rafmacalaba/datause-encoder-data huggingface dataset:
- McKinsey Global Institute. (2010). “India’s Urban Awakening: Building Inclusive Cities, Sustaining Economic Growth”. McKinsey & Company, April 2010.

- McKinsey Global Institute. (2012). “The Shifting Urban Economic Landscape: What Does it Mean for Cities?”. McKinsey & Company, July 2012.

- Michaels, G. (2008). “The Effect of Trade on the Demand for Skill: Evidence from the Interstate Highway System”. _The Review of Economics and Statistics_ , 90 (4): 683–701.

- Mitra, A., A. Varoudakis and M. Véganzonès. (1998). “State Infrastructure and Productive Performance in Indian Manufacturing”. OECD Working Paper No. 139

- Roberts, M., U. Deichmann, B. Fingleton and T. Shi. (2012). “Evaluating China’s Road to Prosperity: A New Economic Geography Approach”. _Regional Science and Urban Economics_ , 42(4): 580–594.

- The World Bank. (2012). “Planning, Connecting, and Financing Cities—Now”. Urbanization Review Flagship Report, The World Bank, Washington DC.

- Ulimwengu, J., J. Funes, D. Headey and L. You. (2009). “Paving the Way for Development? The Impact of Transport Infrastructure on Agricultural Production and Poverty Reduction in the Democratic Republic of Congo”. IFPRI Discussion Paper No. 00944.

- United Nations, World Urbanisation Prospects: The 1999 Revision (New York: United Nations Population Division, 2001).

23
Table 10: Income loss decomposition by age group (as a fraction of the country's total income loss)

||Age|15-24|Age|25-35|Age|36-50|Age|51-65|
|---|---|---|---|---|---|---|---|---|
||_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|
|Albania|16.05|11.35|3.86|22.77|49.37|27.69|35.24|37.93|
|Bosnia and Herzegovina|-|12.89|-|19.62|-|52.6|-|15.95|
|Croatia|5.26|17.88|21.11|20.39|44.49|24.82|28.79|35.33|
|Kosovo|-|15.63|-|29.02|-|39.75|-|21.06|
|Macedonia|13.87|11.76|19.35|17.74|37.56|35.35|29.75|35.17|
|Montenegro|5.45|-|30.83|-|46.16|-|23.43|-|
|Serbia|8.94|9.01|17.94|17.3|34.87|38.7|38.57|35.07|
|Turkey|10.9|13.89|32.17|18.63|46.79|49.56|21.76|21.75|

in the rst and latest available year. The total height is the total income loss due to gender gaps and, as we can also see in Figure 4, Turkey has the largest losses in the rst year while Kosovo displays the largest loss in the second year. In the rst year, the area for age group 4 is the largest one in Serbia, while the area of age group 3 is the largest one for the rest of countries. In the last year, group 4 represents the largest area in Albania and Croatia, while group 3 is the largest for the rest of the countries.

# 5 Concluding remarks

In this paper we calculate gender gaps in labor force participation, employers and self-employed for several Balkan countries (Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia) and Turkey. Our survey data reveal very large gaps in most countries in the two years studied, which reect a puzzling underrepresentation of women in these countries given their relatively high income per capita. We then proceed to provide an estimate of the aggregate losses in terms of income per capita associated with this degree of gender inequality using the occupational choice model proposed in Cuberes and Teignier (2014a).

Our results suggest sizable income per capita losses. On average, about 17% of income per capita is lost due to the gender gaps studied here. Of this loss, about a third can be attributed to the low numbers of women working as employers and/or self-employed. For comparison purposes, Cuberes and Teignier (2014a), using ILO data, obtain an average income loss of 10.5% in Europe, while only Middle East and North Africa, as well as South Asia, have larger average income losses. When we conduct our analysis for dierent age groups, we nd that the small role played in the labor market by women between the ages of 36 and 50 explains the largest fraction of the income losses due to gender gaps in the labor market.

13
enforcement, trauma, stigmatization, and individuals with criminal backgrounds while detained, which may drive their impacts on labor market prospects and firm dynamics upon return.

The levels of education of deportees tend to be low. About 71 percent of the deportees in our sample have completed primary education, whereas only 4 percent have secondary education, and 2 percent have completed technical education or university. Deportees have significantly lower levels of education than Salvadorans living in El Salvador and the United States (13 and 14 percent of them, respectively, has at least 14 years of education, Figure 3). On the other hand, Salvadorans living in the United States have higher educational attainment than their counterparts in El Salvador, which is in line with the idea that repatriated individuals were able to improve their knowledge abroad (Figure 3). We also have data on English proficiency for about 45 percent of our sample, which indicate that 51 percent out of the total of returnees do not speak English.<sup>12</sup> The characteristics of deported individuals suggest that any positive impacts of deportees on Salvadoran firms through a knowledge transfer from a developed to a developing country will be hard to find. Instead, given that most repatriated individuals did not get to live and work in the United States and that they returned to El Salvador within a month of arrival in the United States, any impacts of repatriation flows on Salvadoran firms are likely to reflect the experience of being deported and its impacts on deportees.

# **IV Data**

We combine multiple sources of administrative data to construct a municipal panel of individual firm outcomes and aggregated repatriation inflows.

> 12While we do not observe the sector of economic activity of the jobs of deportees who worked abroad, data from the American Community Survey (ACS) show that most Salvadorans in the United States work in construction, restaurants and hotels, business services, commerce and education and health (see Figure 4). In other words, they are more concentrated in skilled services when compared to their counterparts in El Salvador, who are disproportionately concentrated in agriculture.

9
US and UK, have statutory equality and non-discrimination _laws_ (rather than _constitutional provisions_ ) which are not covered by this study.

# **Areas for Future Analysis**

There are gaps and areas which are not covered in this study which could merit a similar analysis in the future. For instance, in some countries single women need the consent of their male relatives to work outside of the home and travel abroad or even within a country. Discriminatory citizenship laws can prevent married women married to non-nationals from passing citizenship to their spouse and/or their children. Discriminatory labor laws can prevent women from working in certain industries and limit working hours and include other provisions such as maternity leave and differentiated retirement ages impacting on women’s participation in the labor force. Legal recognition of non-monetary contributions (i.e. where child care and household duties can entitle the wife to a share of the marital property on divorce) is not covered here but is an important distinction in comparing the protections of various default marital property regimes (see annex 4).

Case law plays a prominent role particularly in common law countries. It can fill in gaps or progressively interpret statutory laws where there are glaring inadequacies in the legal framework or reinforce negative discriminatory laws. On the positive side, there may be statutory laws on discrimination that we do not cover which offer protection in instances where the constitution is silent or there is no constitution.

Finally we do not look at the implementation of laws- whether good and bad laws on the books are actually applied in practice. The ex-socialist countries in the ECA region had gender equal laws derived from the Soviet Union and so scored highly on our indicators. However, the 2013 ECA World Development companion Report on Gender Equality has found implementation of laws can be weak and in practice there are fewer female land owners than men despite the legal requirement of joint titling for married women.<sup>27</sup> In Vietnam, prevailing social norms that regarded men as head of household rather than a legal requirement undermined the implementation of gender-neutral land titling laws.<sup>28</sup> At an individual country level, Zimbabwe did carry out wide scale reforms over the past fifty years that left it with just one gender gap in the variables that we are looking at but a 2012 NGO CEDAW shadow report<sup>29</sup> shows that the reality on the ground is very different.<sup>30</sup>

> 27 Sattar 2012.

> 28 Klugman, J. & Morton, M. (forthcoming).

> 29 A shadow report is prepared by non-state actors such as not for profits to provide feedback to the CEDAW committee which is independent of an official  State Report and is an important mechanism for women’s networks to highlight areas requiring reform.

> 30 Zimbabwe Women Lawyers Association 2012.

11
The rest of the paper is organized as follows. Section 2 describes the data sets used and presents wage change histograms for selected years, discussing their main features and why they are suggestive of different types of rigidities. Section 3 discusses the methodology used to deal with measurement errors and the joint estimation of the different types of rigidity. Section 4 presents the main results of the paper. In section 5 we extend the analysis of the incidence of wage rigidities to differentiate across worker and characteristics. Section 6 concludes.

# 2 Data

## 2.1 Data Sources and Sample Selection

We use annual administrative employer-employee matched data from Brazil (1995-2002) and Uruguay (1996-2004). The main difference between the two data sets is that while the former is comprehensive, including information for the universe of workers in the formal sector in Brazil, the latter provides us with information on a random sample of workers in Uruguay’s formal sector.

Our data source for Brazil is the labor market census RAIS (Relação Annual de Informações Sociais), an administrative data set collected annually by the Brazilian Labor Ministry. By Brazilian law, all employers in the formal sector must report detailed information for all their workers to RAIS every year. RAIS includes information about workers (sex, age, education) and their jobs (type of contract, occupation, average monthly wage earned during the year, wage earned in December, and the amount of hours usually worked per week), as well as some characteristics of the establishment (sector, region, municipality). Importantly, RAIS also provides firm, establishment, and worker identifiers which, together with the dates of admission and separation, allow us to accurately identify year-to-year job stayers.

We restrict our Brazilian sample to the state of Minas Gerais. We do so for two reasons. First, using the whole RAIS sample, which is huge, would make data handling impracticable. Second, we have confirmed, using representative survey data, that Minas Gerais is well suited to represent the Brazilian economy. In particular, our tabulations, 6
14-B

# Table 4 Determinants of the ParaUel Premium

|||ARG|GHA|MEX'|SUD|TAN|TUR|ZAM|
|---|---|---|---|---|---|---|---|---|
|PORTFOUO DETERMINANTS|||||||||
|Real Money Balances|Short<br>|+|**+**||+-|+|+-|+4s|
|(MIE)|Long|||+|+-||+-||
|Official InterestRate<br> <br>|Short<br>||||+<br>|+-|+-||
|ParityDeviation<br>a|Long||**+**|**+**|**+**||||
|TRADE BALANCE DETERMINA|NTS||||||||
|Official Real Exchange<br>|Short|||+|**+**|**+**|||
|Rate|Long|**+**|**+-**|**+**|**+**|<br>**-**|**+**||
|Aid|Short||||||||
||Long||||||||
|Terms of Trade|Short||||||||
||Long|**-,4+**||||**-**||**-'**|
|Export Tax Trade|Short|-|**+**||||||
||Long||||||||
|Import Tariff Rate|Short|+,-+|||||||
||Long||||||||
|Real Output|Short|**--**|||||||
||Long|**-**|**-**|**+**|||||

## Notes

* refers to an estimated effect that is significaot at the 10% level or better. For details, see Ansu (1991), Table 1; Aron and Elbadawi (1993), Equation (3.1)'; Elbadawi (1993), Equations (12)<sup>and (13); Kamin (1991), Tables</sup> 2 and 3b; Kaminsky (1991), Table 4; Kaufmann and O'Coanell (1993), Table<sup>??; Ozler (1992), Appendix.</sup>

I Effects operating through dollar export prices (+) and dollar import prices (-) were estimated separately and imply conflicting conclusions regarding the terms of trade effect.

2 Results for import tariffs (+) and quantitative restrictions (-)<sup>were both insignificantly different from zero.</sup> 3 See Kaminsky (November 1991). Results reported here are impulse<sup>responses from a structural vector</sup> autoregression including the premium. nominal official exchange rate,<sup>price level. nominal interest rate. and real</sup> output. Short run effects refer to a I-year horizon and long-run<sup>effocts to</sup><sup>_3_years. Contemporaneous effects on</sup> the premium are constrained to zero except in the case of the official<sup>exchange rate. Significance levels are not</sup> reported.

4 Very close to significance at the 10% level.
Treatment E¤ects”, Review of Economic Studies 76(3): 1071-1102.

McKenzie, David (2011) "Beyond baseline and follow-up: The case for more T in experiments", World Bank Policy Research Working Paper no. 5639.

McKenzie, David and Christopher Woodru¤ (2008) “Experimental Evidence on Returns to Capital and Access to Finance in Mexico”, World Bank Economic Review 22(3): 457-82 Platteau, Jean-Philippe (2000) Institutions, Social Norms and Economic Development. Harwood Academic Publishers: Amsterdam.

Ravallion, Martin (2009) “Should the Randomistas Rule?”, The Economists Voice 6(2). Schündeln, Mathias (2006) “Modeling Firm Dynamics to Identify the Cost of Financing Constraints in Ghanaian Manufacturing”, Mimeo. Harvard University.

Somville, Vincent (2011). "Daily Collectors, Public Good Provision, and Private Consumption: Theory and Evidence from Urban Benin", Department of Economics, Namur University (mimeograph) Spears, Dean (2009) “Dosas by the Dozen: Theory and Evidence of Present Bias in Microentrepreneurs”, Institute for Financial Management and Research Working Paper no. 27 Udry, Christopher (1996). "Gender, Agricultural Production and the Theory of the Household", Journal of Political Economy, 104(5): 1010-1046, October Udry, Christopher and Santosh Anagol (2006) “The Return to Capital in Ghana”, American Economic Review Papers and Proceedings 96(2): 388-93.

41
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**_6_**

increase of materials and energy prices.<sup>7</sup> As Table 2 demonstrates. all three groups of finns had roughly the same level of energy and materials costs in 1990: the price impact of the collapse of the CMEA (switching from administered transferable ruble prices for energy and raw materials prices in 1990 to international dollar prices in 1991) shows up in the numbers for the first half of 1991 for AAA firms. By the first half of 1992, both AAA and A groups exhibit increased efficiency in materials and energy use, even compared to the implicitly subsidized numbers of 1990. In fact the decline in the share of materials and energy costs even in the face of a clear relative price increase of in particular energy (relative to output prices) is impressive testimony to the extent of adjustment that took place.. Interestingly, A firns on average have no difficulty covering variable costs, suggesting that their travails.~~~. may be due more to excess labor and the debt overhang, and that with downsizing and restructuring, these firms could have potential.

Table 2: Materials and Energy Costs to Sales (%)

||1990|1991|1991|1992|
|---|---|---|---|---|
|||(1-6)-|(1-12)|(1-()|
|AAA|_52_|58|50|45|
|AA|47|60|61|_50_|
|A|48|47|45|39|

When asked to rate the importance of product mix changes as a factor stimulating increased production on a zero-to-five scale, AAA firms averaged 3.2. while A firms averaged _1.5._

# _Exporr Behavior_

One of the stellar achievements of the Polish economy in 1990 was the huge increase in hard currency exports to the west. Hard currency exports grew by some 40 percent at the same time that total industrial sales fell by 23 percent and CMEA exports shrank by 10 percent. These aggregate numbers strongly suggest a diversion of sales from the home and CMEA markets to the west, indicating that SOEs

> 7 For example. at the strt of the Gulf Crisis in 1990. Poland was importing oil from the Soviet Union at 5 3 per - barrel compared t a world price of 525- Table 5 in Kharas (1991) contains other telling examples of implicit subsidies.
dimension of our methodology. Unfortunately, there is no ideal way to do so. The model estimates may be closer to the truth than direct computations from the CFUWBES, and sector classifications differ from WBES to the CMMHH. These issues, combined with small sample sizes for certain sectors in the WBES, suggest extra caution when using WBES data to derive sector-specific job loss estimates.

# **5.4.1 Jordan**

We first compare wave 1 results for modeled- versus survey-measured approaches. As shown in Table 5-8, the rate of job losses varies a great deal, for these sector categories between 5 and approximately 20 percent ( _chemicals_ and _wholesale, retail, and other services_ , respectively). The job loss projections from the surveyestimated projections deviate somewhat from our modeled projections, particularly for sectors with fewer observations, including _Chemicals, Garments,_ and _Metals and Minerals_ . However, the model broadly predicts which sectors report the most severe reductions in workforce as well as those with less than average declines.

A comparison of wave 2 predictions for all firms (permanently closed and operational) with surveymeasured job loss projections (Table 5-9) shows a reasonable match for some sectors (Chemicals, Hotel, restaurant, and transportation, Machinery, electronics, and construction, wholesale, retail, and other services). The greatest disparity in terms of sector ranking is for Food, Drink, and Tobacco and Wood, Paper, Publishing, and Printing. On the whole, the model, which is based on wave 1 data produces a closer alignment to wave 1 surveymeasured projections than we obtain when we use the same model to predict sector-level job losses forward to wave 2.

_Table 5-8 Jordan: Model-Predicted versus Survey-Measured Projections of Sector-Level PFPS Jobs Losses, Wave 1 / Summer 2020 (with permanently closed firms)_

||Model-<br>Predicted|Survey-<br>Measured|No. of<br>Observations|
|---|---|---|---|
|1. Chemicals|4.9|9.9|26|
|2. Food, drink, and tobacco|8.1|9.2|73|
|3. Garments|18.7|23.6|50|
|4. Hotel, restaurant, and||||
|transportation|17.8|17.0|118|
|5. Machinery, electronics,<br>and construction|16.3|22.6|36|
|6. Metals and non-metallic||||
|minerals|7.6|2.8|14|
|7. Wholesale, retail, and||||
|other services|19.3|19.4|147|
|8. Wood, paper, publishing,<br>and printing|16.7|15.6|21|
|Total|15.8|16.8|485|

Source: CFUWBES1.

Notes: Model-predicted estimates are based on projected jobs lost using a multi-stage model and the employment structure in LMPS. Survey-measured jobs lost are based on directly computed sector-average job losses. In both cases, to estimate the allocation of jobs lost to production and non-production workers, we use the 2013 and 2019 World Bank Enterprise Surveys for Jordan and estimate sector-specific Tobit polynomial models. The calibrated models are applied to estimate the numbers of production and This is somewhat to be expected as there may be new dynamics that emerge over time that affect the link 32
have found that foreign firms usually pay higher wages relative to domestic counterparts.<sup>8</sup> For instance, using the cross sectional and individual panel data from the VHLSS 2002 and 2004, Fukase (2013a) finds that foreign firms pay higher wages relative to domestic counterparts after controlling for workers’ personal characteristics and that longer hours of work in foreign firm jobs relative to working in the informal wage sector are an important component of the wage gains. Furthermore, analyzing foreign wage premium separately by gender, she finds that foreign wage differential relative to informal wage sector jobs for the subset of lower skilled workers (without upper-secondary education) is larger for female than male workers, reflecting low earning opportunities for females in the informal wage sector in Vietnam.<sup>9</sup> The existence of foreign wage premium, in particular, relative to the informal sector, is likely to be another reason why foreign firms are drawing Vietnamese workers from rural areas.

Finally, foreign firm employment is concentrated in a few provinces. Figure 4 shows the foreign sector employment levels in seven provinces (out of 64 provinces) in which foreign enterprises generated the most employment during the period 2000-2007. Foreign sector employment was overwhelmingly concentrated in Ho Chi Minh City (HCMC) and two provinces adjacent to HCMC, namely Binh Duong and Dong Nai provinces, as foreign sector employers in these three provinces alone accounted for about 73 percent of total foreign employment in 2004. Although the employment by foreign firms in two big cities in the North (namely Hanoi and Hai Phong) has grown, the number of individuals employed in the foreign sector in these cities was relatively small compared to the South. The high concentration of foreign job opportunities in the Southeast may be among the reasons why the latter region attracted migrants.

> 8 See Lipsey (2004) for a review of the earlier literature.

> 9 The findings in Vietnam are consistent with Glick and Roubaud’s (2006) study which concludes that Zone Franche (the Export Processing Zones) employment in Madagascar represents a significant step-up in pay for women who would otherwise be engaged in low-wage informal sector work.