rafmacalaba/data-use-mentions-extended huggingface dataset:
agricultural exports alike. In high-income countries (using the 1995 income classification), the share of jobs supported by computer and electronics exports as well as apparel and textiles experienced the strongest decreases (Figure 13, right panel). In emerging markets, the share of jobs sustained by textiles and apparel exports contracted most sharply between 1995 and 2018, but other labor-intensive jobs related to exports of other manufacturing, accommodation and food services, agriculture, and wood and wood products also showed strong contractions (left panel).

**Figure 12: Employment in exports, top 15 expanding sectors (% pt change), 1995-2018, emerging vs. HICs**

|**Top 15 expanding sectors (% pt change), LMICs**|**Top 15 expanding sectors (% pt change), HICs**|
|---|---|
|4.8%<br>Computer, electronic and...|3.1%<br>Administrative and support...|
|3.5%<br>Wholesale and retail trade;...|2.5%<br>Professional, scientific and...|
|1.7%<br>Machinery and equipment, nec|1.4%<br>IT and other information...|
|1.6%<br>Electrical equipment|0.8%<br>Financial and insurance...|
|1.5%<br>Motor vehicles, trailers and...|0.6%<br>Mining and quarrying,...|
|1.0%<br>IT and other information...|0.6%<br>Pharmaceuticals, medicinal...|
|0.9%<br>Mining and quarrying, energy...|0.5%<br>Wholesale and retail trade;...|
|0.5%<br
*_3.3 Efficiency and financial viability_**

The low efficiency of the electricity sector in Pakistan has been a feature of the country for many years. The creation of the independent regulatory body (NEPRA) and the unbundling of WAPDA led to regular collection and publication of statistics concerning various aspects of the operation of the sector, including separate reports on performance evaluation for the state-owned DISCOs and for KE.

To evaluate the performance of state-owned companies it was decided to concentrate on information for a single DISCO for those indicators where information on the aggregate of all DISCOs was not published in the annual State of Industry Reports. Such information was published in company reports and websites but does not cover the whole period from 1998 (the year of unbundling). There are only minor differences in the institutional set-up for the various DISCOs, so that the performance of one can stand as a reliable indicator for all, and therefore for the publicly owned sector as a whole. As a guide to the relationship between institutional factors and performance of the state-sector, the case of LESCO was examined. This was one of the three candidates for privatization under the incoming government of 2013, suggesting that it was considered a “good” performer at that time. In NEPRA’s 2015-2016 Performance Evaluation Report LESCO was ranked third out of the ten DISCOS.

# _3.3.1 Performance_

> 35 For the detailed access regulation index, see the annex.

34
Sant’Anna and Zhao (2020) in column (2) has been implemented using pre-treatment levels of employment, age, region and sectoral dummies as covariates. *10% level, **5% level, and ***1% level.

Table 3: Effects of EU accession on firm performance across sectors

|Dep. variable:|Log TFPR<br>(1)|Log revenue<br>(2)|Log employment<br>(3)|Log exports<br>(4)|
|---|---|---|---|---|
|Post _×_ Germany’s export share (log)|0.048***|0.082**|0.029|0.162**|
|Firm FE|(0.017)<br>Yes|(0.032)<br>Yes|(0.023)<br>Yes|(0.064)<br>Yes|
|Year FE|Yes|Yes|Yes|Yes|
|N (obs.)|134174|134176|134178|63992|
|N (frms)|18355|18357|18359|13245|
|R-squared|0.105|0.110|0.073|0.110|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Germany’s export shares are measured in the period 19942004. TFPR is estimated using the method in Levinsohn and Petrin (2003). When using exports as dependent variable, the sample is restricted to the period 2002-2012 due to a change in the questionnaire, which does not allow for a consistent measure of exports throughout the entire period. The standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level, and ***1
# **2. Trends in economic growth, inequality, and poverty**

# **2.1. Data**

We compile data from the Vietnam Household Living Standards Surveys (VHLSSs), which has been widely employed by the government, the international community, and academic researchers for poverty and inequality analysis for the country. The VHLSSs have been conducted by the General Statistics Office of Vietnam with technical support from the World Bank every two years since 2002. We compile data on all 58 provinces and five centrally controlled municipalities and supplement this data with other data that we collected.<sup>2</sup> In particular, provincial government spending data for the period 2018-2020 is currently unavailable for all the 63 provinces in any official document. To get the most updated data, we manually collected the state spending and investment spending data for 2018-2020 from several sources including the Ministry of Finance’s website, provincial finance departments’ websites, and relevant official documents.

The VHLSSs contain detailed data on individuals and households. Household-level data are collected on durables, assets, production, income, and participation in government programs. Individual-level data are collected on demographics, education, employment, health, and migration. The 1999 Population and Housing Census was used as the sampling frame of the VHLSSs during 2002-2008, while the 2009 and 2019 Population and Housing Censuses were used as the sampling frame of the VHLSSs respectively for 2010-2016 and 2018-2020. Around 3,100 communes were chosen as the primary sampling units out of the list of 10,000 communes for the

> 2 These municipalities are Can Tho, Da Nang, Hai Phong, Hanoi, and Ho Chi Minh City.

5
Policy Research Working Paper 7522

# **Abstract**

Using a data set covering about 277,000 firms across 79 countries over the period 2004–11, this paper examines the evolution of firms’ capital structure during the global financial crisis and its aftermath in 2010–11. The study finds that firm leverage and debt maturity declined in advanced economies and developing countries, even in countries that did not experience a crisis. The deleveraging and maturity reduction were particularly significant for privately held firms, including small and medium enterprises. For small and medium-size enterprises, these effects were larger in countries with less efficient legal systems, weaker information-sharing mechanisms, shallower banking systems, and more restrictions on bank entry. In contrast, there is weaker evidence of a significant decline of leverage and debt maturity among firms listed on a stock exchange, which are typically much larger than other firms and likely benefit from the “spare tire” of easier access to capital market financing.

This paper is a product of the Development Research Group. 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://econ.worldbank.org. The authors may be contacted at ademirguckunt@ worldbank.org, mmartinezperia@worldbank.org, and ttressel@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
To see this, consider the numerical example illustrated in Figure 7. The unshaded area in the top panel depicts the set of ( _τ, θ_ ) types that are hired by an intermediate management public school, while the unshaded area in the bottom panel depicts the set of ( _τ, θ_ ) types that are hired by a low management public school. It is clear that the intermediate management public school hires both more and better types.

In contrast to the people management case, there is a channel operating via household incentives. A further part of the reason that test scores are higher in schools with good operations management practices is because households (students plus parents) exert more effort due to additional _intrinsic_ motivation arising via the shift term _γ_ .

# **3.4.2 Predictions for intermediate school outcomes and evidence from PISA**

Again, we set out predictions relating to intermediate school outcomes (see Remark 2 in Appendix A), and then explore empirically whether they hold in our PISA data.<sup>24</sup>

**Teacher shortages.** The probability of hiring the teacher in an intermediate management public school is higher than the probability of hiring the teacher in a low management public school (via teacher selection). In the numerical example shown in Figure 7, the unshaded area is larger in the top panel relative to the bottom panel.

We take this prediction to the data using the 4 questions in the PISA school principal questionnaire that ask the principal whether they feel that the school’s capacity is hindered by a lack of qualified teachers (see Appendix C). Column (1) of Table 4 shows a negative and statistically significant correlation between the operations management index and the teacher shortage index. A one standard deviation increase in operation score is associated with a 0 _._ 076 standard deviation _decrease_ in the teacher shortage index, significant at the 10 percent level.

**Teacher motivation.** The expected intrinsic motivation of a teacher hired to an
|

_Source_ : Authors’ analysis of the Polity IV index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Table presents pre-treatment predictor variables for Jordan, their corresponding pretreatment sample means, and the weighted averages used to construct Synthetic Jordan. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).

**Table S1.6.** Synthetic Jordan Country Weights, Polity IV Index

||**Weight**|||**Weight**|
|---|---|---|---|---|
|||_Panel A. OIC all_|||
|Mali|0.000||Turkey|0.000|
|Niger|0.000||Egypt|0.000|
|Sierra Leone|0.000||Syria|0.685|
|Nigeria|0.000||Pakistan|0.000|
|Morocco|0.000||Bangladesh|0.000|
|Tunisia|0.315||Malaysia|0.000|
|Iran|0.000||Indonesia|0.000|
|||_Panel B. OIC minus PAK_|||
|Mali|0.000||Turkey|0.000|
|Niger|0.000||Egypt|0.000|
|Sierra Leone|0.000||Syria|0.685|
|Nigeria|0.000||Pakistan|–|
|Morocco|0.000||Bangladesh|0.000|
|Tunisia|0.315||Malaysia|0.000|
|Iran|0.000||
The last two terms in equation (2) represent the second-order behavioral responses of the household. They include the effect of the price change on the quantities produced, delta q, and the quantities consumed, delta c, of each good j. Note that when prices fall due to trade liberalization the quantity produced could decline, thus reducing welfare for a given price, unless the household’s income shifts to other activities that can compensate the decline in income due to the fall of the price of good j. Similarly, consumption could increase as prices fall. Here, it is important not to get confused: if we only do the exercise with one single good j, we would erroneously conclude that welfare would fall as a consequence of the rise of consumption of the cheaper (liberalized) good. The key is to remember the summation sign: consumption of other goods would fall as the liberalized good would have become relatively cheaper. This clarification, however, brings us to the empirical estimations of the framework.

An important feature of this approach, which is consistent with the definition of IG, is that it emphasizes households’ abilities to change income sources and consumption patterns in response to changes in relative prices. With ideal data on household consumption and income patterns over time, combined with data on goods’ prices, it is possible to estimate the behavioral functions. Most countries, however, do not have the ideal panel data set. Instead, it is more common to have cross sections of households in living standards surveys. Table 1 lists 119 countries with surveys since 2000 with this type of data that are available from the World Bank’s Development Data Platform (DDP).<sup>5</sup>

# **B. Estimation Challenges**

Several estimation challenges emerge when attempting to estimate income-source and consumption responses to trade-driven price changes. One concerns price data. In practice, the relevant surveys ask households to report total expenditures by type of good or service, and they are also asked
**Table A6** Impact of Boys Treatment Among Enrolled

||(1)|(2)|(3)|
|---|---|---|---|
||**Boys**<br>**Enrolled**|**Endline**<br>**Control Mean**|**Observations**|
|**A. Female Data**||||
|IPV often index||||
|treatment x post|-0.288<br>(0.184)|0.000|3,662|
|IPV in last year index||||
|treatment x post|-0.312**<br>(0.144)|-0.000|3,662|
|Sexual activity index<br>treatment x post|-0.297***<br>(0.060)|-0.000|3,662|
|**B. Male Data**||||
|Violence attitudes index<br>treatment x post|-0.442***|-0.000|1,542|
||(0.141)|(0.086)||
|IPV often index||||
|treatment x post|0.020|0.000|1,542|
||(0.091)|(0.087)||
|IPV in last year index||||
|treatment x post|0.088|0.000|1,542|
||(0.166)|(0.153)||
|Sexual activity index||||
|treatment x post|-0.097<br>(0.096)|-0.000<br>(0.071)|1,542|

_Notes
**Figure 2 - Morocco GDP growth by Economic Sector 2000-2010**

<!-- Start of picture text --> 30<br>25<br>20<br>15 Agriculture<br>10<br>Industry<br>5<br>Services<br>0<br>Total<br>-5<br>-10<br>-15<br>-20<br><!-- End of picture text --> Source: Morocco Tableau de Bord 2000-2010 Macro indicators would suggest that GDP growth has trickled down to households. According to national accounts, household final consumption growth has closely followed the evolution of GDP growth (Figure 3) and this is in line with developments in the labor market. Despite a growing working age population, Morocco has been able to increase marginally the employment rate and decrease unemployment from 13.4% in 2000 to 9.1% in 2010. While low female and youth labor market participation and the large urban-rural divide continue to pose a problem to the Moroccan economy, the main macroeconomic trends over the past decade have been positive.

**Figure 3 - GDP and household consumption growth 2000-2010**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>GDP growth<br>4<br>HH final cons. growth<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: HCP National Accounts (2000-2010), various publications 6
Results – Growth in Monthly Mean Consumption**

|||**Consumption per**|**capita in 2005 LE**||
|---|---|---|---|---|
||**February 2005**|**February 2008**|**Growth 2008-**<br>**2005 (percent)**|**Annual growth**<br>**rate (percent)**|
|Urban|3,007|3,430|14.1|4.5|
|Rural|1,868|2,057|10.1|3.3|
|**Total Egypt**|**2,352**|**2,641**|**12.3**|**3.9**|

_Source: Authors estimates based on HIECPS 2005-2008 and regional CPI indices. Note: Table uses actual_ _household size to weight data._

> 14 Using CPI index to deflate nominal figures.

9
in the last 12 months [yes = 1]<br>Saved money in the last 12 months [yes = 1]|
|Nutrition|Adolescent’s dietary diversity score [0–1]<br>|
|Mental health|Overall, adolescent feels happy or very happy [yes = 1]<br>Mental health index|

_Note:_ AIDS = acquired immunodeficiency syndrome; HIV = human immunodeficiency virus; IGA = income-generating activity; IHS = inverse hyperbolic sine; SES = socio-emotional skills; SRH = sexual and reproductive health; std = standard deviation; yo = years old.

37
15.07|6.79|12.33|
|Costa Rica|10|Rate of return|15.04|17.75|||
|Ecuador|20|Hybrid|16.28|18.94|||
|El Salvador|40|Hybrid|33.84|38.75|||
|Mexico|10|Rate of return|11.12|7.71|||
|Panama|100|Price cap|20.16|16.52|20.16|16.52|
|Paraguay|0|Rate of return|24.13|13.80|22.02|12.62|
|Peru|60|Hybrid|22.58|12.87|22.56|12.93|
|Uruguay|0|Rate of return|18.81|9.58|15.32|7.52|
|Venezuela|20|Rate of return|6.46|3.29||2.06|

Notes: All prices are at the country level, and correspond to the year 2001, except for prices without taxes in Colombia, which correspond to the year 2000 and were converted into 2001 price levels by using the US Consumer Price Index.

Regulatory regimes and the share of private sector participation in the distribution activity for all countries but Panama, were obtained from Espinasa (2001). The share of private sector participation in the distribution activity for Panama was obtained from the regulator of the public services in that country. In order to harmonize electricity prices across countries, all prices are in 2001 US dollars per KWh of electricity and are expressed in terms of purchasing power parity.

The simple correlations between the share of private sector participation in the distribution activity and household prices with taxes, industrial prices with taxes, household prices without taxes, and industrial prices without taxes (all corresponding to

> 12 The data is from the OLADE data base which only
Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.073|0.080|0.097|0.165|0.189|0.225|0.204|0.277|0.341|
|Panel B Adj.R<sup>2</sup>|0.065|0.074|0.094|0.159|0.186|0.223|0.186|0.270|0.338|
|Nr. of countries|16|16|16|16|16|16|16|16|16|
|Nr. of hh|134196|134196|134196|134196|134196|134196|130866|130866|130866|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. GHSPOP 1km is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector
11 The highest score (six) on their scale goes to countries in which multiple parties compete in elections, even if they do not win seats. For the DPI, we collected sufficient information to replicate this type of indicator for more than 100 additional countries. However, for wider application of their indices we added a seventh point to the scale, in order to identify variation among the large number of countries that exhibit elections where multiple parties run but only one party enjoys great success. Countries receive a seven that have elections in which multiple parties compete and the largest party receives less than 75 percent of the vote. Conceptually, this distinction captures institutional or informal biases against political competitors that are not revealed in our sources; empirically, this seventh category generates much more variability among countries outside of Africa. The scale, then, is:

- 1 no executive/legislature 2 unelected executive/legislature 3 elected, one candidate

- 4 one party, multiple candidates 5 multiple parties are legal, but only one won seats (because other parties did not exist, compete, or win seats).

- 6 multiple parties competes and won seats (but one party one 75 percent or more of the seats)

- 7 the largest party received less than 75 percent of the seats.

As with distinctions between presidential and parliamentary systems of government, numerous subtleties arise in assessing scores for these indices. They are thoroughly detailed in the documentation accompanying the database. For example, many countries have executives _ who are indirectly elected. In our rating scheme, they receive the same competitiveness score as the body that elects them. Therefore, in a parliamentary system, executive elections are as competitive as legislative elections. When there is an electoral college, elections are as competitive as membership in the electoral college. If the executives themselves appoint the electoral college, the systems are scored as two, on a scale of one to seven. Competitively
# **1 Introduction**

Mass displacement of people is common throughout history. The 20th century alone experienced the uprooting of millions of Jews, Hindus, and countless others. More recently, civil wars and conflicts displaced millions of Sudanese, Syrians, and Rohingyas. While significant attention has been given to the short-term costs that mass refugee inflows may cause for hosting countries in the shortterm, less is known about the long-term legacy of such events. In particular, little evidence exists on how large-scale population resettlements affect the social cohesion of receiving communities in the long-run.<sup>1</sup> This question is important because the short-term and long-term effects could be very different.

This paper aims to fill this gap by exploiting the 1923 Greek population resettlement to examine the long-run consequences of mass refugee inflow on the social cohesion of the receiving society. In the aftermath of the Greco-Turkish war of 1919–1922, escaping religious persecutions, the Greek Orthodox population of the Ottoman Empire fled from their homeland in Turkey they inhabited since antiquity. In total, 1.2 million Greek Orthodox were forcibly resettled to Greece, increasing the host population by more than 20 percent within a few months. Confronted with such a sudden influx of refugees, Greece – an agrarian country economically drained by several years of war – called for the help of the League of Nations, which not only provided immediate relief (food and shelter) but also implemented an ambitious resettlement program. Former marshlands were reclaimed and refugees were provided with arable land, livestock and other farm inputs. New houses were build and new schools opened to accommodate the newcomers. Furthermore, given that refugees could not return to their homeland and had to remain in Greece permanently, they were granted the Greek citizenship.<sup>2</sup> Despite these substantial investments, refugees – initially in a state of “utter destitution”<sup>3</sup> – were often met with hostility by the locals, who felt their way of life under threat
|Website/Social Media|57.14|40.69|5.51|
|Mobile Money (MM)||50.64|42.71|

_Notes_ : All variables, except the number of firms, are in % of all firms. Sampling weights applied. The number of firms may vary for some variables. Note that there are additional digital variables for formal firms. “Formal” covers businesses included in in the World Bank Enterprise Survey (WBES). “Micro” covers businesses included in the World Bank Micro Enterprise Survey (WBMES), and “Informal” covers businesses included in the World Bank Informal Sector Enterprise Survey (WBISES).

**Table 2: Summary Statistics**

||Micro|Informal|
|---|---|---|
|Distance of Informal to Closest Formal Firm (Baseline)||0.96|
|Distance of Informal to Closest ES Firm||1.43|
|Distance of Informal to Closest Formal Firm (Enumerated)||0.68|
|Distance of Informal to Closest Formal Firm with Website||1.81|
|Number of Informal Businesses Within Square||12.49|
|Num. of People Who Worked Last Month|2.23|2.04|
|Years in Operation|5.25|3.30|
|Age of Main Owner|35.60|37.72|

7