metadata: authors: 0: author: Reed,Tristan
1: author: Tran,Trang Thu
count: World
docna: 0: docna: The Large-Firm Wage Premium in Developing Economies
docty: Policy Research Working Paper
abstracts: cdata!: Large firms pay higher wages. In
            developing economies, the large-firm wage premium is
            comparable to the average gap between male and female wages,
            or two-thirds of the gap between urban and rural wages.
            There is substantial variation across countries in the share
            of the premium that is explained by sorting of human capital
            into large firms. The average large-firm wage premium
            declines in national income and has declined over time.
            Across industries, it is highest in public utilities and
            commerce. These stylized facts suggest several hypotheses
            about differences between labor markets in developing and
            advanced economies.
repnme: repnme: The Large-Firm Wage Premium in Developing Economies
display_title: The Large-Firm Wage Premium in
            Developing Economies
listing_relative_url: /research/2019/09/31392733/large-firm-wage-premium-developing-economies
publishtoextweb_dt: 2019-09-09T00:00:00Z
no_of_pages: 16
author_keys: 0: author_key: 948891
1: author_key: 1129566
model_extractions: Policy Research Working Paper 8997 The Large-Firm Wage Premium in Developing Economies _Tristan Reed Trang Thu Tran_ Development Economics Development Research Group September 2019
Policy Research Working Paper 8997

# **Abstract**

Large firms pay higher wages. In developing economies, the large-firm wage premium is comparable to the average gap between male and female wages, or two-thirds of the gap between urban and rural wages. There is substantial variation across countries in the share of the premium that is explained by sorting of human capital into large firms.

The average large-firm wage premium declines in national income and has declined over time. Across industries, it is highest in public utilities and commerce. These stylized facts suggest several hypotheses about differences between labor markets in developing and advanced economies.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at treed@worldbank.org (Reed) and ttran6@worldbank.org (Tran).

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Large‐Firm Wage Premium in

# Developing Economies<sup>1</sup>

Tristan Reed Trang Thu Tran

**Keywords:** Large‐firm wage premium; firm size

**JEL Classification:** J31; L25 ; O15

> 1 Reed : <u>treed@worldbank.org; Tran: ttran6@worldbank.org</u>
# Introduction

Larger firms pay higher wages. Moore (1911) first observed this regularity among Italian textile mills, and a subsequent literature has established the large‐firm wage premium (LFWP)‐‐‐or the positive difference between wages paid by large firms and those paid by small ones‐‐‐as a stylized fact of the labor market. Here, we offer the first systematic comparison of the LFWP across developing economies, using a common regression specification and harmonized controls from 69 surveys of individual income across 25 developing economies compiled by the World Bank.

A worker’s wage summarizes their productivity as well as any rents associated with employment in a particular firm. Prior LFWP literature (e.g., Brown and Medoff, 1989; Abowd, et. al., 1999, <mark>Troske, 1999</mark> ) has been primarily concerned with identifying whether the observed LFWP is due to the sorting of higher ability workers into larger firms (worker effects), or due to large firms paying higher wages to otherwise similar workers (firm effects). Overall, sorting is understood to explain at least half of the effect, though its contribution may vary depending on national context, as we confirm here.<sup>2</sup> Identifying this sorting effect requires a panel of workers with firm identifiers, which is not commonly available, especially in smaller low‐income economies or in a format that is comparable across many countries at once. We address this issue in the cross‐section by employing a heuristic benchmarking technique that reports the magnitude of potential sorting on unobserved worker quality.

The analysis reveals five stylized facts about the LFWP in developing economies. First, conditional on occupation, education and experience, the LFWP is comparable to the average gap between male and female wages, or two‐thirds of the gap between urban and rural wages. Second, the LFWP has been declining over time, consistent with what is observed in advanced economies (Bloom, et. al., 2018). Third, the LFWP is also declining in national income.<sup>3</sup> <mark>Fourth, there is large variation across countries in the share of the premium explained by sorting on human capital (i.e., education and experience), conditional on occupation. Fifth, the LFWP in developing economies is low in manufacturing relative to commerce, a pattern that is reversed in advanced economies (B</mark> erlingieri, et. al., 2018) <mark>. In concluding</mark>

> 2 For instance, <mark>in Brazil, Menezes‐Filho, Muendler, and Ramey (2008) find the premium to be entirely explained by selection, whereas in Ghana and Kenya, Söderbom, Teal, and Wambugu (2005) find that a substantial portion remains unexplained.</mark> 3 The LFWP has been estimated in several developing economies, but its variation with income has not been tested systematically. See, for instance, <mark>Dobbelaere (2004) on Bulgaria; Funkhouser (1998) on Guatemala; Söderbom, Teal, and Wambugu (2005) on Kenya and Ghana; Schaffner (1998) on Peru; and Velenchik (1997) on Zimbabwe.</mark>

2
<mark>remarks, we discuss several hypotheses that emerge from these facts regarding systematic differences between the labor markets of developing and advanced economies.</mark>

# Data

The International Income Distribution Database (I2D2) is a harmonized database of households and individuals drawn from nationally‐representative surveys, built to compare income equality across countries. Depending on the country, the I2D2 draws observations from different types of surveys, usually conducted by national statistical agencies, including Household Budget Surveys, Household Income and Consumption Surveys, Labor Force Surveys, and multi‐topic surveys (such as Living Standards Measurement Study Surveys). It includes major economies such as Bangladesh, China, Vietnam, Mexico and Chile. Crucially, along with hourly wages, the data include the size of the worker’s firm, along with indicators of worker demographics, occupation, industry and wage.

We restrict attention to the 69 surveys (25 countries) in which data on the size of the employer firm are provided, which cover 1988‐2015. Where data are available, I2D2 includes a categorical variable for the size of the workers’ firm (for instance, <100 employees, between 100‐500 employees, or more than 500). The breakpoint used varies across countries however, and in the lowest income countries very few or zero individuals report working at firms with more than 500 employees, or even 100. For this reason, to maximize the number of countries in the sample, we define the LFWP as the (log) difference in wages between those workers in firms with 100 or more employees, and those workers in firms with fewer employees. The share of workers in our sample in large firms ranges from 31 percent in China and 26 percent in Chile, to 0.5 percent in Mali and 0.3 percent in the Comoros.

# Results

The LFPW is estimated using the following regression specification, for worker i, in a survey in country k and year t: �,� where wage� is the hourly wage (in real dollars, at current exchange rates); Large� indicates whether worker i’s employer has more than 100 employees; α<sup>�,�</sup> is a country year fixed effect accounting for productivity differences across countries and years; Xi is a vector of Mincerian worker characteristics (i.e. age, age squared, and dummies for completion of primary, secondary and tertiary education); 3
j,k �,� Wi is a vector of job characteristics (i.e., industry and occupation fixed effects); and ε� is a normally distributed i.i.d. error term. The parameter of interest is θ . In some specifications, we run this regression at the country‐year or industry level, allowing for more flexibility in parameter values.

Table 1 presents estimates of this equation, pooling all country years. Column 1 shows the raw unconditional LFWP, at 0.340 (standard error = 0.0116) , implying that, across all countries, wages are on average 34 percent higher in firms with more than 100 employees. Column 2 adds 9 industry fixed effects, which change the coefficient little. Column 3 adds occupation fixed effects, which reduce the premium to 0.276 (s.e. = 0.0118). Column 4 adds the Mincerian human capital controls for schooling and experience, reducing the premium further to 0.180 (s.e. = 0.0125), or an 18 percent difference. The substantial decline in the premium once controlling for occupation indicates that job requirements have specific effects on wages, as does the human capital embodied in the education and experience of the worker. Both factors play a role in explaining the observed (unconditional) premium.

A concern with hedonic wage regressions is that the parameter θ could reflect sorting of workers into firms: selection on unobserved aspects of human capital, not captured by average quality of worker in the occupation (col. 3), or Mincerian human capital controls (col. 4). Such selection would imply the parameter does not have a causal interpretation, though it still accurately measures an empirical compensation differential between firms of different sizes in the labor market. Nunn and Wantchekon (2011), building on the insight of Altonji, Elton and Taber (2005), propose a ratio to gauge how large selection on unobservables would have to be to explain an observed premium. Comparing column 3, which conditions on job observables, and column 4 which adds the Mincerian controls, the ratio is 0.180/(0.276‐0.180) = 1.875, indicating that selection on unobservables must be at least 1.875 times selection on observables to explain the entire premium measured in column 4.

Bloom et al. (2018) show that in the United States, the LFWP has been declining within industries since 1980.<sup>4</sup> To test for this trend, column 5 adds a linear time trend to the regression, which shows a decline in the LFWP over time of ‐8.72 percentage points per decade.

> 4 They find that this decline is explained largely by a decline in the extent to which large firms pay the same workers more, rather than a decline in the quality of workers at large firms.

4
It is helpful to summarize the LFWP by benchmarking its magnitude to other persistent wage differentials observed in the literature: the gender wage gap (Goldin, 1992, <mark>Altonji and Blank, 1999, B</mark> lau and Kahn, 2017), and the urban‐to‐rural wage gap (Harris and Todaro, 1970, Glaeser and Mare, 2001, Combes et. al., 2008, Young, 2013, <mark>Jones, D’Aoust, and Bernard,</mark> 2017).<sup>5</sup> Column 6 adds dummies for whether the worker is female and whether the worker lives in an urban, rather than rural, area. In this specification, the LFWP is almost identical in absolute value to the average discount of female wages to male wages, once controlling for occupation, industry, education and experience. The urban wage premium however remains larger by approximately 50 percent. Column 7 interacts the urban and female dummies with the large firm dummy, to test for heterogeneity in the LFWP by location and worker. The interaction with urban is positive and significant, indicating that the large firm wage premium is even higher in urban areas. The interaction with female is positive but not significant, and also smaller than the urban premium in magnitude.

# The LFWP across developing economies and sectors

An unanswered question in the LFWP literature has been whether the premium varies across countries and levels of development. To investigate the question systematically, we estimate the wage premium separately for each survey, allowing more flexibility in parameters, for instance the returns to human capital as well as occupation and industry wage differentials. Table 2 shows the results of specifications identical to columns 3 and 4 of Table 1 estimated at the country level with year fixed effects, as well as the Nunn and Wantchekon (2011) ratio introduced above. This analysis reveals that across developing economies, there is substantial variation in the importance of human capital sorting in determining the LFWP.

This variation emerges among countries with similar GDP per capita: In Chad for instance (real GDP per capita: $863), the ratio of selection on unobservables required to explain the LFWP (conditional on occupation and industry) must be as large as 2.1 times the observed selection on education and experience. In the Kyrgyz Republic (real GDP per capita: $921), this ratio must be 25.1 times. Further, in Moldova and Nicaragua, the LFWP actually rises when conditioning education and experience,

> 5 Oi and Idson (1999) take this approach in U.S. data and find an LFWP of 35%: comparable to a gender gap of 36% of men over women, and greater than the wage gap of 14% for white over black employees. They also find that the premium is larger for men and larger in the United States than in France, Germany, Italy, and Japan.

5
suggesting that there can be negative sorting, with workers with _less_ human capital selecting into large firms. Clearly, the ability of observable and unobservable human capital to explain the LFWP varies substantially across national contexts.

To investigate whether the LFWP varies systematically with level of national income, Figure 1 displays the LFWP as in column 4 of Table 1, which includes Mincerian controls, and a best fit line between the points. Though there is substantial variance in the premium, with even negative point estimates in Bangladesh, Nicaragua, Honduras and Guatemala, this overall slope is statistically significant and negative, at ‐0.09 (s.e. = 0.037). Further, there is less variance in the estimates as income increases.

Past literature has also been concerned with whether the LFWP varies across industries. Figure 2 displays the LFWP estimated separately for each industry, with fixed effects for country year, and the same controls as in column 4. Some of the differences are not surprising. The premia for public utilities and public administration workers may reflect rents associated with government employment. The high coefficient on commerce however, which encompasses wholesale and retail trade, is surprising given findings from advanced economies. Berlingieri, Calligaris and Criscuolo (2018) identify larger premiums in manufacturing relative to services in OECD countries and argue that this may be explained by a stronger correlation between firm‐size and productivity in manufacturing relative to services.

# Concluding Remarks

Taken together, these new comparable cross‐country results suggest several interesting hypotheses regarding labor markets in developing economies, which warrant further inspection from researchers.

First, that the LFWP is smaller than the urban rural wage gap suggests that average productivity differences across space may be larger than across firms within a location. If true, this would suggest that aggregate productivity may be more responsive to moving factors out of rural and into urban areas, relative to reallocating factors to more productive (larger) firms within cities.

Second, if the LFWP is declining in national income, as observed in Figure 1, it must be that either structural frictions (e.g., access to finance, monopoly rents, higher monitoring costs within firms, or worker search costs) are more pronounced in poor countries, or alternatively that unobserved worker human capital (for instance trustworthiness, which is relevant in the absence of effective legal 6
institutions) is disproportionately important in determining wages in poor countries. For the LFWP to be declining over time, as observed in Table 1, one of these factors must also be declining in its importance in determining wages.

Third, the cross‐industry results specifically suggest that in developing economies, the firm‐size productivity correlation might, if anything, be stronger in services, specifically in wholesale and retail trade, relative to manufacturing, contrary to what is observed in advanced economies. One potential underlying reason is the predominance of small informal (and less productive) businesses in the wholesale and retail sector specifically in developing countries (LaPorta and Shliefer, 2014). Another potential explanation may be that a lack of contract enforcement affects the services sector more disproportionally, for instance due to higher monitoring costs, driving a higher LFWP that compensates for unobservable human capital, such as trustworthiness.

7
# Tables and Figures

_Table 1: Real Wages and Firm Size_

|VARIABLES|(1)<br>ln(wage)|(2)<br>ln(wage)|(3)<br>ln(wage)|(4)<br>ln(wage)|(5)<br>ln(wage)|(6)<br>ln(wage)|(7)<br>ln(wage)|
|---|---|---|---|---|---|---|---|
|Large|0.340|0.335|0.276|0.180|0.252|0.162|0.118|
||(0.0116)|(0.0114)|(0.0118)|(0.0125)|(0.0149)|(0.0124)|(0.0160)|
|Female||||||‐0.167|‐0.176|
|||||||(0.0103)|(0.0121)|
|Large X Female|||||||0.0314<br>(0.0214)|
|Urban||||||0.243<br>(0.0130)|0.235<br>(0.0136)|
|Large X Urban|||||||0.0427<br>(0.0206)|
|Large X Decade<sup>a</sup>|||||‐0.0872<br>(0.0189)|||
|Observations|1,281,234|1,278,100|1,234,681|912,116|912,116|907,619|907,619|
|R‐squared|0.815|0.824|0.832|0.851|0.851|0.855|0.855|
|Country‐Years|69|69|66|60|60|58|58|
|Unique Countries|25|25|23|22|22|20|20|
|Country‐Year FE|YES|YES|YES|YES|YES|YES|YES|
|Industry FE|NO|YES|YES|YES|YES|YES|YES|
|Occupation FE|NO|NO|YES|YES|YES|YES|YES|
|Mincer Controls|NO|NO|NO|YES|YES|YES|YES|

a) normalized so 2000 = 0.

Notes: Wage is hourly, in real US dollars. Large indicates that the employer has more than 100 employees. Mincer controls include age, age squared, and dummies for completion of primary, secondary and tertiary education. Female indicates that the worker is female. Urban indicates the worker lives in an urban area. Heteroskedasticity robust standard errors in parentheses.

8
_Table 2: LFWP by country, with and without Mincer controls_

|**Country**|**ISO**<br>**Code**|**LWFP**<br>**conditional**<br>**on**<br>**occupation**<br>**and**<br>**industry,**<br>**compare**<br>**to col (3)**<br>**of Table 1**|**LFWP**<br>**conditional**<br>**on**<br>**occupation,**<br>**industry,**<br>**education**<br>**and**<br>**experience,**<br>**compare to**<br>**col (4) of**<br>**Table 1**|**Magnitude of**<br>**selection on**<br>**unobservables**<br>**relative to**<br>**education and**<br>**experience**<br>**required to**<br>**explain LFWP**<br>**conditional on**<br>**occupation**<br>**and industry**|**First Year**|**Last**<br>**Year**|**Number of**<br>**observations**|**GDP per**<br>**Capita (2005**<br>**US Dollars)**|
|---|---|---|---|---|---|---|---|---|
|Niger|NER|1.15<br>(0.18)|0.785<br>(0.18)|2.1|2007|2011|5,794|339|
|Comoros|COM|0.936<br>(0.73)|0.621<br>(0.81)|2.0|2013|2013|809|1,373|
|Cameroon|CMR|0.681<br>(0.05)|0.552<br>(0.05)|4.3|2007|2014|21,296|1,311|
|Moldova|MDA|0.315<br>(0.06)|0.397<br>(0.06)|‐4.9|2006|2012|2,412|1,953|
|Chad|TCD|0.47<br>(0.20)|0.32<br>(0.18)|2.1|2011|2011|2,545|863|
|Bahamas, The|BHS|0.324<br>(0.07)|0.267<br>(0.07)|4.6|2001|2001|1,752|31,911|
|Kyrgyz Republic|KGZ|0.272<br>(0.09)|0.261<br>(0.09)|25.1|2011|2011|3,280|921|
|Madagascar|MDG|0.305<br>(0.06)|0.256<br>(0.06)|5.2|2012|2012|16,088|408|
|Albania|ALB|0.265<br>(0.07)|0.246<br>(0.07)|13.1|2005|2005|1,847|3,063|
|Lebanon|LBN|0.289<br>(0.10)|0.227<br>(0.11)|3.6|2011|2011|354|7,453|
|China|CHN|0.292<br>(0.01)|0.2<br>(0.01)|2.2|2002|2013|29,841|4,026|
|Thailand|THA|0.242<br>(0.01)|0.18<br>(0.01)|2.9|1988|1995|61,061|2,893|
|Tajikistan|TJK|0.279<br>(0.09)|0.179<br>(0.31)|1.8|2009|2009|1,301|719|
|Papua New<br>Guinea|PNG|0.175<br>(0.08)|0.137<br>(0.09)|3.6|2009|2009|1,134|1,811|
|Paraguay|PRY|0.157|0.118|3.1|2007|2012|13,680|4,146|
|||(0.05)|(0.05)||||||
|Mexico|MEX|0.186|0.117|1.7|2008|2012|74,013|9,476|
|||(0.02)|(0.02)||||||
|Vietnam|VNM|0.101|0.09|8.6|1997|2010|103,805|1,297|
|||(0.02)|(0.02)||||||

9
|Honduras|HND|0.168<br>(0.01)|0.087<br>(0.01)|1.1|2001|2011<br>|100,016<br>|1,789|
|---|---|---|---|---|---|---|---|---|
|Chile|CHL|0.116<br>(0.01)|0.076<br>(0.01)|1.9|1990|2013<br>|458,846<br>|10,664|
|Guatemala|GTM|0.14<br>(0.03)|0.071<br>(0.03)|1.0|2000|2003<br>|16,448<br>|2,573|
|Nicaragua|NIC|‐0.059<br>(0.05)|‐0.074<br>(0.05)|‐5.2|2005|2005<br>|9,910<br>|1,409|
|Bangladesh|BGD|‐0.465<br>(0.03)|‐0.431<br>(0.03)|12.7|2010|2015<br>|22,904<br>|783|
|Year Fixed Effec|ts|YES|YES||||||
|Occupation Fixe|d Effects|YES|YES||||||
|Industry Fixed E|ffects|YES|YES||||||
|Mincer controls||NO|YES||||||

_Notes:_ Wage is hourly, in real US dollars. Large indicates that the employer has more than 100 employees. Mincer controls include age, age squared, and dummies for completion of primary, secondary and tertiary education. Heteroskedasticity robust standard errors in parenthesis 10
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