rafmacalaba/data-use-mentions-extended huggingface dataset:
Research Release (Tierney et al. 2011).<sup>8</sup> For project outcome ratings, we used the Project Performance Dataset (PPD), a consistent six-point project outcome score based on donor-reported outcome data (Honig, Lall, and Parks 2022). We aggregated from AidData to produce measures of total ODA per country-year, and used the PPD in combination with AidData to construct simple and sizeweighted averages of ratings for projects completed in a given year.

For World Bank projects we use a scraper to download the three main documents of every project - the project information document (PID), the project appraisal document (PAD), and the implementation completion report (ICR). Respectively, they contain the information available at the beginning and end of project preparation, and at project closure. The documents had already been subject to plain text extraction by the World Bank, and we performed a minimal amount of post-processing to clean the files.<sup>9</sup> For project characteristics, we used the data compiled for Ashton et al. (2023). This includes much that is publicly available via the World Bank’s project portal, as well as some internal data on team and management characteristics, and project preparation and supervision steps.

We concentrate on projects in five sectors: health, education, water and sanitation (WASH), energy and fiscal management, due to their combination of prior literature, data availability and size. Together, they account for 35% of aid flows, and one third of PPD projects. When combining sector aid flows and projects with outcomes and country characteristics from the WDI we conducted standard smoothing for noise and minimal interpolation for variables with high missingness.

# _3.3. Linear Methods: Replication, Sectoral Extension and Ratings_

We first replicate the specifications in health, education, and WASH from Mishra and Newhouse (2009) for health, Birchler and Michaelowa (2016) for education, and Ndikumana and Pickbourn (2017) for WASH.
: Authors’ own estimate based on NSSO data. Note: Diff-in-Diff in the last three columns are calculated by deducting the change in urban FLFP rate from the change in rural FLFP rate for respective years.

Historically in India, the education of female children, especially at the secondary and higher levels, was conventionally discouraged due to the perceived social role of females as caregivers.

This social role was equally applicable for before and after marriage— leading to gender 12
). A distinctive element of this channel is the mandate that, in order to justify use of Article XIX, governments were required to provide some evidence of unforeseen events having taking place, and that increased imports of the product were causing injury to the domestic, import-competing industry. Notably, the Article XIX provision has subsequently been transformed into the WTO’s Agreement on Safeguards. We have presented cross-country data on safeguards use over 1995-2013 in substantial detail in Section 3.1.

Overall, countries triggered temporary tariff increases in only 19 instances under Article XIX over 1950-1959; the US requested slightly more than half.<sup>73</sup> Figure 13 displays Article XIX use over 1950-1959 so as to make comparisons to safeguard use in more recent decades. The figure 71For example, an Article XIX action typically involved a tariff line or group of products like ‘hatter’s fur’ (US, 1951), ‘strawberries’ (Canada, 1957) or ‘hard coal’ (West Germany, 1957) rather than a broad industrial classification like ‘chemicals’ or ‘machinery.’

> 72Bown (2004) examines invocations of Article XIX and XXVIII over the 1973-1994 period in one of the relatively few empirical studies seeking to explain why countries used these provisions to implement additional import protection. The evidence there is consistent with a theory that countries invoked these exceptions when they needed to make changes to their trade policies between negotiating rounds and wanted to do so in accordance with GATT rules so as to avoid a dispute and potentially more severe retaliation by affected trading partners. For a theoretical exploration of the different GATT rules on _compensation_ under Article XIX and XXVIII versus under dispute settlement (GATT Article XXIII), see Bown (2002b). A case study of US import policy in the 20th century by Robert Baldwin (1983) suggests that the United States’ use of Article XIX cycled with changes in US trade law. The US law regarding safeguards varied in the stringency of qualifying criteria over decades with the result
1 percent, respectively, among unskilled workers.<sup>3</sup> Similar patterns are observed in other countries in the region, including Tunisia (Mouelhi and Goaied (2017)).

According to survey results by the country’s Haut-Commissariat au Plan, several factors help to explain the low labor force participation rate among women (see World Bank (2018)). Of the 10 million women not currently participating in the labor market, 52.7 percent (more than half) consider household responsibilities as the main reason for staying home–more than 22 points higher than the survey estimate reported by the International Labour Organization (2017, Table 5) for Northern Africa as a whole. Indeed, time use surveys indicate that women continue to perform the vast majority of household tasks (see Attioui et al. (2017)). This factor, however, decreases as the

> 3For workers with tertiary education only, the data are similar: the unemployment rate is 29.7 percent for women and 14.4 percent for men. he unemployment rate for the unskilled (men and women) is lower than for the skilled because they cannot remain openly unemployed for long and often seek employment in the informal economy.

5
69|92.26|55.65|
|Unimproved drinking water|7.40|26.74|3.34|28.31|7.74|44.35|
|**Total**|**100**|**100**|**100**|**100**|**100**|**100**|

_Notes:_ The household wealth index from DHS<sup>4</sup> was used to divide households into 5 quintiles based on their level of wealth. Rich in the table refers to the richest quintile (quintile = 5) and poor to the poorest quintile (quintile = 1).

The average incidence of DD and stunting among under five-year-old in our sample is 12.6 and 15.5 percent, respectively (Table A1), but there is considerable variation across countries (Table A2). Figure 2a shows the average incidence of DD is particularly high in Latin American, Caribbean and Sub-Saharan African countries, where every fifth child surveyed experiencing DD on average among the top 10 countries.<sup>5</sup> Stunting rates also very greatly across countries, ranging from 29 and 26 percent in Timor-Leste and Madagascar, respectively, to a mere 3 percent in Colombia, The Gambia, and the Dominican Republic (Figure 2b). Stunting rates are generally high in Sub-Saharan Africa and in all Asian countries in our sample.

> 3 The following water sources are included in the JMP definition of improved drinking water sources: piped water, borehole or tube wells, protected dug wells, protected springs, rainwater, and packaged or delivered water. Any other sources are considered as an unimproved water source.

> 4 The Wealth index from DHS is defined after accounting for water, sanitation, and hygiene (WASH) variables. We eliminate the WASH variables to create our own wealth index using the Principal Component Analysis.

> 5 The highest incidence of DD (in
# 2 Data

Our primary data source is household survey data on disposable income or consumption available in PIP. We use data from 1,989 surveys covering 168 countries for the period 1991 and 2020. We exclude data before the 1990s as the quality was generally worse then, particularly for low- and middle-income countries. The data are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. We use information on per capita household welfare expressed in 2017 USD PPPs. We use PIP’s public percentile database (version 20230919), utilizing 99 percentiles on the distribution from each income or consumption vector. Concretely, we use the values of income or consumption such that the cumulative density function takes the following values {0.01, 0.02, ... ,0.99} . That is, we retain the 99 poverty lines that result in poverty rates of FF(yy) {1%, 2%, ... ,99%} . The final dataset consists of 196,903 quantile-country-year observations on pairs of daily per-capita welfare and the associated quantile in the distribution.

We combine this survey data with various possible predictors of welfare at the country level. We use data from the World Development Indicators (WDI) of the World Bank, which is one of the largest databases of country-year development indicators spanning a wide range of topics. The WDI contains information on around 1,400 indicators covering topics such as health, agriculture, education, climate change, infrastructure and more. We also use all data from the World Economic Outlook of the International Monetary Fund, which contains dozens of variables on macroeconomic indicators, and all data from the UN’s World Population Prospects, which contains dozens of variables on population, health, and demographics. We compliment GDP data from the sources above with estimates from the Madison Database (Bolt and Van Zanden 2024). We also use country and region classifications
one) banking relationship ( _Multiple Banks Dummy_ ) and a separate variable that records the actual number of banking relationships up to 26 ( _Number of Banking Relationships_ ). About 60% of firms have multiple banking relationships, and the average firm has 2.74 relationships. Finally, we also include a dummy ( _Diversification_ ) for firms that diversify their relationships across banks of different ownership types, i.e., have relationships with at least two different ownership types. Almost half of the firms, 49%, diversify across bank ownership types.

Balance sheet information on the main banks is from the Accounts of Scheduled Commercial Banks, published by the RBI. We include main bank size ( _Log Assets_ ) and three measures of main bank fragility: the ratio of equity capital to total assets ( _Capital Asset Ratio_ ), the ratio of nonperforming loans to total net loans ( _Nonperforming Loans_ ), and the ratio of liquid assets (cash, balance with other banks, and money market instruments) to total assets ( _Liquidity_ ).<sup>21</sup> The average size of main banks is Rs. 351,512.3 **(** US$ 7 billion) in assets.<sup>22</sup> The average main bank fragility indicators are 0.05 for the capital ratio, 0.06 for the nonperforming loans ratio, and 0.19 for the ratio of liquid to total assets.

When we measure main bank fragility in the regressions, we always include the bank’s capital ratio, the most standard measure of bank financial condition. However, we report the regressions both with and without the nonperforming loans and liquidity ratios, since we find these to be more questionable indicators of fragility. Nonperforming loan numbers can be manipulated by banks through “evergreening,” the practice of rolling over overdue loans and/or providing additional credit to cover the required repayments on existing loans. Liquidity ratios may in some circumstances not adequately reflect the lending capacity of banks.
collectively owned enterprises, plus joint stock and shareholding companies and foreign funded enterprises but excluding township and village enterprises. In a broad definition, they also include non-enterprise units, such as government agencies, institutes, non-profit organizations, etc.

7 Three surveys conducted by the Chinese Academy of Social Sciences (CASS) in 1988, 1995, and 1999, respectively, show that the Gini coefficient for the urban area rose from 0.185 in 1988 to 0.225 in 1995 and 0.256 in 1999. The percentage increase was 22% for the period 1988-1995, 14% for the period 1995-1999 and 38% for the whole period 1988-1999. The 1999 survey also shows that the poverty rate was 13.2% for households with unemployed people and only 2.8% for households without unemployed people. See Xue & Zhong (2003).

4
*|2736|3990|5924.3|6696|3996|6247|8568.7|10409|
|**Rural frontier**|2688|3979|6304.7|7074|3984|5788|8979.3|10788|
|**Total**|2568|3921|5889.6|6604|3924|6141|8827|10279|

Source: Compiled from various CAPMAS Poverty assessment updates.

45
restrictions on foreign direct investments and portfolio investmnents in domestic equities.<sup>40</sup>

- To some extent, the Korean capital controls program should have had the opposite effect of the Chilean program. Data from BIS confirm this. Chile's short-term borrowing from banks in the BIS area represents 43 percent of total borrowing, while the corresponding figure for Korea is 68 percent.<sup>4</sup> '

# **3.6. Proper market infrastructure**

- Market infrastructure includes the systems and institutions that facilitate the trade and custody of securities. The necessary functions comprise matching buyers and sellers,

> 40 See Park and Song, 1996, and Johnston, Darbar, and Echeverria, 1997.

> 41 However, we should wait until we could collect more pervasive empirical evidence before we reach any strong conclusions.

14
sup>27</sup> and joined until each created PSU has close to 300 households. Under the proposed approach, original PSUs are never split, just joined to others. Additionally, all original PSUs that were larger than 300 households are designated as a created PSU. The entire process yields 16,297 PSUs.

The resulting Census data is used to draw 500 survey samples to conduct a design-based simulation to establish how a method will perform over repeated sampling from a finite population (Tzavidis et al., 2018). The sampling approach reflects standard designs used in most face-toface household surveys conducted in the developing world, such as those conducted by the Living Standards Measurement Study (LSMS) program of the World Bank (Grosh and Muñoz, 1996), with some simplification for convenience. First, the 32 states that comprise Mexico are treated as the intended domains of the sample. The main indicator of interest for the survey is the welfare measure: household per capita income. The desired level of precision to be achieved in the sample is assumed to be a relative standard error ( _RSE_ ) of 7.5 percent for mean per capita income

> 23Income is defined as money received from work performed during the course of the reference week by individuals of age 12 or older within the household.

> 24Welfare values of 0 and/or missing are a common feature of household surveys.

> 25The median municipality has 1,613 households.

> 26The Intra Censal Survey has its own PSUs, however many of these have too few observations to properly work as PSUs in the created “Census” data.

> 27This assumes that the clusters’ numbering is tied to how proximate each cluster is to one and other.

16
>80% Size of group: 6.7%<br>60%<br>Middle class<br>Share of total transfers: 35.3%<br>40% Share of direct taxes: 11.0%<br>Size of group: 53.8% Vulnerable<br>Total poor Share of total transfers: 44.6%Share of direct taxes: 0.0%<br>20% Share of total transfers: 18.4%Share of direct taxes: 0.0% Size of group: 32.1%<br>Size of group: 7.5%<br>0%<br>-20%<br>-10% 0% 10% 20% 30% 40% 50% 60%<br>Share of total direct transferts received by group (%)<br>Share of total direct taxes paid by group (%)<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016). _Notes_ : The “Total poor” group includes the share of the population living in ultra (0.8 percent), extreme (2 percent) and moderate (4.7 percent) poverty, based on the total market income plus pensions concept. The income thresholds to define the groups are the following: less than $1.25/day for the ultra-poor, $1.252.5/day for the extreme poor, $2.5-4/day for the moderate poor, $4-10/day for the vulnerable, $10-50/day for the middle class, and above $50/day for the wealthy. The size of the bubbles is relative to the size of each group as measured with total market income plus pensions.

The Kakwani index, however, cannot tell if these and other fiscal interventions make the whole fiscal system more (un)equal,<sup
**Long-term Impacts of Global Food Crisis on Production Decisions: Evidence from Farm Investments in Indonesia**<sup>**1**</sup>

Manabu Nose<sup>2</sup> and Futoshi Yamauchi<sup>3</sup> World Bank Key Words: Investment effect, Expectation, Anticipated shock, Liquidity constraint Sector Boards: POV, SOCPT, ARD 1 We thank Luc Christiaensen, Quy-Toan Do, Emanuela Galasso, Ivanic Maros, Owen Ozier, Rachel Heath, and Reno Dewina, Sony Sumaryanto and seminar participants at the World Bank for their useful comments. We are grateful to the Japan International Cooperation Agency for financial support and the Indonesian Center for Agriculture Socio Economic and Policy Studies (ICASEPS) for collaboration. We thank Sony Sumaryanto for sharing agricultural commodity price data, and Dini Inayati for collecting additional price data. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

> 2 Corresponding author: Manabu Nose, The World Bank, 1818 H Street, NW, Washington DC, 20433, USA; Email: <u>mnose@worldbank.org; Tel: +1-(202)-294-5370</u>

> 3 The World Bank, 1818 H Street, NW, Washington DC, 20433, USA; Email: fyamauchi@worldbank.org
# **ABBREVIATIONS AND ACRONYMS**

|ACB|Akiba Commercial Bank|
|---|---|
|BARA|Business Activities Registration Act|
|BDS|Business Development Service|
|BEST|Business Environment Strengthening for Tanzania|
|BOT|Bank of Tanzania|
|BRAC|Bangladesh Rural Advancement Committee, Tanzania|
|BRELA|Business Registration Licensing Authority|
|CEM|Country Economic Memorandum|
|CRDB|Cooperative Rural Development Bank|
|CSSC|Christian Social Services Commission|
|DDSDP|Demand-driven Skill Development Program|
|FBO|Faith-based Organizations|
|FDC|<br>Focal Development Colleges|
|FGD|Focus Group Discussion|
|FINCA|Foundation for International Community Assistance|
|FSDT|<br>Financial Sector Deepening Trust|
|GEMA|Gender Education Management Association|
|GTZ|_Deutsche Gesellschaft für Technische Zusammenarbeit_|
|HBS|Household Budget Survey|
|HE|Household Enterprise|
|ISIC|International Standard Industrial Classification|
|IST|Informal Sector Training|
|ILFS|Integrated Labor Force Survey|
|ITEP|Individual Training Evaluation Program|
|LGA|Local Government Authority|
|MDA|Ministries, Departments, and Agencies|
|MFI|Microfinance Institutions|
|MITM|Ministry of Industry and Trade, Marketing|
|MKUKUTA|_Mkakati wa Kukuza Uchumi na Kupunguza_Umaskini Tanzania|
||(National Strategy for Growth and Poverty Reduction)|
|MKURABITA|_Mpango wa Kurasimisha Rasilimali na Biashara za Wanyonge_|
||Tanzania (National Business and Property Formalization Program)|
|MSME|Micro- and Small and Medium Enterprises|
|MNRT|Ministry of Natural Resources and Tourism|
|M&E|<br>Monitoring and Evaluation|
|NACTE|National Council for Technical Education|
|NSGRP|National Strategy for Growth and Reduction of Poverty|
|NBS|<br>National Bureau of Statistics|
|NGO|Nongovernmental Organization|
|NMP|<br>National Microfinance Policy|
|PIN|<br>Personal Identification Number|
|PMO|Prime
.8%<br>* denote significance|

<mark>Notes: robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.</mark>

# : **Appendix 7 Impacts on World Management Survey and MOPS Management Measures**

## **WMS 2013 Data Collection**

We commissioned the London School of Economics (LSE) team responsible for the Bloom and Van Reenen (2007) World Management Surveys (WMS) to apply their methodology to a random sample of 180 firms representative of the Colombian manufacturing sector, as well as to a subsample of 77 firms in our sample, focusing on firms with 40 or more employees (Table A6.1).

Interviews were done by phone with a manager with thorough knowledge of the production process, typically the plant manager or production manager. The WMS interview is structured as a guided discussion, and is designed to be answered by a manager with thorough knowledge of the production process, typically the production or plant manager. Such discussion lasts between one hour and one hour and a half, and covers the 18 questions related to operations, monitoring, targeting, and people management. The interviewer guides the interviewee by means of open questions, letting him/her speak freely but making sure to have the necessary objective information to score each of the 18 topics using the provided scoring grid. Each of the 18 topics receives a score between 1 (no modern practice is implemented) and 5 (best practice).

60
wage premia which, in general, will differ from the CGE data. Analogous to equations 1 and 2, define: � where g�,� are the wage premia based on averages by skill group and sector in the household data; y′�,� are the average earnings of labor in sector s and skill group e based on the household data; y′�,� are the average earnings of unskilled labor in agriculture based on the household data; and the g′<sup>�</sup> are the predicted values at the household level as a result of the policy change. All right hand side values of equation 3 are known from the initial household data. In order to calculate g��,�� , we define:

> 21 In practical terms, the micro-simulation model recalibrates each household sample weight to match the age, gender, and education projected totals. A new probability distribution can be obtained by solving an optimization problem based on a minimum cross-entropy criterion as in Olivieri et al. (2014). The minimum cross-entropy method assures that the new sets of age, gender and education, deviate as little as possible from the initial distributions. See Wittenberg, (2010) for a technical description and implementation of this method.

31
placement and underperformance of migrants are due to the fact that diplomas earned in their home countries are of no value in the destination countries. Whether this arises from poor education quality or acquisition of non-transferable skills, it requires policymakers to rethink national education policies and global creation of human capital (Mattoo et al. 2008).

The next section reviews the literature on the performance of male migrants United States and the determinants of their performance. Section 3 focuses on the dynamic analysis of the occupational placement over time using census data. It includes three sub-sections: the first introduces the empirical strategy and describes the data; the second presents the results; and the third is dedicated to robustness checks. The impact of country attributes on the occupational performance is evaluated in section 4, which is divided into two sub-sections, one presenting the empirical framework and the other describing the results. Section 5 summarizes the findings and discusses their policy implications.

# **2. LITERATURE REVIEW**

Literature on the performance of immigrants in destination labor markets<sup>3</sup> mostly focuses on their earnings, especially relative to those of the natives with similar education and demographic characteristics. One of the main findings is that migrants in the United States tend to earn less than natives and this gap depends on the country of origin. For example, immigrants coming from countries that more closely resemble the United States (such as Canada and Western Europe) tend to have higher initial earnings relative to those from other countries such as Mexico<sup>4</sup> .

The debate, however, is not on the extent of the initial earning gaps but on what happens to them over time. This is related to the “assimilation effect” which receives special attention as it relates to the accumulation of destination specific human capital (language skills, cultural knowledge and social capital) that increases wages and other measures of performance over time. Chiswick (1978) and subsequent papers<sup>5</sup> claim that, as