rafmacalaba/data-use-mentions-extended huggingface dataset:
- Brune, Lasse, Xavier Giné, Jessica Goldberg, and Dean Yang. 2016. “Facilitating Savings for Agriculture: Field Experimental Evidence for Malawi.” _Economic Development and Cultural Change_ 64(2): 187-220.

- Chen, Rong. 2021. “Mapping Data Governance Legal Frameworks Around the World Findings from the Global Data Regulation Diagnostic.” World Bank Policy Research Working Paper 9615.

- Chiplunkar, Gaurav, and Pinelopi Koujianou Goldberg. 2022. “The Employment Effects of Mobile Internet in Developing Countries.” NBER Working Paper 30741, National Bureau of Economic Research, Cambridge, MA.

- Claessens, Stijn, Jon Frost, Grant Turner, and Feng Zhu. 2018. “Fintech Credit Markets Around the World: Size, Drivers and Policy Issues.” _BIS Quarterly Review_ (September): 29–49.

- Cohen, Michael, and Marc Rysman, 2013. “Payment Choice with Consumer Panel Data.” Working Paper No. 13-6, Federal Reserve Bank of Boston.

- Collins, Daryl, Jonathan Morduch, Stuart Rutherford, and Orlanda Ruthven 2009. _Portfolios of the Poor: How the World’s Poor Live on $2 a Day_ . Princeton, NJ: Princeton University Press.

- Dabla-Norris, Era, Yixi Deng, Anna Ivanova, Izabela Karpowicz, Filiz Unsal, Eva Van Leemput and Joyce Wong. 2015a. “Financial Inclusion: Zooming in on Latin America.” IMF Working Paper No. 15/206, International Monetary Fund, Washington, DC.

- Dabla-Norris, Era, Yan Ji, Robert Townsend, and D. Filiz Unsal. 2015b. “Distinguishing Constraints on Financial Inclusion and Their Impact on GDP, TFP, and Inequality.” NBER Working Paper No. 20821, National Bureau of Economic Research, Cambridge, MA.

- De’, Rahul, Neena Pandey, and Abhipsa Pal. 2020. “Impact of Digital Surge during Covid-19 Pandemic: A Viewpoint on Research and Practice.” _International Journal of Information Management_ , 55(C): 102171
, especially those related to nutrition (for pregnant women and children who are under the age of two) and early childhood care and education services (for children under six years of age). These could include building or revamping health centers, creating community child development groups or playgroups, supporting community preschools or community centers with integrated ECD services and outreach such as home visits, and advocacy activities to sensitize the community about benefits of ECD. They could also include construction grants for pre-primary classrooms that include classrooms, school furniture suitable for pre-primary classrooms, playground materials, and sanitation facilities.

Third, the INDH could provide incentives for performance on ECD outcomes, specifically related to nutrition and early childhood care and education.

Finally, to complement these modifications, raising awareness and sensitizing parents and caretakers so they can make better decisions about ECD (for example, assessing the menu of services) and improve ECD outcomes at home could further enhance impact. This could be done through mass media and technology (such as cell phones, radio, TV) and through reaching key leaders (for example, imams).

# **_7 Conclusions_**

This study examined whether the INDH, a national community-driven human development program in Morocco, had an impact on economic outcomes and human development, focusing specifically on ECD as the foundation of human development and when cycles of poverty begin. The study used three rounds of panel data on rural communities near the cutoff for program targeting to assess program impact. The analysis showed that while the INDH had an impact on economic outcomes during Phase I, the effects had dissipated by 2013. Additionally, the INDH did not have either direct or indirect effects on ECD. Although it did not explicitly target ECD, the focus on improving economic outcomes and social services could have generated 33
13

# **3. The Model and the Decomposition Methodology**

Let us now turn to the Brazilian version of the general semi-reduced-form model for household income and labor supply in Bourguignon et. al. (1998). It is used here to investigate the evolution of the distribution of household incomes per capita over the two decades from the mid- 1970s to the mid-1990s. Specifically, we analyze the distributions of 1976, 1981, 1985 and 1996, and simulate changes between them. The paper covers only Brazil's urban areas (which account for some three quarters of its population). The general model therefore collapses to two occupational sectors: wage earners and self-employed in urban areas."<sup>2</sup> Total household income is given by: Where w; are the total wage earnings of individual i, LW is a dummy variable that takes the value one if individual i is a wage earner (and zero otherwise); **Ti** is the self-employment profit of individual i: L" is a dummy that takes the value one if individual i is self-employed (and zero otherwise); and Y0 is income from any other sources, such as transfer or capital incomes. Equation I is not estimated econometrically. It aggregates infornation on right-hand-side term I (from equations 2 and 4), 2 (from equations 3 and 4) and 3 directly from the household data set.

The wage-earnings equation is given by: (2) _Logw, =_ **_Xi_** _7W± +_ **_eW_** countries.

> 12 We will eventually extend the model to cover rural areas too, by incorporating two additional sectors: wage earners and self-employed in the rural areas. In Brazil, wage earners include employees with or without formal documentation ('com ou sem carteira'). The self-employed are own-account workers ('conta propria').
Similarly to our paper, recent work by Alfaro and Chor (2023) shows that US sourcing has been reallocated away from China and toward other locations, notably Mexico and Vietnam, and that prices of imports from these countries are on the rise.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 studies the impact of the trade war on the reconfiguration of supply chains. Section 4 takes a closer look at where production moved after the shock. Section 5 concludes.

# **_2. Data_**

We use 10-digit import data at the country level from US Customs for 2017 and 2022. There are more than 17,891 products and 157 countries.<sup>2</sup> Tariff data are from Bown (2023). Strategic industries are defined as the 2-digit categories identified as Advanced Technology Products by the US government (see Appendix Table 1 for a list of the industry codes).

Information on country and country-pair characteristics has been obtained from various sources. The Centre d’Etudes Prospectives et d’Informations Internationales (CEPII) GeoDist database (Mayer and Zignago 2011) provides information on bilateral distance in kilometers and an indicator variable that captures if two countries share a border. Data on population and GDP per capita are from the World Bank’s World Development Indicators (WDI). Data on UN voting alignment with the US are from Voeten (2004). Measures of revealed comparative advantage (RCA), export similarity with China, and intraindustry trade with China have been computed using trade data from UN Comtrade. Data on Free Trade Agreements (FTAs) are from Mario Larch’s Regional Trade greements Database based on Egger and Larch (2008). A summary of the main variables is presented in Appendix Table 2.

Prima facie evidence on the reshuffling of the top trade partners from 2017 to 2022 is presented in Figure 3. China’s share fell 5.3 percentage points (ppts). For strategic products, this decline is
|0.656***|
||(0.0454)|(0.0466)|(0.0548)|(0.0507)|(0.0491)|(0.0510)|
|Observations|1843|1843|1843|1843|1843|1843|

Note: This chart is based on answers to question 8, 10, 13, 29 and 30 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

43
traditional areas (carrots) with sufficient mechanisms to recognize failures and quickly phaseout support (sticks); and (iii) _accountability_ , whether the intended beneficiary of innovation policy, society at large, has the information and ability to monitor the policymakers and bureaucrats to ensure that policy is responsive to its needs.<sup>14</sup>

# **III.2 Description of SBIRI and BIPP output achievements**

This subsection describes what we were able to find out about SBIRI and BIPP. The analysis is largely descriptive, given that available DBT data coupled with interviews with DBT staff, DBT partners and beneficiaries were not sufficient to allow an assessment of the benefit-cost of resources spent.

# **_(1) SBIRI: Early-stage funding of SMEs_**

The first PPP initiative created by DBT for domestic early-stage technology development and commercialization, launched in 2005, was the Small Business Innovation Research Initiative. It was modeled on the US SBIR (Small Business Innovation Research) program. A stated objective was to attract a greater number of SMEs to accelerate innovation by engaging in peer-reviewed quality research. Box 3 describes eligibility criteria for public support.

## **Box 3: Funding structure of SBIRI**

SBIRI is open to SME Indian registered and majority-owned enterprises (start-ups and existing firms) that have an in-house DSIR (Department of Scientific and Industrial Research)-certified R&D unit, groups of such firms, and collaborations of such firm(s) with public R&D institutions. The enterprise must not have more than 500 employees engaged in R&D.<sup>15</sup> SBIRI operates in 2 phases, focusing on early-stage, pre-proof of concept funding:

|Phase|Description and funding|
|---|---|
|I<br>II|For establishment of pre-proof of concept of innovation<br><br>80% grant for project costs up to Rs 2.5 million (about $50,000)
|Time Tax (Country level average)|Country-level average of the percentage of<br>firms’ senior management’s time that is spent<br>in dealing with business regulations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Population (lagged, logs)|Total population in the country. Values of the<br>variable used are one year prior (that is, lagged<br>by 1 year) to the fiscal year covered by the ES.<br>In cases where the fiscal year covered by the<br>ES spans more than one calendar year, the<br>population figure is the weighted average over<br>the calendar years covered (lagged by 1 year),<br>where the weights used are the proportion of<br>firms covered by the ES in the calendar year<br>(lagged by one year).<br>Source: World Development Indicators,<br>World Bank.|
|Rule of Law (lagged)|Rule of Law indicator from Worldwide<br>Governance Indicators. Higher values of the<br>variable imply better enforcement of rules and<br>laws. The variable is lagged by 2 years from<br>the fiscal year covered by the Enterprise<br>Survey in the country. In cases where the fiscal<br>year spanned more than one calendar year, the<br>Rule of Law figure is the weighted average<br>over the calendar years covered (lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|Freedom From Regulation (lagged)|Freedom from Regulation indicator from<br>Fraser Institute’s Economic Freedom of the<br>World database
at 60 percent (ILO 2017). Hence, lack of</mark>

> 11 <u>https://reliefweb.int/sites/reliefweb.int/files/resources/64114.pdf, accessed August 2,2018.</u>

> 12 Data from International Labour Organization (ILO) 2016.

> 13 Studies of the agricultural sector in the late 1990s show that poorer women are traditionally responsible for crop and livestock production. Forty‐five per cent of farm family labor is provided by unpaid females (100% for the care of animals) (IFAD 2001) and more recent research shows that women’s responsibility for agricultural production has increased since ( Galièa, Jiggins, and Struika, 2013).

> 14 In Lebanon, Jordan and Turkey.

9
13 There is also no visual evidence of differential pretreatment trends across these region groupings in the raw data. See figure S2 of the supplemental appendix for plots of the mean test score and number of test takers by time to treatment.

> 14 We also looked for district-level heterogeneity in the effect of the program on test taking by interacting the program with dummies for low enrollment, rural, and wealthy districts in separate specifications. None of the interaction terms are statistically significant for girls or boys, indicating that increased access for girls was widely shared across districts. Results appear in supplemental appendix table S2.

15 We have also used administrative data from the Ministry of Education to explore the enrollment effect of the policy by grade. Not surprisingly, the effects are largest in the early secondary grades and diminish as the students’ progress through school. Results appear in figure S3 of the supplementary appendix.

16 Another potential explanation for the results is that additional students retook the exam in response to the scholarship. However, the scholarship covered exam fees only for enrolled students, which would mute this response.

> 17 In columns (9)–(11), we also find no change in the number of private schools in response to the program, either overall or differentially by the characteristics considered. This suggests that private schools experiencing enrollment gains grew in size as a result of the program.

> 18 A potential explanation for the increases in test takers and private school share is migration in response to the program. Although Gambian household surveys lack data on residential migration that would allow us to test directly, we think that such a response is unlikely, because the monetary and psychic costs of migration should exceed the scholarship’s value of less than US$50 annually. Switching schools without changing residence is also unlikely given the sparse geographic distribution of schools, particularly in regions 3–6, as shown in supplemental appendix figure S1. If such switching occurs, it would most likely be at schools close to the border between an eligible
de Estadistica_ - INE). Running since 1968, the survey is representative at national and departmental levels. In 2017, the ECH sample included over 118,200 individuals and 45,300 households. ECH reports information on several topics, including labor market participation, income, housing, health, education, and others. Crucially, the ECH collects detailed information on the characteristics of individuals’ occupation, including income after taxes and contributions (such as net wages, bonuses, tips, capital income, private and public transfers), whether workers contribute to a pension system or not and whether workers and pensioners are covered by FONASA.

11
Policy Research Working Paper 9774

# **Abstract**

This study explores how businesses in Tanzania are impacted by floods, and which strategies they use to cope and adapt. These insights are based on firm survey data collected in 2018 using a tailored questionnaire, covering a sample of more than 800 firms. To assess the impact of disasters on businesses, the study considers direct damages and indirect effects through infrastructure systems, supply chains, and workers. While direct on-site damages from flooding can be substantial, they tend to affect a relatively small share of firms. Indirect impacts of floods are more prevalent and sizable. Flood-induced infrastructure disruptions—especially electricity and transport—obstruct the operations of firms even when they are not directly located in flood zones.

The effects of such disruptions are further propagated and multiplied along supply chains. The study estimates that supply chain multipliers are responsible for 30 to 50 percent of all flood-related delivery delays. To cope with these impacts, firms apply a variety of strategies. Firms mitigate supply disruptions by adjusting the size and geographical reach of their supply networks, and by adjusting inventory holdings. By investing in costly backup capacity (such as water tanks and electricity generators), firms mitigate the impact of infrastructure disruptions. The study estimates that only 13 percent of firms receive government support in the aftermath of floods.

This paper is a product of the Global Facility for Disaster Reduction and Recovery; Urban, Disaster Risk Management, Resilience and Land Global Practice. 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 jrentschler@worldbank.org, ekim11@worldbank.org, aerman@worldbank.org, sdevriesrobbe@worldbank.org, and stephan.thies@fu-berlin.de.

_The Policy Research Working Paper Series disseminates the findings of work in progress
4 supervision was found to be negatively related to the development of the banking sector and the integrity of the system (in other words, positively related to corruption in banking). On the other hand, we found considerable support that strengthening private monitoring was associated with deeper, more efficient, and less corrupt financial systems, but not with greater financial stability. As noted in Section IV, our results here, even with the Survey I data, are a bit weaker because of the smaller sample size used in this paper in order to preserve comparability with the Survey IV data. We continue to stand by the results as presented in BCL (2006). Thus, while many countries have followed the Basel guidelines and strengthened capital regulations and empowered supervisory agencies to a greater degree, existing evidence does not suggest that this will improve banking-system stability, enhance the efficiency of intermediation, or reduce corruption in lending. Although some countries have reformed their regulations to empower private monitoring, consistent with the third pillar of Basel II, there are many exceptions and even reversals along this dimension. Moreover, many countries intensified restrictions on non-lending activities, which existing evidence suggests hurts banking system stability, lowers bank development, and reduces the efficiency of financial intermediation.

Our tempered advice continues to be that countries will benefit from an approach to bank regulation that is grounded in what has worked in practice. In our earlier work, we found that an approach that favors private monitoring, limits moral hazard, removes activity restrictions on banks, encourages competition, including competition by foreign banks, and requires or encourages greater diversification appears to work best to foster more stable, more efficient, and less corrupt financial-sector development. Our earlier findings did not support a hurried adoption of the first two pillars of Basel II by developing countries, but rather stressed the value of first developing the legal, information, and incentive systems in which financial systems flourish to the benefit of everyone. Based on the existing evidence, we continue to believe that this approach is
.115|0.060|0.000***|

Source: authors’ calculation using HIES 2016. Notes:<sup>a</sup> Self-employed farmers are defined as those who had income from at least one crop activity. Those farmers having only livestock activity are not included due to the lack of land information. c Productivity is measured as gross crop income divided by cultivated area in acres. *, **, and *** indicate that the male-female difference is significant at the 10, 5 and 1% level, respectively. ICT = information and communication technology.

11
10.0|-0.274*|-9.8|-0.045*|-2.4|-0.314*|-12.4|
|No. of observations|3266||2043||4781||3392||

_Note:_ Industry dummy variables included but not reported. * Denotes significance at 5 % level.

# **V.  Summary and Conclusions**

To summarize, the empirical evidence provides qualified support for the proposition that Mexico’s CIMO program of integrated training and technical assistance has generally been effective in improving the performance of micro, small and medium size companies. Compared to the control group, CIMO firms increased investments in worker training, had higher rates of capacity utilization, and were more likely to adopt quality control practices. The evidence suggests that these improved intermediate outcomes were associated with increased productivity growth among SMEs that participated in the CIMO program, impacts that were especially strong in the 1991-1993 period. The effects of the program were not apparent in the 1993-95 period, though this result may simply reflect the poor choice of a control group.

This re-examination of the data demonstrates that estimates of program impacts using the same data sets can vary dramatically depending upon the empirical methodology used. The productivity effects of CIMO participation were invariably negative when production functions were estimated in levels, and positive or mixed when estimated in first-differences to address the issue of self-selection bias. These results highlight several lessons. First, it is critical to select an _appropriate control group_ for the program beneficiaries, and to collect panel data for both groups in order to allow comparisons of pre- and post-program participation outcomes. Such a quasi-experimental design is required if the net impacts of the program are to be estimated. Second, there is need to account for and _address sample selectivity issues_ that might arise with the choices firms make about program participation. To the extent that more or less productive 14
# 3.2 Labor force data per education level

It is relatively easier to identify the number and average education level of workers in each country of the world. Several data sources can be used to assess the size and skill structure of the labor force of each country. The size of the working-age labor force (i.e. population aged 25 and over) is provided by the United Nations. Data is missing for a few countries but can be estimated using the CIA world factbook.<sup>8</sup> Labor force data is then split across skill groups using international indicators of education attainment. Here, we follow Docquier and Marfouk (2006) and Docquier, Lowell and Marfouk (2009) in combining di¤erent data sets documenting the proportion of postsecondary educated workers in the population aged 25 and over. They use De La Fuente and Domenech (2006) for OECD countries and Barro and Lee (2001) for non-OECD countries. For countries where Barro and Lee’s measures are missing, they estimate the proportions educated using Cohen and Soto’s measures (see Cohen and Soto, 2007). In the remaining countries where both Barro–Lee and Cohen–Soto data are missing (about 70 countries in 2000), they apply the educational proportions of the neighboring country having the closest enrollment rate in secondary/tertiary education, or the closest GDP per capita.

# 3.3 Description and summary statistics for our sample

Table 1 shows the 1990-2000 ‡ows of immigrants, emigrants and their di¤erence (net migration) for a set of ten large Western European countries and four other groups: (i) three Anglo-Saxon non-European countries (US, Canada and Australia) traditionally attracting large numbers of immigrants, (ii) three large Eastern European countries (Czech Republic, Hungary and Poland) with a range of emigration rates, mostly to Western Europe, (iii) two middle income countries with large emigration rates (Mexico and Turkey) and (iv) three non-OECD countries
We use three waves of household survey data covering the period 1998 to 2013 and information on oil fields to estimate the economic impacts of the oil discovery. We exploit the quasi-experimental nature of the discovery by comparing districts in regions associated with oil (treatment group) and districts in regions without oil (control group).

> 1  Cotet and Tsui (2013) show that oil discoveries increase military spending.

> 2 Aragón, Chuhan-Pole, and Land (2015) provide  a literature review of both cross-country and subnational level impacts of natural resources.

2