Productivity, real wages, and gender. A study in Colombian manufacturing

Andrés Mauricio Gómez-Sánchez; Zoraida Ramírez-Gutiérrez; Isabel Cristina Rivera-Lozada

doi:10.12688/f1000research.161343.1

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Research Article

Productivity, real wages, and gender. A study in Colombian manufacturing

[version 1; peer review: 1 approved with reservations, 1 not approved]

Andrés Mauricio Gómez-Sánchez¹, Zoraida Ramírez-Gutiérrez ², Isabel Cristina Rivera-Lozada³

PUBLISHED 17 Mar 2025

Author details Author details

¹ Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia
² Departament of Accounting, Universidad del Cauca, Popayan, Cauca, Colombia
³ Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia

Andrés Mauricio Gómez-Sánchez
Roles: Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Zoraida Ramírez-Gutiérrez
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

Isabel Cristina Rivera-Lozada
Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

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Abstract

Background

Orthodox microeconomic theory establishes a positive link between employee wages and productivity in competitive markets. However, this perspective often overlooks gender and treats the workforce as a homogeneous category labelled as labor, potentially obscuring issues of gender discrimination. This article addresses the gender wage gap by analyzing, for the first time, at least in an emerging economy such as Colombia, the impact of manufacturing firm’s productivity on wages explicitly including gender.

Method

First, we use the GMM two-equation system proposed by Wooldridge (2009) to obtain consistent and unbiased estimates of output elasticities and TFP, respectively. Secondly, we explore wages-productivity linkage by gender, implementing a dynamic random effect generalized least squares model (GLS) with panel data to deal with endogeneity issues.

Results

Our main findings reveal, among others, that firms with a higher proportion of female workers (female firms) generally have higher productivity than those with a higher proportion of male workers (male firms).

Conclusions

The effect of female firm’s productivity on wages is lower than that of male firm’s productivity, which could indicate gender wage discrimination

Keywords

Wages, Gender, Firms productivity, Manufacturing, Emerging Economy

Corresponding author: Zoraida Ramírez-Gutiérrez

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Gómez-Sánchez AM et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Gómez-Sánchez AM, Ramírez-Gutiérrez Z and Rivera-Lozada IC. Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2025, 14:306 (https://doi.org/10.12688/f1000research.161343.1) First published: 17 Mar 2025, 14:306 (https://doi.org/10.12688/f1000research.161343.1) Latest published: 17 Mar 2025, 14:306 (https://doi.org/10.12688/f1000research.161343.1)

Introduction

The economic literature, particularly within the industrial organization, suggests that firm wages depend on several factors, including firm size (Carlsson, et al. 2016), employee education and skills (Mincer, 1974; Schultz, 1961), economic subsector (Suhányi, et al. 2023), geographic location (Méjean & Patureau, 2010), and the presence or absence of unions (Card, et al. 2017). Nevertheless, productivity is the most critical factor influencing wages (Feldstein, 2008).

Based on the neoclassical microeconomic theory, increases in labor productivity positively affect real wage growth in the long-run (Mankiw, 2015). In line with this, industrial organization literature acknowledges that firm productivity significantly influences employees’ wages. Although empirical evidence supporting this hypothesis varies in strength depending on the country and industries analyzed, productivity is widely recognized as a key determinant of wages, whether at the individual or firm level (Meager & Speckesser, 2011).

Despite this, previous literature has left two issues aside. First, the method used to measure productivity and second, the role of gender in these topics. Regarding productivity measurement, several studies, particularly in macroeconomics, have used the average product of labor as a proxy. This approach is limited as it measures labor productivity rather than firm productivity and only applies in the short-run (Sargent & Rodriguez, 2000). While other studies, particularly in microeconomics, have correctly used total factor productivity (TFP) at the firm level, many studies generate flawed estimates due to the use of statistical methods that generate biased and inconsistent parameters, leading to inaccurate conclusions and recommendations (Gómez Sánchez, 2020).

The second issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases, especially in emerging economies such as Colombia, where industrial subsectors such as Beverages, Garments, Textiles, Pharmaceuticals, or Chemicals, a significant proportion of employees are female. This is an important consideration, as productivity may respond differently to the presence of women than men, and this difference should influence employees' wages.

In this order of ideas, this paper aims to assess the relationship between wages and productivity in firms with a high proportion of female employees or female firms hereafter, compared to firms with a higher proportion of male employees, referred to as male firms hereafter (Tsou & Yang, 2019; Gomez Sanchez, et al., 2025). To do so, we first estimate unbiased total factor productivity using the two-stage method proposed by Wooldridge (2009), and for the first time, we disaggregate the workforce into male and female to obtain gender- a gender-specific productivity estimate rather than an aggregate measure of TFP as the tradition.

In this regard, we implement the methodology in two steps. First, we use the two-equation system proposed by Wooldridge (2009) to obtain consistent estimates of input elasticities and unbiased TFP estimates. This system is jointly estimated under the Generalised Method of Moments (GMM) framework. As a novelty, we separate employees by gender to estimate TFP. Second, we implement a dynamic random effect generalized least squares model (GLS) with panel data to address endogeneity. Moreover, we also include industrial organization covariates, such as exports or innovations, along with wage persistence; that is, firms with higher (or lower) wages in the past tend to continue those wage levels in the present. In addition, the model also deals with potential initial condition problems (Blundell & Bond, 1998).

The data comes from The Annual Manufacturing Survey (EAM) and The Technological Development and Innovation Survey (EDIT), both published by the Statistics Department of Colombia (DANE) for the period 2013-2020. After merging eight waves of EAM and EDIT, we obtain an unbalanced panel with 59,355 observations. Our main findings are summarised as follows: i) Female firms exhibit higher productivity than male firms. ii) The impact of female firms’ productivity on wages is lower than female firms’ productivity, suggesting wage discrimination in the Colombian manufacturing industry. iii) Female labor output elasticity is higher than male elasticity, indicating that women contribute more to firm output than men iv) TFP displays lower input elasticities than gender-specific TFP estimates, suggesting that TFP underestimates firm productivity when gender is considered.

The remainder of this paper is organized as follows. Section 2 examines theoretical and empirical literature. Section 3 describes the data. Section 4 displays TFP and stochastic model estimates. Section 5 discusses the results, and Section 6 offers conclusions.

Methods

Literature review

The existing literature on gender and productivity pay disparities primarily focuses on led-women or led-male firms. These definitions differ slightly from the concept of male and female firms explored in this article. Due to data limitations in EAM survey, we classify firms as female or male based on the proportion of female or male workers employed, respectively, following the approach of Tsou and Yang (2019). Consequently, much of the literature is less concerned with our adopted firm’s definition, which focuses on led-women or led-male firms.

The orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages, as outlined in the efficiency wage theory (Akerlof & Yellen, 1986; Shapiro & Stiglitz, 1984). Nevertheless, this paper demonstrates that wages do not necessarily align with a firm’s productivity. It highlights how female-led firms often exhibit higher productivity than male-led ones, yet wage disparities do not reflect these productivity differences. This phenomenon can be attributed to gender wage gaps masked by the low levels of gender parity worldwide (World Economic Forum, 2024) and employers’ perceptions of female worker's productivity being negatively influenced by traditional caregiving roles and motherhood responsibilities (Todaro et al., 2002).

As Goldin (2014) argues, women have adapted their participation in the labor market over time. Initially, they balanced household and work life, often leaving their employments during motherhood. Subsequently, they increased their educational attainment, equalling or even surpassing that of men to remain competitive in the workforce. Despite these efforts, wage compensation has not reflected women’s labor market resilience. Gender wage gaps persist globally (World Bank, 2024), and wage disparities widen with age.

In Colombia, since the early days of industrialization, women have played a significant role in the workforce, particularly in sectors like Food and Textiles. This participation was largely driven by the need to supplement family income (Santos, 2017; Arango, 1991). Nonetheless, their contributions were often associated with low wages, influenced by religiously rooted work ethics and disciplinary norms (Arango, 1991). More recently, gender wage gaps have persisted despite evidence showing that female-led firms perform better. For instance, women-led firms, identified through the Unified Business and Social Registry (RUES, by its Spanish acronym), account for 59% of Colombia’s 1.2 million registered firms and 1.8 million microenterprises owned by women, according to Emicron Survey (2022) published by National Statistics Department. These firms report higher productivity (34.3% compared to 33.25% for men) and more significant efficiency improvements [25.8% for women versus 17.9% for men] (WCP, 2024).

Santos (2017), considers that despite their higher productivity and significant contributions to the manufacturing sector, wage firms differentials in this country have consistently exceeded productivity gaps. This is theoretically grounded on three factors. Firstly, following Todaro et al., (2002), perceived costs of female labor as hired women are often considered more expensive due to factors related to motherhood, which are believed to affect productivity, despite firms lacking systems for measuring labor costs disaggregated by sex. Secondly, Santos (2017) suggests that wages diverge from efficiency theory: women-led firms take advantage of relatively lower wages to increase profits, contrary to efficiency wage theory. Thirdly, Goldin, (2014) pointed out the sectoral occupational segregation, that is, led-women firms tend to concentrate on specific sectors, perpetuating occupational segregation.

The scarce international literature on these topics shows mixed results. Tsou and Yang (2019) find that Chinese firms with a higher proportion of female workers generally have lower productivity than those with a higher proportion of male workers. However, female workers with a high education level significantly improve the firm’s productivity, especially in small private and foreign firms, but not for medium or large public ones. These contradictory results also depend on the statistical method used. Pfeifer and Wagner (2014) find lower productivity in female firms than in male firms under the OLS method, whilst they obtain opposite results if they use a GMM framework.

TFP estimates by gender

TFP broken down by gender follows Wooldridge’s (2009) method. This framework takes advantage of the GMM estimation to shorten the standard error calculation and avoid using bootstrapping techniques. Expressly, we assume that the production process follows a linearised Cobb-Douglas production function:

(1)

y_{it} = β_{0} + β_{lw} l_{it}^{w} + β_{lm} l_{it}^{m} + β_{k} k_{it} + β_{m} m_{it} + β_{e} {ene}_{it} + ω_{it} + ε_{it}

$y_{it}$ denotes firm’s i production in period t; $l_{it}^{w}$ is the women labor;; $l_{it}^{m}$ is the men labor; $k_{it}$ is the firm’s capital stock; and, $m_{it}$ is the materials consumption, and ${ene}_{it}$ is the electric energy consumption. Besides, $ω_{it}$ is the firm’s productivity, which is only observable or predictable by the firm. As well, $ε_{it}$ represents an error term unobserved or unpredictable by firms.

Olley & Pakes (1992) assume that capital stock evolves according to the perpetual inventory method, and it is determined in the previous period (state variable). Therefore, current productivity shocks do not affect capital stock (Eslava et al., 2013). We also assume that labor by gender and firm’s energy consumption are chosen in the same period, as they are consumed (freely variable factors). However, Ackerberg, et al., (2015) demonstrate that these choices are correlated with $ω_{it}$ , so the equation (1) cannot be estimated by ordinary least squares (OLS), fixed effects (FE), or instrumental variables (IV). In this sense, Levinsohn and Petrin (2003) suggests using the materials demand function as a proxy for unobserved productivity. The demand for materials is as follows:

(2)

m_{it} = m_{t} (k_{it}, ω_{it}, {ene}_{it})

According to Levinsohn and Petrin (2003), this demand is monotonically increasing in productivity, so we can invert this function to express firms’ productivity in terms of observables:

(3)

ω_{it} = m_{t}^{- 1} (k_{it}, ω_{it}, {ene}_{it}) = h_{t} (k_{it}, ω_{it}, {ene}_{it})

$h_{t}$ is an unknown function of $k_{it}$ ; $m_{it}; k_{it}; ω_{it};$ and ${ene}_{it}$ . Replacing (3) in (1):

(4)

y_{it} = β_{0} + β_{lw} l_{it}^{w} + β_{lm} l_{it}^{m} + β_{k} k_{it} + β_{m} m_{it} + β_{e} {ene}_{it} + h_{t} (k_{it}, ω_{it}, {ene}_{it}) + ε_{it}

$h_{t} (∙)$ is unknown, so it is proxied by third-degree polynomials in the respective arguments. Nevertheless, $β_{m}$ and $β_{k}$ are collinear with $h_{t} (∙),$ so we cannot to identify these parameters. Following Olley & Pakes (1992), and Levinsohn and Petrin (2003), it is necessary to introduce the law of motion of productivity as a Markov process:

(5)

ω_{it} = E [ω_{it} | ω_{it - 1}] + ξ_{it} = f (ω_{it - 1}) + ξ_{it}

where

f (∙)

is an unknown function that join productivity in

t

and in

t - 1

plus an innovation term,

ξ_{it},

which is uncorrelated to

k_{it}

. By replacing (5) in (1), we get:

(6)

y_{it} = β_{0} + β_{l} l_{it} + β_{k} k_{it} + β_{m} m_{it} + β_{e} {ene}_{it} + β_{w} {wat}_{it} + f (ω_{it - 1}) + ξ_{it} + ε_{it}

Furthermore, by lagging and replacing equation (3) into (6), we get:

(7)

y_{it} = β_{0} + β_{lw} l_{it}^{w} + β_{lm} l_{it}^{m} + β_{k} k_{it} + β_{m} m_{it} + β_{e} {ene}_{it} + h (k_{it - 1}, m_{it - 1}, {ene}_{it - 1}) + v_{it}

where

v_{it}

is a composed error term (

ξ_{it} + ε_{it})

. Because

h (∙)

is an unknown function, it is proxied by third degree polynomials in the arguments. Equations (4) and (7) are the two-equation systems proposed by Wooldridge (2009); they are jointly estimated by using the GMM method with suitable instruments and moment conditions showed for instance by Gómez Sánchez (2020). Once we estimate equation (1), we get the TFP as a residual by using output elasticities:

(8)

y_{it} - ({\hat{β}}_{0} + {\hat{β}}_{lw} l_{it}^{w} + {\hat{β}}_{lm} l_{it}^{m} + {\hat{β}}_{k} k_{it} + {\hat{β}}_{m} m_{it} + {\hat{β}}_{e} {ene}_{it}) = {\hat{ω}}_{it}

where

{\hat{ω}}_{it}

is the TFP estimated in logs for firm

i

at time

t

.

Data analysis¹

This section describes some key variables used in this study that provide insight into the empirical model. We specifically examine the output elasticity of labor without considering gender and with gender taken into account, as well as the linkage with firms’ salaries, analyzed for the entire sample and by manufacturing sectors.

Table 1 presents the estimates of labor product elasticities using three different methods: Ordinary Least Squares (OLS), Generalized Least Squares (GLS), and Wooldridge’s (2009) two-step method. These estimates are analyzed under two scenarios: one considering the firm's total labor force (Labor) and the other distinguishing employees by gender (women and men’s labor).

Table 1. Estimates of labor output elasticities. Total labor and labor by gender.

	OLS	GLS	Wooldridge	OLS	GLS	Wooldridge
Labor	0.298***	0.252***	0.286***
	(0.002)	(0.003)	(0.002)
Women labor				0.141***	0.100***	0.136***
				(0.001)	(0.002)	(0.001)
Men labor				0.136***	0.137***	0.132***
				(0.002)	(0.003)	(0.002)
Capital	0.042***	0.023***	0.089***	0.045***	0.022***	0.093***
	(0.001)	(0.002)	(0.006)	(0.001)	(0.002)	(0.006)
Materials	0.711***	0.701***	0.684***	0.715***	0.705***	0.690***
	(0.001)	(0.002)	(0.003)	(0.002)	(0.002)	(0.003)
Energy	0.008***	0.056***	0.039***	0.016***	0.061***	0.045***
	(0.001)	(0.002)	(0.003)	(0.001)	(0.002)	(0.003)
Constant	3.096***	3.108***		3.222***	3.257***
	(0.014)	(0.024)		(0.017	(0.026
P-value Wald		0.000	0.000		0.000	0.000
P-value F	0.000			0.000
Observations	67,781	67,781	55,624	65,879	65,879	53,799

As expected, the results generally reveal that all methods yield positive and statistically significant elasticities. Nevertheless, without distinguishing by gender, the product elasticity under Wooldridge’s (2009) method is 0.286. In contrast, the OLS and GLS methods tend to overestimate this figure, likely due to biases from unaddressed endogeneity issues in the Cobb-Douglas production function. When gender is considered, Wooldridge’s (2009) method shows that the product elasticity of women’s labor is 0.136, while that of men’s labor is 0.132. The remaining elasticities corresponding to capital (0.093), materials (0.690), and energy (0.045) fall within the ranges reported by empirical studies on the industrial organization in Colombia (Gómez-Sánchez et al., 2022; Llopis et al., 2022; Sanchis Llopis et al., 2024).

The differential in labor product elasticities between men and women is noteworthy: the elasticity of female labor is higher, implying that women’s average contribution to the firms’ output surpasses that of men. However, it is possible that women’s wages do not correspond to this greater contribution to production. In this regard, we present additional descriptive analyses to explore this idea.

Figure 1 shows that male firms have higher average log-wages (13.52) than female firms (13.09). Besides, in terms of average product of labor (APL), male firms exhibit higher productivity (11.598) than female firms (11.127), seemingly aligning with the prediction of neoclassical theory.

Figure 1. Wages and Productivity by female and male firms. Average in Logarithms.

However, more precisely, measures of a firm’s productivity suggest different results. In the TFP scenario, female firms outperform male firms, with values of 2.554 and 2.476, respectively. Furthermore, in TFP weighted by gender (TFPwm), female firms also perform better (2.622) compared to male firms (2.566). Whilst further analysis is required to establish causality, these findings advise a potential productivity advantage when gender weighting is considered.

Table 2 presents wages and different productivity measures broken down by manufacturing sectors to deepen our analysis. In our preferred scenario (TFPwm), the numbers reveal that in industrial sectors such as Food, Textiles, Leather, Publishing, Coking, chemicals, Electric motors, Vehicles, and Machine Maintenance, female firms’ productivity displays higher productivity than male firms. However, wages are lower for female firms compared to salaries in male firms. In the remaining sectors, there is a clear correspondence between productivity and wages, that is, the more productive, the higher the wages, regardless of firms’ gender orientation.

Table 2. Wages and Productivity. Female and male firms by sector. Average in Logs.

Industry	Male Firms				Female Firms
ISIC Rev.2	Wages	APL	TFP	TFPwm	Wages	APL	TFP	TFPwm
Food	13.874	12.089	2.437	2.501	12.901	11.179	2.452	2.511
Beverages	14.368	12.127	2.612	2.686	12.856	11.365	2.594	2.640
Textiles	13.874	11.427	2.346	2.416	13.036	11.078	2.493	2.554
Garment	12.809	11.585	2.591	2.637	13.092	10.979	2.625	2.727
Leather	12.739	11.168	2.424	2.495	12.724	10.939	2.487	2.547
Wood	12.838	11.263	2.427	2.554	12.132	10.875	2.398	2.450
Paper	14.310	12.128	2.412	2.486	12.982	11.187	2.418	2.463
Publishing	13.214	11.340	2.470	2.524	12.735	11.152	2.533	2.569
Coking	13.925	13.397	2.758	2.826	12.831	12.910	2.949	2.987
Chemical	13.888	12.273	2.615	2.682	13.665	11.466	2.686	2.750
Pharmaceutical	14.685	12.058	2.838	2.888	14.137	11.508	2.803	2.859
Rubber and Plastic	13.505	11.589	2.377	2.453	13.342	11.212	2.377	2.423
Non-Metallic Mineral Prod	13.823	11.554	2.384	2.522	12.784	11.019	2.404	2.439
Metallurgical Products	13.596	11.844	2.442	2.558	12.298	11.525	2.433	2.465
Metal products	13.178	11.204	2.509	2.630	12.552	10.941	2.472	2.520
Manufacture of Electronics	13.293	11.744	2.643	2.705	12.679	10.941	2.488	2.562
Electric motors	13.900	11.432	2.453	2.545	13.018	11.055	2.509	2.563
Machinery and Equipment	13.305	11.239	2.581	2.699	12.200	11.176	2.563	2.603
Vehicles	13.554	11.460	2.424	2.548	13.479	11.431	2.531	2.607
Ships and Boats	14.052	11.523	2.326	2.434	12.459	11.131	2.378	2.424
Furniture	13.065	11.090	2.456	2.558	12.249	11.078	2.447	2.489
Other manufactures	13.364	11.505	2.541	2.636	13.014	11.075	2.561	2.619
Machine Maintenance	13.596	11.673	2.645	2.772	11.966	11.226	2.792	2.830

Other productivity measures, such as TFP, are consistent with the observed results for TFPwm. Nonetheless, APL estimates indicate that both productivity and wages are higher in male firms than in female firms across all industries except the Garment sector.

Empirical model and estimates

To explore the effect of female firm productivity on a firm’s wages, we offer a dynamic generalized least squares model (GLS) with random effects for panel data. The variables are in natural logs except for dummy variables. In addition, we lagged all covariates in one period to deal with possible simultaneity, except factor variables such as time, industry, and firm localization. The model is as follows:

(9)

{lw}_{it} = φ_{0} + α_{0} {lw}_{it - 1} + α_{1} {findx}_{it - 1} + α_{2} {lprod}_{it - 1}^{h, j, p} + γ_{k} Z_{it - 1} + pre \bar{lw} + {loc}_{j} + {year}_{t} + {ind}_{j} + ε_{it}

Where ${lw}_{it}$ signifies the firms wages; ${findx}_{it - 1}^{q, r}$ represents the female firm’s index with q ∈ continuous index and r ∈ dichotomous index. Besides, ${lprod}_{it}^{h, j, p}$ denotes firms productivity; with h ∈ full TFP; j ∈ TFP by gender; and p ∈ APL. Supported in Roberts and Tybout (1997), we introduce the term ${lprod}_{it - 1}^{h, j, p}$ to capture firm’s wages persistence, that is, firms with higher (lower) wages in the past; currently continue to show higher (lower) wages. In this sense, here persistence aims to capture potential gender discrimination. That is, it examines whether firms that paid lower salaries in female firms in the past continue to do so in the present.

The vector $Z_{it - 1}$ includes firm’s control variables according to industrial organisation. It includes the firm’s classification as SMEs (SMEs), to capture if a firm's size influences wages. In line with Schultz (1961), Mincer (1974), and others, we incorporate employee skills (skills) as a proxy for human capital, as higher levels of education and/or training are associated with increased wages. Specifically, skills are the number of employees with master’s degrees. Export ( ${exp}_{it})$ and innovations activities $({inn}_{it}$ ) are included as a part of firm’s internationalisation strategies, as highlighted by De Loecker (2013) and Crepon, Duguet & Mairesse, (1998). According to Schank et al., (2010), firms with strong international trade connections and innovation efforts tend to be more productive and efficient, which typically results in higher employee wages. It is worth mentioning that due to the limited introduction of innovations in Colombian manufacturing, we combine both process and product innovations to achieve accurate statistical representativeness.

As well, the vector $Z_{it - 1}$ accounts for market concentration proxied by Herfindahl-Hirschman index (lihh), and firm’s mark-up (lmarkup). We also include the firms’ age (lage) because, according to the ILO (2015), young small enterprises in Latin America significantly contribute to job creation. On the other hand, following Blundell and Bond (1998), we include pre-sample means of the dependent variable ( $pre_lw 2013$ ) to deal with correlated unobserved firm heterogeneity in the model estimation. Note that we also control for geographic firm localization (loc), macroeconomic shocks (year), and sector characteristics (ind). Finally, $ε_{it}$ is a composed the error term that consist of a fixed effect of firms ( $u_{i})$ and an idiosyncratic error term ( $η_{it})$ .

Results

Table 3 shows the estimates of the empirical modelling. Columns (1), (2), and (3) display TFP results for the full industry, female firms, and male firms, respectively. Columns (4), (5), and (6) present TFP results classified by workforce gender, whilst the final three columns analyze the average product of labor (APL). Each scenario includes continuous and dichotomous indices for female firms and wage persistence.

Table 3. Empirical Estimates.

		TFP			TFPwm			APL
	Industry	Female firm	Male firm	Industry	Female firm	Male firm	Industry	Female firm	Male firm
lrw_t−1	0.701**	0.587**	0.619**	0.668**	0.591**	0.592**	0.701**	0.587**	0.619**
	(0.010)	(0.019)	(0.012)	(0.010)	(0.020)	(0.012)	(0.010)	(0.019)	(0.012)
ltfp_t−1	0.038**	0.055*	0.050**
	(0.015)	(0.031)	(0.019)
${ltfp}_{t − 1}^{wm}$				0.059**	0.057*	0.062**
				(0.015)	(0.031)	(0.019)
lapl_t−1							0.016**	0.021*	0.021**
							(0.005)	(0.010)	(0.006)
smes_t−1	-0.476**	-0.535**	-0.574**	-0.520**	-0.539**	-0.612**	-0.473**	-0.535**	-0.571**
	(0.021)	(0.046)	(0.028)	(0.022)	(0.047)	(0.029)	(0.021)	(0.046)	(0.028)
lskill_t−1	0.006**	0.005**	0.007**	0.006**	0.004**	0.007**	0.006**	0.005**	0.007**
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
exp_t−1	-0.028**	-0.022	-0.024*	-0.030**	-0.023	-0.024*	-0.030**	-0.024	-0.027**
	(0.008)	(0.015)	(0.010)	(0.008)	(0.015)	(0.010)	(0.008)	(0.015)	(0.010)
inn_t−1	0.048**	0.053**	0.047**	0.049**	0.049**	0.047**	0.048**	0.052**	0.047**
	(0.008)	(0.016)	(0.010)	(0.008)	(0.016)	(0.010)	(0.008)	(0.016)	(0.010)
lihh_t−1	0.015**	0.008**	0.014**	0.014**	0.007**	0.013**	0.014**	0.007**	0.014**
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
lmarkup_t−1	-0.002	0.015	-0.026*	-0.019*	0.009	-0.039**	0.025**	0.054**	0.009
	(0.011)	(0.024)	(0.015)	(0.011)	(0.024)	(0.015)	(0.007)	(0.011)	(0.009)
lage_t−1	0.024**	0.015**	0.021**	0.022**	0.015**	0.018**	0.024**	0.016**	0.021**
	(0.003)	(0.005)	(0.004)	(0.003)	(0.005)	(0.004)	(0.003)	(0.005)	(0.004)
pre_lw2013	0.155**	0.279**	0.213**	0.182**	0.275**	0.233**	0.151**	0.274**	0.208**
	(0.008)	(0.017)	(0.010)	(0.009)	(0.019)	(0.010)	(0.008)	(0.017)	(0.010)
loc_t−1	0.040**	0.060**	0.036**	0.042**	0.059**	0.038**	0.042**	0.061**	0.039**
	(0.006)	(0.016)	(0.009)	(0.007)	(0.016)	(0.009)	(0.006)	(0.016)	(0.009)
ind/year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	2.330**	2.138**	2.734**	2.398**	2.141**	2.838**	2.274**	2.086**	2.663**
	(0.105)	(0.220)	(0.131)	(0.104)	(0.198)	(0.136)	(0.107)	(0.225)	(0.135)
Observations	44,272	13,958	30,314	43,187	13,658	29,529	44,299	13,968	30,331
P-value (Wald)	0.000	.0.000	0.000	0.000	.0.000	0.000	0.000	0.000	0.000
Rho	0.087	0.419	0.174	0.131	0.436	0.219	0.085	0.419	0.172

The results are notable. Firstly, all productivity measures positively and statistically significantly impact firm wages. Secondly, the TFP measure without gender differentiation (first three columns) yields lower figures than the gender-specific TFP scenario (columns 4, 5, and 6). Thirdly, as we expected, in this latter scenario, the impact on male-dominated firms is more significant than on female-dominated firms. All else being equal, a 1% increase in TFPwm raises wages by 6.2%, whereas for female firms, the increase is only 5.7%.

The first result eventually supports the neoclassical hypothesis that wages are related to productivity. Nevertheless, in the case of APL (columns 7, 8, and 9), the figures are noticeably lower than those in the first two scenarios. This suggests that using APL to measure a firm’s productivity may be misleading or biased, as it captures short-run employee performance rather than the productivity of the firms as a whole, potentially underestimating the actual impact.

Other covariates reveal additional interesting results. There is evidence of positive wage persistence across all productivity measures, indicating that firms that paid higher (or lower) wages in the past are likely to continue doing so in the future. However, regardless of the productivity measure, the impact is consistently more substantial for male firms than for female firms. Furthermore, the positive and statistically significant estimates of the pre-sample mean of wages $(pr e_{lw 2013})$ , which captures the long-run impact of individual heterogeneity, further support the importance of wages persistence. These findings align with the study by Hansen and McNichols (2020), which suggests that employers’ prior knowledge of employees’ salaries perpetuates historical gender-based wage discrimination.

Firm size reveals that regardless of productivity proxy, SMEs show a negative impact on salaries than large firms, and in addition, female firms SMEs reduce the average wages less than male firms SMEs (-0.539 and -0.612 in the TFPwm case, respectively). These results are consistent with Messina (2019), whose findings reveal that large firms are more profitable and, therefore, pay higher wages, and this is not solely because they attract more skilled workers. Employees with identical qualifications earn better salaries when they work for these firms. As well, Blau and Kahn (2000) highlighted that wage disparities between male- and female-dominated firms could be linked to differing management styles, workplace policies, or labor force composition, and some studies suggest that female leadership may be associated with more equitable pay practices.

Moreover, as we expected, the employees’ skills also positively and significantly affect salaries, as predicted by Human Capital theory. Regardless of TFP measurement, the evidence shows female firms have less impact on salaries than male firms, although when the employees have master's degrees. This is a clear signal of gender discrimination corroborated extensively by many authors, such as Rivera-Lozada et al., (2024) for Colombia, in the context of Kitakawa-Oaxaca-Blinder wage discrimination. In addition, in a typical emerging economy, the magnitude of the parameter (elasticity) is less than one. This suggests low education levels among employees and/or a limited number of hires with master’s degrees or that the manufacturing process does not require a highly specialized workforce.

Internationalization strategies indicate that, for exports of final goods, the results for female firms are inconclusive, as the parameter is not significant. Conversely, for process and/or product innovation activities, the findings support the idea of a positive relationship with wages, with the impact being more significant in female firms than male firms.

According to Gómez Sánchez, (2020), this result may be because in Colombia, small firms tend to be more focused on innovation activities than export activities, whereas the opposite trend is observed in large firms. Furthermore, Dezsö and Ross (2012) argue that gender diversity in leadership can enhance team performance, especially in innovation-driven contexts, as diverse perspectives foster solutions that are more creative and improved decision-making.

For female firms, higher concentration levels tend to boost wages, whilst the evidence related to mark-ups is inconclusive, especially in the TFPwm case. When firms hold significant market power, they may share some of their higher profits with employees through increased salaries, potentially to retain talent or improve productivity. Firms’ age positively and significantly affects wages in all scenarios considered. This evidence supports the findings of ILO (2015), where young small businesses are the ones that contribute the most to job creation. Nevertheless, female firms always display a lower impact than firms with a high male proportion.

Lastly, when firms are geographically located in the Capital District of Bogota (loc), which is the most important economic activity zone in Colombia, female firms consistently have a more significant impact than male firms. Rodríguez-Pose and Crescenzi (2008) emphasize the role of regional dynamics, noting that companies located in economically buoyant areas benefit from knowledge diffusion and network effects, which can amplify the influence of various leadership structures, including those led by women.

Discussion

This document aims to investigate gender wage differentials by exploring the incidence of a manufacturing firm’s productivity on salaries, accounting for gender, in an emerging economy such as Colombia.

The linkage between salaries and a firm's productivity is particularly significant in the Colombian manufacturing industry. Our findings confirm a strong connection between these variables, aligning with the postulates of neoclassical economic theory. However, average labor productivity is inadequate for capturing a firm’s productivity. This metric overlooks crucial complementarities with capital, intermediate inputs, and technology by focusing exclusively on the labor factor. Therefore, Total Factor Productivity (TFP) emerges as a more comprehensive and accurate measure of a firm’s productivity.

Nonetheless, analyzing TFP without disaggregating by gender can obscure critical differences in firms’ productivity. This approach implicitly assumes that all workers contribute homogeneously to production, disregarding structural disparities such as gender gaps in wages, access to training, and working conditions. These omissions can lead to biased interpretations of a firm’s proper productive performance. As previous empirical literature in the industrial organization has largely overlooked the role of gender in TFP measurement, this oversight could result in flawed connections between TFP and variables such as exports, imports, innovation, or ICT, influencing the testing of important hypotheses such as learning by or self-selection.

The wage gaps align with claims made by Goldin (2014) and, more recently, by the World Bank (2024), highlighting women's significant efforts to improve their participation in the labor market. These efforts aim to improve participation rates, productivity, and wage levels. All else being equal, productivity increases in Colombian male firms are associated with higher wage increases than female firms (6.2% > 5.7%). These results are consistent with those of Santos (2017), who claims that wage disparities outweigh productivity differences, contradicting the promise of efficiency theory and evidencing another discriminatory phenomenon in the labor market. This discrimination not only affects women individually through wage disparities but also extends to female-led firms as entities.

However, women often fail to achieve wage increases proportional to their productivity gains. The fact that the labor market allocates higher wages in line with productivity increases predominantly to male firms reveals a discriminatory situation against female firms. Our results, therefore, suggest a possible wage discrimination. Specifically, descriptive statistics reveal that female firms in the Colombian manufacturing industry exhibit higher average productivity than male firms, although average wages show the opposite trend. These findings partially coincide with those of Gomez Sanchez, et al. (2025), who indicate that a higher proportion of female workers leads to higher firm productivity. Furthermore, female firms show higher productivity levels than male firms, and the elasticity of labor output of female employees is higher than that of male employees, indicating that women contribute more to firm output than men.

Finally, the fact that wages in female firms are lower than in male firms, despite their higher productivity, may indicate problems of labor exploitation as well. The results on female firms in Colombia call for further exploration of productivity differences between female and male firms. In particular, it is worth considering whether the productivity gains of female firms are due to the lower wages paid to women. As Santos (2017) postulates, this claim may not be far-fetched given the historical context of the Colombian textile industry, where female firms traditionally paid lower wages to women. Our results indicate that this situation persists over time, suggesting that the productivity of female firms continues to depend on female wage exploitation.

In conclusion, we can point out that gender wage discrimination goes beyond individual differences between men and women to extend to female and male firms in Colombia. This situation could reveal labor exploitation that would be conditioning the productivity of female firms and has been perpetuated since the dawn of industrialization in Colombia. These findings highlight the need for public policies that address gender pay disparities by recognizing and rewarding productivity regardless of gender. These could include gender-sensitive cost accounting systems, incentives for inclusive hiring practices, and targeted efforts to break occupational segregation. Otherwise, persistent wage gaps will continue undermining gender equity in the labor market.

Ethical considerations

Ethical approval and consent were not required.

Reporting guidelines

Reporting guidelines were not required. This study is not related to clinical topics.

Data availability

Zenodo: EAM-EDIT 3. https://doi.org/10.5281/zenodo.14867593 (Gómez Sánchez et al., 2025).

The project contains the following underlying data:

EAM-EDIT 3.xlsx. (A merge of two databases: Annual Manufacturing Survey (EAM) and Technological Development and Innovation Survey (EDIT)).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

References

Ackerberg DA, Caves K, Frazer G: Identification Properties of Recent Production Function Estimators. Econometrica. 2015; 83: 2411–2451. Publisher Full Text
Akerlof GA, Yellen JL: The Fair Wage-Effort Hypothesis and Unemployment. En. Q. J. Econ. 1986; 105(o2): 255–283. Publisher Full Text
Arango LG: Mujer, religión e industria: Fabricato, 1923-1982. Medellín: Universidad de Antioquia y Universidad Externado de Colombia; 1991. Reference Source
Blau FD, Kahn LM: Gender differences in pay. J. Econ. Perspect. 2000; 14(4): 75–100. Publisher Full Text
Blundell R, Bond S: Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 1998; 87(1): 115–143. Publisher Full Text
Card D, Lemieux T, Riddell WC: Unions and wage inequality. What do unions do? Routledge; 2017.
Carlsson M, Messina J, Skans ON: Wage adjustment and productivity shocks. Econ. J. 2016; 126(595): 1739–1773. Publisher Full Text
Crepon B, Duguet E, Mairesse J: Research, innovation and productivity: An econometric analysis at the firm level. Econ. Innov. New Technol. 1998; 7(2): 115–158. Publisher Full Text
De Loecker J: Detecting learning by exporting. American Economic Journal: Microeconomics. 2013; 5(3): 1–21. Publisher Full Text
Dezsö CL, Ross DG: Does female representation in top management improve firm performance? A panel data investigation. Strateg. Manag. J. 2012; 33(9): 1072–1089. Publisher Full Text
Eslava M, Haltiwanger J, Kugler M, et al.: Trade and market selection: Evidence from manufacturing plants in colombia. Review of Economic Dynamics. 2013; 16(1): 135–158. Publisher Full Text
Feldstein M: Did wages reflect growth in productivity? J. Policy Model. 2008; 30(4): 591–594. Publisher Full Text
Goldin C: A grand gender convergence: Its last chapter. Am. Econ. Rev. 2014; 104(4): 1091–1119. Publisher Full Text
Gómez Sánchez AM: Topics on firms’ estrategic desicions in a developing country: the case of manufacturing in Colombia.2020. Reference Source
Gomez-Sanchez AM, Ramirez-Gutierrez Z, Rivera-Lozada IC: Gender and firm’s productivity in Colombian manufacturing.2025. Manuscript submitted for publication.
Gómez Sánchez AM, Ramirez Z, Rivera Lozada IC: EAM-EDIT 3. [Dataset]. Zenodo. 2025. Publisher Full Text
Gómez-Sánchez AM, Máñez-Castillejo JA, Sanchis-Llopis JA: ICT Effects on Firms' Export Decisions: Evidence for Colombian Manufacturing. International Review of Entrepreneurship. 2022; 20. Reference Source
Hansen B, McNichols D: Information and the persistence of the gender wage gap: Early evidence from California's salary history ban (No. w27054). National Bureau of Economic Research; 2020. Publisher Full Text
ILO. International Labor Office: Pequeñas y medianas empresas y creación de empleo decente y productivo. Oficina Internacional del Trabajo; 2015. Reference Source
Levinsohn J, Petrin A: Estimating production functions using inputs to control for unobservables. Rev. Econ. Stud. 2003; 70(2): 317–341. Publisher Full Text Reference Source
Llopis JAS, Castillejo JAM, Sánchez AMG: The dynamic linkages between exporting and importing in Colombian manufacturing. Latin American Economic Review. 2022; 31: 1–33. Publisher Full Text
Mankiw G: Macroeconomía. Versión para América Latina. Ciudad de México: Cengage Learning; sexta edición2015.
Meager N, Speckesser S: Wages, productivity and employment: A review of theory and international data. EEO; 2011. Reference Source
Méjean I, Patureau L: Firms' location decisions and minimum wages. Reg. Sci. Urban Econ. 2010; 40(1): 45–59. Publisher Full Text
Messina J: La productividad de las empresas y los salarios de los trabajadores.2019; 13. Reference Source Reference Source
Mincer J: Schooling, Experience, and Earnings. New York: Columbia University Press; 1974.
Olley GS, Pakes A: The dynamics of productivity in the telecommunications equipment industry. Technical report. National Bureau of Economic Research; 1992. Reference Source
Pfeifer C, Wagner J: Age and gender effects of workforce composition on productivity and profits: Evidence from a new type of data for German enterprises. Contemporary Economics. 2014; 8(1): 25–46. Publisher Full Text
Roberts MJ, Tybout JR: The Decision to Export in Colombia: An Empirical Model of Entry with Sunk Costs. The American Economic Review. 1997; 87(4): 545–564. Reference Source
Rodríguez-Pose A, Crescenzi R: Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Reg. Stud. 2008; 42(1): 51–67. Publisher Full Text
Rivera-Lozada IC, Gómez-Sánchez AM, Rivera-Lozada O: The gap that Matilda will bridge: a look at the Colombian case. F1000Res. 2024; 13: 588. PubMed Abstract | Publisher Full Text | Free Full Text
Sanchis Llopis JA, Mañez JA, Gómez-Sánchez AM: The dynamic links between product and process innovations and productivity for Colombian manufacturing. Applied Economic Analysis. 2024; 32(94): 62–82. Publisher Full Text
Santos D: Faldas por pantalones: las obreras y la brecha salarial en la industria colombiana en 1945., Tiempo&economía.2017; 4(2): 31–65. Publisher Full Text
Sargent TC, Rodriguez ER: Labor or total factor productivity: Do we need to choose? Ottawa, ON, Canada: Department of Finance; 2000. Reference Source
Schank T, Schnabel C, Wagner J: Higher wages in exporting firms: self-selection, export effect, or both? First evidence from linked employer-employee data. Rev. World Econ. 2010; 146(2): 303–322. Publisher Full Text
Schultz TW: Investment in human capital. Am. Econ. Rev. 1961; 51(1): 1–17. Reference Source
Shapiro C, Stiglitz JE: Equili-brium Unemployment as a Worker Discipline Device. Am. Econ. Rev. 1984; Vol. 74(3): pp. 433–444. Reference Source
Suhányi L, Suhányiová A, Kádárová J, et al.: Relationships between average wages in the manufacturing sector and economic indicators of the manufacturing sector in the region of Visegrad Group countries. Sustainability. 2023; 15(5): 4164. Publisher Full Text
Todaro R, Godoy L, Abramo L: Desempeño laboral de hombres y mujeres: opinan los empresarios. Cadernos pagu. 2002; 197–236. Publisher Full Text
Tsou MW, Yang CH: Does gender structure affect firm productivity? Evidence from China. China Econ. Rev. 2019; 55: 19–36. Publisher Full Text
WCP: Empresas lideradas por mujeres son más productivas, tienen mayor calidad y son más sostenibles. Colombia productiva; 2024. Reference Source
Wooldridge JM: On estimating firm-level production functions using proxy variables to control for unobservables. Econ. Lett. 2009; 104(3): 112–114. Publisher Full Text
World Bank: Women Business and the Law 2024. World Bank Group; 2024. Reference Source
World Economic Forum: Global Gender Gap Report 2024. World Economic Forum; 2024. Reference Source

Footnotes

1 Data authored by Gómez Sánchez, A. M., Ramirez, Z., & Rivera Lozada, I. C: EAM-EDIT 3. [Dataset]. Zenodo. 2025. https://doi.org/10.5281/zenodo.14867593

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Author details Author details

¹ Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia
² Departament of Accounting, Universidad del Cauca, Popayan, Cauca, Colombia
³ Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia

Andrés Mauricio Gómez-Sánchez
Roles: Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Zoraida Ramírez-Gutiérrez
Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing

Isabel Cristina Rivera-Lozada
Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Gómez-Sánchez AM, Ramírez-Gutiérrez Z and Rivera-Lozada IC. Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2025, 14:306 (https://doi.org/10.12688/f1000research.161343.1)

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Reviewer Report 29 Aug 2025

Ronald M. Hernandez, Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Lambayeque, Peru

Approved with Reservations

https://doi.org/10.5256/f1000research.177354.r400226

The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this ... Continue reading

The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this as a relevant and original study. However, there are several areas for improvement that should be addressed to strengthen the research:

The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity.
Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples.
Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity.
Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use.
The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Education, social relations, consumer psychology, applied technologies

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 02 Jun 2025

Davide Villani, Joint Research Centre, European Commission, Seville, Spain

Not Approved

https://doi.org/10.5256/f1000research.177354.r381152

The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers ... Continue reading

The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers tend to have higher productivity than those with a higher proportion of male workers. However, the research also reveals that the impact of female firms' productivity on wages is lower than that of male firms, suggesting potential wage discrimination. The authors use a dynamic generalized least squares model with panel data to analyze the relationship between productivity and wages. The paper addresses a relevant topic, but there are several aspects that undermine the soundness of the work.
Let me be more precise.

Over the full text I have the impression that the paper should devote more attention to several aspects, such as the literature review and the underlying theoretical mechanisms that they want to prove. Some ideas are only sketched and/or not properly referenced. For example:
- In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages”. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest.
- You argue that “… issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases …”. However, you do not describe what these problems and biases are.
Moreover, there is some imprecision with the theory. This imprecisions affect the core concepts of the paper. For example, the authors argue that "orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages" and than claim that this is not necessarily the case for Colombian firms. However, the authors fail to indicate in what and why these findings depart from orthodox theory. Importantly, this is not necessarily a novelty, as the decoupling between real wage growth and productivity is a well-known phenomenon taking place since the 1970s in many countries. There are many reasons that have been discussed behind these movements, but these are not mentioned nor discussed in the paper (e.g. lower bargaining power of workers, international trade etc.).
I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as markup changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored.

I also have different comments about the empirical analysis. Here two important ones:

In table 1, The authors conclude that the contribution of women is higher than that of men. However, this statement seems to be not properly supported by the data. Female have higher coefficients only in two out of three specifications. Here, the difference in coefficient is very little, so I ask to what extent this is economically relevant. When the situation is the opposite (GLS) the difference is much marked. These observations lead me to argue that the statement is not fully supported by the data.
In the empirical model the authors use random effects in the model. Why do you use random effect rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion to this.

References
- Shaikh, 1980 Laws of production and laws of algebra: the humbug production function, The review of economics and statistics
- Felipoe and McCombie 2010 What is Wrong with Aggregate Production Functions. On Temple’s Aggregate Production Functions and Growth Economics International Review of Applied Economics, 24(6): 665–684
- Elmslie, B. and W. Milberg (1996), The productivity convergence debate: a theoretical and methodological reconsideration, Cambridge Journal of Economics

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Political economy and inequality

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Davide Villani, Joint Research Centre, European Commission, Seville, Spain
Ronald M. Hernandez, Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Peru

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Ronald M. Hernandez, Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Lambayeque, Peru

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Approved With Reservations

The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this as a relevant and original study. However, there are several areas for improvement that should be addressed to strengthen the research:

The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity.
Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples.
Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity.
Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use.
The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Education, social relations, consumer psychology, applied technologies

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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02 Jun 2025 | for Version 1

Davide Villani, Joint Research Centre, European Commission, Seville, Spain

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The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers tend to have higher productivity than those with a higher proportion of male workers. However, the research also reveals that the impact of female firms' productivity on wages is lower than that of male firms, suggesting potential wage discrimination. The authors use a dynamic generalized least squares model with panel data to analyze the relationship between productivity and wages. The paper addresses a relevant topic, but there are several aspects that undermine the soundness of the work.
Let me be more precise.

Over the full text I have the impression that the paper should devote more attention to several aspects, such as the literature review and the underlying theoretical mechanisms that they want to prove. Some ideas are only sketched and/or not properly referenced. For example:
- In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages”. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest.
- You argue that “… issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases …”. However, you do not describe what these problems and biases are.
Moreover, there is some imprecision with the theory. This imprecisions affect the core concepts of the paper. For example, the authors argue that "orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages" and than claim that this is not necessarily the case for Colombian firms. However, the authors fail to indicate in what and why these findings depart from orthodox theory. Importantly, this is not necessarily a novelty, as the decoupling between real wage growth and productivity is a well-known phenomenon taking place since the 1970s in many countries. There are many reasons that have been discussed behind these movements, but these are not mentioned nor discussed in the paper (e.g. lower bargaining power of workers, international trade etc.).
I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as markup changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored.

I also have different comments about the empirical analysis. Here two important ones:

In table 1, The authors conclude that the contribution of women is higher than that of men. However, this statement seems to be not properly supported by the data. Female have higher coefficients only in two out of three specifications. Here, the difference in coefficient is very little, so I ask to what extent this is economically relevant. When the situation is the opposite (GLS) the difference is much marked. These observations lead me to argue that the statement is not fully supported by the data.
In the empirical model the authors use random effects in the model. Why do you use random effect rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion to this.

References
- Shaikh, 1980 Laws of production and laws of algebra: the humbug production function, The review of economics and statistics
- Felipoe and McCombie 2010 What is Wrong with Aggregate Production Functions. On Temple’s Aggregate Production Functions and Growth Economics International Review of Applied Economics, 24(6): 665–684
- Elmslie, B. and W. Milberg (1996), The productivity convergence debate: a theoretical and methodological reconsideration, Cambridge Journal of Economics

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Political economy and inequality

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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[1] Ackerberg DA, Caves K, Frazer G: Identification Properties of Recent Production Function Estimators. Econometrica. 2015; 83: 2411–2451. Publisher Full Text

[2] Akerlof GA, Yellen JL: The Fair Wage-Effort Hypothesis and Unemployment. En. Q. J. Econ. 1986; 105(o2): 255–283. Publisher Full Text

[3] Arango LG: Mujer, religión e industria: Fabricato, 1923-1982. Medellín: Universidad de Antioquia y Universidad Externado de Colombia; 1991. Reference Source

[4] Blau FD, Kahn LM: Gender differences in pay. J. Econ. Perspect. 2000; 14(4): 75–100. Publisher Full Text

[5] Blundell R, Bond S: Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 1998; 87(1): 115–143. Publisher Full Text

[6] Card D, Lemieux T, Riddell WC: Unions and wage inequality. What do unions do? Routledge; 2017.

[7] Carlsson M, Messina J, Skans ON: Wage adjustment and productivity shocks. Econ. J. 2016; 126(595): 1739–1773. Publisher Full Text

[8] Crepon B, Duguet E, Mairesse J: Research, innovation and productivity: An econometric analysis at the firm level. Econ. Innov. New Technol. 1998; 7(2): 115–158. Publisher Full Text

[9] De Loecker J: Detecting learning by exporting. American Economic Journal: Microeconomics. 2013; 5(3): 1–21. Publisher Full Text

[10] Dezsö CL, Ross DG: Does female representation in top management improve firm performance? A panel data investigation. Strateg. Manag. J. 2012; 33(9): 1072–1089. Publisher Full Text

[11] Eslava M, Haltiwanger J, Kugler M, et al.: Trade and market selection: Evidence from manufacturing plants in colombia. Review of Economic Dynamics. 2013; 16(1): 135–158. Publisher Full Text

[12] Feldstein M: Did wages reflect growth in productivity? J. Policy Model. 2008; 30(4): 591–594. Publisher Full Text

[13] Goldin C: A grand gender convergence: Its last chapter. Am. Econ. Rev. 2014; 104(4): 1091–1119. Publisher Full Text

[14] Gómez Sánchez AM: Topics on firms’ estrategic desicions in a developing country: the case of manufacturing in Colombia.2020. Reference Source

[15] Gomez-Sanchez AM, Ramirez-Gutierrez Z, Rivera-Lozada IC: Gender and firm’s productivity in Colombian manufacturing.2025. Manuscript submitted for publication.

[16] Gómez Sánchez AM, Ramirez Z, Rivera Lozada IC: EAM-EDIT 3. [Dataset]. Zenodo. 2025. Publisher Full Text

[17] Gómez-Sánchez AM, Máñez-Castillejo JA, Sanchis-Llopis JA: ICT Effects on Firms' Export Decisions: Evidence for Colombian Manufacturing. International Review of Entrepreneurship. 2022; 20. Reference Source

[18] Hansen B, McNichols D: Information and the persistence of the gender wage gap: Early evidence from California's salary history ban (No. w27054). National Bureau of Economic Research; 2020. Publisher Full Text

[19] ILO. International Labor Office: Pequeñas y medianas empresas y creación de empleo decente y productivo. Oficina Internacional del Trabajo; 2015. Reference Source

[20] Levinsohn J, Petrin A: Estimating production functions using inputs to control for unobservables. Rev. Econ. Stud. 2003; 70(2): 317–341. Publisher Full Text Reference Source

[21] Llopis JAS, Castillejo JAM, Sánchez AMG: The dynamic linkages between exporting and importing in Colombian manufacturing. Latin American Economic Review. 2022; 31: 1–33. Publisher Full Text

[22] Mankiw G: Macroeconomía. Versión para América Latina. Ciudad de México: Cengage Learning; sexta edición2015.

[23] Meager N, Speckesser S: Wages, productivity and employment: A review of theory and international data. EEO; 2011. Reference Source

[24] Méjean I, Patureau L: Firms' location decisions and minimum wages. Reg. Sci. Urban Econ. 2010; 40(1): 45–59. Publisher Full Text

[25] Messina J: La productividad de las empresas y los salarios de los trabajadores.2019; 13. Reference Source Reference Source

[26] Mincer J: Schooling, Experience, and Earnings. New York: Columbia University Press; 1974.

[27] Olley GS, Pakes A: The dynamics of productivity in the telecommunications equipment industry. Technical report. National Bureau of Economic Research; 1992. Reference Source

[28] Pfeifer C, Wagner J: Age and gender effects of workforce composition on productivity and profits: Evidence from a new type of data for German enterprises. Contemporary Economics. 2014; 8(1): 25–46. Publisher Full Text

[29] Roberts MJ, Tybout JR: The Decision to Export in Colombia: An Empirical Model of Entry with Sunk Costs. The American Economic Review. 1997; 87(4): 545–564. Reference Source

[30] Rodríguez-Pose A, Crescenzi R: Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Reg. Stud. 2008; 42(1): 51–67. Publisher Full Text

[31] Rivera-Lozada IC, Gómez-Sánchez AM, Rivera-Lozada O: The gap that Matilda will bridge: a look at the Colombian case. F1000Res. 2024; 13: 588. PubMed Abstract | Publisher Full Text | Free Full Text

[32] Sanchis Llopis JA, Mañez JA, Gómez-Sánchez AM: The dynamic links between product and process innovations and productivity for Colombian manufacturing. Applied Economic Analysis. 2024; 32(94): 62–82. Publisher Full Text

[33] Santos D: Faldas por pantalones: las obreras y la brecha salarial en la industria colombiana en 1945., Tiempo&economía.2017; 4(2): 31–65. Publisher Full Text

[34] Sargent TC, Rodriguez ER: Labor or total factor productivity: Do we need to choose? Ottawa, ON, Canada: Department of Finance; 2000. Reference Source

[35] Schank T, Schnabel C, Wagner J: Higher wages in exporting firms: self-selection, export effect, or both? First evidence from linked employer-employee data. Rev. World Econ. 2010; 146(2): 303–322. Publisher Full Text

[36] Schultz TW: Investment in human capital. Am. Econ. Rev. 1961; 51(1): 1–17. Reference Source

[37] Shapiro C, Stiglitz JE: Equili-brium Unemployment as a Worker Discipline Device. Am. Econ. Rev. 1984; Vol. 74(3): pp. 433–444. Reference Source

[38] Suhányi L, Suhányiová A, Kádárová J, et al.: Relationships between average wages in the manufacturing sector and economic indicators of the manufacturing sector in the region of Visegrad Group countries. Sustainability. 2023; 15(5): 4164. Publisher Full Text

[39] Todaro R, Godoy L, Abramo L: Desempeño laboral de hombres y mujeres: opinan los empresarios. Cadernos pagu. 2002; 197–236. Publisher Full Text

[40] Tsou MW, Yang CH: Does gender structure affect firm productivity? Evidence from China. China Econ. Rev. 2019; 55: 19–36. Publisher Full Text

[41] WCP: Empresas lideradas por mujeres son más productivas, tienen mayor calidad y son más sostenibles. Colombia productiva; 2024. Reference Source

[42] Wooldridge JM: On estimating firm-level production functions using proxy variables to control for unobservables. Econ. Lett. 2009; 104(3): 112–114. Publisher Full Text

[43] World Bank: Women Business and the Law 2024. World Bank Group; 2024. Reference Source

[44] World Economic Forum: Global Gender Gap Report 2024. World Economic Forum; 2024. Reference Source

Productivity, real wages, and gender. A study in Colombian manufacturing

Abstract

Background

Method

Results

Conclusions

Keywords

Introduction

Methods

Literature review

TFP estimates by gender

(1)

(2)

(3)

(4)

(5)

(6)

(7)

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Data analysis1

Table 1. Estimates of labor output elasticities. Total labor and labor by gender.

Figure 1. Wages and Productivity by female and male firms. Average in Logarithms.

Table 2. Wages and Productivity. Female and male firms by sector. Average in Logs.

Empirical model and estimates

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Results

Table 3. Empirical Estimates.

Discussion

Ethical considerations

Reporting guidelines

Data availability

References

Footnotes

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