rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Financial Development (Credit/PIB), Property Rights and Poverty (Headcount poverty)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Private Credit/GDP (log)|-0.084|-0.078|0.371|0.436|
||(4.04)***|(4.16)***|(2.00)**|(2.29)**|
|GDP per capita ($US, log)|-0.454|-0.454|-0.452|-0.505|
||(8.89)***|(8.01)***|(7.55)***|(8.47)***|
|Property Rights (log)|||-0.299|-0.339|
||||(1.87)*|(2.04)**|
|Private Credit/GDP (log) * Property Rights (log)|||-0.138<br>(2.60)***|-0.153<br>(2.77)***|
|Inflation (log)||0.377||0.331|
|||(3.93)***||(3.21)***|
|Trade Openness (log)||-0.013||0.068|
|||(0.15)||(0.84)|
|Constant|5.857|5.906|6.870|7.059|
||(18.48)***|(15.79)***|(12.27)***|(12.26)***|
|Observations|69|69|65|65|
|Number of countries|34|34|33|33|
|Chi square (Wald test)|192.79|207.64|210.12|320.39|
|Pseudo R2|0.43|0.44|0.44|0.45|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 4. Financial Development (Credit/PIB), Property Rights and Poverty (Income of the poor)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Private Credit/GDP (log)|0.103|0.086|-0.218|-0.312|
||(4.45)***|(5.15)***|(1.18)|(1.77)*|
|GDP per capita ($US, log)|0.212|0.239|0.202|0.223|
||(5.88)***|(8.66)***|(4.87)***|(4.90)***|
|Property Rights (log)|||0.012|0.108|
||||(0.06)|(0.61)|
|Private Credit/GDP (log) * Property Rights (log)|||0.096<br>(1.80)*|0.117<br>(2.29)**|
|Inflation (log)||-0.371||-0.419|
|||(3.52)***||(4.56)***|
|Trade Openness (log)||-0.214||-0.203|
|||(3.28)***||(3.12)***|
|Constant|1.716|2.429|1.746|2.107|
||(6.95)***|(7.36)***|(2.98)***|(3.27)***|
|Observations|67|67|63|63|
|Number of countries|34|34|33|33|
|Chi square (Wald test)|104.16|519.75|206.72|378.54|
|Pseudo R2|0.39|0.33|0.34|0.32|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

14
Table 6. Monetary regime, income shocks and household consumption

|Countries:|Crisis,hig|h-income|Crisis,mid|dle-income|
|---|---|---|---|---|
|Dependent variable:|Staple foods|Other<br>consumption|Staple foods|Other<br>consumption|
|Rural|0.0209**|0.00|0.00|-0.0407***|
||[0.00971]|[0.0140]|[0.0120]|[0.0118]|
|Low income|0.118***|0.02|0.200***|-0.0389**|
||[0.0166]|[0.0239]|[0.0170]|[0.0167]|
|Income shock|0.122***|0.251***|0.191***|0.327***|
||[0.0115]|[0.0165]|[0.0146]|[0.0144]|
|_Floating currency_|||||
|_* Income shock_|-0.0400**|0.116***|0.01|-0.0715***|
||[0.0182]|[0.0262]|[0.0251]|[0.0247]|
|Observations|5'412|5'412|6'988|6'988|
|Countries|5|5|6|6|
|CountryFE|yes|yes|yes|yes|
|Method|OLS|OLS|OLS|OLS|
|R2|0.06|0.23|0.11|0.14|

_Notes: Floating currency countries are countries which are not member of the euro-zone and did not have a pegged currency at the end of 2007. Among the high-income crisis countries these are UK and Sweden, among the middle-income countries these are Hungary and Ukraine. The reported coefficients are estimates from a linear probability model. All regressions include country fixed effects. See the Appendix for a definition of all dependent and explanatory variables._

# **4.2 Meso Level: Foreign Bank Ownership**

Financial globalization, i.e. cross-border lending and the entry of **foreign-owned banks** , was a crucial determinant of credit growth and economic growth in Eastern Europe prior to the crisis (EBRD,2010). However, recent evidence suggests that the reliance on foreign creditors and foreign owned banks may have exacerbated the crises in the region, as these withdrew funding more sharply than domestic banks (De Haas and Van Horen 2013, Popov and Udell 2012, De Haas and Van Lelyveld 2012, De Haas et al 2012). We therefore examine whether the market share of foreign banks affected the transmission of banking crises to households in Eastern Europe. We focus our analysis on the middle-income crisis countries and compare the three countries in which foreign banks dominate the banking sector (Hungary, Latvia, Ukraine) to the three countries where foreign banks have only a minor market share (Kazakhstan, Russia, Slovenia).

23
decreased considerably for the self-employed, falling in real terms by around 20 percent. In contrast, individuals who escaped poverty were lifted by about a 15 percent growth in average salaries. This together with the earlier evidence on employment reported above indicates that subpar performance in labor markets hurt the chronically poor during this period.

Figure 6: Average Returns to Assets by Worker/Household (2015 GEL/month) Georgia 2009 and 2015 <!-- Start of picture text --> 600 532 2009<br>479 482<br>500 2015<br>417 404<br>367<br>400<br>272<br>300<br>200 358 408 367 139 153 117<br>100 266 186 175 306 264 72 94 27 54 58 49<br>46 54 108 80 20 19 43 32<br>0<br>Earnings by worker from Earnings by worker from Earnings by household Value by household of<br>salaries self-employment from agricutural activities agricultural in-kind<br>consumption<br>2015 GEL/month<br><!-- End of picture text --> _Source_ : Own elaboration based on the 2009 and 2015 WMS. Average earnings reported only for household with positive income for each category.

Figure 7: Coverage of Selected Transfers by Welfare Status, Georgia 2009-2015 <!-- Start of picture text --> 70.060.0 55.5 61.9 60.1 60.6 2009<br>50.0 2015<br>36.5<br>40.0<br>30.0 59.0 56.2 52.4 54.9 14.3<br>20.0 10.6<br>10.0 23.2 14.7 3.11.9 8.3<br>0.0<br>Pensions TSA<br><!-- End of picture text --> Figure 8: Average Transfer by Beneficiary Household (2015 GEL/month), Georgia 20092015 <!-- Start of picture text --> 300 266 2009<br>2015 232<br>250 206 208 206<br>181 192<br>200<br>150 121<br>100<br>50 125 130 148 140 111 119 80 102<br>0<br>Pensions TSA<br>2015 GEL/month<br><!-- End of picture text --> _Source_ : Own elaboration based on 2009 and 2015 WMS. _Source_ : Own elaboration based on 2009 and 2015 WMS.

21
# **Table 14. Contemporaneous and Lagged Effects of Legally Enforceable IPAs on U.S. Majority-Owned Affiliate Sales in Manufacturing**

||(1)|(2)|(3)|
|---|---|---|---|
|WTO-X IPR LE = 2 IPAt|0.268***<br>(0.064)|0.041<br>(0.085)|0.079<br>(0.079)|
|High-IP × WTO-X IPR LE = 2 IPAt|–0.013<br>(0.069)|0.142<br>(0.095)|0.103<br>(0.093)|
|WTO-X IPR LE = 2 IPAt–1||0.282***<br>(0.086)|0.125<br>(0.098)|
|High-IP × WTO-X IPR LE = 2 IPAt–1||–0.164*<br>(0.096)|–0.022<br>(0.121)|
|WTO-X IPR LE = 2 IPAt–2|||0.166**<br>(0.078)|
|High-IP × WTO-X IPR LE = 2 IPAt–2|||–0.143<br>(0.095)|
|**Total effect time t (****_β_IPA +****_β_High-IP,IPA)**|**0.255*****|**0.183****|**0.182****|
|_F_-test of total effect|17.93|5.19|5.60|
|**Total effect t–1**||**0.118**|**0.103**|
|_F_-test of total effect||2.37|1.17|
|**Total effect t–2**|||**0.023**|
|_F_-test of total effect|||0.09|
|Observations|5,523|5,283|5,042|
|_R_<sup>2</sup>|0.907|0.913|0.921|
|Country-sector FEs|Y|Y|Y|
|Year FEs|Y|Y|Y|

Robust standard errors clustered by country-industry in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1 Our final exercise is to consider the impacts of these groupings of IPAs on U.S. relatedparty exports to partner countries. Such exports are widely considered in the literature to capture intermediate inputs sent to U.S.-owned affiliates for local assembly or production of final goods. One advantage of these data is that they exist for relatively detailed NAICS industries, which correspond closely to our high-IP sectors used in the trade analysis, permitting us to return to that issue. The results are in Table 15 for the three IPA definitions. They suggest a marked heterogeneity across agreement types. Specifically, U.S.-partnered agreements see notable increases in related-party exports of pharmaceuticals and medical devices, the primary subject matter of many TRIPS-plus demands. In this context, perhaps the increases in third-party exports 36
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The Impacts of Disasters on African Agriculture: New Evidence from MicroData

**Philip Wollburg**<sup>**1,2**</sup> **, Yannick Markhof**<sup>**1,3**</sup> **, Thomas Bentze**<sup>**1**</sup> **, Giulia Ponzini**<sup>1</sup>

**JEL codes: Q54, N57, O13, Q15, C81, C83**

**Keywords: agriculture, climate change, disaster risk, survey data, Africa, loss and damage**

1 Development Economics Data Group, World Bank; 2 Wageningen University & Research; 3 United Nations University, UNU-MERIT.

This paper received funding support from the 50x2030 Initiative to Close the Agricultural Data Gap and the World Bank Research Support Budget grant “On the Measurement of Agricultural Productivity Trends in Africa”. The authors are grateful to Douglas Gollin, Erwin Bulte, Gero Carletto, Ruth Hill, Stephane Hallegatte, Talip Kilic, Travis Lybbert and participants of the CSAE 2023 Conference, the ICAS IX Conference, the EAAE 2023 Congress, and at the World Bank for their comments and feedback. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Place and child health: The interaction of population density and sanitation in developing countries Payal Hathi, Sabrina Haque, Lovey Pant, Diane Coffey, and Dean Spears<sup>_∗_</sup> JEL classification: J00, I1 keywords: sanitation, infant mortality, height, population density, developing countries

> _∗_ Hathi: r.i.c.e.. Haque: World Bank Water and Sanitation Program. Pant: r.i.c.e. Coffey: Princeton University Office of Population Research, Centre for Development Economics at Delhi School of Economics, and r.i.c.e. Spears: Centre for Development Economics at Delhi School of Economics and r.i.c.e. (Corresponding author: dean@riceinstitute.org) We are grateful for comments from seminar participants at the World Bank in Delhi. This project would not have been possible without the support from and dedicated work of the World Bank WSP in Bangladesh, and especially Ahasan Hoque, to facilitate the GIS matching. Part of the work of r.i.c.e. on this study was funded by the United States Agency for International Development under Translating Research into Action, Cooperative Agreement No. GHS-A-00-09-00015-00. This study was made possible by the support of the American people through the United States Agency for International Development (USAID). The findings of this study are the sole responsibility of the authors, and do not necessarily reflect the views of USAID or the United States Government.
- 10 -

links strengthen, new world trading blocks could emerge. Low growth in Europe would lead to slow growth of imports from developing countries. At the same time increased protectionism there would not only mitigate against improved competitiveness and efficiency, but would also reduce the opportunities for LDCs to earn foreign exchange, to service their debts. Thus, creditor countries could find themselves in the dilemma, of providing capital to developing countries for industrial development, but then denying market access to their products. The recent increase in non-tariff barriers is discussed by Laird and Yeats (1988). Further analysis is given by Yeats (1989) that producers in industrial countries can expect increased competition from the developing countries in clothing, footwear, leather products, and wood manufactures in the 1990s.

14. Availability and Cost of CaRital. World capital markets revived strongly in 1988. However, flows to developing countries have fallen (see Table 7). Details are given in World Debt Tables (1988) and Quarterly Review (1988). Net flows to developing countries for 1988 are expected to be US$16 billion, similar to 1987's US$15.8 billion. The overall stock of external debt by end 1988 for these countries is expected to increase by US$24 billion. While this compares with US$100 billion in 1987, much of that increase (about US$70 billion) was due to the accounting impact of dollar depreciation; some of the rest was due to extension of maturity (short- to long-term debt), and interest capitalization. The net transfer of resources from the highly indebted countries (HICs) on account of long-term debt, US$21.8 billion in 1987, is estimated at
# Proposed Mandate of WARPO

1. To advise the Government on national policies and strategies for balanced utilization and conservation of water resources, will a perspective of the needs of all users in the system and particularly socio-economically disadvantaged groups.

2. To administer water legislation through issuance of standards, regulations and administrative rules for goveming water rights, land-use rights for the purpose of water resource management, including location of industries and urban centers, pollution control, limiting flood damage risk, and managing regional supply shortages.

3. To issue environmental quality standards for water resources and regulation and rules for their administration, including comprehensive standards for measuring compliance with environmental objectives, public health and instream water quality and quantity conditions.

4. To issue rules and regulations for quality of water service standards, including community water supply, standards for irrigation delivery, etc. for government owned, user group owned and private utility operations.

5. To administer water rights including issuance of licenses, etc. on national scale and overseeing of performance of the system.

6. To prepare, revise and update a comprehensive water resource master plan including detailed projections of supply, demand, balancing needs of different users (including disadvantaged socio-economic groups), environmental safeguards, economic efficiency, and long term preservation of national interests, using a system of participatory planning at various stages of the plan development..

7. To interface with other public and private organizations for evaluating and approving the water resource utilization plans of all subsector's, and private and public user groups, in context of management of the scarce resources, environmental and water quality maintenance, priority needs of specific communities, municipalities, industry, agriculture, fisheries, navigation, and various instream users.

8. To advise water resource users on balanced development of their facilities and provide guidance for long-term water use.

9. To monitor, collect, consolidate and disseminate standardized water resource data needed for planning and regulating the water-use environment, including data on surface and ground water, draught, flood, salinity, water quality, erosion, land-use, etc.

The proposed WARPO's position vis-a-vis the existing water sector institutions is shown in Figure 4, and the structure of its main units in the revised form are discussed below.

28
distance decreases the probability that an MNE sets up a subsidiary in a country, but increases the size of the FDI flow if the MNE makes an investment. Moreover, FDI in the previous year, the rate of return on equity and the number of employees positively and significantly affect the probability of investment, whereas they do not affect the size of the investments. Age has a significant negative effect on both the intensive and extensive margin, whereas geographic diversification increases the probability of an MNE making an investment, while decreasing the size of FDI flow.

We also estimate the Heckman model separately for resource and non-resource FDI decisions.<sup>13</sup> The estimates for non-resource-related FDI flows (Column 3) are similar to those of total FDI flows. This is not surprising; in the firm-level data set 88% of the observations concern non-resource FDI flows. However, the estimates for the resource sector differ (Column 2). Political conflict, the type of conflict and the diversification moderator are no longer significant. In line with our hypotheses, the effect sizes are also considerable smaller than the effects on non-resource-related FDI flows. However, the number of MNEs in the resource-related FDI regression is small and therefore, the estimates of the coefficients are relatively imprecise. This concern in combination with the sample selection effect, explained above, lowers our confidence in the Heckman estimates for the resource sector.

# 6. THE CASE OF HYDROCARBONS: THE ROLE OF ECONOMIC RENTS AND GEOGRAPHIC CONSTRAINTS ON LOCATION CHOICE

This section examines the factors behind the insensitivity of resource-related FDI to political violence by focusing on the two mechanisms discussed in the theory section: the size of the economic rents and the geographic constraints on location choice. We focus on the oil and gas industry because no data on rents and location choice are available for other natural resource industries. The BP Statistical Review of World Energy provides a large data set containing information on global oil, gas reserves and prices, obtained from government sources and published data. As a proxy for geographic constraints on location choice, we use proven global reserves of oil and gas that can be extracted from known reservoirs with reasonable certainty in the future. We first standardize the oil and gas reserves data (because oil and gas reserves are measured in different units) and subsequently take the average of the two measures to obtain our _gasoilreserves_ variable. We then interact this score with battle-related deaths 35
19 include LeGrand (1982) and O'Higgins and Ruggles (1981b) for the United Kingdom, Gruske (1985) for West Germany, and Leu et al. (1985) for Switzerland.

Of these, only O'Higgins and Ruggles (1981a and 1981b) allocate expenditures by variables other than income, including number of earners per household, race, and sex. However, they do not examnine these variables in conjunction with income. In none of the studies is household income adjusted for household size or composition. Nor do the studies consider the benefit effects of government spending on the intra-household distribution of resources.

Table 1 summarizes the main findings of this literature. There is general agreement that among the studies that specific govenmment services have a pro-poor distribution in percentage terms when benefits are calculated as a percentage of income. Transfer payments are clearly pro-poor, and the allocation of so-called pure public goods depends greatly on one's essumptions about the distribution of their benefits among households.

However, examining the distribution of expenditure (not as a percentage of income) on specific govermment services in each group can yield very different answers about progressivity (DeWulf, 1975; and Meerman, 1979), as the results reported in Table 1 also indicate. Given the different conclusions reached from these two benefit incidence measures (expenditure and expenditure as a percentage of income), we recommnend following O'Higgins and Ruggles (198 la and 1981b) who report both sets of figures. We examine these results in greater detail below. Specific Government Services Specific or "allocable" govefrnment services include education, agriculture, highways, and a variety of in-kind transfers (including transfers in the fonn of health care, housing, and food).<sup>2 1</sup> Educational benefits are typically distributed on the basis of school-age children per household. In the United States education benefits are generally found to be pro-poor in percentage terms and pro-rich in dollar terms. Similar results are found for the United Kingdom (O'Higgins and Ruggles, 1981a and LeGrand, 1982) and Switzerland (Leu et al.). However, such conclusions may be premature insofar as the studies surveyed typically ignored the following considerations: (i) the investment return to educational achievement across income groups,
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**Table 4: Selection of India by ethnic origin of company contacts -- oDesk experience levels**

||||||Total sample<br>Third an|<br>With prior<br>d later contract|With prior<br>s for company|contact|
|---|---|---|---|---|---|---|---|---|
||Total<br>contract|Initial|Initial<br>restricted to|Subsequent|<br>with two or<br>more prior|<br> <br>successful<br>experience in|<br>unsuccessful<br>experience in|<br>Without prior<br>experience in|
||sample|contracts|repeat users|contracts|contracts|India|India|India|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|||Dep<br>Estimates|endent variable<br>include fixed e<br>Panel A: T|is a (0,1) indic<br>ffects for year<br>otal sample, ex|ator for choosi<br>x job type x co<br>cluding Indian|ng a worker in<br>untry of compa<br>companies|India<br>ny contact||
|Probability that hiring contact|0.047***|0.059***|0.070***|0.042***|0.037***|0.029|0.064**|0.016|
|is of ethnic Indian origin|(0.010)|(0.012)|(0.016)|(0.012)|(0.014)|(0.019)|(0.032)|(0.013)|
|Observations|157,922|35,863|21,289|122,059|100,770|59,203|12,717|28,850|
|Mean of dependent variable|0.289|0.319|0.311|0.280|0.274|0.346|0.272|0.125|
|Relative effect|0.163|0.185|0.225|0.150|0.135|0.084|0.235|0.128|
||||Panel B: Panel|A conditional|on a worker in|India applying|||
|Probability that hiring contact|0.041***|0.073***|0.099***|0.029**|0.019|-0.010|0.064|0.050|
|is of ethnic Indian origin|(0.011)|(0.016)|(0.021)|(0.014)|(0.016)|(0.020)|(0.039)|(0.032)|
|Observations|71,668|20,525|11,644|51,143|40,651|27,674|5,048|7,929|
|Mean of dependent variable|0.637|0.559|0.570|0.669|0.677|0.739|0.684|0.455|
|Relative effect|0.064|0.131|0.174|0.043|0.028|-0.014|0.094|0.110|

Notes: Contract-level regressions estimate propensities to select a worker in India by the ethnic origin of the company contacts. The sample excludes company contacts located in India. The dependent variable is an indicator variable for selecting a worker located in India. Panel A documents the whole sample, and Panel B considers cases where a worker from India applies for the position. Column headers indicate sample composition. Initial and subsequent contracts are from the perspective of the company contact. Regressions are unweighted, include fixed effects for year x job category x country of company contacts, and report standard errors that are two-way clustered by originating company and worker. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.