rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7: The effect of competition on wage rigidity: Euro area vs non-euro area**

||Euro<br>|area<br>|Non-eu<br>|ro area<br>|Euro<br>|area<br>|Non-e<br>|uro area<br>|
|---|---|---|---|---|---|---|---|---|
||DNWR/<br>FW|DRWR/<br>FW|DNWR/<br>FW|DRWR/<br>FW|DNWR/<br>FW|DRWR/<br>FW|DNWR/<br>FW|DRWR/<br>FW|
|Perceived comp =<br>strong|0.763**|1.033|0.581***|1.290*|||||
||(0.018)|(0.762)|(0.000)|(0.080)|||||
|Perceived comp =<br>weak|0.926|1.158|0.616**|1.388|||||
||(0.715)|(0.463)|(0.023)|(0.156)|||||
|Perceived comp =<br>none|1.155|0.780|0.260**|0.627|||||
||(0.636)|(0.514)|(0.014)|(0.277)|||||
|Price comp =<br>likely|||||0.956|0.889|0.867|0.915|
||||||(0.744)|(0.225)|(0.372)|(0.657)|
|Price comp =<br>not likely|||||0.825|0.803**|0.951|1.059|
||||||(0.203)|(0.041)|(0.770)|(0.784)|
|Price comp =<br>not at all|||||1.172|1.032|0.791|1.087|
||||||(0.465)|(0.840)|(0.449)|(0.796)|
|Observations|43|19|32|30|69|82|29|87|

Notes: The table presents the estimated odds ratios for nominal wage rigidity vs flexible wage and real wage rigidity vs flexible wage. Worker skill groups, % permanent workers, % labour cost, dummy variables for different types of union contracts and sector, size and country fixed effects are added in all specifications. Robust P-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The full regression estimations are presented in Appendix 9.

# **_5.4. Estimation results – labour market institutions_**

In the above-described regressions, almost all the dummy variables for countries have highly significant estimates for the odds ratios of both types of rigidity vs flexible wage setting. As country effects appear to have an important impact on wage rigidity, national labour market institutions are a natural suspect as the cause of the differences between countries. Previous research in this area has demonstrated that indicators of the institutional environment, such as collective bargaining coverage and employment protection, are significantly correlated with real wage rigidity and nominal wage rigidity. We extend this analysis to more countries, exploiting the substantial cross-country variation in the institutionalisation of the wage-setting process between the euro area and non-euro area economies. In all our specifications we look 24
interact with the sectoral output share of such firms. Fully-owned FDI, in contrast, is more likely to be an export-platform with fewer local linkages and technology leakages.

Finally, income per capita ( _income_ ) positively mediates the FDI spillover effect on domestic labor productivity from partially-owned FDI, while there is no effect for fully-owned FDI (column 11). This reflects the importance of national competitiveness and development, given the possibility of more local linkages for partially-owned foreign firms.

Overall, the results clearly indicate that spillovers from partially-owned FDI are positively mediated by various national and institutional factors. In contrast, national and institutional characteristics exert no or even a negative effect on spillovers from fully-owned FDI.

# **5 Conclusions**

In this paper, we use a cross-section of more than 25,000 manufacturing firms in 78 low- and middle-income countries from the World Bank’s Enterprise Surveys Indicator Database to identify the mediating factors that determine intra-industry productivity spillovers. Besides OLS, this paper also applies an instrumental variables approach to addresses the potential endogeneity between FDI spillovers and domestic firm productivity. We differentiate between major types of mediating factors, namely the FDI spillover potential, domestic firms’ absorptive capacity, and differences in host country factors and institutions. This is the first study to our knowledge that interacts FDI with all three types of mediating factors against productivity outcomes.

We find evidence for negative FDI spillovers overall, which we relate to increased competition in the short term for market share (both domestic and export) and skilled labor. Regarding the FDI spillover potential, we find a positive and significant impact in the overall sample for market orientation in the OLS regressions – the higher the share of FDI output sold domestically, the greater productivity spillovers to domestic firms. Splitting the sample into three productivity groups, we find that larger output share sold to the domestic market and larger share of local inputs contributes to higher spillovers for low- and medium-productivity firms.

Regarding a domestic firm’s absorptive capacity, almost all variables show the expected coefficient signs, suggesting that a lower technology gap, firm technology, firm size, proximity to other firms, and export behavior interact positively with foreign output share, although results vary by type of firm. In sum, the mediating factors are more significant for low-productivity firms. The results imply that firm productivity is a major mediating factor of FDI spillovers, as negative 34
7

# _Data_

Our analysis is based on the 1996 individual and household survey for Djibouti (EDAM 1996), which makes it possible to estimate individual returns to schooling and other wage determinants across various categories, including men, women, public, and private (formal sector). The sample is restricted to 2,011 wage earners between 12 and 65 years of age. Table 1 contains characteristics of the workers examined for this section, excluding employers.

|**Table 1:Selected Descriptive statistics of Djibouti Wage Earners in 1996**|
|---|

||**All**|**Ma**|**le**|**Fem**|**ale**|**Pu**|**blic**|**Private**|**(Formal)**|**Self-E**|**mployed**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Mean**<br>**STD**|**Mean**|**STD**|**Mean**|**STD**|**Mean**|**STD**|**Mean**|**STD**|**Mean**|**STD**|
|monthly wages/earnings (USD)|288.2 (373.5)|354.5|(362.6)|168.8|(363.0)|375.4|(288.8)|348.5|(262.5)|240.0|(563.4)|
|age|35.0 (11.0)|36.8|(10.7)|31.7|(10.7)|35.8|(10.4)|34.7|(9.7)|38.2|(11.7)|
|female (%)|35.7|||100.0||13.2||30.0||53.6||
|married (%)|58.4|74.6||29.2||69.9||63.3||58.5||
|% in public sector|43.6|58.9||16.2||||||||
|% in private (formal) sector|13.4|14.6||11.3||||||||
|% in private (informal) sector|20.0|9.9||38.3||||||||
|% self employed|22.6|16.3||33.8||||||||
|% obtained certificate|1.4|1.5||1.1||2.2||3.3||0.0||
|% with no formal education|51.4|40.8||70.5||47.1||47.1||74.8||
|% with primary schooling|18.3|23.2||9.5||23.4||26.3||11.9||
|% with middle schooling|10.0|11.4||7.7||15.2||13.7||3.3||
|% with secondary schooling|3.7|4.4||2.4||5.6||5.9||1.8||
|% with vocational schooling|3.4|3.9||2.5||5.4||4.8||0.9||
|% with university education|3.2|4.6||0.7||5.4||5.2||0.7||
|No of. Uncensored observations|2011|1293||718||878||270||453||

_Note: standard deviations (STD) are not reported for dummy variables. Source: Author's calculation based on EDAMS 1996._ Wages are the sum of cash earnings. Monthly wages<sup>6</sup> are obtained by dividing reported monthly wages by imputed monthly hours. The average monthly earning is USD 288 with males earning proportionally more on average (USD 354 vs. USD 169 for females). %. Public sector monthly wages are on average higher by USD 26.9 relative to wages in the private formal sector.

Forty-three percent of workers are employed in the public sector, 14 percent in the private formal sector, and 22 percent in the informal sector. Males make up 64.3 percent of overall wage earners. More than 75 percent of male workers are married and more than 58 percent work in the public sector. In contrast, 74 percent of female workers are unmarried and only

> 6 Daily wages are converted in monthly figures by multiplying with 26 days. Similarly weekly wages are converted to monthly wages by multiplying with 4.2 weeks.
# **1 Introduction**

Women are remarkably underrepresented in elected office around the world. As of January 2023, women accounted for only 26.5 percent of national parliament members globally. This gender gap in political leadership is prevalent in nearly all countries; e.g., women comprised 29 percent of the US Congress and 31 percent of the UK Parliament in 2023.<sup>1</sup> In fact, only six countries have 50 percent or more women in parliament. In India, the context of our study, merely 12.2 percent of the national and state legislators in 2021 were female. Besides equity considerations, women’s underrepresentation in government has significant welfare implications. Numerous studies have shown that increasing the share of female leaders not only results in more favorable outcomes for women (Beaman et al., 2012; Chattopadhyay and Duflo, 2004; Iyer et al., 2012), but also leads to better development outcomes more broadly (Baskaran et al., 2021; Bhalotra and Clots-Figueras, 2014; Brollo and Troiano, 2016; ClotsFigueras, 2012; Dollar et al., 2001; Miller, 2008; Swamy et al., 2001).

The underrepresentation of women in elected positions can result from multiple factors such as a lower rate of entry into politics and a lower rate of career progression within politics for women (Brown et al., 2020).<sup>2</sup> Even if female politicians are interested in contesting elections, voters may be gender-biased against female candidates and parties may be unwilling to field female candidates due to their lower chances of victory (Le Barbanchon and Sauvagnat, 2022; Sanbonmatsu, 2002; Schwarz and Coppock, 2022; Teele et al., 2018). In majority rule elections when voters trade off gender bias against leader competence, less competent leaders may be elected if voters are sufficiently biased, causing societal welfare loss (Krishna and Morgan, 2011).

We examine whether exposure to well-implemented public policies or programs increases voters’ prioritization of politician competence over their gender. Such a re-weighting of voter priorities may occur regardless of whether the effective policies are implemented by male or female leaders (though exposure to effective female leaders may have a larger impact on re-prioritization; Beaman et al. (2009); Bhavnani (2009)). By leading voters to elect more competent men and women in place of less competent men who previously benefited from voters’ gender bias, the number of women elected to office may increase following observable effective governance.

To the best of our knowledge, the question of whether competent delivery of public programs can increase women’s elected leadership is as yet unanswered. Administrative ca-

> 1 Source: Parline Data, Inter-Parliamentary Union ( `https://data.ipu.org/)` .

> 2For instance, women may have lower ambition (Lawless and Fox, 2010), may lack female role models (Beaman et al., 2009), may be averse to competitive environments (Gneezy et al., 2003), and may face gender stereotypes and opposition from their families, among other things.

1
-7-

large and inefficient government sector should<sup>necessarily affect the</sup> growth rate, rathar than simply the level, of GDP?

To address this theoretical difficulty, Ram (1986) derived<sup>an</sup> expression for output growth as a function of growth rates<sup>in government</sup> spending. He found a strong, positive impact of<sup>government current</sup> consumption on output growth. The goal of this section<sup>is to build on work</sup> by Robinson (1971), Feder (1983), and Ram (1986), to develop<sup>a theoretical</sup> framework for measuring the impact of taxation, government<sup>expenditures,</sup> capital, and labor supply on output growth.

Before deriving the model, it is useful to review<sup>some</sup> shortcomings of cross-country regression analysis. The usual criticism of these comparisons is that countries are suff'ciently dissimilar that<sup>they</sup> cannot be pooled together in a single data set; it makes little sense<sup>to</sup> interpret regression estimates based on, e.g., France and Burundi. While this paper restricts its attention to Sub-saharan Africa, the criticism<sup>is</sup> a general one for all regressions -- do the observations, whether of individuals, countries, or years, behave according to a similar structural model? If yes, then the reported regression results will provide<sup>estimates</sup> of the average, or representative parameters values. If not, the diversity should be readily reflected in insignificant regression<sup>coefficients (also</sup> see Landau, 1986).

A second problem with any regression is the possibility that measured independent variables proxy for the true, but unmeasured, factors which determine output growth. For example, countries with<sup>large mining</sup> sectors often rely heavily on corporate taxation. Downturns suffered by some mining industries during 1974-82 could therefore have led to a measured, but spurious, negative effect of corporate taxation on output growth. To correct at least partially for this problem, the regressions
order to assist regulators in the design of policies that would minimize opposition to environmental regulation, we have assessed the relative costs of three alternate policies that achieve a 20 percent reduction in carbon dioxide emissions. We find that the market based “cap and trade” regulation is the least costly of the three policies, followed by emission intensity standards (which is reasonably efficient). Energy efficiency policies are significantly more costly at achieving the same level of carbon dioxide reduction compared with cap and trade and emissions intensity standards.

In our sensitivity analysis, we find that there are drastic differences in the economic and environmental impacts between model simulations without and with the endogenous productivity effects of our model with increasing returns to scale and foreign direct investment. In particular, with a CRTS model, the welfare gains are insufficient to finance the costs of the least efficient environmental regulation.

# **References**

- Antweiler, W., B. R. Copeland, M. S. Taylor (2001), “Is Free Trade Good for the Environment?” _The American Economic Review_ , 91: 877-908.

- Arnold, Jens M., Beata S. Javorcik and Aaditya Mattoo (2011), “Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,” _Journal of International Economics,_ Vol. 85 (1), 136-146 _._

- Arnold, Jens M., Beata S. Javorcik, Molly Lipscomb and Aaditya Mattoo (2012), “Services reform and manufacturing performance: evidence from India,” World Bank Policy and Research Working Paper Series Paper No. 5948, January.

- Babiker, M. H., K. E. Maskus and T. F. Rutherford (1997), “ Carbon Taxes and the Global Trading System,” Paper prepared for presentation at the International Energy Workshop and Energy Modelling Forum Meeting at IIASA, June 23-25, 1997, Laxenburg, Austria.

- Cole, M.A., A. J. Rayner, J. M. Bates (1998), “Trade liberalization and the environment: The case of the Uruguay Round,” _The World Economy_ , 21, 337-347.

- Copeland, B. R., and M. S. Taylor (1994), “North-South trade and the environment,” _The Quarterly Journal of Economics_ , 109: 755-787.

- Copeland, B. R. and M. S. Taylor (1995), “Trade and Transboundary Pollution,” _American Economic Review_ , 85(4): 716-73.

- Dean, J. M. (1992), “Trade and the Environment: A Survey of the Literature,” In: P. Low (Ed.), _International Trade and the Environment_ , Washington, D.C.: The World Bank, 15-28.

- Dean, J. M. (2002), _International Trade and the Environment_ , Aldershot UK: Ashgate Publishing.

33
system closer to the frontier.<sup>25</sup> The government, in its role as financial market participant, can also have an important role. For instance, in 1999, the U.S. Treasury started a program to make all federal benefit payments though electronic transfer accounts, using subsidies to encourage banks and beneficiaries to open such accounts (Caskey, Ruiz Duran and Solo, 2006; Claessens, 2006).

Market-enabling policies, however, find their limits in the state variables. Deficiencies in state variables require more profound structural reforms, mostly outside the reach of even financial sector policy makers. We denote such reforms as _market-developing_ policies. One often neglected area are distortions in the input markets for financial service provision, including labor and communication markets. A deficient phone infrastructure and lack of competition in the telephone market can drive up costs for financial institutions.<sup>26</sup> A deficient transportation infrastructure can increase the cost of outreach into more remote areas, even through innovative techniques such as mobile banking. Generally high costs of doing business, due to widespread corruption, insecurity, or even deficient electricity provision, can drive up the fixed cost element of financial service provision with negative repercussions for outreach to low-value/high-volume customers. Finally, economic size can be a restricting factor as discussed above. Small economies should therefore put a premium on encouraging entry of foreign banks that are able to reap benefits of scale economies across subsidiaries in different countries, on integrating their

> 25 Compare Financial Services Authority (2000) and the literature quoted therein. See Ashraf, Karlan and Yin (2006a,b) for an example of how the introduction of commitment savings product can impact savings rates.

> 26 Compare the detailed discussion for the case of South Africa in World Bank (2004b).

21
<!-- Start of picture text --> _________________ _Figure 4: Distribution of the working-age population by region<br>Fast AsM and Pacific (working-age populationi 1175 Europe and Central Asia (%%orking-age population 324 Latin America and Caribbean (working-age population<br>million) million) 294 million)<br>ViLcs tinciniplnyed niot in laIor ser ices uneniployed notinlabor unemployed not in alaor<br>I 9%;, 2% rorce 28° 6% Ibre firce<br>;~~~~~~~~~~~~8 ° 28 66' 0,1 44<br>d(l;lrs 0 1 ) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~iind u s r<br>agiculture indiotry agriculture 1 4 agiculture<br>46% 24° 164 |2%<br>-I<br>Sliddle Fast anid Nirtilh Afiic a (w%orkinig-ige population I O.E.C.D. (sorking-age population 53 1 million) _ _ _ _ __ South __ Asia ______ (workinig-age population 744 __ _____ million) _-_<br>155 million)<br>unemploed not in labor senlces unemployed iiot in labor<br>cer C: uel 42°o 6°o force industry 13°% 2% force<br>10%~~~~~~~~~~~~~~~~~~~~~~~~~~~3'<br>ariL-lbr-.,,,,,i,,. Ilture<br>I co f4orc;e m idustry 3 agriculture b<br>6n O ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- - - - - - -- -- .<br>Maib-Saliarairl Africa (wAorkinig-age 1/. o~~~~~~~~~~~~~~ .. population 31 4n~LOi ... .....- . iaiioor<br>rs sets - -xices ~~~~~~~~~~. unemployed ..... ......... l;rce<br>41%17°% 4°b 32°'%<br>agriculture~ ~ ~ ~~~~~frc inus 315rcltr<br>41°b |~~~~~~~~~~~~~~~9<br><!-- End of picture text -->
Table A.18: Postnatal health investments: Hindus vs Muslims, REDS

|||Months|breastfed||R|eceived >=|one vacci|ne|Medic|ines/ special f|ood during i|llness|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Hindus|Hindus|Muslims|Muslims|Hindus|Hindus|Muslims|Muslims|Hindus|Hindus|Muslims|Muslims|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|First girl * Female|-4.0527*|-3.2497*|-0.2781|-1.8091|-0.0764*|-0.0445|-0.0032|0.058|-65.5785*|-115.2283**|120.6338|61.8803|
||[1.9879]|[1.5970]|[5.6447]|[3.4082]|[0.0401]|[0.0486]|[0.1394]|[0.1030]|[35.5459]|[41.1597]|[83.6157]|[144.8619]|
|First girl * Female * Post1|3.0801|2.13|-6.0742|-5.9526|0.1022**|0.0773*|-0.0486|-0.1137|66.1963|107.7535*|-102.2985|-58.7846|
||[2.5654]|[2.5635]|[5.4057]|[3.4693]|[0.0458]|[0.0374]|[0.1049]|[0.1274]|[48.7842]|[61.5769]|[85.5359]|[125.8920]|
|First girl * Female * Post2|2.9154|1.7382|-2.0404|-0.4341|0.0663**|0.0862**|0.0033|-0.0115|117.3814**|172.0607**|-60.7643|-43.5918|
||[2.5750]|[2.2636]|[6.4090]|[8.0712]|[0.0296]|[0.0344]|[0.1024]|[0.1322]|[55.0821]|[66.3537]|[101.5542]|[165.4993]|
|N|9,726|9,726|734|734|18,138|18,138|1,619|1,619|9,079|9,079|849|849|
|_Female_*_Post1_ & _Female_*_Post2_|x||x||x||x||x||x||
|_1st Girl_*_Post1_ & _1st Girl_*_Post2_|x||x||x||x||x||x||
|_Xijt_|x|x|x|x|x|x|x|x|x|x|x|x|
|_1st Girl_ x Birth year FE||x||x||x||x||x||x|
|_Female_ x Birth year FE||x||x||x||x||x||x|
|_Female_ x State FE||x||x||x||x||x||x|
|_Female_ x Birth order FE||x||x||x||x||x||x|
|Birth order x State FE||x||x||x||x||x||x|
|State-specifc time trends||x||x||x||x||x||x|
|_Firstborn girl_ x State FE||x||x||x||x||x||x|
|_Firstborn girl_ x Birth order FE||x||x||x||x||x||x|

NOTES: This table reports investment effects for children of second- and higher-order birth order using the REDS sample, separately for Hindus and Muslims. Each column is from a separate regression. We control for _Female_ , _Firstborn girl_ , and fixed effects for birth year, birth order, and state in all columns. The vector _Xijt_ comprises mother’s age at birth, caste, educational attainment of child’s parents, and mother’s birth cohort. Breastfeeding results are based on the last two surviving births of a mother and restrict the sample to children above age 2. Vaccination and health expenditure results are based on all surviving children of a mother. Standard errors in parentheses are clustered by state. *** 1%, ** 5%, * 10%.
<!-- Start of picture text --> 50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0% zzxazs PezaeSay<br>5.0% Vi a t<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>m<5S =5-9 ©10-14 15-19 m>=20<br><!-- End of picture text --> _ SS
commonly find that in the aggregate subsidies are at least mildly "progressive" in that they are higher for the poor as a percentage of initial income or expenditure. In contrast, absolute benefit levels often increase with the welfare indicator; the poor get less in absolute amount than the rich, though inequality is still reduced. Finally, an implicit urban bias is common in that public spending amounts are often found to be higher in more urbanized areas.

However, study after study highlights the importance of looking at within-sector components. For example, in the education sector, benefit incidence analyses typically find that primary, and often secondary, schooling are propoor both in absolute levels and percentage terms. This is a consequence of the fact that in most developing countries poorer families (at least as measured on a per capita income basis) tend to have more and younger children, and that poorer children are more likely to attend school at the primary than at other education levels. Tertiary education, in contrast, is invariably prorich. The overall progressivity will often depend on the composition of education spending. For example, as only a small share of the education budget is devoted to university training in the economies of Eastern Europe, the overall distribution of education expenditures is propoor (Milanovic, PSP). In comparison, high university subsidies imply a flat overall distribution of education benefits for Malaysia, despite the unusually strong progressivity of both primary and secondary spending (Hammer and others, PSP). Generally, the more educationally advanced a country, and the more developed its private sector, the more public spending on higher education levels reach poor children.

These general patterns are supported by Selden and Wasylenko's investigation of the education sector in Peru, and the Hammer and others study of Malaysia (both in PSP). The former also find that spending levels are higher in urban areas and that, as beneficiaries of education subsidies, girls are deprived relative to boys. Milanovic finds that in the Eastern European economies too, the distribution of education benefits are progressive though decreasingly so the higher the 23