rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: Summary statistics on individuals’ knowledge about BREGS rules**

||||Ro<br>|und 1<br>|||Rou<br>|nd 2<br>||
|---|---|---|---|---|---|---|---|---|---|
|||Wo<br>|men<br>|M<br>|en<br>|Wo<br>|men<br>|M<br>|en<br>|
|||Mean|s.d.|mean|s.d.|mean|s.d.|mean|s.d.|
|Heard|Has heard of BREGS|0.73|0.44|0.95|0.21|0.88|0.33|0.98|0.15|
|_Work score_||_1.51_|_1.52_|_2.60_|_1.88_|_2.10_|_1.56_|_3.14_|_1.83_|
|Days|Entitled to 100 or thinks 90 days|0.11|0.31|0.37|0.48|0.13|0.34|0.38|0.49|
|Gender|Both men & women can demand work|0.51|0.50|0.57|0.50|0.62|0.49|0.68|0.47|
|BPL|Non-BPL families can demand work|0.38|0.48|0.53|0.50|0.57|0.49|0.69|0.46|
|Work lag|Work is to be provided within 15 days|0.02|0.13|0.06|0.25|0.07|0.26|0.14|0.34|
|Unempl.|Knows about unemployment<br>allowance|0.11|0.32|0.29|0.45|0.22|0.42|0.41|0.49|
|Wage rate|Knows wage rate|0.21|0.41|0.44|0.50|0.26|0.44|0.45|0.50|
|Wage lag|Wages to be paid within 2 weeks|0.08|0.27|0.12|0.32|0.10|0.30|0.14|0.35|
|Contractor|Contractors not allowed|0.11|0.32|0.24|0.43|0.13|0.34|0.27|0.44|
|_Facilities knowl_|_edge score_|_0.98_|_1.29_|_1.39_|_1.41_|_1.12_|_1.20_|_1.55_|_1.29_|
|Crèche|Knows of childcare facilities|0.19|0.39|0.19|0.39|0.12|0.32|0.18|0.38|
|Water|Knows of drinking water facilities|0.42|0.49|0.59|0.49|0.53|0.50|0.67|0.47|
|Shade|Knows of shade facilities|0.21|0.40|0.36|0.48|0.38|0.48|0.51|0.50|
|First aid|Knows of first aid facilities|0.18|0.38|0.26|0.44|0.10|0.30|0.20|0.40|
|Board|Knows of info board|--|--|--|--<br>|0.01|0.11|0.06|0.23|
|Demand|Knows work has to be demanded|--|--|--|--<br>|0.52|0.50|0.74|0.44|

_Notes_ : Household weighted. Approximate sample sizes: (a): “Has heard of BREGS”: N=2,800 for women; N=2300 for men; N=2000 for others; (b): “Has heard of BREGS”: N=2,000; Women R1: N=1,500; N=2,000 for others. Questions about the information board and having to demand work were not asked in R1. The “work score” is an overall measure of knowledge about the scheme’s employment aspects, based on the number of correct answers to the 8 employment related questions. The “facilities score” is an overall measure of awareness of the facilities and amenities that BREGS mandates must be provided at work sites (crèche, drinking water, shade and first aid kits). The stipulated 100 days of work is sometimes referred to as 3 months and hence interpreted by the public as 90 days, which we take to be an acceptable answer to the question.

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# **4.1 Infrastructure/Habitation Opportunities**

Figure 2 shows that the HOI is significantly different from the coverage rate for all habitation opportunities. Comparing across opportunities over the last 20 years, there has been a small improvement in electricity and sanitation, but hardly any progress on clean water. The difference between HOI and coverage is the greatest for access to sanitation, indicating that inequality between children of different circumstances is high for that access. Overall, if one focuses on the recent decade of civil conflict (1998– 2008), changes in habitation opportunities have not been substantive and opportunities remain fairly constant.

**Figure 2. Opportunities for Infrastructure/Habitation**

<!-- Start of picture text --> Opportunities for Habitation in 1985 Opportunities for Habitation in 1998<br>Opportunities<br>Opportunity<br>HOI Electric light HOI Sanitation<br>HOI Electric light HOI Sanitation HOI Clean water Confidence Interval<br>HOI Clean water Confidence Interval Coverage<br>Coverage<br>Source : Authors ' calculation with Household Survey 1998<br>Source : Authors ' calculation with Household Survey 1985<br>Opportunities for Habitation in 2002 Opportunities for Habitation in 2008<br>Opportunities Opportunities<br>HOI Electric light HOI Sanitation HOI Electric light HOI Sanitation<br>HOI Clean water Confidence Interval HOI Clean water Confidence Interval<br>Coverage Coverage<br>Source : Authors ' calculation with Household Survey 2002 Source : Authors ' calculation with Household Survey 2008<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> 15
<mark>Charlton, A., & Davis, N. (2007). Does investment prrdomotion work?.</mark> _<mark>The BE Journal of Economic Analysis & Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1).</mark>

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- Choe, Jong Il. 2003. “Do Foreign Direct Investment and Gross Domestic Investment Promote Economic Growth?” _Review of Development Economics_ 7 (1): 44–57.

- Coughlin, Cletus C.; Terza, Joseph V.; Arromdee, Vachira. BState Characteristics and Location of Foreign Direct Investment within the United States. Review of Economics and Statistics, 73, 4, 1991, pp. 675Y83.

<mark>Crescenzi, R., Di Cataldo, M., & Giua, M. (2019). It’s not about the money! EU funds, local opportunities, and the Brexit vote.</mark> _<mark>EU Funds, Local Opportunities, and the Brexit Vote (November 9, 2019). LEQS Paper</mark>_ <mark>, (149).</mark>

- Das, Sanghamitra. 1987. “Externalities, and Technology Transfer through Multinational Corporations: A Theoretical Analysis.” _Journal of International Economics_ 22 (1–2), 171–82.

- Demir, Firat and Chenghao Hu. Institutional Differences and the Direction of Bilateral Foreign Direct Investment Flows: Are South–South Flows any Different than the Rest?. _The World Economy_ 39(12): 2000-2024.

Devereux, Michael P., Rachel Griffith, and Helen Simpson, 2007. "Affiliate Location Decisions, Regional Grants and Agglomeration Externalities." Journal of Public Economics, Elsevier 91(3-4): 413–35.

<mark>Erramilli, M. K., & Rao, C. P. (1993). Service firms’ international entry-mode choice: A modified transaction-cost analysis approach.</mark> _<mark>Journal of marketing</mark>_ <mark>,</mark> _<mark>57</mark>_ <mark>(3), 19-38.</mark>

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Fosfuri, A., Motta, M., & Ronde, T. (2001). Foreign Direct Investment and Spillovers through Workers' Mobility. _Journal of International Economics, 53(1)_ , 205-222 <mark>Freund, C., & Moran, T. (2017). Multinational investors as export superstars: How emerging-market governments can reshape comparative advantage.</mark> _<mark>Peterson Institute for International Economics Working Paper</mark>_ <mark>, (17-1).</mark> Freund, Caroline, and Martha Denisse Pierola. 2012. “Export Surges.” _Journal of Development Economics_ 97 (2): 387–95.

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levels. These differences do not seem to be related to differences in patterns of government expenditure.

The rest of the paper proceeds as follows. Section 2 gives a brief description of conditions in rural Peru. Section 3 outlines the extent to which traditional models of volunteering may be relevant to rural Peru. Section 4 describes the data set used for the analysis. Section 5 reports the amount and types of voluntary activity, and provides estimates of the determinants of volunteering. Section 6 concludes by discussing some of the implications for public policies in Peru.

# 2. The setting

Peru is divided into three geographic regions--coast, sierra (or highlands), and selva (or jungle).<sup>2</sup> About 30% of Peru's population of 23 million lives in rural areas, 14% of them on the coast, 67% in the sierra, and 19% in the selva.

By virtually any measure of welfare, residents of rural areas appear to be worse off than their urban counterparts. According to the 1994 LSMS, per capita expenditures in rural areas are about half of what they are in urban areas, and the proportion of people who live below the poverty line is about 25 percentile points higher (68.3%, compared to 42.0%). About two-thirds of total expenditures made by rural households are devoted to food, compared to less than 50% for urban households.

Health and nutrition indicators in rural areas are very poor: the 1991-92 Demographic and Health Survey estimated that fully 53.4% of children under the age of five suffer from chronic malnutrition (stunting), one of the worst rates in Latin America. The Total Fertility Rate in rural areas is high, 6.2 children per woman, but infant mortality is also high, about 78 per 1000. Indicators of educational attainment calculated from the LSMS show that about 12% of schoolaged children in rural areas are not enrolled in school, and more than 20% of those aged 15 or older are illiterate. In every case, the comparable statistic for urban areas is about half that for rural areas, except for adult illiteracy, which only affects 5% of adults in urban areas.

A large fraction of the population of the rural sierra and the rural selva speaks a native Indian language as their mother tongue. The languages spoken are principally Quechua and Aymara in the sierra, and Shipibo and Campa in the selva. In keeping with the literature (for example, MacIsaac and Patrinos, 1994), I define these people as indigenous. According to the LSMS, 56.3% and 26.4% of households in the rural sierra and rural selva, respectively, are

> 2 This section is based on my own calculations from the LSMS, Cuanto (1995), World Bank (1999), and the 1991-92 Peru Demographic and Health Survey (DHS).

3
# **D Excerpts from the Human Rights Council Report**

The following is a compilation of excerpts drawn from the United Nations’ Human Rights Council Report on Myanmar regarding the “Clearance Operations” in Rakhine State executed by the Myanmar military (referred to below as the _Tatmadaw_ ) in late August and early September of 2017. These excerpts describe the indiscriminate nature of the violence perpetrated against the Rohingya during these operations. We caution the reader as several of these excerpts are difficult to read. We have left out the most graphic descriptions but direct the reader to the report itself (A/HRC/39/CRP.2) for further evidence of the random nature of violence during the Operations.

- During subsequent operations in villages and towns, the Tatmadaw did also not attempt to distinguish civilians from military objectives. Such indiscriminate attacks resulted in civilian men, women and children being injured or killed, with large numbers of civilians being driven away from their homes and villages. (P.35)

- Information therefore strongly indicates that airstrikes and shelling were used indiscriminately as a more general tactic in the context of “clearance operations,” in essence attacking the civilian population as a whole as opposed to being used against specifically identified military targets. (P.35)

- The operations were designed to instill immediate terror, with people woken by intense rapid weapons fire, explosions, or the shouts and screams of villagers. Structures were set ablaze and Tatmadaw soldiers fired their guns indiscriminately into houses and fields, and at villagers. (P.178)

- Many Rohingya were killed or injured by indiscriminate shooting. Rohingya villages were approached without warning, usually from more than one direction, and often in the early morning, by armed Tatmadaw soldiers.... Members of the security forces, primarily Tatmadaw soldiers of the Western Command and the 33rd and 99th LIDs, shot assault rifles towards the Rohingya villages from a distance, not targeting any particular military objective or making any distinction between ARSA fighters and civilians. Men, women and children were all shot at. Many victims referred to the volume of gunfire, with some describing it as “raining bullets.” Many were shot and killed or injured while attempting to flee. (P.205)

- One young girl described the operation in Maungdaw Township: “When the soldiers came to my village, we all ran, and they shot at us. We were around 50 people, and maybe half of us were shot. The people shot fell down while they were running. Some died and some escaped. Somehow, I escaped.”’ (P.205-206)

- One man from Kyein Chaung village tract, known in Rohingya as Boli Bazar, in northern Maungdaw Township explained the circumstances in which his daughter was killed: “I don’t know how many people died that day. The military, they were just shooting at whomever. They were shooting at people whenever they saw them, on the

67
_Figure 12 - Share of affected households by quartile and different loss thresholds_ <!-- Start of picture text --> 70% 65% 66%<br>61%<br>60%<br>50%<br>40%<br>40% 36%<br>28%<br>30% 26%<br>23%<br>20% 16% 17%<br>14%<br>8%<br>10%<br>0%<br>Q1 Q2 Q3 Q4<br>If loss over 1% threshold (2018) If loss over 20% threshold (2018)<br>If loss over 50% threshold (2018)<br><!-- End of picture text --> Other characteristics associated with larger relative losses include access and distance to economic opportunity and garbage collection. Regression analysis, presented in Table 23 in Appendix 4, indicates which factors are associated with larger losses in relation to income. As previously mentioned, distance to work of the breadwinner, measured in travel time, is associated with increased risk of exposure. Results suggest that shorter distance to work is also associated with larger relative losses for households. This confirms that accepting higher flood risk to live closer to economic opportunities comes at a high prize. It is also found that having the garbage collected by either private or informal operators is associated with lower relative losses than unauthorized heap or public garbage collection.

# 3.3 The 2018 flood cost the people of Dar es Salaam between 2 and 4% of city GDP

There is limited information about the gravity of the 2018 flood event. However, it was observed that schools were closed during the peak of the flood, and the Bus Rapid Transport system was down. Nonetheless, this was not considered an unusual event in Dar es Salaam. To get an idea of the total cost of an event of this gravity, sample weights were used to extrapolate the impact on households in the sample to the population of the rest of the city. Again, these very rough estimates should be taken with caution.

To generate these estimates, it is assumed that households within each flood risk category are exposed to similar levels of risk (equally likely to be exposed in the 2018 flood event) and similar impacts and losses. Since flood risk categories are designed to reflect flood exposure, there is a fair degree of confidence about the first assumption that households within each flood risk category face similar risk. The second assumption suggests that households are also facing similar impacts _when exposed_ . This is a bold assumption – especially since a lot of heterogeneity is observed in how the households are affected. There is no way to confirm or reject this assumption, so it is important to use these results as indicative rather than absolute. With those two assumptions, the total cost of the 2018 flood event for affected households in Dar es Salaam is estimated. This estimate includes the cost that households carry themselves and excludes all the costs that the government captures through damages and repairs to public goods.

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# Import Demand in Developing Countries

## by Riccardo Faini, Land Pritchett, and Fernando Clavijo

## Table of Contents

Introduction ...................................... 1 1. The Traditional Approach ....................... 3 2. Cross-Country Patterns ......................... 8 3. The Impact of Foreign Exchange Availability .... 10 4. Modeling Non-Tariff Barriers Directly .......... 17 5. Conclusions .................................... 20 Appendix .......................................... 22 References ........................................ 27 Footnotes ......................................... 29

> *** Thls** paper sumLres oUgong work at the Trade Policy Dilvision on import behavior In developing countries. hlUe providing new evidence, the paper draw on two main sourcesg Prltchett (1987) and Bertola and Faini (1987). We are very grateful to Bela **Saiassa,** Mario Blejer, Engo Grilli, Jaime de Kelo, Viod Thomas, and JiS Tybout for their very valuable camonts. Abdel Senhadii-Semlall provided skilled research **aslstance at** various stages and Karla Cabana skillfully typed the manuscript.
Such procedure allows inferring daily oil production at any site, which we can then aggregate to obtain yearly production as reported in Table A.9. Column 1 repeats the estimates with the main sample, cited in the text, assuming venting when no radiant heat is observed. We also report our estimates including the November 2015 observations at Al Hussein North in Column 2. The inclusion of Al Hussein North results in an increase of 5,000 bpd on average in 2015.

# **A.4.5 Alternative site production state classifications**

In this section we show the results are not sensitive to the alternative RH assignment rules for sites that are deemed to be venting and discusses the resulting inferences. Figure A.11 displays the estimated July 1 to December 31, 2014 Daesh oil production in thousands of barrels per day for various assignments of parameters employed in the production estimate. The y-axis represents the number of consecutive days a site without electric lights is assumed to be venting before it is declared non-productive. The x-axis represents the absolute deviation from the lunar cycle that must be achieved to determine electric lights are present. A lower threshold allows observations with faint light to qualify as lit with electric lights. The panels 1710, 1810, and 1910 represent the temperature in K that is assigned in the computation of radiant heat when there is a single spectral band detected. Last, the shaded area shows the July 1 to December 31, 2014 Daesh production in thousand of barrels per day with the corresponding values for venting days, electric lights threshold, and temperature are assigned. The primary estimates in the paper assume 7 days venting, lights threshold of .6, and temperature of 1810. The figure shows that this estimate is invariant to the assumptions. The upper bound of the estimate considering all parameters increases from 56,000 bpd to around 83,000 bpd.

44
|MUSprim_comp|_0.814_|_1.244_|
|---|---|---|
||_(0.231)_|_(0.352)_|
|MUSpostpri|_0.977_|_0.814_|
||_(0.199)_|_(0.256)_|
|*** p <= 0.001<br>** p <= 0.01<br>* p <= 0.05|||

Standard deviations in parentheses. Uneducated, upper caste, Hindu, unmarried, central region are the omitted categories.

23
9

||**Table 1. Description of reforms indices**<br>|
|---|---|
|Reforms indices|Description|
|Financial sector|<br>Financial sector legal reforms<br><br>Regulation and supervision of financial institutions<br><br>Restructuring and privatization of financial institutions<br><br>Bank regulation and supervision|
|Fiscal sector|<br>Tax policy (excluding trade policy) and revenue administration measures;<br><br>Expenditure (including arrears clearance and poverty reduction);<br><br>Debt management measures;<br><br>Auditing, accounting, and financial controls;<br><br>Fiscal transparency (including publication, parliamentary oversight);<br><br>Central Bank financing to Government or the public sector;<br><br>Pensions and social sector reforms (including social safety nets, health, and<br>education);<br><br>Anti-corruption legislation or policy.|
|Real sector|<br>Private sector and regulatory environment reform (non-financial sector);<br><br>Public firm reform and privatization (including pricing and subsidies) and<br>restructuring other than pricing;<br><br>Price controls and marketing restrictions;<br><br>Labor market reform.|
|Trade sector|<br>Changes in trade regime and policies (excluding customs reforms).|
|Source: Authors’classi|fication based on MONA database.|

# **B.   The World Bank Enterprise Surveys (WBES) database**

We use the WBES to compute firms’ productivity. The WBES is a collection of firm-level surveys in developing countries based on a representative sample (random stratified sampling) of the economy’s private sector mainly. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures in most countries of the world. The surveys follow common guidelines in design and implementation, thereby allowing cross-country analyses. We use the standardized questionnaires and data set over the period 2006 to 2014 in order to be able to match firm level productivity and structural reforms data from the MONA data set. The standardized data set has a pseudo-panel structure. While the data set contains information on 117,358 firms in 136 countries, this paper focuses on developing countries under an IMF program and having at least one round of the WBES. In addition, our identification strategy requires matching firm-level data with reform programs within the 3 previous years. The median sample size is 360 firms, with only three having samples over 1,000 observations (Bangladesh, Pakistan, and Ukraine). The WBES contains information on firms’ performance such as employment, investment, and sales. The existence of retrospective information on
are taken from the years 1997-98. The ICRG indicators used span several years from 1984 to 1997. The former indicators measure aspects of govemance such as: graft, government effectiveness, regulatory burden, the rule of law, political instability and violence, and voice and accountability. The latter indicators measure corruption, the extent to which the rule of law is respected, bureaucratic quality, contract repudiation and expropriation risk.

Other variables used in this paper are: an indicator for the freedom of the press, 1999, from Freedom House which ranges between 1 and 100, a variable indicating the extent of state ownership of the press (poss) and television (toss) both taken from Djankov et al (2001), newspaper circulation (circu) defined as circulation per 1000 people, 1996 - from the United Nations Educational, Scientific, and Cultural Organisation, Statistical Yearbook. All three of these variables have been shown to be associated with institutional quality (Djankov et al, 2001, Besley, Burgess and Pratt, 2002 ). The GNP per capita figures are an average of the years 199296 in the regressions with the KKZ variables and 1979-83 for the ICRG measures. They are taken from the World Development Indicators, 2000, World Bank. Many of the regressions control for the legal origin of the country - whether English, French, Scandinavian or German. How long a country has been in existence as an independent nation can be expected to have an effect on institutional quality since institutions develop slowly over time. This variable is aiso used to assess the robustness of the transparency variable. Finally, a dummy variable which distinguishes between countries that have a FOI law and those that do not is used as a controlling variable. This indicator is composed from data held by Article 19 of International Center and Censorship, (ICC,1993) and World Bank research and shown in Table 2.17

> 17 **Author's** compilation.

15