rafmacalaba/datause-encoder-data huggingface dataset:
poverty line (World Bank, 1999).

Household characteristics are summarized in Table 2. The Nepal survey contains detailed information about travel time to a number of di ff erent facilities. Given that Nepal is a very mountainous country, distance in Km is not a relevant measure for most of the country; travel time is a more accurate measure of isolation in this case. We see that, on average, surveyed households live on average more than two hours of travel time from a market, the maximum value being 40 hours.<sup>9</sup> The median is around 1 hour. Distance to local markets is the fi rst isolation measure djk that we use in our empirical analysis. Given the nature of the terrain and the spatial dispersion of households, djk varies between individuals within the same ward. Travel times to the nearest school and health facility are much shorter: on average households are located around 20 minutes from the nearest school and one hour from the nearest health facility. The quality of schools and health facilities varies widely across locations, however.

Average total annual consumption (non-durables and durables) is reported in US$.<sup>10</sup> The total value of assets is reported next. This includes land, livestock, agricultural equipment, and fi nancial assets. As is customary, wealth distribution is quite unequal (high standard deviation) and highly skewed, with the median representing around one-fourth of the mean. Parental background variables are reported as well, such as land inherited by the household, education level of the father of the household head, and whether the head’s father was employed in a non-farm occupation. Later on we use these variables to predict migration out of one’s birth ward. In 1996, towards the end of the NLSS survey, a Maoist insurgency began to take root in rural Nepal. Since the insurgency initially limited itself to attacking a few police stations,

> 9 Our measure of isolation, ‘distance to markets’ is computed as the average of the travel time to fi ve di ff erent types of markets, namely market centers, hat/bazar, krishi center, cooperative center and local shops. Taking any single one of them leads to the loss of many observations. This information is recorded independently for each household.

> 10 Using the exchange rate of 56.8 Rupees to the dollar which prevailed at the time of the survey. For reference, in the regression analysis we use (logged) Rupees.

12
**Table 1: Effect of the Interventions on Exposure to Handwashing Promotion**

||**Province lev**|**el intervention**||**District leve**|**l intervention**||
|---|---|---|---|---|---|---|
||**Mass me**|**dia treatment**|**Community t**|**reatment sample**|**School co**|**mponent sample**|
||Control mean|Effect of treatment|Control mean|Effect of treatment|Control mean|Effect of treatment|
|**Exposure**<sup>**+**</sup>|||||||
|High exposure to the treatments|0.147|-0.0039|0.147|0.0551|0.2|-0.0275|
|(HW message through three channels)||(0.037)||(0.037)||(0.041)|
|Medium exposure to the treatments|0.432|-0.000929|0.432|0.145***|0.499|0.065|
|(HW message through two channels)||(0.058)||(0.051)<br>[33.5%]||(0.050)|
|Low exposure to the treatments|0.674|-0.065|0.674|0.106**|0.717|0.0637*|
|Clustered standard errors in parentheses; *** p<0.<br>(HW message through one channel)<br>+Channels: mass media (radio and/or printed materi|01 ** p<0.05 * p<0.1<br>als), promotional events and|(0.051)<br>personal training/educatio|nal sessions|(0.045)<br>[15.7%]||(0.042)<br>[8.9%]|

**Table 2: Effect of the Interventions on Handwashing Determinants**

||**Province lev**<br>|**el intervention**<br>||**District leve**<br>|**l intervention**<br>||
|---|---|---|---|---|---|---|
||<br>**Mass me**|<br>**dia treatment**|<br>**Community t**|<br>**reatment sample**|<br>**School co**|<br>**mponent sample**|
||Control mean|Effect of treatment|Control Mean|Effect of treatment|Control Mean|Effect of treatment|
|<br>**Knowledge**|||||||
|Events that require handwashing (summary index)|0.225|0.02<br>(0.019)|0.225|0.009<br>(0.020)|0.222|0.008<br>(0.019)|
|Best method to wash hands|0.879|-0.003<br>(0.021)|0.879|0.0512*<br>(0.027)<br>[5.8%]|0.882|0.044**<br>(0.021)<br>[4.9%]|
|Not washing hands with water and soup is main<br>cause of diarrhea<br>Clustered standard errors in parentheses; *** p<0.01 ** p|0.94<br><0.05 * p<0.1|-0.006<br>(0.014)|0.94|0.0313**<br>(0.012)<br>[3.3%]|0.94|0.011<br>(0.014)|

36
**Table 5: Antitrust and Legal Standard for Type of Monopolistic Practice.** Absolute monopolistic practices are as defined in Article 53 of the Federal Economic Competition Law. Such practices “shall be null and void...and the Economic Agents that engage in such practices shall be subject to the sanctions provided in this Law, regardless of any criminal or civil liability that may arise.” Practices that are not absolute monopolistic practices are relative monopolistic practices as defined in Articles 55 of the Law, which shall be punished “unless the Economic Agent proves these practices produce gains in efficiency and favorably impact upon the process of economic competition and free market access, thus overcoming their possible anticompetitive effects, and consequently result in an improvement of consumer welfare.” Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

|Change in|(1)<br>the log<br>of<br>sales|(2)<br>the log<br>of the<br>wage bill|(3)<br>operating<br>proft<br>margin|
|---|---|---|---|
|Absolute monopolistic practices X sanction(=1)|0.029|0.029|-0.570|
||(0.039)|(0.035)|(4.597)|
|Financial sanction (=1)|0.042|0.049*|-0.174|
||(0.028)|(0.025)|(3.130)|
|Absolute monopolistic practices (=1)|-0.009|-0.038*|-0.307|
||(0.025)|(0.022)|(3.161)|
|Constant|-0.068***|-0.042***|1.721|
||(0.014)|(0.012)|(1.515)|
|Observations|90|90|90|
|Meta analysis framework|RE|RE|RE|
|Outcome change estimator|SDID|SDID|SDID|
|Sanction in absolute case|0.071***|0.078***|-0.744|
||(0.027)|(0.024)|3.368|
|Second Kampala Institutional and Infrastructure Developmen|t ProjeMartin Onyach-Olaa|
|---|---|
|Second Kampala Institutional and Infrastructure Developmen|t ProjeMartin Onyach-Olaa|
|Second Kampala Institutional and Infrastructure Developmen|t ProjeMartin Onyach-Olaa|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Water Supply,Sanitation and Hygiene Project|Habab Taifour|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Ethiopia Local Government Development Project II|Dinkneh Tefera|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|Senegal SafetyNet operation|Thomas Bossuroy|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
offer short-lived benefits and are seen to be justified by their provision of insurance or emergency relief such as public works and cash transfer schemes.

The criticisms of econometric targeting could reflect either methodological inadequacies or informational/data limitations. On the former, standard regression-based calibration of the PMT score will tend to work less well toward the extremes of the distribution of household consumption. By its design, a standard regression line passes through the means of the data. The residuals will be positively correlated with the dependent variable (more so the higher the variance of the residuals given exogenous regressors).<sup>8</sup> One can expect the method to have a tendency to overestimate living standards for the poorest and underestimate them for the richest, though the degree to which this is problematic for targeting accuracy is unclear. Indeed, it is theoretically possible that the PMT method predicts that nobody lives below a poverty line for which even a sizable share of the population is deemed to be poor based on observed consumptions. Another possibility is that the variables used are not sufficiently good proxies for household consumption. In other words, that there is an information problem.

The paper aims to provide a systematic assessment of the reliability of econometric targeting as a tool for social policies aiming to reduce poverty. We assess what appears to be the most common form of what we call “Basic PMT,” as well as some alternative methods using extra covariates and methods that are arguably more appropriate when it is recognized explicitly that the PMT is for antipoverty policy making. A natural counterfactual for assessing any form of PMT is a uniform allocation—the same for everyone. Various other counterfactuals of interest to policy makers are examined. The study also considers less finely-targeted options to econometric targeting, which are uniform only within stipulated categories.

While Latin America has attracted the bulk of the past research on PMT, we study the method using survey data for the world’s poorest region, Sub-Saharan Africa (SSA). This is also the region where existing social spending has been least effective in reaching the poorest.<sup>9</sup> The specific countries studied are Burkina Faso, Ethiopia, Ghana, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda, being all those countries in SSA with recent and reasonably comparable surveys in the World Bank’s Living Standards Measurement Study (LSMS).<sup>10</sup> For a subset of these countries we also have panel data.

> 8 If the regression model is _y_   _x_   with _Cov_ ( _x_ ,  )  0 then _Cov_ ( _y_ ,  )ˆ  _Var_ (  )ˆ  0 (in obvious notation). 9 For evidence on the point see Ravallion (2016, Chapter 10).

> 10 Existing government safety net programs in Ghana, Malawi, Nigeria, and Tanzania, and World Bank projects in Burkina Faso, Ethiopia, Ghana, Malawi and Niger use PMT, often in combination with geographic and community-based targeting. At the time of writing, PMT is also being considered for Mali.

4
imperfect granularity (i.e. Both lead to deviations from the asymptotic single risk factor framework. Empirical evidence for developed countries indicates that the impact on economic capital is larger for sectoral concentration than for name concentration. Using German data (but with a hint that the conclusions are generalizable to other continental European banks), Duellmann and Masschelein (2007) find that economic capital increases from 7.8% in the case of the most diversified benchmark portfolio to 11.7% for a portfolio concentrated in one sector. However, there is also theoretical and empirical evidence that shows how lending specialization may be beneficial and reduce risk or increase (risk-adjusted) returns. Winton (2000) shows theoretically that it is likely that the bank’s monitoring effectiveness is lower in new sectors, with the effect that diversification lowers average returns on monitored loans, as banks are less likely to improve monitoring incentives, and is more likely to increase the bank’s chance of failure. The aforementioned effects theoretically imply a reduction in the probability of default. Empirical evidence by Acharya et al. (2006) for Italy and by Hayden et al. (2007) for Germany documents that specialization in certain industries is indeed accompanied by lower loan loss rates. Boeve et al. (2010) find that German cooperative and saving banks exert more and better monitoring if they are specialized rather than diversified. Empirical evidence from Brazil, by Tabak et al. (2011) also hints to the fact that loan portfolio concentration seems to improve the performance of banks in both return and risk of default. In addition, these authors also document that the loan portfolios of Brazilian banks are more concentrated compared to e.g. Germany, Italy and the U.S. While the existing literature focuses either on single countries or syndicated lending (Cai et al. (2013)), our paper is the first cross-country study on the relationship between lending specialization and bank performance and risk, which also allows testing for cross-country variation in the relationship. While other studies have focussed on banks’ diversification in interest and non-interest business, we use unique and novel data to shed light on lending specialization.<sup>2</sup>

> 2See De Jonghe (2010), Demirguc-Kunt and Huizinga (2010) and Stiroh and Rumble (2006), among others, for studies on interest vs. non-interest business of banks.

4
- Wallsten, S. (2005), “Regulation and Internet Use in Developing Countries. Economic,” _Development and Cultural Change_ . 501-523.

- World Bank (1994), _World Development Report, Infrastructure for Development,_ Washington, D.C.

- Zhen-Wei Qiang, C., and A. Pitt (2004), “Contribution of information and Communication Technologies to Growth,” World Bank Working Paper No 24, Washington D.C.

26
workers is _L-LR-LM_ . Considering that the probability of obtaining a job in the urban sector over a given period is given by the ratio of jobs created to the number of job seekers (i.e. assuming that all jobs turn over in each period and random job selection), the probability of finding a job for an unemployed worker is thus λ _LM / (L-LR-LM)_ . Denoting _w_ and _r_ the urban wage and the rural income respectively, the expected urban-rural income differential _δ_ is then: Assuming that migration responds to the income differential _δ_ implies that _L-LR_ should be considered a function of _δ_ . From (2.1), any policy affecting the rate of job creation λ will thus have an impact on the expected income differential _δ_ and, in turn, on the urban labor supply _L-LR._ The Todaro paradox will occur if the increase in labor supply _d(LLR)/dλ=(d(L-LR)/dδ).(dδ/dλ)_ is greater than the increase in labor demand _d(λLM)/dλ=LM_ . Using (2.1), the condition for an increase in the unemployment level is as follows: The interpretation is as follows: if the elasticity of urban labor supply with respect to the income differential between urban and rural areas (the LHS in 2.2) is greater than the income differential –written as a percentage of the urban wage– multiplied by the unemployment rate in the urban area (the RHS in 2.2), an increase in job creation will trigger an increase in the number of unemployed workers. Todaro argues that this formalizes what happened in Kenya in 1964 when the Kenyan government and private employers agreed to immediately increase employment levels by 15 percent but only managed to increase overall unemployment by attracting new workers to cities. However, empirically testing the values of these parameters is somewhat tricky.

11
<!-- Start of picture text --> S 40% -<br>J<br>=<br>5 30% -<br>4<br>s<br>2 20% -<br>coal<br>o><br>2 10%-<br>s<br>=<br>2 om%-<br>-—<br>e<br>8 -10% -<br>¢<br>9<br>-<br>£ -20% -<br>=<br>B -30% -<br>=<br>S -40% -<br>2008 2010 2012 2014<br>|| Brazil World Average<br><!-- End of picture text -->
# **Figure 10. Extreme weather event risks and macro‐financial risks**

## A. Physical (disaster) risk vs. macroeconomic risk

## B. Weather event & macroeconomic risks by region

<!-- Start of picture text -->
1.8<br>100%<br>1.6 BIH 90% 3 2<br>1.4 80% 4 6 1 5<br>1.2 MMR 70% 0 6 0<br>THA 60% 9<br>1<br>0.8 OMN 50% 6 4 3<br>0.6 CHN BGD VNMSRBNIC KHM 40%30% 0 2 8<br>0.40.2 SWECHELUXAUSISR BOLMWI MOZJAM 20%10% 3 8 5 6<br>0<br>0 NOR 0% 1 0 1<br>0 20 40 60 80 100 EAP ECA LAC MENA SA SSA<br>EIU economic risk index (100=High), 2017 low both high climate high macro high both<br>C. Physical (disaster) risk vs. banking risk D. Weather event & banking risks by region<br>1.8 100%<br>1 1<br>1.6 BIH 90% 3 3<br>1.4 80% 5 6<br>2<br>1.2 MMR 70% 2<br>THA 60% 0 11<br>1<br>0.8 OMN NIC 50% 6 8<br>SRB KHM 40% 5 8<br>0.6 CHE BGD VNM 30% 1 0<br>0.4 SWE LUXAUS ISR JAMCHN MOZ 20% 8 1 2<br>0.2 NOR 10% 3 2 4<br>ZAF 1<br>0 0% 0<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU banking risk index (100=High), 2017 low both high climate high banking high both<br>E. Physical (disaster) risk vs. public debt risk F. Weather event & public debt risks by region<br>1.8 100%<br>1.6 BIH 90% 2 1 3<br>4 4 1<br>1.4 80%<br>6<br>1.2 THA MMR 70% 0 3 10 0<br>60%<br>1 KHM<br>OMN<br>SRB VNM 50% 5 8<br>0.8 6<br>0.6 BGD NIC 40%30% 7 1 2<br>0.4 SWENZLCHE AUS BGR HRV MOZ SDN 20% 8 5 2<br>0.2 CHL 10% 3<br>0 LUX SGP VEN 0% 1 0 1<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU debt risk index (100=High), 2017 low both high climate high debt high both<br>Average Losses natural disasters (%GDP)<br>Average losses natural disasters (%GDP)<br>Average losses natural disasters (%GDP)<br><!-- End of picture text -->

Note: Panels A, C, and E: Countries above (below) country median in Notre Dame Readiness indicator in green (red). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low macro‐ financial risks, low climate‐high macro‐financial risks, high climate and macro‐financial risks) by region. Thresholds are country median values of EIU indicator or climate variable as of 2017 (historical country median for the case of fuel exports). Numbers in columns are the number of countries in each category. EIU data for Saudi Arabia as of 2016.

Source: EIU Credit Risk Model, GermanWatch, Notre Dame GAIN, WDI. Staff calculations.

36
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 7: Regional log Formal Employment - Pre-Trends - 1987-1990 <!-- Start of picture text --> 6.5<br>5.5<br>4.5<br>3.5<br>2.5<br>1.5<br>0.5<br>1987  1988  1989  1990<br>-­‐0.5<br>-­‐1.5<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the change in regional log formal employment from 1986 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects. Positive estimates imply larger formal employment declines in regions facing larger tariff declines. Standard errors adjusted for 112 mesoregion clusters.

46
|(7)<br>(8)<br>Cognitive<br>Days Healthy|0.039<br>0.313<br>(0.078)<br>(0.167)|0.098<br>-0.317<br>(0.087)<br>(0.190)|743<br>698<br>0.312<br>0.066<br>0.415<br>0.000<br>0.421<br>0.002<br> in the six-week followup survey.<br>or the baseline measure of the<br>hrough a double-selection<br>eatment relative to the control<br>utcomes that compose the<br>lumns 2-6). ‘Well-being’ is an<br>ast week they felt ‘good’. Higher<br>present improved well-being.<br>thy reported in the prior week.<br>e for the test of equality between<br>t of equality, adjusted for|
|---|---|---|---|
|(6)<br>Stress|0.234<br>(0.096)|-0.003<br>(0.107)|698<br>0.036<br>0.010<br>0.038<br>mes collected<br> controlling f<br>etermined t<br>e specifed tr<br>bset of the o<br>s subset (Co<br>ays in the p<br>her scores re<br> of days heal<br>usted p-valu<br> for this tes|
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>PS Index<br>Wellbeing<br>Life Satis.<br>Locus of Cont.<br>Sociability|Work<br>0.161<br>0.421<br>0.045<br>0.184<br>0.084<br>(0.064)<br>(0.122)<br>(0.095)<br>(0.098)<br>(0.087)|Cash<br>0.041<br>0.230<br>0.019<br>0.090<br>-0.005<br>(0.074)<br>(0.148)<br>(0.120)<br>(0.114)<br>(0.092)|Observations<br>698<br>698<br>698<br>698<br>698<br>Shrp. q-val Work<br>0.006<br>0.312<br>0.066<br>0.204<br>Test: Cash = Work<br>0.048<br>0.178<br>0.807<br>0.306<br>0.223<br>Shrp. q-val: Cash = Work<br>0.286<br>0.555<br>0.343<br>0.287<br>**Notes:** This table reports the treatment efect of the employment and cash arms on a variety of outco<br>Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and <br>dependent variable (ANCOVA), camp and enumerator fxed efects, and sociodemographic controls as d<br>LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). Each row presents the coefcients of th<br>group (which received a small weekly cash payment).<br>All outcomes are standardized and drawn from the six-week followup survey, in which we ask only a su<br>Psychosocial Index collected at endline. The ‘PS Index’ presented here is therefore only an index of thi<br>index of two questions asking i) how the respondent feels today on a six point scale, and ii) how many d<br>values for ‘Stress’ represent _reduced_ stress. All remaining outcomes are likewise presented such that hig<br>‘Cognitive’ represents an index of three arithmetic questions, and ‘Days Healthy’ measures the number <br>Standard errors are clustered at the block level. The row labeled “Test Cash=Work” refects the unadj<br>the Cash and Work arms, while the row labeled “Shrp. q-val Cash=Work” refects the adjusted p-value<br>multiple hypothesis testing of Anderson (2008) for all outcomes in this table.|

49
# Figure 1 Impact of Investigations and Duties on Import Prices

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## Knitted Sweaters

<!-- Start of picture text --> 14L<br>o10<br>I"<br>ti l<br>62S4 II Im 41414 I MsIm I34S484 I N~wet 84<br>Woven Pants Men's<br>~~~~~ ~ ~~~~~~soe 9 \<br>1100<br>5<br>1 234 12 34 1 2 41 283411,2834<br>I9881 1641 9 iSe193 I"<br><!-- End of picture text -->