rafmacalaba/datause-encoder-data huggingface dataset:
Table B6: Mechanism at play, destinations averaging fewer than 10 foreign investors are excluded

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|(6)<br>log(# MNEs)|(7)<br>log(K)|
|---|---|---|---|---|---|---|---|
|Uncertainty (standardized)|1.023*<br>(0.518)|-0.237<br>(0.624)|1.789***<br>(0.630)|1.903***<br>(0.608)|0.075**<br>(0.029)|-0.092***<br>(0.031)|-0.015<br>(0.033)|
|Uncertainty _×_ Developing||2.109***<br>(0.735)||||||
|Observations|2,047|2,047|1,187|1,170|1,170|1,170|1,170|
|R-squared|0.799|0.800|0.796|0.802|0.722|0.894|0.453|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All co|untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 10 foreign investors are excluded from these calculations. “HHI” refers to the HerfindahlHirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

31
> <sup>22</sup>

to controls either for initial financial sector development or initial income, both of which are highly correlated with these two political variables. A one standard deviation increase in the initial number of continuous years of competitive elections is associated with an increase in the rate of growth of the financial sector of 0.6 percentage points per year and with an increase in the size of the financial sector in 2000 of as much as 25 percent of GDP. Results for newspaper circulation are still larger.

**Table 2b:  Two-stage least squares estimates of Table 2a**

||Con<br>competi<br>|tinuous yea<br>tive electio<br>|rs of<br>ns(1975)<br>|Average<br>|Newspaper Ci<br>(1975-2000)<br>|rculation<br>|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_IV_)|Level<br>2000<br>(_IV_)|Level<br>2000<br>(_IV_)|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_)|Level<br>2000<br>(_IV_)|
|Political variable|.002<br>(_.004_)|.02<br>(_.006_)|.03<br>(_0.0_)|.0003<br>(_.002_)|.002<br>(_.007_)|.002<br>(_0.0_)|
|Land (thousands<br>of square miles)|-.00005<br>(_.03_)|-.00038<br>(_.15_)|-.0004<br>(_.13_)|-.00001<br>(_.48_)|.0002<br>(_.34_)|.0002<br>(_.29_)|
|Average|-.00001|-.0025|-.0025|-.02|.004|.005|
|population<br>(millions)|(_.96_)|(_.63_)|(_.61_)|(_.20_)|(_.17_)|(_.09_)|
|Log real||.01|||-.008||
|income/capita<br>(1975)||(_.91_)|||(_.93_)||
|Private sector<br>lending (1975)|-.06<br>(_..04_)|||-.12<br>(_.004_)|||
|_F_-statistic on<br>instruments (first<br>stage)|6.2|4.2|4.7|7.3|4.5|12.5|
|Hansen_J_-statistic<br>(Chi-squared_p_-<br>value)|.04|.02|.09|.05|.04|.05|
|_N_|66|71|79|78|92|104|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Instruments for the fourth and eighth regressions are three dummy variables indicating whether a country has British, French or Spanish colonial origins. Constants not reported.
reported GBV of any kind in the preceding 30 days, while those selected to receive the training were significantly more likely (7 p.p.) to report GBV. This imbalance is accounted in the empirical approach (Section 4.3).

# **4.2 Attrition and non-compliance**

The end-line survey conducted an intensive tracking exercise to ensure paneling of the respondents. This led to a match of 525 cross-border traders to the original baseline sample (84% of the sample), as 103 traders could not be re-identified due to the fake names and contacts they gave during the baseline survey. As shown in Figure 3, the 16% attrition rate of the sample was distributed evenly across assignment groups.

There were several univariate differences between the attrited and non-attrited groups, including along baseline covariates measures (Appendix A.2) and study outcomes of interest (Appendix A.3). Econometric estimations (probit and linear probability model) indicated that, at the 1% level of significance, traders not retained in the final sample were less experienced traders on average. At the 5% level of significance, attrited traders also owned fewer bikes and more radios, and more were likely to reside in the DRC (Table 4). This suggests that the final sample included slightly more experienced small-scale traders who were more likely to rely on bicycles to cross the border from Rwanda to DRC for their small-scale trading activities and were less likely to access border information through radio broadcasts. Attrited traders (at the 10% significance level) were also more likely to have electricity access and be married when compared with “non-attrited” traders (Table 4). Based on the Ramsey RESET test, we did not reject the null hypothesis that there were no additional attrition correlates.

The end-line sample of 525 traders comprised 262 traders assigned to receive the training and 263 traders assigned to be control traders (Figure 3). However, the program implementers found it challenging to ensure the compliance with the randomized assignment. Despite the familiarity of the implementing NGO with the study zone, only 110 of the 262 traders assigned to the treatment group actually attended the training (take-up of 42%). In addition, there were 64 traders among the 263 traders assigned to the control group who attended the training. This yielded a compliance rate of 35% in the treatment group and of 63% in the control group. This two-sided non-compliance 20
Hence, any evidence of an increase in employment found in this paper implies that inter-provincial capital mobility is not perfect and not immediate.

There is also a concern that we might pick up a long-term trend of structural transformation in different provinces. This is the case if provincial tariff reduction is correlated with provincial trend of labor reallocation across sectors. To address the concern, we control for province-level changes in the share of sectoral employment between 1998 and 2002, using data from VHLSS 1998 and 2002 .

# **4.3 Expected effects**

Exporting to the United States would benefit labor in Vietnam due to an increase in laborintensive products. Export firms would expand production and hire more labor. Hence the BTA is expected to have a direct impact on employment of manufacturing industries in Vietnam. Added to this direct impact is an upstream effect on supporting industries which supply to manufacturing industries. In this paper, we focus on local service supporting industries. We expect that employment of supporting service industries such as accounting, consulting, advertising, protection and cleaning services also increases.

An important spillover mechanism works through an increase in aggregate demand. The increase in manufacturing income could result in higher demand for local services such as retails, restaurants and entertainment. This would cause higher demand for employment in those industries. New job opportunities from manufacturing and service sectors imply labor reallocation away from agricultural sectors. Hoang et al (2014) find that an increase in nonfarm participation decreases labor involvement in agricultural sectors in rural Vietnam during the period 2002-2008. We hypothesize that the BTA decreases agricultural employment, as agricultural laborers move 15
<!-- Start of picture text --> WORLD REAL EXPORTS<br>Base Low case Middle case High case<br>101<br>100<br>99<br>98<br>97<br>96<br>95<br>94<br>Figure 3: AMR Impact on Livestock Production<br>WORLD LIVESTOCK PRODUCTION<br>Base Low case Middle case High case<br>102<br>100<br>98<br>96<br>94<br>92<br>90<br>88<br>Base = 100<br>Base = 100<br><!-- End of picture text -->

# **_Impacts of AMR on Livestock Production_**

The shocks to the livestock sector were modeled as both a decrease in productivity because of the greater prevalence of untreatable disease and as a reduction in its exports due to restrictions imposed by its trading partners. This trade-related behavior could include a so-called “fear factor” and contributes to the reductions in livestock production by making it less profitable. Livestock is a small part of the global economy (about 2% of world GDP), so its reduced productivity has a minor influence on global indicators. The sector is relatively more important in the economies and exports of low- and lower-middle-income countries than in the wealthier countries, however. Also, the sector plays a substantial development role and makes a major contribution to nutrition, 13
Bowers, Alex, Ryan Sprott, and Sherry Taff. 2013. “Do We Know Who Will Drop Out? A Review of the Predictors of Dropping out of High School: Precision, Sensitivity, and Specificity.” _The High School Journal_ 96(2): 77-100.

Cardenas, Mauricio, Rafael de Hoyos, and Miguel Székely. 2015. “Out of School and Out of Work Youth in Latin America: a Persistent Problem in a Decade of Prosperity.” _Economia_ 16(1).

Estado de la Nación. 2013. _Estado de la Educación Costarricense_ . San José, Costa Rica: Estado de la Nación.

European Commission. 2013. _Early Warning Systems in Europe: Practice, Methods, and Lessons_ . Thematic Working Group on Early School Leaving. Brussels: European Commission.

Frazelle, Sarah and Aisling Nagel. 2015. _A Practitioner’s Guide to Implementing Early Warning Systems_ . Washington, D.C.: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educaitonal Laboratory Northwest.

Hotelling, H. 1933. “Analysis of a Complex of Statistical Variables Into Principal Components.” _Journal of Educational Psychology_ , 24: 417-441 and 498-520.

Jensen, Robert. 2010. “The (Perceived) Returns to Education and the Demand for Schooling.” _The Quarterly Journal of Economics_ 125 (2): 515-548.

Jolliffe, I.T. 2002. _Principal Component Analysis_ . New York: Springer-Verlag New York, Inc.

Fryer, Roland. 2013. “Information and Student Achievement: Evidence from a Cellular Phone Experiment.” NBER Working Paper 19113.

Gleason, P., and M. Dynarski. 2002. “Do We Know Whom to Serve? Issues in Using Risk Factors to Identify Dropouts.” _Journal of Education for Students Placed at Risk_ 7(1): 25–41.

Heller, Sara, Anuj Shah, Jonathan Guryan, Jens Ludwig, Sendhil Mullainathan, and Harold Pollack. “Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago.” NBER Working Paper No.21178. Cambridge, MA: National Bureau of Economic Research (NBER).

Janosz, M., I. Archambault, J. Morizot, and L.S. Pagani. 2008. “School Engagement Trajectories and their Differential Predictive Relations.” _Journal of Social Issues_ 64(1): 21–40.

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19
_Table 1: Rates of Missingness in GPS-Based Plot Areas in Selected Datasets Generated by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS ISA) & Survey Instructions on the Required Spatial Coverage of GPS-Based Plot Area Measurements_

|**_Survey_**|**_Rate of_**<br>**_Missingness_**|**_Required Spatial Coverage of_**<br>**_GPS-Based Plot Area Measurements_**|
|---|---|---|
|Niger Enquete Nationale sur les Conditions de<br>Vie des Menages et l’Agriculture 2011|29%|Measure all plots in the same enumeration area as<br>the household.|
|Nigeria General Household Survey - Panel<br>2012/2013|13%|Measure all plots in the same district of the<br>household and within 3 hours of travel, regardless<br>of mode of transportation.|
|Tanzania National Panel Survey<br>2010/2011|22%|Measure all plots within 1 hour of travel from the<br>household, regardless of mode of transportation.|
|Uganda National Panel Survey<br>2011/2012|44%|Measure all plots in the same enumeration area as<br>the household.|

Recognizing the need to address the problem of missing data for increasing the usability of household survey data, (Kilic, et al., 2017) use the LSMS-ISA data from Tanzania and Uganda to show that the missing GPS-based plot areas indeed constitute a non-random subset of the unit-record data and that the missing data can be simulated by Multiple Imputation (MI). In their analysis of plot-level land productivity, the authors document the non-trivial effects of using the datasets that are completed based on MI.

Underlined by the relatively recent adoption of GPS-based land area measurement in national household surveys, and the evolving appreciation of the recent methodological research on addressing (ultimately unavoidable) missingness in GPS-based land area measures, there is a continuing need to work on two fronts. The first is to offer operational guidance for survey practitioners and data users, including agricultural and development economists, regarding “acceptable” rates of item non-response in GPS-based areas for “distant” plots whose areas could instead be simulated. The second is to further elevate the importance of relying on easily-accessible, model-based simulation approaches, including MI, to judiciously address missingness in public use datasets that are at the core of development research.

4
2 4. A summary of each renewable energy technology, including an assessment of the sensitivity of its economic viability to lower oil prices, is presented in Chapter 2. Conclusions and policy recommendations that can be drawn from this analysis are preserted in Chapter 3. Before proceeding to this discussion, however, it is useful to outline what has actually happened in international and domestic oil markets.

# Actual Oil Price Changes

_5._ From November 1985 to July 1986 the average FOB price of crude oil on world markets slid from above US$ 28/bbl to below US$ 11/bbl. Since mid 1986, crude oil FOB prices remained relatively stable between US$ 15/bbl and US$ 18/bbl. As crude oil prices are projected to continue within this range in the medium term, the general economic conclusions reached in this paper are expected to be valid for some time to come.

<!-- Start of picture text --> Diesel & Fuel Oil Retail Price<br>Changes in Current US$ by Region<br>30% 9 Month Current US$ Price Change '85-'86<br>20% Diesel . Fu ... .<br>-1 .,. .. . 0% , .. .. ... ... ,.. .. .. ..,. .. .. .. .,,, ..... .. .<br>0 sia Africa Latin merica Asia Africa Ltin Anmerica<br>I 90 Least Drop _ Region Average C Most Drop<br>Source: see Annex<br><!-- End of picture text --> Figure 1 Retail Price Changes in Current US Dollars 6. Data collected from almost 20 developing countries indicate that most countries made only slight retail price adjustments in response to the -60% drop in international oil prices between November, 1985 and July, 1986.3' In fact, a few countries actually raised nominal retail prices during this period. Figures 1 and 2 illustrate the magnitude of diesel and fuel oil retail price in the appendix.

Retail petroleum product price changes from late 1985 to mid 1986 in fourteen developing countries are tabulated

**3/**
_Credit markets_ . According to the recent survey mentioned earlier, firms in Chile do not undertake more innovation because of the associated high technical risk and long gestation periods. This could be seen as broadly mapping into the market failures standard in the literature: individual firms cannot handle the lumpiness, risk and long gestation periods of innovation projects. This points clearly to credit market failures.

Recent micro-estimates for Chile by Benevente, de Gregorio and Nuñez (2005) suggest that own rates of return to R&D are high. In particular, they estimate rates of around 30% to R&D, whereas the (gross) returns to physical capital are 16%. The higher private rates of return to R&D may be due to its higher risk, but it may also be associated with the fact that it is harder to finance, both because of higher risk together with absence of venture capital, and because of the fact that R&D leads to the accumulation of assets that are harder to use as collateral.

Innovation surveys again suggest that the vast majority of financing of innovative activities is internal suggesting potentially an inability to share risk. That said, there is no consensus on why specialized institutions, such as venture capital, have not taken hold in Chile. Some VC firms have folded allegedly for lack of “deal flow” suggesting that there is inadequate financing at the early stages of idea development that would generate demand downstream. However, recent entrants into the market suggest that deal flow is adequate, but that the design of previous VC operations failed to pay sufficient attention to the provision of complementary management and mentoring services that are generally part of VC packages.<sup>13</sup> Further, legislation has tied the development of specific institutions to the intermediation of pension funds assets and hence burdened them with inappropriate regulation on risk taking (Arrau 2002). This said, it is worth highlighting that VC is virtually absent in Spain and Italy so it is difficult to assert that this missing market is an insuperable barrier to gains in TFP.

> 13 Discussions with Eduardo Bitran and Patricio Arrau respectively

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<!-- Start of picture text --> Polley Research<br>Population, Health, and Nulrltlon<br><!-- End of picture text --> WPS 952

- This paper a product of the Population, Health, and Nutrition Division, Population and Human - Resources Department is part of a larger effort in the department to quantify the costs of malnutrition and its alleviation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Otilia Nadora, room S6-065, extension 31091 (August 1992, 66 pages).

Horton summarizes what is known about unit costs, the cost structure, cost-effectiveness, and financing of eight nutrition interventions: matemal and child health (MCH) feeding, school feeding, nutrition education, the promotion of breastfeeding, targeted food subsidies, micronutient supplementation, micronutrient fortification, and growth monitoring. Among items that she reports:

* Mass media nutrition education campaigns and the promotion of breastfeeding cost about **_$1-$5_** per beneficiary; face-to-face nutrition programs cost more ($23 per beneficiary in the Dominican Republic).

> e Food distribution programs of different types have fairly similar costs. For distributing about 1,000 calories a day per beneficiary per year $75 for untargeted food rations, $64 for targeted food rations, $74 for MCH and school feeding programs, and $134 for highly targeted feeding programs. Micronutrient interventions cost from $0.04 to $4 per person-year of protection; supplementation is more expensive than fortification.

There is little difference in cost between programs operated by nongovenmuent organizations and those operated by governitiits. The more expensive programs are not necessarily less costeffective, but may include more complementary inputs.

> * The cost per death averted was about $1,500 for both a targeted supplementary feeding program in Tamil Nadu and a vitamin A capsule distribution scheme in Bangladesh.

> * The cost per child removed from moderate. and severe malnutrition ranged from $33 (Tamil Nadu) to $331 (targeted food subsidy, Pbilippines) to $493 (face-to-face nutrition program, Dominican Republic).

* Nutrition expenditures seem to account for about 10 percent of health spending, both for donors and for individual countries (Chile is an outlier with 35 percent).

Impact data on these topics are scarce, and these estimates should be interpreted cautiously.

* Medium-sized feeding programs (100,000 to 500,000 beneficiaries) are the least expensive.

The Policy Research Working Paper Series disseninates the fndings of work under way in the **Bank.** An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The fmdings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
We estimated the impact of access to water on our outcome indicators using both approaches (8) and (10) and got very similar results.<sup>8</sup> Given that the second approach based on (10) gives the correct standard errors, we report and focus on this estimation method and its results in what follows.

Whether one accepts the assumption of conditional exogeneity must depend on the data available and the setting. In principle it appears more likely that one could collect geographic data relevant to both outcomes and infrastructure placement, while one might be justifiably skeptical of any claim that one could collect data on all the relevant individual characteristics, which would include many latent preference and knowledge parameters at the individual level. So the ability of our estimation strategy to deal with endogenous placement across individuals within a given area is desirable.

# **5. Data and descriptive statistics**

We use rural household and community data from national consumption surveys spanning Sub-Saharan Africa (Madagascar, Malawi, Rwanda and Uganda), South Asia (India, Nepal and Pakistan), North Africa (Morocco) and the Middle East (Yemen). Countries meeting three criteria are included. The first is the availability of a good quality and comprehensive household survey containing household level information on water access, and either distance or time to the nearest water source used; data on household members' time spent, or participation in, market and non-market productive activities; and a wide range of other household and individual socioeconomic characteristics. The surveys must also contain disaggregated and detailed community level data on access to facilities, commodity prices, presence of labor markets, and other characteristics not directly affected by water access for the communities where sample households reside. Finally, among the countries meeting these criteria, we select those where rural access to water is a concern for at least part of the rural population, and where rural households typically spend substantial amounts of time collecting water.

Table 1 gives an overview of the datasets used, including the survey period, and the number of sample households and rural communities (typically corresponding to the primary sampling units, or PSUs) covered. The latter form the basis of the rural

> 8 Results based on both (8) and (10) are available from the authors.

14