rafmacalaba/data-use-mentions-extended huggingface dataset:
likely may have a global component, as countries tend to “specialize” in the same form of sovereign theft at the same time. In the next section we explore a number of such potential factors more systematically.

# **5.  Correlates of Sovereign Theft Episodes**

In this section we investigate in more detail a set of potential correlates of sovereign theft episodes that have been discussed in the literature on defaults and expropriations. Our starting point is the core empirical specification in Kraay and Nehru (2006), who investigate the correlates of "debt distress", defined as episodes of debt servicing difficulties marked by exceptional financing in the form of recourse to the Paris Club or the IMF, as well as arrears accumulation. In a large sample of developing countries they find that debt distress is more frequent in countries with high levels of external debt, with weak policy performance, and in countries experiencing adverse macroeconomic shocks. One immediate difference however is that Kraay and Nehru (2006) study debt servicing difficulties vis-a-vis both private and official creditors, while the dataset we study in this paper covers only defaults against private creditors.<sup>5</sup> We begin by considering how expropriation and sovereign default events are related to the stock of external debt owed to private creditors and the stock of FDI in a country. Data on these are taken from the Sovereign Wealth of Nations dataset by Lane and Milesi-Ferretti (2007). We also measure policy performance using the World Bank's Country Policy and Institutional Assessment (CPIA) data, which covers all World Bank borrowers since 1978. Finally, we use as proxy for macroeconomic shocks real per capita GDP growth.

In addition, we consider two characteristics of the political system, both taken from the Database of Political Institutions by Beck et al. (2001). The first is the ideology of the government of in power, measured with a dummy variable taking the value one if the

> 5 Rescheduling of debts owed to official creditors (via the Paris Club in
sup>|(0.014)<sup>_∗∗∗_</sup>||
|||[0.001]<sup>_∗∗∗_</sup>|[0.001]<sup>_∗∗∗_</sup>|[0.008]<sup>_∗∗∗_</sup>||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Questions about ante and post natal care, vaccinations and illness/injury episodes are asked for the past one month. Questions about child birth are asked about the past year. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.2:** Maternal Utilization

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Maternal utilization index**(SUR)||0.046|0.130|_−_0.031|888|
|||(0.045)<br>[0.322]|(0.054)<sup>_∗∗_</sup><br>[0.079]<sup>_∗_</sup>|(0.054)<br>[0.463]||
|(1) ANC/PNC visits index|0.000|_−_0.038|
at customs but instead are recorded by other government/private institutions in the countries.

> 24 The specific series used for this conversion is PA.NUS.FCRF taken from the World Development Indicators (whose original source is the IMF’s International Financial Statistics). The series is based on an annual average of daily exchange rates determined by national authorities or in the legally sanctioned exchange market.

> 25 http://unstats.un.org/unsd/tradekb/Knowledgebase/Calculation-of-dollar-value-in-trade-statistics-Current-value-or-constantdollar-value.

12
to be 16 percent higher, points to the potential for improving management capabilities among other firms and sectors. By comparison, in Eastern Croatia, exporters are not significantly better managed than the rest of the firms.

The paper beyond this point is organized as follows: Section 2 compares firms in Eastern Croatia with Zagreb and other regions of Croatia on key parameters surveyed, including firm capabilities other than management (e.g. skills, innovation, technology adoption), outcomes on firm performance (e.g. labor productivity), and the constraints that firms face across different regions of Croatia. Section 3 introduces the methodology for computing management scores and compares management capabilities in Eastern Croatia with other regions of the country, and across countries. Section 4 presents some descriptive results on firm characteristics that are associated with management capabilities in Eastern Croatia relative to other regions in Croatia. Section 5 presents a simple regression framework for assessing the association between management practices and firm performance. Section 6 explores the drivers of good management practices. Section 7 concludes with policy recommendations.

# **Section 2: Firm Capabilities Survey**

In this section, we utilize the firm level survey to understand the differences between firms across regions in Croatia.

# **_2.1 Firm Performance, Technology Adoption and Obstacles – Comparing firms across regions_**

We compare firms based on three broad categories across Zagreb, Eastern Croatia and other regions in Croatia, excluding Zagreb and Eastern Croatia. The three categories are classified as follows: (1) firm performance – we use sales/employee (labor productivity) and gross value added (GVA) per employee as the two indicators; (2) innovation and technology adoption. For innovation, we assess whether a firm has registered itself for a new trademark, patent, utility model or industrial design and for the latter, we review the use of technology for quality and control management and introduction of new products and processes; and (3) the different types of obstacles the firms face during their operations
examines the impact of an incumbent’s electoral defeat in the most recent election on village-level and individual-level outcomes. Using our survey data, we first verify that these electoral results induce a leadership change, as expected. Specifically, we show that an incumbent’s defeat increases the likelihood that the village head in our survey is someone different from the incumbent candidate who competed in the most recent election. The RD point estimate is 83.5 p.p., significant at the 1% level (see Table 2, column 1, and Figure 2, panel a). We also estimate the effect of a turnover on the village head’s tenure. The RD point estimate is roughly (minus) five years, slightly less than the _de jure_ term of six years (see Table 2, column 2, and Figure 2, panel b).

Thus, electoral turnovers translate into leader turnovers at the village level, but there is imperfect compliance. While our baseline specification is a sharp RD estimation of the effect of turnovers ( _γ_ in equation 1), in the Appendix we also report fuzzy RD estimates where we use 1 ( _marginjt >_ 0) as an instrument for village head turnover to account for this imperfect compliance. In this case, the endogenous regressor is a dummy equal to 1 if the village head in our survey sample is a different individual from the incumbent who competed in the most recent election—the dependent variable in column 1 of Table 2. Thus, the sample for this fuzzy RDD estimation is restricted to the _N_ =443 villages in which an incumbent competed in the most recent election and we were able to survey the current village head.

**Bureaucratic Turnover.** Although the majority of village officials have tenured positions, newly elected village heads may seek to reorganize the village government by appointing new officials or by reshuf-

10
second is the relative higher weight of processed foods consumption, as was observed in Table _2_ , albeit the lower ultra-processed foods consumption.

_Table 8_ - Changes in medical expenditure

||**Processed and ultra-process**<br> <br>|**ed foods**<br>|
|---|---|---|
||Low<br>Medium|Upper|
|**1**|6.55%<br>6.56%|6.58%|
|**2**|3.17%<br>3.17%|3.18%|
|**3**|2.01%<br>2.02%|2.02%|
|**4**|1.45%<br>1.45%|1.46%|
|**5**|0.99%<br>1.00%|1.00%|
|**6**|0.70%<br>0.71%|0.71%|
|**7**|0.48%<br>0.48%|0.48%|
|**8**|0.31%<br>0.31%|0.31%|
|**9**|0.19%<br>0.19%|0.19%|
|**10**|0.06%<br>0.06%|0.06%|

Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019; and SIH/SUS. Estimates assume a price shock of 20%.

Figure 5 depicts the progressive pattern of the long-term estimated medical expenditure changes after implementing a taxation on processed and ultra-processed foods. The lowest consumption decile presents the highest gains, with
Agricultural and rural households in low-income settings often have higher illiteracy rates than the general population, for example. This is illustrated in Figure 4, which presents the literacy rates for households with no income from agriculture vis-à-vis those earning more than 30% of total income from agriculture for 26 low- and middle-income countries.<sup>9</sup> If a meaningful share of respondents can be expected to have literacy difficulties, text message-based and Interactive Voice Response (IVR) phone survey modes are likely unsuitable as these usually require the respondents to read and/or write.

_Figure 4. Adult literacy rate (ages 15+), by share of total income from agriculture._ <!-- Start of picture text --> Income from agriculture > 30% No income from agriculture<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source: Authors’ calculation based on the FAO Rural Livelihoods Information System (RuLIS) database_

# _Response fatigue and incentivization_

Fatigued respondents may cease to answer truthfully, refuse to answer questions, or stop participating in a survey over time. Various factors can contribute to the respondent burden, including survey complexity and length. For instance, Ambler et al. (2021), through an experiment in Ghana, find that the roster position of a household member has implications for activities reported in the labor module, with the movement of an individual down in the roster by one position reducing the number of their reported productive activities by 2.2%. In a recent experimental study, Abay et al. (2021) show that delaying a module on dietary diversity by 15 minutes led to underestimation of the dietary diversity score due to respondents’ fatigue in a phone survey in Ethiopia.

Recall length also contributes to respondent burden and has
(0.04)|-0.02*<br>(0.01)|-0.01<br>(0.01)|0.11|0.07|0.48|
|Progressive gender attitudes index|0.01<br>(0.06)|-0.01<br>(0.02)|-0.03<br>(0.02)|0.75|0.56|0.47|
|B**argaining power measures**|||||||
|Legally married|-0.05<br>(0.07)|0.01<br>(0.02)|-0.03<br>(0.02)|0.49|0.83|0.21|
|Wife's age|0.00<br>(0.00)|0.00<br>(0.00)|0.00<br>(0.00)|0.43|0.34|0.68|
|Wife owns land|0.03<br>(0.08)|0.00<br>(0.02)|-0.03<br>(0.02)|0.72|0.49|0.36|
|Wife's years of education|0.02<br>(0.01)|-0.00<br>(0.00)|-0.01*<br>(0.00)|0.13|0.06|0.27|
|Wife in paid work last week|-0.15**|-0.00|0.03|0.04|0.02|0.33|
||(0.07)|(0
The variable SID Camps _i_ represents the standardized weighted inverse distance of each grid/individual to the 33 refugee camps, calculated as follows: Here, SID Camps _i_ is the product of the inverse distance of each grid unit _i_ to each of the 33 main refugee camps _j_ in Cox’s Bazar. The distance from each geographic unit to each refugee camp is calculated using the Euclidean distance formula, measured from the centroid of each geographic unit (or coordinate location of each individual) to the centroid of each camp. _Wc_ represents the displaced population weights of each camp in 2023. The location of each of the 33 areas is illustrated in 3. The standardization of camp Figure SID Camps _i_ facilitates the interpretation of results. The geographic variation of SID for 4. the district of Cox’s Bazar, where the analysis is centered, is illustrated in Figure Our main coefficient of interest is _α_ 1. It describes the effects of increasing SID Camps by one standard deviation in each of the outcomes. In simpler terms, it describes the effects of being physically closer to the most populated refugee camps before and after the Rohingya arrived in 2017.

# **III.B Data**

This paper combines multiple sources of information described below. All data sources are restricted to the Cox’s Bazar district as it is the area most plausibly experiencing the majority of the effects of the refugee camps.

# **III.B.1 Remote Sensing Data**

1. _Night light density_ . Data on night light density comes from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The former spans 1992–2013 and the lat-

12
regular municipal functions.<sup>13</sup> However, there were rules that prohibited replacement. To ensure that WWS positions were newly created, Government provided municipalities with technical assistance to illustrate tasks that were eligible for program support, and followed up with inspections.

# **2 Data**

This paper uses data from a unique household survey commissioned by the State Employment Agency and administered during December 2010-March 2011. The field work for the survey was carried out GfK Custom Research Baltic. The WWS Household Survey represents the population of registered unemployed persons. The sampling strategy required data analysis of the registered unemployed population from State Employment Agency data. All five regions of Latvia - Kurzemes, Latgale, Riga, Vidzemes, and Zemgales - were sampled. Data on the registered unemployed population were divided into four strata within each region: <u>Strata 1:</u> people enrolled in WWS for less than six months prior to the survey (Treatment 1 or T1) <u>Strata 2:</u> WWS applicants during August-November 2010 that were wait-listed (Control 1 or C1) <u>Strata 3:</u> people laid off during August-October 2009, who became WWS beneficiaries and completed a stint of WWS at least six months prior (Treatment 2 or T2) <u>Strata 4:</u> people laid off during August-October 2009 but did not register for WWS program (were not interested in the WWS program) (Control 2 or C2) A random sample of 1,000 people was drawn from each Strata 1 (T1) and Strata 2 (C1); and, a random sample of 500 people was drawn from Strata 3 (T2) and Strata 4 (C2). In this paper, we call the individuals who were originally selected in random sampling as _assigned_ individuals irrespective of the group they belong to. The questionnaire was administered to

> 13Municipalities outsourcing is particularly common among the larger and wealthier municipalities.

7
|0.215|0.006|0.145|0.974|0.434|
|Mean in Control Group|-1.410|0.028|0.013|0.484|0.771|0.000|0.000|0.000|
|Sample Size|1,032|1,167|756|1,090|1,169|185|196|196|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The height-for-age z-score is calculated using the 2006 WHO child growth standards. The parenting practices score is the percentage score on a set of parenting practices. The Malawi Developmental Assessment Tool is a test of fine motor skills, language, and hearing administered directly to the child. The reported child difficulties and reported pro-social behaviors are created using the Strengths and Difficulties Questionnaire (http://www.sdqinfo.com/c3.html). Additional details on the outcome variables can be found in Appendix A and Appendix B. Baseline values of the following variables are included as controls in the regression analyses: gender of the child, age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
Release 2) put together by Borchert et al. (2020) for our baseline results for three reasons: first, to estimate Equation 4 theory-consistent and to be able to include trends for globalization, we need data on international and domestic trade flows (Yotov 2021). In contrast to other standard sources for trade data such as UN Comtrade or CEPII’s Baci (Gaulier and Zignago 2010), the ITPDE covers both, inter- and intranational trade. Second, deep trade agreements typically cover trade in both goods and services (Mattoo, Rocha, et al. 2020) while tariffs are only applicable for trade in goods. It could be possible that trade agreements do not reduce NTBs for goods trade but do so for services. To get a full picture of the trade-creating effect of NTBs, we would therefore like to include also services. Lastly, the ITPDE covers the whole world while the comparable WIOD data only report trade for 44 countries (Timmer et al. 2015). The ITPDE

> 5 WITS is available at https://wits.worldbank.org/.

11
decrease with a higher tari¤ which implies a larger trade discrepancy, given a ...xed level of export quantity.

In summary, this simple model delivers the following testable hypothesis:

1. Trade discrepancies increases with the restrictiveness of NTMs imposed by the importing countries

2. Origin fraud and product classi...cation fraud exist, particularly for trade between highincome importing countries and developing exporting countries 3. The e¤ects of importing country NTMs on trade discrepancies are larger for trade between high-income importing countries and developing exporting countries 4. The e¤ects of importing country NTMs on trade discrepancies are larger for homogeneous products than for di¤erentiated products 5. The e¤ects of tari¤s on trade discrepancies could be positive

# 3 Data

The tari¤ and NTM data used in this paper are from the UNCTAD TRAINS database. Trade data originates from the UNSD COMTRADE database. The NTM data is collected only periodically, and the data utilized in this exercise was collected between 2015 and 2019. As NTM data generally varies little across time, the data on NTM is assumed to re‡ect the existing NTMs as of 2018. Consequently, trade and tari¤ data are matched to the same year.

For the purpose of this paper, we focus on Border NTMs, while retaining other types of NTMs as control variables.<sup>13</sup> Border NTMs include costs incurred by imported goods such

> 13NTMs are distinguished between border and non-border variables on the basis of the international classi...cation of non-tari¤ measures (UNCTAD, 2019) and de...ned as custom measures by Ederington and Ruta (2016)

12
among others, Neidhofer et al. (2018), Azam and Bhatt (2015), Emran and Shilpi (2015), Hertz et al. (2008), Thomas (1996)). Among the few contributions on income mobility, see Fan et al. (2020) on China. The recent analysis of intergenerational occupational linkages in developing countries includes Bossuroy and Cogneau (2013), Emran and Sun (2015) and Emran and Shilpi (2011).

The literature on intergenerational mobility in Indonesia is small, with only a handful of studies available. We are aware of three studies on Indonesia that can be broadly classified as dealing with intergenerational issues in education, and all of them study the effects of the same policy experiment: a large scale school construction program in the 1970s, originally studied by Duflo (2001).<sup>12</sup> Hertz and Jayasundera (2007) analyze the effects on IGRC in schooling for the children, and find that the exposure to new schools weakened the intergenerational persistence for men, but not for women. In a recent paper, Mazumder et al. (2019) focus on the long-term effects of the program on the second generation children, i.e., children of those who benefited from the school construction when they were children themselves. Since most of these second generation children have not completed their schooling, it is difficult to estimate IGRC in schooling attainment. They thus focus on the academic performance of the children which would be correlated with the final educational attainment of a child. They show that the children of the mothers exposed to the large scale school construction in the 1970s had significant gains in school examination scores. But they do not find any effects of the fathers. In a related paper, Akresh et al. (2018) study the effects of school construction on the socioeconomic well-being of the first generation directly exposed to the program and the intergenerational effects on school attainment using the SUSENAS 2016 cross section data.

# **(
‐specific wage data at the firm‐level, the model is modified in two ways: First, the analysis on trade and the female wage share combines the Enterprise Survey dataset with sectoral wage data by gender, available from Household Surveys. This assumes equal relative wages across all firms in a sector. Such wage data are available for 30 countries in the Enterprise Surveys, allowing to estimate the following female wage share equation: Second, the analysis assumes equal wage rates for female and male workers, _w_<sup>_F_</sup> _= w_<sup>_M_</sup> _= w._ Since _S_<sup>_F_</sup> = _w_<sup>_F_</sup> _L_<sup>_F_</sup> _/ wL_ , the dependent variable becomes the female labor share in total employment, _femsh= L_<sup>_F_</sup> _/ L._ Similarly, the model no longer controls for relative wages, since we assume ln( _w_<sup>_M_</sup> _/w_<sup>_F_</sup> ) = ln1 = 0. While assuming equal wage rates between female and male workers is a strong assumption, we argue that the fixed country‐sector effects included in the model partially correct for this. The female labor share equation takes the following form: Equation (2) becomes our baseline specification, allowing us to include all 64 countries in the analysis, while equation (1) serves as a robustness test. Both models consider four types of trading firms: (1) 5
_** _F(t)_ **_-_** _Q(t_ **1)**

where **S** denotes the dollar value of the stock at the end of the period,F is the net flow during the period, **_Q_** is the market price of the asset in current **US** dollars, and 5is a rate of physical depreciation. The key issue concerns the measurement of **_Q._** In the case of FDI, we take **_Q_** to follow the replacement value of physical capital. For inward FDI, this is captured **by** the investment deflator of the host country. For outward FDI, a detailed breakdown of flows **by** destination is not available, and hence we use a weighted average of investment deflators, with

> In a large number of cases, the asset figures reported **by** this source just reflect accumulated dollar flows at historical exchange rates, while in others some adjustments are introduced for exchange rate changes. In a few rare cases, the asset figures are expressed at market value.

> **1o** For most countries, initial FDI stocks are obtained from **OECD (1967),** which reports direct investment assets of each industrial country disaggregated **by** country of destination; this provides also the basic source of initial values for developing-country inward FDI. For portfolio equity assets and liabilities, stock information is generally not

> available, although this is not too serious a problem given that portfolio flows are a relatively recent phenomenon, absent an initial stock, we set the starting value at zero. For industrial country loan assets and liabilities, as wil as for developing country loan assets, we take as initial stocks those reported **by** the BoP, Rider's (1994) data, or national sources whenever available.

14