rafmacalaba/datause-encoder-data huggingface dataset:
|Log Inspected Firms<br>per firm in city<br>Log Inspected Firms<br>per firm in city<br>OLS<br>OLS<br>(2)<br>(3)<br>0.139<br>0.183<br>[0.026]***<br>[0.048]***<br>-0.156<br>-0.095<br>[0.015]***<br>[0.015]***<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>5,269<br>5,240<br>0.24<br>0.37<br>28.82<br>14.65|the regression of the log of the number of inspected firms|te dummies, distance to the nearest labor office, its square<br> the state and interactions with other state variables, city|tude, city latitude and city longitude. Other state variables<br>sportation cost is the transport cost between each city and<br>urban areas, in 1970, 1980, and 1991. Variables described|
|---|---|---|---|
|Dependent Variable:<br>Log Inspected Firms<br>per firm in city<br>Method:<br>OLS<br>(1)<br>City distance to the nearest labor office (hours) * Inspectors per firm in the state<br>0.068<br>[0.017]***<br>City distance to the nearest labor office (Average Marginal Effect)<br>-0.237<br>[0.012]***<br>City distance to the nearest labor office (hours)<br>Yes<br>City distance to the nearest labor office squared<br>Yes<br>City distance to the nearest labor office (hours) * State Level Institutional Quality<br>No<br>City distance to the State capital city (hours)<br>No<br>City distance to the State capital city squared<br>No<br>City distance to the State capital city (hours) * Inspectors per firm in the state<br>No<br>City distance to the State capital city (hours) * State Level Institutional Quality<br>No<br>City transportation costs<br>No<br>City transportation costs squared<br>No<br>City transportation costs * Inspectors per firm in the state<br>No<br>City transportation costs * State Level Institutional Quality<br>No<br>City Institutional quality<br>No<br>City altitude, latitude and longitude<br>No<br>State level dummies<br>Yes<br>City level characteristics in 91, 80 and 70<br>No<br>Observations<br>5,284<br>R squared<br>0.22<br>F-test (H0: Distance to the nearest labor office (hours) * Inspectors per firm in the state = 0 )<br>15.95|Robust standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. The table reports the least squares estimates of|per firm in the city on the distance to the nearest labor office (hours) interacted with the number of labor inspectors in the state. The controls are sta<br>and interactions with state level variables, distance to the state capital city, its square and interactions with the number of inspectors per firm in|transportation costs, its square and interactions with the number of inspectors per firm in the state and interactions with other state variables, city alti<br>include average access to justice, political concentration, management quality in public administration and the GDP per capita in the state. City tran<br>the nearest capital city in 1995. We also include the log of total population, per capita income, average years of schooling and share of population in|
nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situations where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations.

To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of residence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “Global Trends”, “Mid-year trends”, “Asylum trends”, to a “Statistical Yearbook”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).

Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “Refugees (including refugee-like situations)”, “Asylum seekers”, “Internally Displaced Persons”, “Returnees”, “Stateless” persons, and “Others of concern”. A situation is a pair country of origin/country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25,000 people. It is referred to as protracted if it is major for at least 5 continuous years.

The database, and therefore our analysis, is limited to refugees under UNHCR protection.

It does not include asylum seekers, i.e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “refugee-like situations”, i.e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e.g., undocumented Rohingya originating from Myanmar 4
Policy Research Working Paper 10441

# **Abstract**

This study examines the effects of communal violence, that is violence inflicted upon a particular group along ethnic or religious lines, on women’s marital outcomes. Communal violence, with its accompanying psychological and physical trauma, can have important socioeconomic consequences. The effects could be very different for women who are often disproportionately subject to sexual violence during such events. Using individual-level survey data from India and a difference-in-differences approach, the study shows that women’s age of marriage decreased, and the probability of getting married before the age of 18 increased after the Hindu-Muslim riots in Gujarat in 2002. The Gujarat riots stand out as one of the most significant and abrupt occurrences of communal violence in post-independence India, marked by widespread violence against women, thus providing a setting to study the impacts of such violence on women. Event study and synthetic control methods suggest that these effects are prominent two years after the riots and have increased over time. Women married after the riots also had fewer years of education and poorer social and economic status, such as a lower probability of employment and lower autonomy in household decision-making.

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 dghose@worldbank.org.

_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
- Oreopoulos, P., T. Von Wachter, and A. Heisz (2012): “The short-and long-term career effects of graduating in a recession,” _American Economic Journal: Applied Economics_ , 4, 1–29.

- Packard, T., U. Gentilini, M. Grosh, P. O’Keefe, R. Palacios, D. Robalino, and I. Santos (2019): _Protecting All: Risk Sharing for a Diverse and Diversifying World of Work_ , The World Bank.

- Pallais, A. (2014): “Inefficient hiring in entry-level labor markets,” _American Economic Review_ , 104, 3565–99.

- Perry, G., W. F. Maloney, O. Arias, P. Fajnzylber, A. Mason, and J. Saavedra-Chanduvi (2007): _Informality : Exit and Exclusion_ , The World Bank.

- Pissarides, C. A. (1994): “Search unemployment with on-the-job search,” _The Review of Economic Studies_ , 61, 457–475.

- Restuccia, D. and R. Rogerson (2013): “Misallocation and Productivity: Editorial,” _Review of Economic Dynamics_ , 16, 1–10.

- ——— (2017): “The Causes and Costs of Misallocation,” _Journal of Economic Perspectives_ , 31, 151–174.

- Rocha, R., G. Ulyssea, and L. Rachter (2018): “Do lower taxes reduce informality? Evidence from Brazil,” _Journal of Development Economics_ , 134, 28–49.

- Schanzenbach, D. W. and M. R. Strain (2021): “Employment Effects of the Earned Income Tax Credit: Taking the Long View,” _Tax Policy and the Economy_ , 35, 87–129.

- Shehu, E. and B. Nilsson (2014): _Informal employment among youth: Evidence from 20 school-to-work transition surveys_ , ILO Geneva.

- Ulyssea, G. (2010): “Regulation of entry, labor market institutions and the informal sector,” _Journal of Development Economics_ , 91, 87–99.

- ——— (2018): “Firms, informality, and development: Theory and evidence from Brazil,”

- _American Economic Review_ , 108, 2015–47.

- ——— (2020): “Informality: Causes and consequences for development,” _Annual Review of_

- _Economics_ , 12, 525–546.

- Van der Klaauw, B. and J. C. Van Ours (2013): “Carrot and stick: How reemployment bonuses and benefit sanctions affect exit rates from welfare,” _Journal of Applied Econometrics_ , 28, 275–296.

45
of this corridor is also referred to as the Trans-Sahelan Highway stretching over 4,400 kilometers, of which 50% of the network has been paved. Paving of the missing link between Salo (in CAR) and Quesso in the Democratic Republic of Congo would benefit Chad in terms of access to the coast and other transit countries. However, delays to attention on the missing links have been attributed to the relevant states not according the corridor the same level of priority.<sup>5</sup>

- Landlocked countries’ internal and external access depends to the degree which its neighbors improved the quality of their transport logistics and customs procedures: it is estimated that a one standard deviation improvement in a landlocked country’s logistics together with one standard deviation improvement in its neighbors’ logistics would raise the landlocked country’s exports by 74 percent.<sup>6</sup> Beyond-the-border institutional reforms facilitating trade and transport in a neighborhood can greatly increase the efficiency and reliability of logistics chains. Exploring corridor approaches that have worked well elsewhere, as in Southeastern Europe would be an avenue through which landlocked countries in the Sahel can mitigate their geographic disadvantage.

- Roads harassment by a multiplicity of control points that collect significant illegal payments is generalized. The Improved Road Transport Governance (IRTG) initiative's report shows that, in the West Africa region, these hindrances – in terms of number of checkpoints, bribes, and delays during the shipments of goods from gateway to the place of delivery – are more important in Mali, even though their magnitude has significantly decreased over the recent period (Table 5). Individual payments (bribes) at each check point, though small, add up sufficiently to deter traders with small volumes, especially the poor and women traders, which results in many such traders engaged in informal trade.

Table 5: Road harassment in selected West African countries

||Number of<br>per 100 km|Road Controls<br>|Bribes per 1|00 km|Delays per|100 km Trip|
|---|---|---|---|---|---|---|
||2005|2013|2005|2013|2005|2013|
|Burkina Faso|5.5|1.6|4,410|2,140|22|17|
|Côte d’Ivoire|-|1.9|-|2,675|-|8|
|Ghana|2|1.8|1,960|679|21|18|
|Mali|4.6|2.6|12,250|3,775|38|26|
|Senegal|-|1.3|-|1,614|-|14|
|Togo|1.5|0.9|1,470|597|16|7|

_Source:_ World Bank, 2018 calculations using IRGT Initiative data

- Cross-border trade is expensive and inefficient due to the difficulty of obtaining import and export licenses. It takes an inordinate amount of time to file for licenses, and bribes or informal payments may be expected.

- Unorganized and Fragmented Domestic Transportation Market: The transport sector in these three countries is dominated by a large number of individual or

> 5 UN-OHLRSS (2019).

> 6 Behar and Manners (2008).

17
that children who received non ‐ cognitive development interventions exhibited greater scholastic and labor market success as adults, even controlling for cognitive abilities.

The World Bank’s Skills Toward Employment and Productivity (STEP) Measurement Survey is designed to measure eight personality traits, which include the big five: (i) “extraversion,” (ii) “conscientiousness,” (iii) “openness to experience,” (iv) “neuroticism,” (v) “agreeableness,” (vi) “grit,” (vii) “hostile attribution bias,” and (viii) “decision-making style” (Pierre _et al_ . 2014), and it has been implemented in developing countries. Several studies have used the STEP taxonomy to test the association between personality traits and labor market outcomes in a developing country setting. In Peru, Cunningham _et al_ . (2016) find that perseverance (“grit”) and a variable representing both “openness to experience” and “extraversion” correlate closely with some labor market outcomes. Similarly, in Colombia, Acosta _et al._ (2015) find that personality traits have little effect on wages, but play a stronger role in labor market participation.

In Central Asia, Ajwad _et al_ . (2014a), using data from the Uzbekistan Jobs, Skills, and Migration Survey, determine that employed workers have better cognitive skills and personality traits than individuals who do not participate in the labor force, and that scores for “decision-making style” are especially low among non-participants. Using the same instrument in the Kyrgyz Republic, Ajwad _et al_ . (2014b) find that workers with higher personality trait indicators are also more likely to supervise the work of others, while in Tajikistan Ajwad _et al_ . (2014c) find that returning migrant workers have significantly higher cognitive skills and personality trait indicators than nonmigrants. In China, using data from the Gansu Survey of Children and Families, Glewwe _et al_ . (2013) find that both cognitive and non-cognitive skills strongly influence the decision to stay in school or enter the labor force, with significant differences between boys and girls. Moreover, Chua and Chun (2016) use STEP data to study job and qualification mismatches across different countries.

# b) _The Bangladeshi Labor Market_

Bangladesh has made substantial progress in economic and social development in recent decades. Its gross domestic product grew at an average rate of 6.1 percent per year over the past ten years, driving a substantial decline in the poverty rate. With a gross national income of US$1,190 per capita in 2015, Bangladesh is categorized as a lower-middle-income country. Bangladesh’s 5
# **<u>Table 1 – Fajnzylber et al. (2011) First-Stage Estimates for All Micro-Firms</u>**

|Formality Measures<br>|Eligible firms b<br>(All micro<br>|efore-after<br>-firms)<br>|
|---|---|---|
|License to operate|0.116***|(0.034)|
|Legal entity|0.075***|(0.022)|
|Micro-firm registration|0.063***|(0.020)|
|Registered with tax authorities|0.072***|(0.022)|
|Paid taxes|0.031**|(0.016)|
|Paid socialsecurity|0.043***|(0.015)|

Source: Fajnzylber et al. (2011, p. 273), Table 5.

Note: ***, ** Statistically significant at 1 and 5 percent respectively. Standard errors in parentheses. The estimates include as covariates, age, gender, and education of the firm’s owner, household size, dummies for the reasons of why the entrepreneurs decided to start business, and dummies for state and industry variables. The sample considers firms 3-20 months old and entrepreneurs aged 20-65 without college degrees.

The results presented in table 1 suggest that the program had a large impact on formalization rates. Given the percentage of ‘control’ firms with license to operate of 22.4 percent, 11 percentage points is equivalent to an effect of about 50 percent.<sup>8</sup> As discussed below, the impact on formalization rates found in Fajnzylber et al. (2011) seems to be mostly driven by firms created in November 1996, that is, the date the provisional measure was drawn up. Figure 2 illustrates the discontinuity with the threshold defined in December 1996 by Monteiro and Assunção (2012). The discontinuity practically disappears. This explains why Monteiro and Assunção (2012) found limited impact of the program on firms’ formalization rates.

**Figure 2 - Proportion of Formal Firms Before and After SIMPLES Threshold: December 1996**

<!-- Start of picture text --> 0 5 10 15 20<br>Age - in months<br>lpoly smooth: (mean) license lpoly smooth: (mean) licencse<br>(mean) license<br>.4<br>.35<br>.3<br>Formalization Rate .25<br>.2<br><!-- End of picture text --> 8 The authors then use the law as an instrumental variable and estimate the impact of formalization rates on firms’ performances through the weighted two-stage least squares.

8
# **uazrterlv .*-d GrCin D innd** Block

For purposes of estimation, the demand for feed grains is broken into the demand for feed, industry, exports, farmer owned reserve, government<sup>owned</sup> stocks, and feed grain price which implicitly determines free stocks through an identity. Feed demand depends on cattle, hog, and broiler numbers since all three types of livestock are heavy users of corn as well as on the ratio of corn price to meat price. The specification of export and inventory equations follows the earlier discussion.

Estimated Equation for DLVKFG DLVKFG - 14.25 - 6.776 Ql - 14.54 Q2 - 10.63 Q3 - 406.6 RPAFCMT (10.53) (1.638) (2.759) (2.140) (170.7) + .001126 COF + .0001552 PIGOF + .000001684 BROF (.0006086) (.0001591) (.000004695) + .4622 DLVKFG(T-1) (.09593) R- = .937, R2 - .926, DW - 1.90, ay - 2.914, Data = 1973:1 - 1987:3 Estimated Equation for DDINDFG DDINDFG = -7.373 + .5827 Q1 - .1042 Q2 + .2549 Q3 - .1264 RPAFC (5.800) (.4309) (.4317) (.4334) (.2524) + 2.518 RPYD (1.600) R<sup>2</sup> = .210, R<sup>2</sup> = .136, DW = 2.18, **ay** = 1.159, Data 1973:1 - 1987:3 Estimated Equation for XFG XFG - 1.473 - .3727 Ql - 2.797 Q2 + 1.041 Q3 + .4605 XFG(T-4) (3.182) (.9574) (1.106) (.9665) (.09736) - .9691 PCX + 9.339 EXR (.4480) (2.943) R2 - .676, R2 _ .639, DW - .922, acy - 2.556, Data - 1973:1 - 1987:3 Estimated Equation for CKFORFGE 18
Figure 2 shows the evolution of the bilateral nominal exchange rate between the Dominican Republic and Haiti during the period January 1979-September 2007, using monthly data. It suggests that the exchange rate between the two countries has been quite volatile—especially after the countries switched to a flexible exchange rate (19- for the Dominican Republic and 19- for Haiti) and during periods of financial crisis.<sup>7</sup> Thus, the reduction in exchange rate volatility and transactions costs in foreign exchange that a CCA could bring would entail substantial benefits for the two countries.

# **2.  Similarity of Inflation Rates**

The similarity of inflation rates (at levels consistent with overall macroeconomic stability) is one of the most common criteria used to evaluate the viability of a CCA. If inflation rates diverge too much between two potential members of a union, the ability to implement a common monetary policy (which follows from the adoption of a common currency and the shared goal of price stability) may be significantly hampered.

> bring about greater exchange rate stability among themselves and boost intraregional trade.

> 7With higher frequency data (weekly or daily), the data would show even higher volatility during certain periods.

11
# _Regressions_

Tables 3 and 4 provide the regression results for the drivers of labor productivity. We explore the determinants of labor productivity in a pooled sample of all firms in the UMICs and the HICs (Table 3) and separately for firms in the UMICs and the HICs (Table 4). For the pooled sample regressions and to capture the difference in labor productivity between UMICs and the HICs, we use a dummy variable equal to 1 if the firm belongs to UMICs and 0 otherwise (UMICs). Following the existing literature, several determinants of labor productivity are considered.

We begin with firm-size as defined above ( _Employment_ ). One view is that productivity tends to increase as firms become larger. Among other factors, the positive relationship between firm-size and productivity is often attributed to greater allocative efficiency for the relatively larger firms and the presence of fixed costs that generate economies of scale, which tend to favor the relatively larger firms (Bartelsman et al. 2013, Tybout 2000). However, the opposite case of diminishing labor productivity with firm-size is also possible due to decreasing returns to scale overall or due to diminishing returns to labor. Firm-size is also considered a vital proxy measure for several firm attributes that could potentially affect labor productivity such as access to finance, access to raw materials and product markets, tendency to innovate, exporting activity, firmefficiency and growth (see for example, Acs and Audretsch 1988, Cohen and Klepper 1996, Diaz and Sánchez 2008, Pagano and Schivardi 2003, Söderbom and Teal 2004).

Substantial work has also been done linking the productivity and age of the firm. Agerelated effects among surviving firms may be due to a number of reasons, including scale economies gained from expansion over time, vintage effect due to younger firms employing new and improved technology or equipment, selection effects which weed out inefficient firms 11
**Figure 14: Product Space for Products Not Exported by Penang, 2000**

<!-- Start of picture text --> 5 10 15 20<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation

**Figure 15: Product Space for Products Not Exported by Penang, 2007**

<!-- Start of picture text --> 5 10 15 20 25<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation 22