rafmacalaba/data-use-mentions-extended huggingface dataset:
4,930 households, detailed data at the household and individual level, and a labor module that encompasses separate sections on agriculture and non-agriculture enterprises for company owners and self-employed workers.

It should be noted that the three surveys do not use an exactly similar questionnaire, and therefore are not perfectly comparable. Annex 1 discusses the harmonization effort undertaken to render the interest variables as comparable as possible.

# **I. Overview of Haiti’s Labor Market Trends from 2001 to 2012**

As of 2012, Haiti’s population was estimated at 10.8 million people,<sup>4</sup> 7 million of whom were of working age (Figure 1). Among those, 74 percent were active in the labor market, meaning that they were either employed or willing to start a job on short notice if offered one.<sup>5</sup> Two-thirds of the 1.4 million people defined as unemployed were discouraged by the tough labor conditions and were not actually searching for work, while the remaining 0.5 million were actively looking. Unemployment as defined by the International Labour Organization, which considers only people looking for a job, stood at 12 percent, high for Latin America, but in line with other countries in the Caribbean.

> 4 Haiti’s last Census dates to 2003. Total population for 2012 refers to estimates from the 2012 household survey, based on projections by Haiti’s statistical office, the _Institut Haitien de Statistiques et Informatique_ (IHSI).

> 5 This number refers to an extended definition of unemployment, which considers not only those currently looking for a job (the ILO’s definition), but also those who are not looking due to discouragement by labor market conditions but who would be willing to take a job if offered one. For more details on definitions used in this paper, see Box 2.

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contain information on housing characteristics as well as information on each usual household member and visitors present on the reference night, including the relationship to the head of household, sex, age, marital status, migration, literacy, education and employment. The census also contained questions on fertility of females aged 15 and over, and infant mortality. The census dataset contains about 1.4 million records of children under five.

A set of geographic indicators is also used in this analysis. Because Cambodia has a rich collection of geographic data, indicators on a range of characteristics could be generated. These indicators included distance calculations, land use and land cover information, climate indicators, vegetation, agricultural production and flooding. A number of datasets from various sources were compiled into a GIS and these indicators were generated for all villages and communes in Cambodia. Very coarse resolution data were summarized at the commune level, while high resolution data were attributed to individual villages. Distances from villages to roads, other towns, health facilities, and major rivers were calculated from the center of the villages. Indicators based on satellite data with varying temporal resolutions included land use within the commune (agricultural, urban, forested, and so forth), the Normalized Difference Vegetation Index (NDVI) to proxy agricultural productivity, and the degree to which the area was lit by nighttime lights as a proxy of urbanization. Relatively stable indicators including soil quality, elevation, and various 30-year average climate variables were derived from other composite datasets. We have also generated the village-level means from the census data. It should be noted that the village-level means do not have to be taken from the variables that also exist in the CDHS dataset. This is because the village-level means, as with other geographic variables, can be linked to both the census and the survey datasets. Inclusion of these geographic variables and their cross terms with other individual-level and household-level variables has improved substantially the ability to explain the variation of anthropometric indicators.

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<!-- Start of picture text --> Figure 2.2: Share of farmers using improved inputs, 2005/06‐2013/14<br>1. Input Use, 2005/06‐2013/14<br>90%<br>78%<br>80%<br>72% 73%<br>68%<br>70%<br>61%<br>60%<br>50%<br>40%<br>30%<br>20% 16% 15% 14% 15%<br>13%<br>10% 5% 5% 4% 5% 6%<br>0%<br>Inorganic fertilizer Pesticide Seed purchase<br>2005/06 2009/10 2010/11 2011/12 2013/14<br>2. Input use by crop, average 2005/06 ‐ 2013/14<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Inorganic Fertilizer Use Pesticide Use Hybrid Seeds Use<br>Source: Staff calculations using UNPS 2005/06‐2013/14.<br>agriculture<br>Percentage of households engaged in<br>each crop<br>Percentage of households among those growing<br><!-- End of picture text --> Input use is low in Uganda compared to all other African countries for which comparable LSMS‐ISA data are available. Previous studies such as Sheehan and Barrett (2017), and Binswanger and Savastano (2014) 6
firm-destination size by definition, and thus obscures the firm-size effect on exports in a gravity equation.

Several of the stylized facts uncovered using the Database are broadly consistent with recent trade models with heterogeneous firms such as those reviewed in Bernard, Jensen, Redding, and Schott (2011) and Redding (2011), while other facts point to the need to extend and modify the existing models to accommodate them. In particular, with respect to dynamics, the high and correlated entry and exit rates point to tremendous uncertainty when firms enter export markets.

The rest of the paper is structured as follows. Section II describes in detail the construction of the Database based on customs exporter-level data in each country while Section III lists, defines and presents summary statistics on the measures included in the Database. Section IV presents six stylized facts based on the Database. Section V discusses possible avenues for future research and policy analysis using the Database.

# **II. Constructing the Export Dynamics Database Using Customs Data at the Exporter-Level**

The measures included in the Database are computed using customs data from 45 countries at the exporter-product-destination-year level.<sup>8</sup> Pooling across the datasets for all countries, we obtain 15 million unique observations at the country-firm-product-destination-year level<sup>9</sup> This is the raw dataset that we use to construct the Database.

> 8 The providers of the raw datasets for each country were mostly governmental agencies, mainly customs offices. Appendix 1 provides a complete list of the countries included in the Database, the periods for which data is available, and the data sources. For Brazil, Egypt, Estonia, Laos, New Zealand, Norway, Portugal, Spain, Sweden, and Turkey we calculated the measures without having permanent access to the raw customs data at the exporter-product-destination-year level. A few of the data providers authorize the access by researchers outside the World Bank to the raw customs data at the exporter-product-destinationyear level. The list of countries for which such data access is authorized is available
# **1. Introduction**

Urban areas around the world are attractive places for people looking for opportunities for a better life. About 55 percent of the world’s population lived in urban areas in 2018 and that number is likely to grow by 68 percent by 2050 (United Nations 2019). Urban agglomerations spur economic growth through productivity gains within economic sectors and structural transformation (Duranton and Puga 2004; Glaeser et al. 1992; Glaeser and Gottlieb 2009; Michaels, Rauch, and Redding 2012). At the same time, urban residents tend to be vulnerable to climatic and environmental shocks triggered by the increase in economic activities and are often pushed back to or remain trapped in poverty (Hallegatte et al. 2017). Without proper mitigation and adaptation measures against climate change, the benefits of urbanization could be negated (Mukim and Roberts 2022).

In this context, this paper attempts to test the following two hypotheses. The first is that people are more likely to become or stay nonpoor in larger or denser cities, compared to smaller or less densely populated towns. The second hypothesis is that large or dense cities that are more exposed to climatic and environmental shocks offer residents a lower chance to become or stay nonpoor, compared to cities of similar size but not exposed to such shocks. By empirically testing those hypotheses, we investigate the following key question: _Do climatic and environmental shocks hamper the key function of urban agglomerations as the escalator out of poverty in the developing world?_ Confirming this question is critically important as it underscores the need for policy interventions to help achieve inclusive and green growth through urban development.

We develop an analytical approach to addressing the questions with and without panel datasets by combining panel (or synthetic panel) household surveys with climatic datasets. The synthetic panel method is a useful approach to analyzing poverty dynamics when panel household survey datasets are not available. We develop synthetic panel datasets out of repeated cross-sectional household surveys in Chile between 2011 and 2015 and Colombia between 2008 and 2010.
nearly all public schools in Brazil. These indices are well-validated and can be used by researchers interested in studying the role of management in education systems across a far wider range of countries and schools than was previously possible.<sup>1</sup> All three indices, WMS, PISA, and Prova Brasil, show a strong, positive (within-country) correlation between school management and student learning outcomes, echoing recent causal evidence from randomized controlled trials in the U.S. [Fryer, 2014, 2017].

Our second contribution is to develop a framework to explore why management matters for schools.

> 1 For example, see Wössmann [2016] for a review of education systems research using large, cross-country surveys.

1
# **7. Conclusions**

Using different types of bilateral data that reflect international financial transactions (cross-border portfolio investments, syndicated bank loans, M&As, and greenfield investments), this paper offers a comprehensive and systematic account of how economies in the EAP region have integrated with the rest of the world since the 1990s. The paper shows, first, that EAP is increasingly more connected internationally, both with itself and with the rest of the world. Second, despite being relatively less important, EAP’s integration with the South has grown faster than that with the North. Third, EAP’s connections with the North involve more arm’s length financing than those with itself and the South, where FDI matters more. Fourth, there is significant heterogeneity in the financial connections of EAP. The more developed EAP economies integrate in a way that is similar to that of the North (with a larger role in EAP’s arm’s length financing), whereas less developed EAP economies integrate more similarly to South economies (with a larger participation in EAP’s FDI financing).

The broad set of stylized facts presented in this paper leads to some conclusions and conjectures that could be of relevance for researchers and policy makers interested in the EAP region and in cross-border financial integration patterns more broadly. Here we highlight a few.

The findings we uncover, related to the different patterns of integration with the North and the South and across different economies in EAP, are consistent with arguments in the literature that state that financial development has an important role in international financial transactions and in how EAP has engaged financially across borders. That is, whereas other factors are surely at play, the sophistication of financial markets as economies become richer might well explain the patterns we find. Two aspects of financial development can matter. One is the availability of financial instruments that allow savers to invest and lowers the costs and risks of those investments. This might prompt EAP economies (in particular the less developed ones)
26|Chinese companies create most jobs in the communications sector (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|27|Chinese companies in manufacturing sector create the most jobs (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|28|<br>Chinese companies in manufacturing sector create the most jobs per project (2003-<br>2015)||
||fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|29|<br>China is ffth largest creator of jobs. India creates the most jobs (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|29|
|30|US is a top donor for Kenya. Chinese fnance does not meet OECD/DAC aid criteria<br>(2013)<br>OECD DAC aid database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|

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quality consultants. Obviously the trade-off was that this led to a small cross-sectional sample size. We discuss the estimation issues this generates in section III.D below.

<u>Treatment and control plants: The 17 firms in our sample had 28 plants. Due to</u> manpower constraints we could collect detailed performance data from only 20 plants, so we designated 20 plants as “experimental” plants and randomly picked 6 control plants and 14 treatment plants. As Table 1 shows, the treatment and control firms were not statistically different across any of the characteristics we could observe.<sup>13</sup> The remaining 8 plants were then the “non-experimental plants”: 3 in control firms and 5 in treatment firms. These nonexperimental plants did not themselves receive consulting services, but data on their management practices and organizational and IT outcomes were collected in bi-monthly visits.

<u>Timing: The consulting intervention was executed in three waves because of the capacity</u> constraint of the six-person consulting team. The first wave started in September 2008 with 4 treatment plants. In April 2009 a second wave of 10 treatment plants was initiated, and in July 2009 the diagnostic phase for the 6 control plants was carried out. Firm records usually allowed us to collect data going back to a common starting point of April 2008.

We started with a small first wave because we expected the intervention process to get easier over time due to accumulated experience. The second wave included all the remaining treatment firms because: (i) the consulting interventions take time to affect performance and we wanted the longest time-window to observe the treatment firms; and (ii) we could not mix the treatment and control firms across implementation waves.<sup>14</sup> The third wave contained the control firms. We picked more treatment than control plants because the staggered initiation of the interventions meant the different treatment groups provided some cross identification for each other, and because we believed the treatment plants would be more
18 to estimate the impact of exogenous shifts in social capital on income. This eliminates the difficulty created by the potentially simultaneous determination of income and social capital. The drawback is that one must have valid instruments, and worse, the validity of an instrument depends entirely on theoretical arguments about the structure of the model since at least some set of the 'just identifying' assumptions cannot be directly tested.

As mentioned above, questions were posed to households about individual's 'trust" in various groups. We posit that certain of the these "trust" variables, particularly an individual's trust in strangers and trust in various government officials, are not affected directly by household income nor do they affect income directly, but that greater levels of trust do lead to higher village social capital". Column 2 of table 3 shows the IV estimates of the social capital impact using cluster level data based on those assumptions. The estimated effect of social capital is substantially larger than the OLS estimates and is now strongly statistically significant. This supports a view that social capital is an exogenous determinant of income because if social capital were purely a consumption good and higher incomes led to greater social capital then the IV estimates of the effect of social capital should have been _lower_ than the OLS estimates, instead of much higher. The higher estimate is consistent with a lack of joint determination of the two variables and a large degree of measurement error in our social capital variable since measurement error leads to bias towards zero'<sup>8</sup> .

" A recent investigation using cross national data from the World Values Surveys does find a strong bivariate correlation between expressed degrees of trust and membership in associations (La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1997).

**"'** The fact that the IV estimates are higher may reflect measurement error in the village level social capital variable. Since we are using only between 15 to 20 households per cluster, the
added per employee, log wage per employee, log sales per employee, log pro fi ts per employee, log capital per employee, log total employment and technology dummy). As expected (from our results in table 1) the OLS estimates are both negative, and (as expected from table 2) the IV estimates are positive and large. For example, an increase in the fraction of employed informal workers by 1 percentage point leads to an increase in productivity of 3.5% (an elasticity of 0.81) and an increase in wages of 1.7% (an elasticity of 0.39).

# 6. Conclusion

This paper has two main themes. First, we study the incentives fi rms face to employ informal workers. In particular, we study the role of enforcement of labor regulation (in the form of labor inspections and labor fi nes) on the behavior of fi rms. Second, we analyze how enforcement of formal employment affects fi rm performance. Stricter enforcement reduces the access of fi rms to unregulated labor and can damage their productivity by increasing labor costs. Using a combination of fi rm level data on illegal employment and fi rm performance, and administrative data on enforcement of regulation, we show that law enforcement reduces informal employment by fi rms. However, it also reduces the fi rm’s wages, productivity and investment. We argue that this is due to the fi rm’s limited access to fl exible labor.

Our paper shows that enforcement of labor regulation in the form of fi nes can be effectively used as a tool for fi ghting informality. However, our paper also shows that informal employment is an important source of unregulated labor for Brazilian fi rms, allowing them to operate more efficiently, and increasing their incentives to invest in new technology. Both of these conclusions have important implications for the design of labor regulation and of its enforcement.

# 7. Appendix A - Data

The data used in the paper comes from a variety of sources
- Transparency (ie regulator has statutory obligation to explain basis for decisions)

- Redress (ie appeal rights)

This list includes a key aspect of regulatory practice – transparency. However, the study only looks at whether and how this is covered in the law. Indeed, it seems that the study is only a study of Electricity/Energy _Laws_<sup>28</sup> . In consequence, there is no indication of whether, how and how far the letter of the law is reflected in regulatory practice nor how the spirit of the law is interpreted.

The Prayas Report and, to a lesser extent, Stern and Holder (1999) show how regulatory _practice_ can and is typically significantly different from what legal provisions would lead one to expect. This is confirmed in numerous one-off case studies as discussed in Kessides (2004). Similarly, Stern (2000) and (2002) – as well as the EBRD 2001 Transition Report on Energy - would suggest that the actual performance of regulatory frameworks for electricity in transition economies is significantly different from the relatively optimistic picture set out in the Kennedy tables.

In fairness, Kennedy’s paper supplements the tables with brief descriptions of the electricity regulatory experience in Hungary, Kazakhstan and Moldova. These descriptions do bring out clearly the gap between de jure provisions and de facto experience. Nevertheless, counting the positive entries as a proportion of the total, Kennedy’s Table 2 provides a picture for Central Europe and the Baltic States of a glass that is over 80% full. This is not what one finds in the case studies, nor what one finds in the papers referred to above.

The importance of this example is not to criticize the Kennedy paper. Rather it clearly demonstrates that data based on analysis of laws without collection of information on practice are, at best, seriously problematic; and, at worst, may be biased and misleading.

# **3.3.2 The 2002 Indian Ministry of Power Study**

There are other studies
**Figure 3: Export-employment (upper) and export-income elasticities (lower panel), by disaggregated sector**

<!-- Start of picture text --> 0.49 0.47<br>0.42<br>0.35 0.31<br>0.21 0.18 0.15 0.11 0.11<br>-0.11 -0.13 -0.16<br>-0.23<br>-0.30 -0.33<br>1.36<br>0.78 0.75 0.72 0.71 0.68 0.64 0.63 0.63<br>0.56 0.53 0.48<br>0.38 0.36 0.35 0.33 0.32 0.31 0.28<br>0.16<br>-0.25<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at the country-sector level have been estimated using employment (labor income, resp.) (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions controls for country and sector fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported.

In sharp contrast to manufacturing, aggregate business services exports show no significant association with within-sector employment creation, while their potential to generate labor income is much higher. Across the sample of 45 disaggregated sectors, only wholesale and retail trade, land transport, and professional services exports are positively associated with employment growth in these sectors
LiTS. For the remaining regions of the world, for which we use information from GDIM, we plot the values from the cohorts born between the 1950s and the 1980s. In almost every country, upward mobility is lowest for the younger generations. The patterns of upward mobility across the world are heterogeneous. In South Asia, the Middle East and North Africa, Sub-Saharan Africa, and East Asia and the Pacific, the younger generation have witnessed an increase in upward mobility. In Latin America, in highincome countries, and particularly in Europe and Central Asia, younger generations have lower upward mobility than older generations. In Europe and Central Asia, the median upward mobility of the cohort born in the 1990s was about 40 percent less than those born in the 1930s or 1940s.<sup>15</sup> The secular increase in education levels might explain part of this movement in the more educated

> 15 The largest differential in upward mobility between people born in the 1930s and the 1990s is in Kazakhstan, where upward mobility for the youngest generation was almost 85 percent lower than for the oldest generation. In this country, while older generations had established careers and resources from the Soviet era, young people faced a collapsing economy, high unemployment, and dismantled social safety nets. The rapid shift to a market economy, coupled with industrial decline and disrupted education reforms that were adapted to fit local political agendas rather than embracing their intended purposes, left the youth ill-equipped for new economic realities (Silova 2005).

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