rafmacalaba/data-use-mentions-extended huggingface dataset:
psychic costs of moving. Drawing on the data from the ENOE, a rotating panel survey conducted in Mexico, we show that migration to the United States is more likely for initially married than for unmarried individuals among the individuals who co-resided with their parents prior to migration.<sup>2</sup> The final section of the paper then attempts to draw out the implications of this changing nature of the family for migration decisions, research, and policy. A first implication is that migration decisions may be dynamically and locationally inconsistent, since the family unit that is being considered in the decision at one location and point in time is different from what the same individual would consider if they move elsewhere. Second, it is difficult to visualize the spouse and children you are giving up by not moving somewhere different, and this inability to picture counterfactuals and the opportunity cost of not moving can result in a bias towards the status quo of not migrating. Third, to the extent that they anticipate the possibility of these changes, non-migrant family members might be reluctant to finance the migration of an unmarried individual. Fourth, extreme care needs to be taken when attempting to ask what is the impact of migration on the migrants’ family, since in many cases this might be an ill-defined question, as the counterfactual of not migrating could also involve an entirely different family. We give examples of which treatment effects can and cannot be recovered as a result, and discuss what types of questions in the migration literature are more and less likely to be affected by counterfactual families.

Our paper contributes to two main literatures. The first is a methodological literature which focuses on how to identify migration impacts given selection into migration. The main focus of this literature has been on self-selection on the basis of individual and fixed family characteristics (e.g., Borjas, 1987; Gibson et al., 2013). We show how the endogeneity with family structure affects migration decisions and impacts even when the main factors influencing individual self-selection are held
-0.034**|-0.003|-0.001|
||(0.012)|(0.015)|(0.004)|(0.006)|
|Mean in control|0.107|0.141|0.037|0.047|
|Adjusted R-squared|0.065|0.074|0.037|0.036|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers from other households The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers; in column 2 it is the number of households (capped at 3) that the respondent’s household receives food transfers from; in column 3 it is a DV equal to 1 if the respondent’s household ever gives food transfers to other households; in column 4 it is the number of households (capped at 3) that the respondent’s households gives food transfers to. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village food transfers.

39
At the end of the endline survey, there was approximately equal representation between the treatment (750) and control (757) groups, with 394 and 391 fewer observations from each group respectively. This was despite ongoing conflict keeping enumerators from going to a few counties due to insecurity.<sup>6</sup> As a robustness check on results, the difficult‐to‐reach study participants (those reached only through intensive tracking) are reweighted to regenerate the baseline sample. This recognizes that the hard‐to‐reach may be more representative of the outstanding missing observations in the endline data than those reachable through the phone survey.

The main approach for measuring outcome variables was through face‐to‐face interviews that were conducted as part of baseline and endline surveys described above. In addition, risk preferences, trust attitudes and engagement in crime and violence were assessed using experimental data collected during these face‐to‐face interviews from decisions reported over lotteries, trust games and elicited through list experiments (see appendix 1 for full methodological details).

The hypotheses of this study are grouped into 2 main families of outcomes – socio‐economic outcomes, and psychological and behavioral outcomes. It is possible that these families of outcomes were differently affected by the intervention. For instance, it is possible that participants who failed to access the grant but participated in the training did not experience any negative effects on their socio‐ economic situation while suffering negative effects on their psychological and behavioral well‐being. Table 1 Main outcomes of interest provides detailed information on the individual outcomes we studied in each category.

# 4. Methodology

# 4.1 Selection into treatment arms

Selection into treatment arms was a two‐stage process. In the first stage participants from the control group and the original treatment group were randomly selected according to the originally planned experiment. A balance test on these baseline study participants shows no systematic differences between the original control and the treatment groups (Table2). We find weak evidence that the control group
different manner than initially planned. Food transactions should have been reported by the households themselves through the use of a diary, in which every two days they would record the daily food transactions that had occurred; and three enumerator visits to each household were initially envisaged. However, this procedure was not implemented. Instead, only one visit was completed in 93 percent of cases in the IOF 2008/09. It should not come as a surprise that many households showing low calorie consumption also reported fewer than ten transactions during the recall period (7 days); this is likely the result of limited supervision during the survey fieldwork.

Finally, the lack of official conversion factors for non-standard measurement units could have significantly affected calorie calculations. Two main assumptions are used in computing calorie consumption in the 2008/09 survey, namely (i) that one piece/unit of bread is set to weigh 200 grams in every province, although it is uncertain whether this is in fact true across the entire country, and (ii) that one coconut is assumed to contain 800 grams of edible parts, regardless of its actual size, ripeness or freshness.

Problems related to caloric intake, and the considerable disparities between wellbeing indicators and poverty figures, also appear to affect the 2002/03 data. These flaws in data collection and analysis

> 6 “Engel’s law” refers to the typical observation that as income rises, the share of food consumption in total expenditures falls, or, in other words, that food is a normal good, with an income elasticity of demand between 0 and 1.

> 7 _Inquérito aos Agregados Familiares sobre Orçamento Familiar_ : “Household Survey of Family Budgets”, the precursor to the IOF.

4
# **Restructuring** of **Insider-Dominated Firms:** A Comparative **Analysis**

# **1. Introduction**

A large number of recent studies suggest that privatization and the ensuing change in corporate governance are essential conditions for enterprise restructuring in transition economies. State enterprises are generally found to be less efficient, have excess labor and higher wages, and tend to accumulate losses (Frydman et al, 1997, Pohl et al, 1997). Relatedly, privatization has been found to lead to significant improvements in firm performance (Megginson et al., 1994; Hare and Peev, 1995).

There is less agreement on whether particular methods of privatization and modalities within these methods yield more restructuring. A detailed survey of case study evidence (Carlin et al, 1995) shows, for a sample of Czech, Hungarian, Russian, and Bulgarian enterprises, that firrns owned by foreign investors perforr best, but there is little difference between local insider (manager and employees) and outsider dominated firms. Frydman et al. (1997), using a large data set of Czech, Hungarian, and Polish enterprises, find that private firms outperform stateowned firms, but that outsider-dominated firrns do not outperform insider-dominated firms. Smith et al. (1997) find that foreign dominated enterprises in Slovenia have the highest growth in value-added, while insider-controlled firms have a higher average growth when compared to firms controlled by outside local investors. Earle and Estrin (1997) and Linz and Krueger (1998) find no evidence of a robust relation between methods of privatization and enterprise restr-ucturing in Russia, using several alternative specifications. Similar conclusions are reached in Estrin and Rosevear (1 998) for Ukrainian enterprises.

In this paper we extend the literature by investigating the effect of different modalities of privatization to insiders on the restructuring process in two forner Soviet republics-Georgia and Moldova--using enterprise survey data for 1995-97. Enterprise restructuring was similar in companies where incumbent managers received significant ownership stakes for free and in still state-owned companies
household surveys to construct an index to measure the distance of household dwellings to the nearest service provider. This indicates whether households in Jordan have access to services such as education, health or other infrastructure that either increase consumption or creates savings. Two criteria were used to identify these services: the depth of reforms and transformation that occurred in these sectors in the past decade, this is the case for education and health, or the lack of access and lack of public investments in the case of sewage networks<sup>47</sup> . Sixth, sectoral<sup>48</sup> employment for various household members is also examined. This helps capturing the changes in consumption due to sectoral transformation that occurred between 2002 and 2010. Changes that occurred in value added, productivity and employment, and which often translates into changes in household returns (income or wages) coming from those sectors. Finally, the paper controls for regional residency in order to capture the effect of governorates on household spending<sup>49</sup> .

_V.3. Services and Income Sources as Determinants of Welfare Growth_ Tables 8 and 9 (in Annex) display the findings from running the Re-centered Influence Function (RIF) regression for 2002 and 2010. The signs and the statistical significance of the covariates coefficients across the different percentiles reveal that the regional welfare bias towards Amman and urban areas has been indeed been reduced in the past decade. With 6 out of the 11 coefficients turning positive and statistically significant across the entire population distribution, findings suggest that consumption levels in the governorates are catching up with the capital and consequently reducing the household welfare gap. This confirms earlier results on the role of the decline in intra-regional disparities and it effects in reducing overall inequality in Jordan<sup>50</sup> . Furthermore, the economic transformation that occurred in the past decade in Jordan seems to have accentuated the role of rent in increasing household welfare as opposed to labor income. Indeed Tables 8 and 9 suggest that receipt
Life Index, the United Nations Human Development Index, the World Bank Human Capital Index, the World Press Freedom Index, and Yale University’s Environmental Performance Index.

# **Potential mechanisms**

While the different components of each tool form an integral whole, the self-reinforcing five SPI pillars help showcase a virtuous data cycle (Figure A.1 in Dang _et al._ (2023)). For specific illustration, Figure 6 suggests a theory of change, where the five SPI pillars together result in greater data production transparency that enables more data investment and analysis capability and facilitates data flows to key users. The key users—which consist of the government, international organizations, civil society, academia, and the private sector—ensure greater data accountability, provide better policy making and service delivery, and obtain more business efficiency. A better functioning economy, in turns, can contribute more resources to strengthen the country’s statistical capacity. This whole process leads to better development outcomes. Conceptually, this process applies not just to the SPI, but other assessment tools as well. More generally, this theory of change is consistent with the economic theory of asymmetric information (information economics), whereby better information flows among different stakeholders in the economy (including firms, workers, investors, and traders) produce more economic benefits and enable the economy to operate more smoothly (Stiglitz, 2002).

This process is broadly consistent with the cumulating evidence from a growing literature in economics that investigates related data topics. These include the studies that analyze the SCI (Appendix A, Table S1). Other recent studies suggest that increased data transparency is found to have beneficial effects on GDP and economic growth (Arezki _et al._ , 2020; Islam and Lederman, 2020), external borrowing costs (Kubota and Zeufack, 2020) as well as a positive relationship with more democracy (Janus, 2022). More data dissemination, as measured by the amount of data that countries reported to the World Development Indicators
- 22 -

assets to respond to stimuli provided by technological or policy induced changes. Households which do not have material or human resources remain at the periphery of growth and eventually aggravate the demand constraint. It is for this reason that a discussion of poverty is important for understanding processes and prospects of growth.

45. Poverty is an imprecise concept and difficult to measure, but whatever definition and standard is adopted, there is no dispute that there are great numbers of poor in Sub-Saharan Africa who are not able to secure the basic necessities of life. An understanding of the nature and extent of poverty in this region is an essential prerequisite to the design and implementation of strategies to alleviate the problem. The most salient feature of poverty distribution within countries of the region is its concentration in rural areas. Substantial rural-urban income gaps are documented in several studies. From the mid 1960s to the mid 1970s the average smallholder family in Tanzania had a standard of living of only just over one-half of the average urban wage earner family. During this period, the absolute income gap between the two groups became wider. A survey in Zambia in the 1970s found that ninety-five percent of rural households had incomes lower than the average income of urban workers.

46. In addition to the rural-urban spatial distribution of poverty, there are pockets of poverty within specific countries which derive from the physical isolation or ecological vulnerability of the region. Examples of such poverty pockets include northeastern Somalia, northwestern Mozambique, eastern Senegal, northern Niger and Mali and parts of Botswana.
||||
|At least 4 antenatal consultations|0.526|0.058|0.007|0.648|6068|
|Timely initiation of antenatal care<sup>e</sup>|0.726|0.127|-0.098|0.940|5682|
|Postnatal consultation within 3 days after<br>discharge from maternity<sup>f</sup>|||0.18***|0.001|3565|
|**Panel C: Family planning**<sup>**c**</sup>||||||
|All women: any method|0.633|-0.006|-0.046|0.930|9343|
|All women: Modern FP method<sup>g</sup>|0.290|0.025|-0.047|0.956|9343|
|Want to delay or stop: any method|0.698|0.006|-0.063|0.487|5958|
|Want to delay or stop: Modern method<sup>g</sup>|0.375|0.018|-0.058|0.862|5958|
|**Panel D: Blood pressure measurement fo**|**r adults ag**|**ed 40 an**|**d above**<sup>**d**</sup>|||
|Blood pressure measurement in past year|0.497|0.122|0.032**|0.034|9885|

Note: data from household surveys. Unless otherwise noted, regressions are of the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for catchment areas’ exposure to the collaborative quality
## **2.3 The Education Sector in Rwanda**

Rwanda uses the 6-3-3 pre-tertiary education system, which comprises 6 years of primary education; 3 years of middle school (Ordinary level); and 3 years of senior secondary school (advanced level). At the end of primary school (Grade 6), students sit for a mandatory national exam and obtain the primary leaving certificate, which allows them to move on to junior secondary education. This primary school exit examination is the focus of this study.

The exam consists of five subjects (mathematics, science, social studies, Kinyarwanda, and English). Performance in this examination is the key metric for progression into middle school (O-Level). Usually, the best-performing students in the national exams are automatically awarded places in boarding schools. Although students with low performance in the national exam are promoted to the middle school level, they usually remain in the same schools for what is commonly known as 12 years of basic education (12YBE). The average age of starting primary school education in Rwanda is 7, although most of the students complete primary school at age 14 as shown in Table 1.<sup>6</sup>

# **3 Data**

## **3.1 Test Scores**

Our primary dataset is standardized test scores of the universe of primary school students in the primary school national exams between 2012 and 2019, obtained from the National Examination and School Inspection Authority (NESA). It contains the test scores of nearly 1.5 million students who sat for the exam during the period. The dataset contains the standardized test scores in each of the five subjects (mathematics, science, social studies, Kinyarwanda, and English); grade points in each subject; age and gender of the student; and unique school ID. We match this data with a dataset on the geolocation of each school using the school IDs. Figure 3 shows the geographic footprint of primary schools and coffee mills in
- Gertler, P., M. Gonzalez-Navarro, T. Gracner, and A. D. Rothenberg (2022): “Road Maintenance and Local Economic Development: Evidence from Indonesia’s Highways,” Working Paper 30454, National Bureau of Economic Research.

- Glaeser, E. L. and G. A. Ponzetto (2018): “The Political Economy of Transportation Investment,” _Economics of Transportation_ , 13, 4–26.

- Herrendorf, B., R. Rogerson, and Ákos Valentinyi (2014): “Chapter 6 - Growth and Structural Transformation,” in _Handbook of Economic Growth_ , ed. by P. Aghion and S. N. Durlauf, Elsevier, vol. 2 of _Handbook of Economic Growth_ , 855 – 941.

- IPUMS-I (2019): “Integrated Public Use Microdata Series, International: Version 7.2 [dataset],” Minnesota Population Center, Minneapolis, MN, https://doi.org/10.18128/D020.V7.2, (last accessed 11 December 2019).

- Jedwab, R. and A. Storeygard (2021): “The Average and Heterogeneous Effects of Transportation Investments: Evidence from Sub-Saharan Africa 1960 - 2010,” _Journal of the European Economic Association_ , 20, 1–38.

- Kanbur, R. and A. J. Venables, eds. (2005): _Spatial Inequality and Development_ , WIDER Studies in Development Economics, Oxford: Oxford University Press.

- Kelly, M., J. Mokyr, and C. Ó Gráda (2023): “The Mechanics of the Industrial Revolution,” _Journal of Political Economy_ , 131, 59–94.

- Kochenderfer, M. J. and T. A. Wheeler (2019): _Algorithms for Optimization_ , MIT Press.

- Lipscomb, M., A. M. Mobarak, and T. Barham (2013): “Development Effects of Electrification: Evidence from the Topographic Placement of Hydropower Plants in Brazil
as offering circulars, prospectus, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires. While data for issues in the U.S. start in the 1970s, coverage of other markets starts later, with most regional databases starting in 1991.<sup>11</sup> Therefore, we restrict our sample to the period 1991-2008.

In additional, unreported analyses, we considered several subsets of these data.<sup>12</sup> First, we were concerned that including data for the onset of the recent global financial crisis might affect the results. Consequently, we re-did all the analyses reported throughout the paper using data for only the period 1991-2006 and obtained similar conclusions. Second, our sample includes bond issues by both financial and non-financial firms. We include all firms in our analyses because we want to provide a comprehensive view of bond markets around the world. Although a priori financial and non-financial firms might differ in their use of domestic and international bond markets, we obtained results similar to those reported throughout the paper when restricting the sample to non-financial firms. Third, there are some firms that are very active in debt markets, conducting many issues and capturing a significant fraction of the overall debt issuance activity. Therefore, as an additional robustness check, we re-estimated all our regressions excluding the top five percent of the firms in terms of the number of debt issues and obtained similar results.

> 11 The SDC database is divided into twelve regional sub-databases covering different markets: Asian Pacific Domestic (Hong Kong SAR, China; Indonesia; Malaysia; Philippines; Singapore; Taiwan, China; and Thailand); Australian/New Zealand Domestic (Australia, New Zealand, and Papua New Guinea); Canadian Domestic (Canada); Continental European Domestic (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia,