rafmacalaba/data-use-mentions-extended huggingface dataset:
much more pronounced in the lagging regions: for the pooled sample, the party effect in lagging regions is almost twice that in rich regions. Moreover, when we estimate that effect separately for urban and rural regions, the positive coefficient is robustly significant only in lagging economic areas, and more so in poor rural areas. Thus, access to the ruling party network appears to have especially strong payoffs in lagging rural areas.

# **_5d. Effects of Informal Finance on Components of Consumption_**

The richness of the CHFS data allows us to decompose consumption into two components: daily consumption (expenditures on food, clothing, necessities, rent, housekeeping, transportation, and communications), and long-term consumption (expenditures on health care and education). Distinguishing between them is important because, while daily consumption is more or less ‘pure’ consumption, long-term consumption has a much stronger connection to investment, which can improve future consumption. Tables 9 and 10 report regression results that use daily consumption and long-term consumption, respectively, as the dependent variable.

In general, informal loans do not appear to affect _daily_ consumption in rural areas (see 2SLS results in Table 9). However, informal loan amounts are associated with significantly _reduced_ daily consumption levels in urban regions. Increasing informal loans by 20 percent leads to a reduction in daily consumption by 1 percent. Interestingly, party affiliation is robustly associated with higher daily consumption levels, but only in rural regions.

In sharp contrast, informal finance significantly facilitates _long-term_ consumption (see Table 10). Increasing the amount of informal loans used by a household by 10 log points (i.e., 0.1) would increase long-term consumption by 4 log points in both urban and rural areas in the 2SLS models. In contrast to the patterns for daily consumption, party affiliation is associated with significantly higher long-term consumption levels only for urban residents. Thus, rural residents must resort to informal loans for longterm welfare, while urban residents are able to rely on both the party
Thus, her findings suggest that CCT programs targeted at women are beneficial for the majority of them, but that there is a subset of families with low-education male heads who tend to hold more traditional values for whom violence increases.

# **6.1.3  Effects on Marriage and Fertility-related Behaviors of Adolescents**

Gulametova-Swan (2009) studies the effect of the Mexican _Oportunidades_ program on adolescent girls‟ decisions about marriage and fertility. Adolescent youth enrolled in the program were required to attend health talks, where topics such as prevention of pregnancy were discussed. Gulametova-Swan‟s (2009) analysis is based on data from the 2002–2004 urban _Oportunidades_ evaluation data samples, which were nonexperimental. Program participation was

> 25 The impacts of _Oportunidades_ in urban areas were assessed using propensity score matching estimators.

50
scale, with total scores ranging from 6 to 30, and a higher score indicating worse mental health. The index is then standardized to have a mean of zero and standard deviation of one.

Respondents in the BNLA survey were also asked about whether they had experienced or witnessed any traumatic events before arriving in Australia. These include extreme living conditions (e.g., lack of food, water, shelter or medicine), war/conflict, violence, kidnapping or imprisonment, political/religious persecution, natural disaster, and/or other events. We then create an indicator of any trauma experience, interacted with the time indicator, as the instrumental variable in our analysis. We also examine the intensity of trauma experience (measured by total events experienced by refugees) as a robustness check. For the heterogeneity analysis, we obtain information on a wide range of refugee characteristics before and after resettlement such as time spent in refugee camps, social networks, and local/government benefits received. Table B1 provides summary statistics of all the variables used in our analysis.

# **References**

- Andersen, M. (2015). Heterogeneity and the effect of mental health parity mandates on the labor market. _Journal of Health Economics_ , _43_ , 74-84.

- Correa‐Velez, I., Barnett, A. G., & Gifford, S. (2015). Working for a better life: Longitudinal evidence on the predictors of employment among recently arrived refugee migrant men living in Australia. _International Migration_ , _53_ (2), 321-337.

- Edwards, B., Smart, D., De Maio, J., Silbert, M., & Jenkinson, R. (2018). Cohort Profile: Building a New Life in Australia (BNLA): the longitudinal study of humanitarian migrants. _International Journal of Epidemiology_ , _47_ (1), 20-20h.

- Flatau, P., Smith, J., Carson, G., Miller, J., Burvill,
Lanka, Kyrgyz Republic and Zambia, raising concerns over collateralized debt obligations, debt transparency and weak governance in countries that are large recipient of financing from China.

The assessment of the impact of BRI on countries’ debt sustainability outlook faces significant challenges due to lack of comprehensive and consistent information on investments and financing terms. Accurate and comprehensive debt data are important for policy makers so that they make informed borrowing decisions, thus safeguarding debt sustainability and macroeconomic stability. Yet, there is no official source of BRI investment nor systematic information of the size and terms of public and private debt financing, as well as non-debt financing related to identified BRI projects. This paper therefore relies on a variety of data sources and makes several strong assumptions, which are discussed in detail below, to establish a range of estimates for the impact of BRI on debt sustainability in 45 countries. In particular, it assumes that on average 60 percent of BRI identified investment would be financed through public or publicly guaranteed debt.<sup>6</sup> The paper estimates that BRI may be associated with heightened debt dynamics in 12 out of 43 (or 28 percent) low and middle-income countries, over the medium term. Of these 12 countries, 10 have already high debt vulnerabilities before BRI. At least two additional countries would experience a severe worsening of their debt situation over the medium term, as a result of the estimated BRI financing. These estimates are bound estimates: they assume an optimistic impact of BRI investment on growth, no materialization of additional fiscal risks and no negative feedback effects from debt to growth.

Over the long-term, including the full impact of BRI investment, one-third of countries (11 out of 30 with available data) are projected to have a higher debt-to-GDP ratio as a result of BRI. In these 11 countries, the debt-to-GDP ratio resulting from BRI is projected to be higher compared to a scenario excluding BRI. However, BRI is estimated to increase the debt-to-GDP ratio only in two countries relative to at end-2016, the base-year
# **Improving Business Practices and the Boundary of the Entrepreneur:**

# **A Randomized Experiment Comparing Training, Consulting, Insourcing and Outsourcing**<sup>*****</sup>

Stephen J. Anderson, _McCombs School of Business, UT Austin_ David McKenzie, _Development Research Group, the World Bank_ _Keywords:_ Business Support Programs; Business Practices; Firm Growth; Entrepreneurship; Boundary of the Entrepreneur, Boundary of the Firm; Insourcing; Outsourcing; Business Services Marketplace.

_JEL Codes:_ O12, L26, L24, M31, M41.

* This study is registered in the AEA RCT Registry and the unique identifying number is AEARCTR-0002769.We gratefully acknowledge funding from the Growth and Employment (GEM) project, the World Bank’s Competitiveness Policy Evaluation Lab (Compel) program with contributions from the United States Agency for International Development (USAID) and the Facility for Investment Climate Advisory Services (FIAS), and from the Economic Development and Institutions project funded by UKAID. We thank Johanne Buba for her tireless efforts in launching this pilot, and Kofi-Boateng Agyen for his operational work on this project; the Project Implementation Unit of the GEM project; and especially Toks Fayomi for outstanding project management. Helpful comments from Tim Rose and various seminars are gratefully acknowledged.
few of them. Some of these issues require further studies for better understanding and are highlighted in the next section.

# **_5.1 Quality of Infrastructure_**

The existing literature on infrastructure and economic growth deals mostly with the quantity of infrastructure. Studies analyzing the impacts of infrastructure quality are limited due to the lack of time-series data representing the quality of infrastructure. A few studies, however, address this issue, although the findings are inconclusive. For example, Calderon and Serven (2010) find, using data from more than 100 countries, a positive relationship between infrastructure 26
|Price<br>(US$/Mt)|Trade<br>Balance<br>(US$ Mn)|Employment<br>(Numbers)|
|---|---|---|---|---|---|---|---|---|
|**Survey**<br>**data(2016)**||**1,029,709.0**|**695,255.0**|**334,454.0**||**770.0**||**732,461.0***|
|Normative<br>Baseline|1,401.6|1,002,010.2|524,520|861,521.5|384,030|624.11|(413.5)|142,280|

Source: Survey data and Model output Interestingly, because of the low import price (in part due to the duty waiver) the simulation also suggested that to maximize consumer/producer surplus, Sierra Leone should import 861,521.5 Mt of rice and export 384,030Mt of rice. However, this would leave the country with a substantial rice trade balance deficit of US$413.5 million. Under the current technical and policy environment for the rice sector in Sierra Leone, maximizing consumer and producer surplus would also lead to a sharp reduction in employment in the rice sector. The results of the simulation show employment falling to 142,280, from 669,340 in the initial baseline—a substantial reduction of nearly 79 percent. Most of this reduction in labor would come from the ecologies where no rice would be produced. While this outcome may be optimal in terms of the maximization of consumer and producer surplus, it does not appear to be feasible in terms of employment and the balance of
after the program (second difference)<sup>3</sup> . See Ravallion (2009) for a discussion of commonly used evaluation techniques<sup>4</sup> .

This study is organized as follows. The following section introduces the economic environment of the Eastern European Union Industrial firms and the existing overdraft facility policy schemes. Section 3 briefly explains the methodologies of Data Envelopment Analysis (DEA) Frontiers (with Bootstrap procedure) and propensity score matching. Section 4 describes the data and it presents the key descriptive statistics. The empirical results are discussed in Sect. 5. The final section summarizes and concludes this study.

# **2 Background**

# 2.1 The Eastern European Union Companies

The EU enlargement to 27 countries in 2004 and 2007 constitutes a historical benchmark in the forming of the European space. In contrast to previous enlargements, the entering of Eastern European countries has peculiar characteristics due to the large number of nations entering the EU and due to the heterogeneity in its parameters and levels of development (Hay, 2003: 13).

Although the Eastern European countries have the common trait of their recent history linked to the Soviet Union and the fall of the Berlin Wall, their entry into the European club required the compliance of three conditions: democratic institutions, a market economy capable of insertion in a competitive world and the capacity to take on the Union‘s goals. These conditions were an important impulse to leave that common trait behind and, to a certain extent, have allowed differentiating the strengths and weaknesses of each country.

On the other hand, one must emphasize that this important enlargement in terms of numbers did not maintain a proportional relation with its economic relevance. Comparing the UE15 (2004) and taking into account data of that year, the contribution in population of the Eastern European nations is of about 100 million inhabitants, which translated into a 28% increase in the EU population whereas in added terms, the increase in GDP was of nearly 7%,
design. The authors find that the discontinuity design gives good approximations for almost all outcome indicators.

The method is not without its drawbacks. It is assumed that the evaluator knows _M i_ and (hence) eligibility for the program. That will not always be the case. Consider (again) a meanstested transfer whereby the income of the participants is supposed to be below some predetermined cut-off point. In a single cross-section survey, we observe post-program incomes for participants and incomes for non-participants, but typically we do not know income at the time the means test was actually applied. And if we were to estimate eligibility by subtracting the transfer payment from the observed income then we would be assuming (implicitly) exactly what we want to test: whether there was a behavioral response to the program. Retrospective questions on income at the time of the means test will help (though recognizing the possible biases), as would a baseline survey at or near the time of the test. A baseline survey can also help clean out any pre-intervention differences in outcomes either side of the discontinuity, in which case one is combining the discontinuity design with the double difference method discussed further in section 7.

Note also that a discontinuity design gives mean impact for a selected sample of the participants, while most other methods (such as social experiments and PSM) aim to give mean impact for the treatment group as a whole. However, the aforementioned common-support problem that is sometimes generated by eligibility criteria can mean that other evaluations are also confined to a highly selected sub-sample; the question is then whether that is an interesting sub-sample. The truncation of treatment group samples to assure common support will most likely tend to exclude those with the highest probability of participating (for which nonparticipating comparators are hardest to find), while discontinuity designs will tend to include only those with the lowest probability. The latter sub-sample can, nonetheless, be relevant for deciding about program expansion; section 9 returns to this point.

Although impacts in a neighborhood of the cut-off point are
believe this is only a minor issue and the use of year fixed effects should account for year-to-year changes in detection common to the entire sample.

# **3 Methodology**

We develop a matched _agreement-level_ approach that aims to control for the potential endogeneity of environmental provisions. We create an agreement-level panel by aggregating outcome variables of interest across RTA signatories by year. For example, we create an annual time series of deforestation associated with the US-Peru trade agreement by adding forest loss in Peru and forest loss 9
Returns to Low-Skilled International Migration: Evidence from the BangladeshMalaysia Migration Lottery Program<sup>*</sup> Mushfiq Mobarak<sup>†</sup> Iffath Sharif<sup>‡</sup> Maheshwor Shrestha<sup>§</sup>

**Keywords** : International migration, Government intermediation, lottery, welfare

**JEL codes** : F22, O12, O15,

> * We thank Bangladesh Bureau of Manpower, Employment, and Training (BMET) for providing the administrative lottery applicant data to make this study possible. We are thankful to David McKenzie, Ganesh Seshan, and Manjula Luthria for helpful discussions and feedback. Corey Vernot provided excellent research assistance. IPA Bangladesh was superb in managing fieldwork for data collection. Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund and the DEC Research Support Budget grant within the World Bank.

> † Yale University, Deakin University, NBER and CEPR. Contact: ahmed.mobarak@yale.edu

> ‡ The World Bank. Contact: isharif@worldbank.org

> § The World Bank. Contact: mshrestha1@worldbank.org
Between 2007 and 2009, the Marriage Transitions in Malawi (MTM) project collected innovative, longitudinal data from a random sample of nearly 1,200 initially never-married women and men in the Central Region of Malawi, ranging in age from 14-20 for females and 1725 for males. The study was designed to understand the links between pre-marital relationships and sexual activity, the transition into marriage, socioeconomic status, and HIV/AIDS.

Respondents provided detailed information on socio-economic characteristics, marriage and fertility, and sexual partnering. Two particular features of this longitudinal data set stand out. First, respondents were interviewed at short intervals, up to five times within a 24-month window. Most panel studies in Sub-Saharan Africa conduct survey rounds at a minimum of yearly intervals (and often longer), which necessitates a reliance upon retrospective reporting of events, and may bias estimates due to recall error (such as on dating and marriage).<sup>3</sup> Second – and most relevant for this study – respondents who left the sample, due to relocation outside of the study site, were tracked by the research team. Such tracking allows us to identify the socioeconomic characteristics and demographic behaviors of mobile respondents.

There are multiple ways to define migration. Using the MTM data to explore the socioeconomic dimensions of migration, we take several approaches to measuring migration. In the most general approach, we look at the distance in kilometers (based on GPS data) between the baseline (summer 2007) and follow-up (summer 2009) residence. That is, we compare the location of physical dwelling in which our sample of young adults resides using objective measures of location (rather than self-reported distances from baseline). A second approach is to look at moves out across administrative/economic boundaries, such as villages, Traditional

> 3 The issue of recall bias in reporting events is also relevant for migration data. For example, lacking panel data which follows movers over time, studies may need to use life history calendars to collect information on moves
-0.15|(0.15)|[0.44]|-0.00|(1.01)|1,101|
|Syrians pay more in taxes|0.06|(0.14)|[0.49]|0.03|(1.01)|1,078|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The first outcome titled “list experiment” uses a different specification specific to the list experiment. All respondents were asked how many of the listed groups they’d accept as a neighbor. Half saw a list that included Syrian refugees, and the other half saw a list that did not include Syrian refugees. (The other 3 groups were Persons with disabilities, People in poverty, and Someone who does not follow the law). The reported coefficient is the coefficient on an indicator for seeing the list including Syrian refugees.

- The other outcomes that require definitions are: Share friends Syrian is the share of the 3 friends they visit regularly who are Syrian refugees. Share advice Syrian is the share of the 3 people they seek and give advice to who are Syrian refugees. Kids have Syrian friends is an indicator equaling 1 if the children in the household have any Syrian friends, and is missing for households without children.Kids share rec space is an indicator equaling 1 if the children in the houseold share recreational space with Syrian refugee children. Accept Syrian marriage is a standardized 5-point likert scale regarding “To what degree would people in your community feel comfortable accepting the marriage of their son/daughter/sister/brother to a Syrian refugee?” Accept Syrian neighbor is a standardized 5-point likert scale regarding “To what degree would people in your community feel comfortable being neighbors with a Syrian refugee?” Syrians pay more in taxes is a standardized 5-point likert scale where an increase corresponds
Second, the pattern of results speaks against this interpretation. We find that entry increased disproportionately in industries where women establishment owners were more concentrated before the reforms nationally. It seems to us unlikely that there would have been systematically more under-reporting in industries where women’s ownership rates were very high.

# _Potential Channel: Access to Government Contracts or Business_

A second potential channel is that placing women in political positions led to increased government contracts and related business flowing to women-owned establishments, which prompted the higher entry rates. This could have been due to politicians enacting explicit setasides of government contracts for women, favoritism in how contracts were awarded (or the weakening of a prior bias towards male-owned businesses), and/or the general redistribution of government expenditure towards industries and goods where women-owned businesses were better able to secure and fulfill contracts (without explicit set-asides or changes in how contracts are awarded).

Two questions on the NSSO survey inquire about the extent to which the manufacturing establishment either buys from or sells to the government. The questions are only included on the surveys after 2000, so we are unable to undertake a full quantitative analysis in a pre-post format of their response such as we did in Table 3 on other dimensions. Yet, the data are useful for understanding that it is unlikely that the access-to-the-government hypothesis accounts for the bulk of our observed response.

This is most easily observed with the raw statistics. The data for 2000 and 2005 report very limited government business dealings for most manufacturing establishments—less than 1% of establishments report output being sold to government agencies in any year. Even if substantial under-reporting existed for this channel, this very low share is mismatched to the large entry response observed after the elections.

Likewise, the data in their raw format do not align very well with the strong householdbased nature of the women’s entry. In the 2000 survey, 61% of those establishments that report government business dealings are establishments that are also based in households. While a majority, this share is significantly less
The results suggest that these countries should be closely scrutinized and action plans to mitigate a debt disaster should be set.

**Figure 5:** Country indices

<!-- Start of picture text --> ALB DZA AGO ARG ARM AZE BGD BLR BLZ<br>−5 05<br>BOL BIH BWA BRA BGR CMR CHN COL COG<br>−5 05<br>CRI DJI DMA DOM ECU EGY SLV GAB GEO<br>−5 05<br>GHA GTM GUY HND IND IDN IRN JAM JOR<br>−5 05<br>KAZ KEN LBN LSO MRT MUS MEX MDA MNE<br>−5 05<br>MAR MMR MYS NIC NGA PAN PAK PRY PER<br>−5 05<br>PHL ROU RUS RWA SEN SRB SLB ZAF LCA<br>−5 05<br>SDN SWZ TJK TZA THA TUN TUR UKR UZB<br>−5 05<br>2002 2011 2002 2011 2002 2011 2002 2011 2002 2011 2002 2011<br>VUT VNM ZMB<br>−5 05<br>2002 2011 2002 2011 2002 2011<br>Exposure to public debt risk<br><!-- End of picture text --> The results can also be summarized for a cross-section (i.e., a given year). The vulnerability and the resilience indices jointly determine the ”Red-Square” measure (see Figure 6). The horizontal line depicts the vulnerability indices (centered on zero) for public and external debt, while the vertical line represents the resilience index.

A country that has rising vulnerabilities and a reduction in resilience falls into the red square. Countries with a reduction in vulnerabilities and an increase in resilience find themselves in the green square and are possibly less vulnerable to default risk. The two pink squares still indicate risk, but less than the red-square measure. The red square highlights countries such as Argentina, Lebanon, and Pakistan with heightened public debt exposure.