rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9734

# **Abstract**

Migration has been demonstrated by various studies to be closely linked to improvements in individual- and household-level outcomes. Rather than examining the effects of migration, this paper explores whether an economic shock in United States negatively affected migrant households in rural Guatemala. Treating the Great Recession as a natural experiment affecting migrant and non-migrant households differently, the paper puts the spotlight on the effect on child anthropometry, including longer-term indicators of height-for-age z-scores. Panel data on children and multiple children in households enable double- and triple-difference estimation. In relative terms, migrant households fared far worse than non-migrant households over the period. In particular, large advantages in child anthropometric status for the youngest children in migrant households in 2008, just prior to the crisis, were substantially diminished four years later. The findings underscore the possible fragility of the benefits of migration, particularly in the face of a substantial economic shock, and point to the potential importance of deepening social safety nets.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at gcarletto@worldbank.org, jmalucci@middlebury.edu, savant.m.shrestha@gmail.com, and mackenzie.f.stewart@gmail.com.

_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
**Table 1: Characteristics of Microenterprises and Verification of Randomization**

|||Fu|ll Sample|||Trimm|ed Sample||
|---|---|---|---|---|---|---|---|---|
||N|Control|Cash|In‐kind|N|Control|Cash|In‐kind|
|_Variables Using to Stratify or Match_|||||||||
|Monthly profits in January 2009|781|128|132|131|753|103|99|115|
|Female|793|0.60|0.60|0.61|765|0.62|0.62|0.62|
|High Capture|793|0.58|0.58|0.57|765|0.58|0.58|0.57|
|High Baseline Capital Stock|793|0.49|0.49|0.49|765|0.48|0.48|0.48|
|Male in Male dominated industry|793|0.18|0.19|0.18|765|0.18|0.18|0.18|
|Male in Mixed industry|793|0.21|0.21|0.21|765|0.20|0.20|0.20|
|Female in Female dominated industry|793|0.29|0.29|0.29|765|0.30|0.29|0.30|
|Female in Mixed industry|793|0.31|0.31|0.31|765|0.32|0.32|0.32|
|_Other Variables_|||||||||
|Monthly profits in October/November 2008|
schooling outcomes.For grades 5 and 7, the data reports whether a student passed the exam and if they passed with more than 60 percent marks.

In Table 5, we show that the passing rate in government schools falls by 1.8 percent for grade 7 students (Columns (ii) and (iv)) with the coefficient significant at the 10 percent significance level, whereas there is no effect on grade 5 (Columns (i) and (iii)). The program effects passing with more than 60 percent marks much more significantly. Both private and government school 7th graders do worse on this measure. This effect is almost twice as large for government schools than the private schools and the difference is statistically significant (Columns (vi) and (viii)).<sup>24</sup>

# **5.4 Mechanisms**

In order to shed light on the mechanism, we examine the employment outcomes of children in our DID framework. The data on child labor come from the National Sample Survey Organization (NSSO) employment and unemployment surveys (rounds 2004-05 to 2008-09) and we employ earlier rounds to carry out a falsification check. The data asks individuals to identify their principal occupation in the last month. We examine two outcome variables: Child reports working (employed, self employed or unpaid family labor) and child reports doing chores (housework or free collection of goods). We restrict our sample to 206,321 non-disabled children aged between 5 and 15 from the two rounds and look at their reported principal activities. The indicator for ‘working’ equals to 1 if a child’s reported principal activity is working in the household enterprises (paid or unpaid), as wage employee, or in other types of work, and 0 otherwise. The indicator for doing chores is 1 if a child’s reported principal activity is attending to domestic duty or doing any other housework, and 0 otherwise.<sup>25</sup>

> 24The program
_Table 2: Main Entry Points for Imports in the Western DRC, 2007_

|**_Customs office:_**|**% shar**<br>|**e of import**<br>|**s interms of **<br>|
|---|---|---|---|
||_value_|_weight_|_transactions_|
|Matadi|65.05|84.17|65.20|
|Boma|0.02|0.02|0.06|
|Kinlau(fuelport)|0.54|0.65|0.32|
|Banana|1.22|0.16|0.57|
|Kimpangu(Angolan border)|0.00|0.00|0.00|
|Kinshasa Port(ONATRA freightport)|15.92|10.50|10.68|
|Kinshasa Airport|9.47|0.27|15.82|
|Kinshasa Beach Ngobila(ONATRA ferry port)|0.07|0.11|0.28|
|Other Kinshasaports|7.72|4.12|7.07|
|_Total_|_100_|_100_|_100_|

Source: own computations, based on data from DGDA.

**Almost no DRC imports from RC find their way into official trade statistics.** According to the data summarized in Table 2, the two customs offices likely to clear imports originating in neighboring countries, the ferry port Kinshasa Beach (RC) and Kimpangu (Angola), record next to no imports.<sup>9</sup> Only ONATRA’s Kinshasa freight port declares a significant volume of imports, accounting for some 16 percent of the value of Western DRC imports. Given that there is no formal upriver trade (with CAR and Cameroon) left, this points towards non-negligible formal imports from Brazzaville. These imports could be of two kinds
but rather to measure revisions of intertemporal plans and to shed light on the determinants of such revisions.

# 5. **Conclusion**

The consequences of making low quality intertemporal choices can be dire, especially among the poor in developing countries. Low-income farmers can go hungry for long periods if they fail to make good smoothing decisions. They can fail to rise out of extreme poverty if they miss opportunities to make high return investments. We conducted an experiment among Malawian farmers to investigate why their intertemporal choices may appear not to serve their individual self-interest. More precisely, we provide the first field evidence on the causes and correlates of decisions to revise prior intertemporal choices. The experiment allowed subjects to make an intertemporal allocation of substantial funds they would receive at two future times 30 days apart. This future 30-day period was timed to occur during a period of low income and low food stores, and thus consumption smoothing of substantial amounts of future income is very difficult. Several weeks later, prior to the first disbursal of funds, we revisited study participants and allowed them to revise their previous allocations over the same 30-day period. We examine these revisions of allocations for evidence of self-control problems as well as other potential mechanisms behind intertemporal choice revision.

We provide new evidence for the existence of self-control problems. Revisions of allocations toward the present are positively associated with measures of “present”-bias from an earlier baseline survey, as well as the (randomly assigned) closeness in time to the first possible date of money disbursement.<sup>50</sup> These results complement existing evidence for self-control problems that are based on demand for commitment devices (e.g., Ashraf et al., 2006, and Beshears et al., 2011). In contrast to pre-existing work on demand for commitment devices, our approach has the advantage of allowing even naïve individuals (who are not aware of their self-control problems) to contribute to estimates, since naïve as well as sophisticated respondents can display revision behavior. In
provides descriptive statistics of labor characteristics by the category of city size: 1) population up to 15,000; 2) between 15,001 and 40,000; 3) between 40,001 and 150,000; 4) between 150,000 and 500,000; and 5) Addis Ababa. Compared to smaller towns, labor force participation rates are lower in larger towns, while their unemployment rates are higher. Based on the UEUS/LFS data, around 75 percent of working-age urban populations are active labor forces, 83 percent of whom are employed and 17 percent are unemployed. The labor force participation rates are 72 percent in Addis Ababa (64.1 percent for female and 81 percent for male), while the rates in small towns (town size less than 15,000) are 80 percent. By contrast, unemployment rates tend to be higher in larger towns, and it is 20.6 percent for women and 12.6 percent for men in Addis Ababa.<sup>10</sup> In terms of employment types, wage employment (private and public) is common in larger towns, whereas the majority of workers are self-employed in smaller towns. The data show that 48 percent of urban workers are engaged in self-employment, 21 percent are private employees, and 23 percent are public employees. Unskilled workers tend to engage in self-employment jobs (commonly in the sectors of agriculture, manufacturing, and construction).

A third of urban workers are engaged in informal jobs. The UEUS/LFS allows to define informal jobs in two ways: whether the job has a book account or whether the job has a license. According to the former definition, 23 percent of private employees and 90 percent of self-employed workers are in the informal sector. Based on the license definition, only 9 percent of wage workers in the private sectors are informal, while nearly 60 percent of self-employed workers are informal.

The average hours worked per week is only 35 hours in small towns,
which are any data and information produced or commissioned by public bodies; and (ii) Open Data, which are data that can be freely used, re-used and distributed by anyone, only subject to (at the most) the requirement that users attribute the data and that they make their work available to be shared”.<sup>11</sup> Common types of OGD include, but are not limited to, data on maps, land ownership, census, government budget, government spending, company registration, legislation, public transport, international trade, health, education, crime statistics, environment statistics, election results, and contracts.

While there is considerable conjecture about the precise nature and categorization of public sector benefits that can be derived from these types of data, OGD has been tied to a number of positive public externalities. For its part, the World Bank has cited a number of benefit streams, including: (i) fostering economic growth and job creation; (ii) improving the efficiency, effectiveness, and coverage of public services; (iii) increasing government transparency, accountability, and citizen participation; and (iv) facilitating better information sharing within government itself.<sup>12</sup> Likewise, the OECD describes similar benefits, which include: (i) improving government accountability, transparency, responsiveness and democratic control; (ii) promoting citizens’ self-empowerment, social participation and engagement; (iii) building the next generation of empowered civil servants; and (iv) creating value for the wider economy.<sup>1314</sup> Recognizing this potential, dozens of governments have created OGD platforms to support the proactive release of high-value data sets that can be used to improve government transparency, accountability, and performance. Notable examples include the United States and the United Kingdom, who pioneered the release of government data sets in an effort to make government more transparent through platforms such as _data.gov_ and _data.gov.uk_ . Since then, numerous
and knowledge of the parental leave program and increase fathers' intention of taking leave. Together with the Social Security Institute of Uruguay (BPS), we designed and implemented an information intervention delivered via email and text messages (SMS) targeting workers contributing to BPS who were recent parents and/or of reproductive age.<sup>3</sup> Two parallel experiments were implemented, building on insights from behavioral science regarding drivers of behavior and intention formation (Ajzen 1991, Gollwitzer and Sheeran 2006, Michie et al. 2011). The first one focused on female and male workers and tested the effect of an information message (treatment) against no messages, i.e. regular access to existing publicly available information to date (control). The second experiment focused on recent parents. Here we tested the differential effects of three messages: (i) information about the parental leave program (same as in experiment one), and two “information plus” messages, (ii) a message emphasizing the benefits of men's involvement in childcare through the testimony of a father, and (iii) a message emphasizing the importance of planning and sharing childcare between parents.

> 2 For example, according to the OECD Family Database (https://www.oecd.org/els/family/database.htm) in 2021 across OECD countries fewer than 50 men for every 100 live births used parental leave benefits. 3 We consider individuals between 18 and 42 years of age.

2
Costs include direct costs of third parties, such as a forwarder’s or broker’s account for entry/clearance services, and costs incurred by companies in employing personnel and facilities to achieve customs/trade law compliance and planning. While the pilot surveys are of US companies, SITPRO intends to expand the surveys to the EU, Brazil, India, China, Japan, South Korea, and Eastern Europe.

# _<u>PERINOM Database</u>_

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15 ____________________________________________________________________________________________________________
br><!-- End of picture text --> Sources: Environmental Performance Index; World Economic Forum; World Bank, World Development Indicators database.

A. World Economic Forum ranking of 140 countries according to the quality of their infrastructure. 1= best, 140 = worst. B. Logistic Performance Index Surveys conducted by World Bank and Turku School of Economics. 1 = extremely underdeveloped by international standards; 7 = well developed and efficient by international standards. D. The Environmental Performance Index is constructed through the calculation and aggregation of 20 indicators reflecting national level environmental data, including child mortality, wastewater treatment, access to drinking water, access to sanitation, and air pollution (average exposure to PM2.5). These indicators use a “proximity-to-target” methodology, which assesses how close a particular country is to an identified policy target. Scores are then converted to a scale of 0 to 100, with 0 being the farthest from the target (worst observed value) and 100 being closest to the target (best observed value). E. Probability of dying between birth and exactly five years of age expressed per 1000 live birth. Latest data are for 2020.
2006). Predicting cognitive control from preschool to late adolescence and young adulthood. _Psychological Science 17_ (6), 478–84.

- Ersche, K., A. Turston, S. Pradhan, E. Bullmore, and T. Robbins (2010). Drug addiction endophenotypes: impulsive versus sensation-seeking personality traits. _Biological Psychiatry 68_ (8), 770–3.

- Fafchamps, M., D. J. McKenzie, S. Quinn, and C. Woodruff (2014). When is capital enough to get female microenterprises growing? Evidence from a randomized experiment in Ghana. _Journal of Development Economics 106_ (1), 211–226.

- Fox, R. C. (2004). Observations and reflections of a perpetual fieldworker. _The ANNALS of the American Academy of Political and Social Science 595_ (1), 309–326.

- Group, N. E. (2003). Suitability of different reference periods for measuring household consumption. results in pilot survey. _Economic and Political Weekly 38_ (4), 25–31.

- Holt, C. and S. Laury (2002). Risk aversion and incentive effects. _American Economic Review 92_ (5), 1644–1655.

- Jamison, J. and D. Karlan (2011). Measuring preferences and predicting outcomes. _Working paper_ .

- Karlan, D., R. Knight, and C. Udry (2015). Consulting and capital experiments with microenterprise tailors in ghana. _Journal of Economic Behavior and Organization 118_ , 281–302.

- LeCompte, M. D. (1987). Bias in the biography: Bias and subjectivity in ethnographic research. _Anthropology & Education Quarterly 18_ (1), 43–52.

lix
# **Courts and Business Registration: Evidence from Serbia**<sup>**1**</sup>

Miriam Bruhn, Caleb Cho, Andreja Marusic, Ha Nguyen, José-Daniel Reyes, Trang Tran

# **JEL Classification:** O17; O12; D22; L26.

**Keywords:** Business registration reform, courts, firm creation.

> 1 We thank the Serbian Business Registers Agency and in particular the Director Zvonko Obradovic, Registrar of Companies Miladin Maglov, and Registrar of Financial Accounts Ruzica Stamenkovic for providing us with access to firm level data.
WPS3840 Estimating Trade Restrictiveness Indices* Hiau Looi Kee† Alessandro Nicita‡ Marcelo Olarreaga§

# Abstract

The objective of this paper is to provide indicators of trade restrictiveness that include both measures of tariff and non-tariff barriers for 91 developing and developed countries. For each country, we estimate three trade restrictiveness indices. The first one captures the extent to which trade policies at home affect domestic welfare. This follows the work of Anderson and Neary (1992, 1994 and 1996). The second index captures the impact of trade distortions on each country’s import bundle. This follows the work of Anderson and Neary (2003). The last index focuses on market access and summarizes the trade distortions imposed by the rest of the world on each country’s export bundle. All indices are estimated for the broad aggregates of manufacturing and agriculture products. Results suggest that poor countries (and those with the highest poverty headcount) tend to be more restrictive, but they also face the highest trade barriers on their export bundle. This is partly explained by the fact that agriculture protection is generally larger than manufacturing protection. NTBs contribute more than 70 percent on average to world protection, underlying their importance for any study on trade protection.

JEL classification numbers: F10, F11, F13 Keywords: Trade Restrictiveness Indices World Bank Policy Research Working Paper 3840, February 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We are grateful to William Cline, Alan Deardorff
total volume transferred permanently was 19.7 million **m**<sup>**3**</sup> **.** The data for the New South Wales state, which exists between 1984 and 1991 is summarized in Table 1. From the data it can be seen that annual transfers vary, although the trend is increasing, and the average unit value of water (in nominal Australian $) vary between I cent and 13 cents.

The accumulated experience with the transfer of water among users (mainly water agencies providing water to agricultural farmers) both over time and across regions can shed light on relative importance of potential problems associated with water transfers. Pigram et al. (1992) surveyed the agencies involved in water transfers and the results are presented in Table 2.

The range of potential problems in table 2 may be indication of the set of benefits and concerns associated with the implementation of transferable water rights elsewhere. While some potential problems, such as capacity and water and soil salinity, are associated with the physical short years than in regular rainy years. In such events available water is transferred from staple to perennial crops.
# **4. Results and discussion**

In this section, we first evaluate the impact of hurricanes on economic activity and assess the mitigating role of mangroves against hurricanes in the Caribbean region. Using the hurricane windstorm model— and the nightlight data set to quantify the causal effects of the hurricane windstorm model on local economic activities—we categorize three hurricane damage indexes, which provides a higher level of detail. In a second step, we include mangrove data and characteristics to evaluate its potential mitigation effect of the impact of hurricanes on economic activity. The main descriptive statistics for the variables in the sample used in this section can be found in Annex 4.

# **4.1.Impact of hurricanes on nightlight**

We initially explore the impact of hurricanes on nightlight using data for 29 Caribbean countries and territories. This comprises more than 2 million observations due to the grid-cell measurement for the 2000–12 period.<sup>19</sup> The econometric results show a negative, statistically significant, association between nightlight and the hurricane damage index. Table 3 provides estimates for the two-way fixed effect equation described in Section 3 and using three specifications (columns 1 to 3) to include a potential lagged effect of hurricanes on nightlight and to discriminate the effect in low-elevation coastal zones. Particularly, the size for the estimate for , which measures the impact of hurricanes on nightlight, shows that nightlight in cells that experience Category 3 hurricane winds ( , wind speed of 203 kilometers per hour) decrease by 0.35 units ( ≈ −1.75 ∗0.2ββ ).<sup>20</sup> Given that the average level of nightlight intensity in 2000 was 12.1,<sup>21</sup> the effect is equivalent to a 2.9 percent decrease in local economic activity. ff= 0.2 Table 3 shows that the negative association between nightlights and the hurricane damage index seems to be short-lived. In column 2, it is possible to