rafmacalaba/datause-encoder-data huggingface dataset:
and Sjöholm 2005), which shape both domestic and foreign firm characteristics as well as the transmission channels for spillovers (Paus and Gallagher 2008).

Using a cross-section of more than 25,000 domestic manufacturing firms in 78 low- and middle-income countries from the World Bank’s Enterprise Surveys Indicator Database, <mark>we assess how mediating factors influence intra-industry productivity spillovers to domestic firms from FDI.</mark> This paper contributes to the growing body of research on mediating factors for FDI spillovers in several ways: First, most studies are limited to examining a single mediating factor, with the majority focusing on the absorptive capacity of domestic firms. Fewer studies analyze the role of foreign investors or host country characteristics and the institutional context for FDI-enhanced productivity spillovers. To our knowledge, only Havranek and Irsova (2011) control for all three types of mediating factor. Their meta-analysis uses the t-statistic of existing FDI spillover estimates from other studies as the dependent variable. In contrast, this study estimates the impact of FDI directly on productivity, and introduces all three types of mediating factors in the form of interaction terms with the FDI presence variable. While the methodological approach of this study is similar to Blalock and Gertler (2009), their study focuses only on a domestic firm’s absorptive capacities.

Secondm related to the relatively low number of studies that take into account the characteristics of foreign investors is the fact that studies have neglected the specific dynamics within global value chains (GVCs). Global production networks are “led by large firms based typically in the industrialized countries, and relying often on complex networks of suppliers around the world” (Milberg and Winkler, forthcoming). The potential for FDI spillovers, however, is determined by the GVC in which foreign firms operate and by the specific GVC dynamics, including the FDI motive and sourcing strategy, among others (Paus and Gallagher 2008). This study addresses this gap in the literature by including two measures that proxy for foreign investors’ FDI motive and sourcing behavior.

Third, most studies adding mediating factors focus on one or a few variables only. Even the meta-analysis by Meyer and Sinani (2009) covers only seven institutional variables<sup>4</sup> . This study takes a more comprehensive approach, focusing on four variables reflecting the FDI spillover

> 4 In addition, their study also differs in terms of methodology, as it uses the t-statistic of FDI spillover estimates from existing research as the dependent variable.

3
less than the average wage for _s+1_ graduates at each age throughout their work lives. In other words, when calculating the difference between wages of girls with _s_ level of education and those with _s+1_ level of education, we adjust downward by 10% the _s+1_ wages. Second, based on scarcer research from developing countries, we assume that the less educated girls, when gaining an additional level of education, would earn 20% less than the currently observed average wage for girls with _s+1_ level of education.

We make yet another adjustment to account for the possibility that the influx of more educated girls into the labor force will reduce the equilibrium wage for that skillssegment of the labor market. Following on Behar (2004), we use labor demand elasticities of -0.56 to -0.8 for sub-Saharan Africa. Thus, for each 1% increase in the share of the labor force with a higher level of education, we decrease the average wage by 0.56% and 0.8%. For countries in Asia, we draw on Goldar (2008) and apply an elasticity of -0.4. In Latin America and the Caribbean the elasticity is -0.2, as in Fajnzylber and Maloney (2005).

The data come from various sources. The returns to education that are estimated in Mincer equations are drawn from country studies. The average wage is taken from the ILO’s KILM database. The number of girls dropping out of each grade level is from the World Bank’s EDStats.

To allow for some degree of comparison across countries, we state the final cost as a share of the country’s most recent GDP. This also allows us to generate all calculations in local currency but still have comparability ex-post. It adjusts for the size of the economy and, to some degree population, allowing for a better sense of the magnitude of the gain in the context of the country’s output. Finally, it better communicates the implications for economic growth than a raw currency amount would. The value of annual GDP is taken from the World Bank’s WDI.

# **b. Methodology for computing the cost of inactivity and joblessness**

Following Cunningham and García-Verdú (forthcoming) and Chaaban (2007), the estimate captures the opportunity cost of girls’ inactivity and joblessness in regard to lost wages and productivity. The ―inactivity rate‖ is the share of girls age 15-24 who are not working; this includes girls who are both in school and out of school. We consider two 8
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

|**Region**|**Regional**<br>**head-**<br>**quarters**|**Manufact-**<br>**uring**|**Materials**<br>**manageme**<br>**nt**|**New**<br>**product**<br>**introductio**<br>**n**|**After-sales**<br>**service**|**Tecnology**<br>**developme**<br>**nt**|
|---|---|---|---|---|---|---|
|San Jose, CA, U.S.||X|X|X|||
|Atlanta, GA, U.S.||X|X|X|X||
|Westborough, MA, U.S.||X|X|X|||
|Suwanee, GA , U.S.||X|X|X|X||
|Fremont, CA, U.S.||X|X|||X|
|Everett, WA, U.S.||X|X||||
|Raleigh, NC, U.S.||X||X|||
|Aguadilla, Puerto Rico, U.S.||X|||||
|Aguada, Puerto Rico, U.S.||X|||||
|Los Angeles, CA, U.S.|||||X||
|Austin, Texas, U.S.|||||X||
|Memphis, TN, U.S.|||||X||
|Louisville, KY, U.S.|||||X||
|San Jose, CA, U.S.||||||X|
|Vaughn, Canada|||||X||
|Calgary, Canada||X|||||
|Guadalajara, Mexico||X|X||||
|Monterrey, Mexico||X|X|X|||
|São José dos Campos, Brazil||X|X|X|X||
|Hortolândia, Brazil||X|||X||

_Source:_ Solectron company website

# **References**

Akinwande, Akintunde, Douglas Fuller, and Charles Sodini. 2005. “Leading, Following or Cooked Goose: Explaining Innovation Successes and Failures in Taiwan's Electronics Industry.” In _Global Taiwan: Building Competitive Strengths in a New International Economy._ ed. Suzanne Berger and Richard Lester, paper 3. Armonk, NY: ME Sharpe.

- Balconi, Margherita. 2002. “Tacitness, Codification of Technological Knowledge and the Organisation of Industry.” _Research Policy_ 31: 357–79.

Baldwin, Carliss, and Kimberly Clark. 2000. _Design Rules; Unleashing the Power of Modularity_ . Cambridge, MA: MIT Press.

Barnard, Bruce. 2009. “Container Carrier Losses Reach $11 Billion.” _The Journal of Commerce Online_ Dec. 22, 2009. Accessed at <u>http://www.joc.com/maritime/container-carrier-lossesreach-11-billion</u> on January 15, 2010.

Bonaglia, Frederico, Andrea Goldstein, and John Mathews. 2007. “Accelerated Internationalization by Emerging Multinationals: The Case of the White Goods Sector.” _Journal of World Business_ 42: 369–83.

Borrus, M., D. Ernst, and S. Haggard (eds.). 2000. _International Production Networks in Asia_ , Routledge: London and New York.

46
tertiary education, where the vast majority of beneficiaries (over 85 percent) are concentrated in the top quintile. See Figure 3, below. The methodology for assigning beneficiaries in scenarios 1 and 2 (see Table 8 above) does not alter these findings.

Figure 3: Distribution of beneficiaries for education <!-- Start of picture text --> Distribution of enrollment (a) Distribution of enrollment (b)<br>800 400 800 400<br>686 718 699 694 684 676<br>700 350 700 653 651 641 350<br>606<br>684<br>600 661 644 300 600 634 648 300<br>603 610<br>500 250 500 573 250<br>400 483 200 400 462 200<br>136<br>300 150 300 150<br>98<br>200 84 100 200 100<br>100 24 33 53 64 50 100 18 28 41 62 46 50<br>1 1 2 7 1 0 0 6<br>- - - -<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>(Poorest 20%) (Richest 20%) (Poorest 20%) (Richest 20%)<br>Scenario 1 Total Basic High Tertiary Scenario 2 Total Basic High Tertiary<br>Welfare composition of enrollment (c) Welfare composition of enrollment (d)<br>100% 100%<br>90% 16% 90% 16%<br>80% 41% 80% 40%<br>70% 70%<br>60% Q5 (Richest 20%) 60% Q5 (Richest 20%)<br>85% 86%<br>50% Q4 50% Q4<br>Q3 Q3<br>40% 40%<br>Q2 Q2<br>30% 30%<br>Q1 (Poorest 20%) Q1 (Poorest 20%)<br>20% 20%<br>10% 21% 2% 10% 22%<br>7% 7% 1%<br>0% 0%<br>Basic High Tertiary Basic High Tertiary<br>Scenario 1 Scenario 2<br>Source: LCMS VI<br>Note: Poorest quintile represents the lowest consumption level; richest quintile captures the part of the distribution<br>with the largest consumption per capita.<br>Total and Basic Education (thousands)<br>High School and Tertiary Education (thousands) High School and Tertiary Education (thousands)<br>Quintile participation<br>Total and Basic Education (thousands)<br>Quintile participation<br><!-- End of picture text -->

# **5.1.2. Healthcare Services**

Those individuals who reported being ill or injured during the 15 days before the survey follow a uniform distribution across socioeconomic groups. (See ―Injured or Ill‖ in Figure 4.) The same distribution is observed both for those who did not report visiting any public facility and among those visiting a public 23
Policy Research Working Paper 10653

# Unraveling the Factors behind Women’s Empowerment in the Labor Market in Colombia

_Ana María Iregui-Bohórquez Ligia Alba Melo-Becerra María Teresa Ramírez-Giraldo Ana María Tribín-Uribe Héctor M. Zárate-Solano_ Development Economics Global Indicators Group December 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
# **Table 10. Growth and credit regulations**

|VARIABLES|(1)<br>growth|(2)<br>growth|(3)<br>growth|(4)<br>growth|(5)<br>growth|(6)<br>growth|(7)<br>growth|(8)<br>growth|(9)<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-7.337***|-7.339***|-6.732***|-8.078***|-8.048***|-8.084***|-9.175***|-9.279***|-11.10***|
||(-5.221)|(-5.226)|(-3.727)|(-4.491)|(-4.689)|(-4.680)|(-5.116)|(-4.998)|(-5.332)|
|FDI||0.000659|0.000453|-7.04e-05|2.29e-05|-8.50e-05|-0.000461|-0.000591|0.00138|
|||(0.517)|(0.515)|(-0.0712)|(0.0242)|(-0.0884)|(-0.387)|(-0.498)|(0.885)|
|Country risk (ICRG)|||0.190**|0.102|0.0923|0.0939|0.0882|0.0904|0.164**|
||||(2.355)|(1.560)|(1.446)|(1.500)|(1.357)|(1.400)|(2.020)|
|Government consumption||||-0.202**|-0.169**|-0.167**|-0.116|-0.123|-0.113|
|||||(-2.575)|(-2.063)|(-2.048)|(-1.420)|(-1.488)|(-1.316)|
|Rents from natural resources|||||0.0559*|0.0559*|0.00739|0.0202|-0.0180|
||||||(1.716)|(1.704)|(0.221)|(0.606)|(-0.514)|
|Political freedom and civil||||||||||
|<br>rights (FH)||||||0.234|0.236|0.280|-0.0209|
|||||||(0.613)|(0.662)|(0.753)|(-0.0576)|
|Trade (X and M)|||||||0.0634***|0.0622***|0.0577***|
||||||||(3.581)|(3.533)|(3.178)|
|Corruption (CPI)||||||||0.159|0.0633|
|||||||||(0.550)|(0.177)|
|Credit to private sector|||||||||-0.0158<br>(-1.031)|
|Lagged GDP growth|0.429***|0.429***|0.382***|0.363***|0.348***|0.350***|0.323***|0.313***|<br>0.256***|
||(9.171)|(9.177)|(5.335)|(4.763)|(5.094)|(5.061)|(4.903)|(4.734)|(3.108)|
|Delta DTF (credit system)|0.0678***|0.0674***|0.136***|0.140***|0.140***|0.140***|0.123***|0.123***|0.126***|
||(2.734)|(2.715)|(3.586)|(3.452)|(3.440)|(3.424)|(3.270)|(3.247)|(2.989)|
|Constant|66.03***|66.06***|53.10***|73.14***|72.24***|71.64***|75.99***|76.17***|91.90***|
||(5.307)|(5.313)|(3.095)|(4.339)|(4.546)|(4.462)|(4.703)|(4.692)|(5.019)|
|Observations|1,004|1,001|751|682|676|671|666|656|537|
|Number of cod2|175|175|131|124|124|123|123|121|118|
|chi2|93.94|93.97|139.9|129.1|139.8|136.6|100.8|99.40|91.30|
|Robust z-statistics in<br>parentheses||||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||||

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from Arellano-Bond dynamic panel estimator regressions of growth on the change in the distance to the credit system regulatory frontier. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

26
<!-- Start of picture text --> OPEX* Markups<br>Includes PT (2017) correction for intangible capital<br>Low RMAN Industries High RMAN Industries<br>2 2<br>1.81.6 an- 1 .86<br>pad eee 14 a ae<br>124 12<br>ne Sta eeee ~~ ee<br>~ ete TT Oe ek. cee<br>1 1<br>SESRS PSS SQSsegseesegscranss SESRSL SS SASSI OSaSsegecans4<br>-- -- - 25th percentile ——— median<br>--------- 75th percentile ©————— 90th percentile<br><!-- End of picture text -->
_Panel B: Inequality_

|||Dropping||Dependent va|riable: Gini index<br>Eldi 10|Eldi 10|S.E.||
|---|---|---|---|---|---|---|---|---|
||Balanced<br>panel|countries with<br>few<br>subregions|Excluding<br>USA|Excluding<br>India|xcung<br>percent cold<br>countries|xcung<br>percent hot<br>countries|clustered<br>country<br>level|Spatially-<br>corrected<br>Conley S.E.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|0.285***|0.363**|0.288***|0.386***|0.274**|0.258***|0.213**|0.285**|
||(0.086)|(0.142)|(0.086)|(0.086)|(0.111)|(0.085)|(0.087)|(0.143)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of countries|134|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,604|3,990|4,448|4,888|4,547|4,674|4,957|4,958|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In column (8), we use the Conley standard error (Conley, 1999) to adjust for spatial correlation based on a radius of 200 km following Graff Zivin et al. (2020). *** p<0.01, ** p<0.05, * p<0.1 53
Table 10: Returns and political regime type.

|||Spe|cificationa:||
|---|---|---|---|---|
||A|B|IC|D|
|IRIS Indicator of Regime Ty<br>Dummy variable by regime t|pe (IIRT) (1<br>ipe (default|9 7 4-1 98 7b; N=72<br>isleastdemocrati|5)<br>c, IIRT= 1)||
|IIRT=2|-0.48|-0.56|0.24|0.49|
||(.767)|(.733)|(.885)|(.766)|
|IIRT=3|5.46|1.61|3.52|1.04|
||(.107)|(.636)|(.296)|(.757)|
|IIRT=4|-0.076|-3.68|-0.82|-3.35|
||(.967)|(. 058)|(.657)|(.091)|
|_IIRT=5_(most democratic)|3.94|0.322|4.17|0.458|
||_(.055)_|(.892)|(.043)|(.847)|
|Alesina democracy index (A<br>Dummy variable by democ|DI) (19 7 4-19<br>level, def|8 2b;N=369)<br>ault is least demo|cratic (ADI=3)||
|ADI=2|3.10|2.86|2.38|2.41|
||(.225)|(.257)|(0.349)|(.345)|
|ADI= 1 (most democratic)|2.93|-1.27|1.93|-1.52|
||1 (.117)|(.542)|(.311)|(.465)|

Notes: a) for description of the variables included in each specification see table 2; b) Annual values for time period; c) p-levels in parenthesis. Source: Authors' calculations.

In summary, there is no clear pattern that suggests that countries with more democratic regimes (as classified by the available indices) have better projects, as measured by ERRs.

The very least democratic countries perhaps do more poorly than others, but moving towards a democracy from other levels not have any empirically discernible impact on project returns.

- 31 -
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# **3. Empirical framework**

The sales growth equations that we estimate is based on the panel data structure that Goddard, Wilson and Blandon (2002) propose as a framework for testing Gibrat’s law, that is, roughly the proposition the expected growth rate of a firm is independent of its initial size. Let _y it_ log annual sales of business _i_ in year _t_ . A specification of the DGP of _yit_ nesting Gibrat’s law is Δ _yit_ = α _i_ + δ _t_ + (β− )1 _yit_ −1 + _u it_ ; _uit_ = ρ _u it_ −1 +ν _it_ (1) where α _i_ and<sup>δ</sup> _t_<sup>are firm effects and time effects respectively,</sup> ν _it_ is a zero-mean, error term distributed iid normal with varianceσν2 ; ρ is a persistence parameter, and β is a ‘convergence’ parameter governing the relationship between the size of a firm and its growth rate. Gibrat’s law impliesβ= 1 , in which case all firms grow at a uniform mean rate (unrelated to size), and the variance and concentration of the size distribution increases over time. If β< 1 , on the other hand, firm size is mean-reverting, smaller firms growing faster, and the variance of the distribution tends to an equilibrium value. For the purpose of estimation we use the following re-parameterization of the process:

* * Δ _y it_ = α _ii_ + δ _t_ + (β− )1 _yit_ −1 + ρΔ _yit_ −1 + υ _it_ (2) * * where α _i_ = α _i_ 1( − ρ), δ _t_ = δ _t_ − ρδ _t_ −1, υ _it_ =ν _it_ + ρ1( − β) _yit_ −2 so that υ _it_ =ν _it_ under the null β= 1.

We bring business environment variables into this picture by assuming that they account * * for some of the unobserved individual firm and time effects subsumed underα _i_ + δ _t_ . * * The easiest way of identifying the potential effect of access to finance on α _i_ + δ _t_ is probably to include the financial variables that are commonly included in econometric investment functions to test for liquidity constraints, that is, to augment equation (2) by including lagged profitability, (Π / _K_ ) _it_ −1 , and lagged indebtedness, ( _D_ / _K_ ) _it_ −1 among the right hand side variables. The rationale for the augmentation is that terms in such financial variables naturally emerge from the structural investment equation that we estimate in a companion paper.<sup>4</sup> If the same variables do indeed explain any part of the observed variation in investment rates, one would expect then to enter firm growth equations significantly as well.

We seek to assess the effects of corruption, labor regulation and power shortages on * * α _i_ + δ _t_ by appending indicators thereof to equation (2) along side of financial variables, or by interacting them with the more basic regressor, that is, with initial size. Again the justification for this is that institutional variables enter investment functions as influences on the user cost of capital or through their association with uncertainty. To the extent that

> 4 See Honorati and Mengistae (2007). The equation is due to Bond and Meghir (1994).

10
this simulation is favorable to the poorest households with little distributional impact for the richest 70% of the population in urban areas. On the other hand, the sim2b (panel b) is slightly positively sloped below the 50<sup>th</sup> percentile and slightly negatively sloped above this 50<sup>th</sup> poverty line. In this case, the simulation is most favorable to the center of the distribution (between the 40<sup>th</sup> to the 70<sup>th</sup> percentile.

**Figure 5: Growth incidence curve for Urban**

**(a) Simulation 1c                                                (b) Simulation 2b**

<!-- Start of picture text --> Absolute propoor curves Absolute propoor curves<br>URBAIN - Simulation 1c URBAIN - Simulation 2b<br>.05 .23 .41 .59 .77 .95 .05 .23 .41 .59 .77 .95<br>Percentiles (p) Percentiles (p)<br>Difference Upper bound of 95% confidence interval Difference Upper bound of 95% confidence interval<br>Null horizontal line Null horizontal line<br>.02 .01<br>.01 0<br>0<br>-.01<br>-.01 -.02<br>-.02 -.03<br><!-- End of picture text --> Source: Computed by authors from ELIM 2006 with DASP package We complete the pro-poor analysis with a presentation of results from the Ravaillon and Chen (2003), Kakwani and Son (2003) and Kakwani and Pernia (2001) indices. Results are presented in Table 10 where PPG is used for pro-poor growth, PRG for pro-rich growth, PPR for pro-poor recession, PRR for pro-rich recession and PSPPG for non-strictly propoor growth<sup>37</sup> .

> 37 For Kakwani and Pernia (2001), when growth is positive and when ψ>1, it indicates that poor benefit proportionally more from growth compared to the rich. When ψ<-1 growth is pro-rich. For  0<ψ<1, the growth is non strictly pro-poor and with -1<ψ<0 growth is non strictly pro-rich. For Kakwani and Son (2003), the equation of growth rate equivalent to poverty implies that growth will be pro-poor (pro-rich) if γ* is greater (smaller) than γ. If γ* takes a value between 0 and γ, growth will be accompanied by an increase in inequality but with a decrease in poverty (this is when we use NSPPG). For further explanation of these indices, the reader can consult Boccanfuso and Menard (2009).

25
Table 10. Africa subsample: Service VS. Manufacturing

||S|ervice Interacti|on<br>LP||Service|LP||Manufacturin|g<br>LP|
|---|---|---|---|---|---|---|---|---|---|
|Dep. Variables|TFP|Log LP|<br>Growth|TFP|Log LP|<br>Growth|TFP|Log LP|<br>Growth|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|FemHead|-0.265|-0.281***|-0.080***|-0.083|0.005|0.048***|-0.252|-0.279**|-<br>0.076***|
||(0.166)|(0.096)|(0.025)|(0.240)|(0.066)|(0.015)|(0.155)|(0.097)|(0.023)|
|FemHead*Service|0.523|0.296**|0.131***|||||||
||(0.404)|(0.107)|(0.024)|||||||
|Foreign|0.662**|0.697***|0.109***|0.114|0.681***|0.078**|0.787***|0.732***|0.140***|
||(0.216)|(0.100)|(0.021)|(0.287)|(0.224)|(0.036)|(0.244)|(0.112)|(0.038)|
|OwnLargest|-0.324|-0.644***|-0.117***|-0.987**|-0.729**|-0.132***|-0.109|-<br>0.558***|-0.104*|
||(0.181)|(0.182)|(0.037)|(0.354)|(0.252)|(0.032)|(0.261)|(0.128)|(0.053)|
|Middle|-0.043|0.211**|0.024|-0.352|0.083|0.009|0.054|0.339***|0.039|
||(0.087)|(0.096)|(0.022)|(0.289)|(0.115)|(0.022)|(0.062)|(0.109)|(0.028)|
|Large|0.124|0.620***|0.086***|-0.136|0.354|0.063|0.212|0.793***|0.106***|
||(0.148)|(0.154)|(0.023)|(0.311)|(0.253)|(0.071)|(0.240)|(0.236)|(0.025)|
|Age6_10|0.356*|0.146**|0.013|0.906*|0.088|0.010|0.277|0.227**|0.011|
||(0.166)|(0.058)|(0.012)|(0.429)|(0.052)|(0.011)|(0.160)|(0.094)|(0.021)|
|Age10plus|0.373**|0.236**|0.022|0.939***|0.131|0.004|0.267|0.384***|0.040|
||(0.142)|(0.083)|(0.014)|(0.272)|(0.081)|(0.013)|(0.174)|(0.114)|(0.025)|
|Exporter|0.169*|0.030|0.023|0.491**|-0.058|0.033|0.079|0.097|0.006|
||(0.081)|(0.209)|(0.023)|(0.165)|(0.251)|(0.037)|(0.089)|(0.175)|(0.017)|
|Initial LP|||-0.144***|||-0.145***|||-<br>0.142***|
||||(0.010)|||(0.011)|||(0.009)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,329|5,279|4,500|266|2,946|2,509|1,063|2,333|1,991|
|Adjusted R<sup>2</sup>|0.143|0.279|0.623|0.176|0.211|0.607|0.138|0.369|0.646|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

In Africa, the share of female-headed firm in service sectors is 11.4 percent and in manufacturing sectors is 9.9 percent.

40
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