rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Baseline balance tests for 2010‐2012 Pooled Cohorts (ITT), Full Sample**

||Control|Treatment|Difference|p‐value|N|
|---|---|---|---|---|---|
|_Demographics_||||||
|Female (%)|0.640|0.630|‐0.010|0.610|4101|
|AGEI (women aged 16‐24) (%)|0.319|0.336|0.017|0.350|4101|
|Dalit (%)|0.090|0.077|‐0.012|0.365|4037|
|Janajati (%)|0.421|0.468|0.048**|0.024|4037|
|Muslim (%)|0.017|0.025|0.008|0.269|4037|
|Age|24.537|24.242|‐0.294|0.249|4101|
|Currently Married (%)|0.580|0.594|0.014|0.463|4101|
|Any Children (%)|0.505|0.526|0.021|0.248|4101|
|Completed SLC(10thgrade) (%)|0.163|0.105|‐0.059***|0.000|4101|
|_Employment_||||||
|Any IGA in past month (%)|0.594|0.619|0.025|0.182|4101|
|Any non‐farm IGA in past month (%)|0.266|0.307|0.041**|0.012|4101|
|Earnings in past month (NRs)|1201.970|1295.522|93.552|0.285|4069|
|Earnings > 3000 in past month (%)|0.172|0.197|0.025*|0.094|4101|
|Trade‐specific IGA in past month (%)|0.154|0.189|0.035**|0.014|4101|
|Hours workedpast month|62.774|71.502|8.728***|0.008|4101|
|_Empowerment_||||||
|Any savings (%)|0.585|0.604|0.019|0.311|4080|
|Total Cash Savings (NRs)|3114.676|3177.379|62.703|0.832|4080|
|Entrepreneurship Score (0‐32)|15.151|14.865|‐0.286|0.235|4101|
|Financial Literacy|0.636|0.609|‐0.028|0.109|4101|
|Analytical ability (Raven's test, 0‐5)|2.944|2.911|‐0.033|0.516|4101|
|Any mentor related to work|0.597|0.603|0.006|0.761|4099|
|Self‐Regulation Score (0‐16)|10.500|10.370|‐0.131|0.167|4101|
|Self‐Confidence Score (0‐27)|19.015|19.014|‐0.001|0.990|4100|
|Any money of your own (%)|0.702|0.673|‐0.029*|0.083|4094|
|Control over earnings, if has earnings (%)|0.670|0.662|‐0.008|0.697|2511|
|Control over own savings, if has savings (%)|0.600|0.561|‐0.040*|0.073|2466|
|Control over household spending (0‐8)|3.401|3.351|‐0.050|0.685|4088|
|Mobility (0‐9)|4.773|4.692|‐0.081|0.179|4101|
|_Family and Reproductive Health_||||||
|Number of children|1.130|1.124|‐0.006|0.911|4101|
|Currently Pregnant (self or partner, if applicable)|0.038|0.038|‐0.000|0.971|3923|
|Desired number of children|2.220|2.180|‐0.040|0.249|4099|
|Use any type of contraception, if applicable (%)|0.754|0.773|0.019|0.305|2613|
|HIV Knowledge score (0‐3)|1.124|1.155|0.031|0.318|4046|
|Household food insecurity (0‐4)|0.540|0.555|0.015|0.534|4007|
|HH consumes eggs,meat,fish ≥ 5 timesper week|0.348|0.347|‐0.001|0.958|3582|

Notes: This table reports average values for treatment and control groups, with p‐value of a Student's t‐test for equality of means between the two groups. The tests are conducted on the panel sample (those interviewed at baseline and follow‐up). Standard errors are clustered by training course. “ITT” indicates that treatment is defined as having a score that qualifies the respondent for an EF training course. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

28
**<u>Table 1: BRICS Adjusted Trade Openness Ratios, 2012</u>**

||**Real GDP per**<br>**Capita, PPP, $**<br>**(1)**|**Actual**<br>**Trade/GDP(%)**<sup>*****</sup><br>**(2)**|**Potential**<br>**Trade1/GDP(%)**<br>**(3)**|**Potential**<br>**Trade2/GDP(%)**<br>**(4)**|**Potential**<br>**Trade3/GDP(%)**<br>**(5)**|
|---|---|---|---|---|---|
|Russian Federation|15,177|51.3|102.6|70.0|39.9|
|Brazil|10,264|26.9|98.3|61.8|47.1|
|China|7,958|58.7|95.6|32.8|42.8|
|India|3,341|55.4|86.1|25.8|27.6|
|South Africa|9,860|59.6|97.9|80.4|73.3|

Source: World Bank _Trade Competitiveness Diagnostics._ _Notes_<sup>:</sup> Predicted trade ratio is estimated by regressing the actual trade-to-GDP ratio on: the log of real per capita in column (3); the log of real per capita and log of population in column (4); the log of real per capita, log of population, and the log of arable land in column (5).

* Actual trade-to-GDP ratio for China is for 2011.

0
testable predictions. Results are at the bottom of Table 1. The model implies that each imported case per capita should contribute a case per capita to the incidence in the destination in the month entered. With a p-value of 0.79 on the Wald test, the coecient is not signicantly dierent from 1. Additionally, the _β_ 1 parameter estimated represents the expected number of humans infected per infected human per day if transmission eciency were perfect, _C_ , times the actual transmission eciencies from infected mosquitoes to humans and humans to mosquitoes, _b_ and _c_ . Using estimates drawn from the epidemiology literature, an average monthly value of 0.273 is expected for _bcC_ .<sup>22</sup> I nd that the coecient is signicantly dierent and larger, implying that the parameters estimated by the epidemiological model would underestimate the externality. Finally, to measure whether there is an externality beyond a case being detected in one location versus another location due to travel, I test whether the sum of imported incidence and lag incidence is equal to 1. Their sum is signicantly dierent from 1 at the .05 level.

I simulate a baseline scenario based on the preferred model using Arellano-Bond and compare it to the actual data. I draw values for the parameters of the model from their estimated distributions, which are estimated using a bootstrap where I resample observations in the data 10,000 times. I use these draws to calculate an incidence path for the year and average paths across 500 draws. In Figure 3 panel a, I average across health post areas by district and compare to actual incidence. Similarly, I simulate incidence under the assumption that there is no imported incidence. Panel b of Figure 3 compares the results of this simulation to the baseline simulation. Imported incidence represents a substantial portion of the malaria incidence, especially for Richard Toll and Saint-Louis. On average annually per health post, travel represents 41% of the incidence.

> 22Based on Vercruysse, Jancloes, and Van de Velden (1983) the average daily vectorial capacity in Senegal, C, is 1.13. Based on Gething et al. (2011), average transmission from infected humans to mosquitoes, c, is 0.161. The transmission from mosquitoes to humans, b, is 0.05 based on the linear model in D. L. Smith, Drakeley, et al. 2010. This gives an average daily value of 0.0091, or a monthly value of 0.273 for _bcC_ .

20
Table 3: Unemployment rates in the sample countries at the time of the first and the second observation

||**Arge**|**ntina**|**Br**|**azil**|**M**|**exico**|
|---|---|---|---|---|---|---|
||first<br>observation|second<br>observation<br>(6 onths)|first<br>observation|second<br>observation<br>(1 month)|first<br>observation|second<br>observation<br>(3 months)|
|Male|||||||
|15-18|31.4|31.5|6.0|5.8|5.1|5.0|
|19-24|20.5|19.8|7.5|6.7|3.3|3.0|
|25-44|93|94|37|35|14|13|
|<br>Female|.<br>|.|.|.|.|.|
|15-18|37.8|38.1|7.3|6.7|8.8|7.9|
|19-24|24.1|22.7|8.6|7.9|5.4|4.6|
|25-44|11.3|11.1|4.3|4.0|2.6|2.3|
|<br>1<sup>st</sup>inco<br>|mequintile<br>|||<br>|||
|15-18|54.0|41.2|9.8|8.2|8.7|7.2|
|19-24|47.3|32.4|11.9|10.3|6.1|4.6|
|25-44|329|222|69|57|24|20|
|<br>5<sup>th</sup>inco|.<br>mequintile|.|.|.|.|.|
|15-18|14.1|16.7|5.3|5.4|4.5|5.3|
|19-24|7.3|8.8|6.5|6.0|2.6|2.5|
|25-44|2.4|3.4|2.8|2.7|1.1|1.0|

# **Table 4: Transition Matrix**

||**% moving in**|**to unemp**|**loyment**|**% moving o**|**ut of unempl**|**oyment**|
|---|---|---|---|---|---|---|
||**Argentina**|**Brazil**|**Mexico**|**Argentina**|**Brazil**|**Mexico**|
|Male|||||||
|15-18|5.6|1.4|1.8|64.0|59.6|84.7|
|19-24|9.9|2.8|1.9|62.5|53.5|79.6|
|25-44|6.1|1.8|1.0|61.5|53.1|77.3|
|Female|||||||
|15-18|3.6|1.0|1.6|66.5|62.6|85.2|
|19-24|7.2|2.4|1.8|65.2|55.9|83.0|
|25-44|4.9|1.3|0.9|70.5|58.8|84.3|
|1<sup>st</sup>incomequ<br>|intile<br>||||||
|15-18|6.5|1.6|1.9|65.0|63.8|83.7|
|19-24|11.6|3.5|2.2|63.1|55.2|81.6|
|25-44|9.5|2.1|1.1|65.1|59.4|83.5|
|5<sup>th</sup>incomequ|intile||||||
|15-18|0.9|0.8|1.1|73.1|64.3|89.6|
|19-24|4.0|2.2|1.3|71.6|58.0|83.3|
|25-44|2.3|1.2|0.6|66.7|55.5|78.6|

19
Table 7: Potential Short-Run Impacts of Brexit by Sectors

||SITC Rev 4|Exp|ort|Import Weighte|d Average|OTRI|Trade|Impact|
|---|---|---|---|---|---|---|---|---|
|Sector|Description|$Bil.|%|MFN Tarif (%)|Elasticity|%|$Bil.|%|
|0|Food and live animals|4.39|2.74|8.51|-13.33|6.09|-3.10|-70.79|
|1|Beverages and tobacco|2.57|1.61|1.93|-1.14|14.37|-0.05|-1.76|
|2|Crude materials|3.03|1.89|0.47|-18.63|0.05|-0.03|-0.91|
|3|Mineral fuels|19.29|12.06|0.75|-10.69|0.16|-0.30|-1.56|
|4|Animal and veg. oils|0.37|0.23|5.64|-26.56|7.80|-0.69|-185.02|
|5|Chemicals|28.75|17.97|2.65|-1.69|4.21|-0.80|-2.80|
|6|Manufacturing material|16.42|10.27|2.60|-4.59|3.52|-2.01|-12.21|
|7|Machinery and transport|67.00|41.89|3.38|-1.60|2.67|-1.04|-1.56|
|8|Misc. manufacturing|17.20|10.75|3.82|-2.95|6.95|-2.18|-12.69|
|9|Other Commodities|0.93|0.58|0.00|-0.49|0.00|0.00|0.00|

30
Our surveys and pre-analysis plan enable us to trace out the causal chain from providing training through to changes in business outcomes, and to examine the different mechanisms through which training may or may not have an effect. Since we examine a number of different mechanisms, in Appendix 10 we provide sharpened q-values that hold constant the false discovery rate when reporting results for specific outcomes. All outcomes at the three-year horizon that have p-values below 0.05 also have sharpened q-values below 0.05, whereas only the one year impacts that are significant at the 1 percent level have sharpened q-values below 0.10.

# **6.1. Changes in Business Knowledge, Practices, and Use of a Mentor**

The first step in the causal chain is for training to lead to changes in the business knowledge and business practices of the women taking training. Business knowledge was assessed through giving respondents a description of a business and then asking them seven questions that involve calculating the revenue, value of stock on hand, variable costs, total expenses, profits, fixed costs, and break-even point. This proved very difficult for most participants, with the median respondent only getting 2 out of 7 questions right, and only 0.5 percent getting all the answers correct. This question was only asked in the first long follow-up survey and was asked of both those with surviving businesses as well as those whose business had closed down. Column 1 of Table 5 shows that there is no significant treatment effect or spillover effect on business knowledge. This is consistent with the financial literacy results of Carpena et al. (2011) who find that financial literacy training does not improve performance on questions involving numerical calculations.

We measure business practices through a set of 26 questions that measure the marketing, recordkeeping, buying and stock control, and financial planning of the firm. These questions are only measured in the long follow-up survey rounds and only for firms that survive. These questions have been shown to correlate strongly with business performance in a range of countries by McKenzie and Woodruff (2015), and to predict future survival and growth of the firm. The mean firm in the pure control group is employing 53 percent of these practices.

Column 2 shows that the impact of being invited to training is a statistically significant increase in the use of business practices at both the 1 and 3 year horizons: treated firms have a 0.05 to 0.07 increase in the proportion of practices used. This is approximately a 10-13 percent increase on the control mean. There is a marginally significant positive spillover in business practices to untreated firms in the same marketplaces over 1 year, however the magnitude is very small (0.01), and the three-year impact is smaller still and not significant. The increase in practices is three times as 17
# **Table 5. SEO Average Issuance Size**

|||Full sample|||Full sample||Low- and M|iddle-Incom|e Countries|Low- and|Middle-Incom|e Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|0.829***|||0.882***|||0.962*|||1.076**|||
||(0.287)|||(0.280)|||(0.524)|||(0.514)|||
|Stock Market Total Value||-0.028|||-0.034|||0.197|||0.263||
|Traded (% of GDP)||(0.302)|||(0.308)|||(0.575)|||(0.511)||
|Stock Market Turnover|||-0.397*|||-0.453**|||-0.243|||-0.236|
|Ratio|||(0.211)|||(0.227)|||(0.316)|||(0.288)|
|Foreign Direct Investment,|0.002|0.005|0.004|0.002|0.004|0.003|-0.032|0.006|-0.019|-0.025|0.019|-0.007|
|net inflows (% of GDP)|-0.004|-0.005|-0.005|-0.004|-0.005|-0.005|(0.252)|(0.269)|(0.257)|(0.237)|(0.252)|(0.240)|
|Political Stability and<br>Absence of Violence|-0.192|-0.297|-0.098||||-0.607|-0.610|-0.626||||
||(0.357)|(0.376)|(0.365)||||(0.556)|(0.615)|(0.575)||||
|Investment Profile||||0.147|0.078|0.215||||0.304|0.334|0.305|
|||||(0.141)|(0.155)|(0.169)||||(0.239)|(0.258)|(0.232)|
|Constant|17.52***|20.80***|21.86***|15.99***|20.11***|20.10***|16.62***|19.80***|20.72***|14.35***|17.55***|18.84***|
||(1.190)|(0.822)|(0.650)|(1.726)|(1.309)|(1.417)|(2.283)|(1.608)|(1.178)|(2.800)|(2.083)|(1.584)|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Time Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Observations|364|292|318|355|286|312|147|132|142|144|132|142|
|R-squared|0.040|0.031|0.032|0.046|0.031|0.042|0.095|0.065|0.066|0.107|0.075|0.074|

_Note:_ The table reports fixed effects panel regressions using 5 years of data. The outcome variable is SEO average issuance size in million USD. FDI net flows as percentage of GDP, political stability and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

17
49 THE HOUSING MARKET OF KUMASI <!-- Start of picture text --> Figure 3.6: Total consumption expenditures by Tenure<br>Owners Family Housers<br>40 40<br>e e<br>r r<br>e e<br>n n<br>e 0 ..... .. . . t 10 ............... .... ......<br>0 ~~~~~~~~~~~~~~~~~~~~~~~~~~0 /<br>lT 5000 5-10 K 10-15 K 15-20 K 20-25 K GE 25 K LT 5000 5-10 K 10-15 K 15-20 K 20-25 K GE 25 K<br>Median, 16300 Cedis Median 11650 Cedis<br>Renters Sub-Renters<br>40 40<br>e e<br>r r<br>e e<br>n n<br>0- MrA O'~~~~0-<br>IT 5000 5-10 K 10-IS K 15-20 K 20-25 K GE 25 K LT 5000 5-10 K 10-65 K 15-20 K 20-25 K GE 25 K<br>Median: 12100 Cedis Median: 11600 Cedis<br><!-- End of picture text --> There are some variations by tenure; owners have higher mean and median consumptions than any other group and they are more concentrated in the higher consumption classes. Family housers have very slightly lower consumptions than any other group but their distribution is not dissimilar from that of renters. However, when consumption levels, even for owners, are converted to hard currency, they constitute very little purchasing power. The owners' mean monthly consumption of C19,600 only represents US$220 at the official exchange rate in 1986. Looking at the data another way, there is a positive correlation between household consumption and house-ownership. Whereas only 5 percent of all households in the lowest group are owners, 22 percent in the highest group are owners. This is close to the distribution of consumption within the owner group. However, while 11 percent of family housers are in the lowest consumption group, they constitute 42 percent of all the households in that group; at the other end, they are only 19 percent of the households in the highest consumption group.

Although household consumption in Kumasi is low by international standards, the size of households (discussed below) leads to even lower per capita
Policy Research Working Paper 6713

# **Abstract**

This paper contributes to the long-standing debate on the merits of decentralized beneficiary targeting in the administration of development programs, focusing on the large-scale Malawi Farm Input Subsidy Program. Nationally-representative household survey data are used to systematically analyze the decentralized targeting performance of the program during the 2009–2010 agricultural season. The analysis begins with a standard targeting assessment based on the rates of program participation and the benefit amounts among the eligible and non-eligible populations, and provides decompositions of the national targeting performance into the inter-district, intra-district inter-community, and intra-district intra-community components. This approach identifies the relative contributions of targeting at each level. The results show that the Farm Input Subsidy Program is not poverty targeted and that the national government, districts, and communities are nearly uniform in their failure to target the poor, with any minimal targeting (or mis-targeting) overwhelmingly materializing at the community level. The findings are robust to the choice of the eligibility indicator and the decomposition method. The multivariate analysis of household program participation reinforces these results and reveals that the relatively well-off, rather than the poor or the wealthiest, and the locally well-connected have a higher likelihood of program participation and, on average, receive a greater number of input coupons. Since a key program objective is to increase food security and income among resource-poor farmers, the lack of targeting is a concern and should underlie considerations of alternative targeting approaches that, in part or completely, rely on proxy means tests at the local level.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
set of 20 household accounts integrated into the social accounting matrix. Of these 20 households, ten are rural and ten are urban, ranked according to income. Due to some problems with the consistency of the household data, however, we employ one representative household in this model.

.

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In appendix A, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. Excluding those East African Customs Union countries that are also COMESA members, COMESA includes Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius, Seychelles, Sudan, Swaziland, Zambia and Zimbabwe.<sup>22</sup> Trade shares for the ―Africa‖ region in our model are the sum of East Africa Customs Union plus COMESA. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in appendix A. We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in tables A2 and A3 of appendix A.

# **Tariff Data**

**Tariff and Sales Tax Data** .  We started with MFN tariff rates at the eight digit level taken from

the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php. These tariff</u> rates were then aggregated to the sectors of our model, using simple averages.

We obtained data on the total taxes on imports and the total value of imports and took the ratio to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent.<sup>23</sup> That is,

> 22 To avoid double counting we exclude from COMESA the members of the East African Customs Union. 23 Economic Survey (2006, pp. 103, 115).

22
**Table 12: Main Panel Regressions, Georgia**

||Percentage<br>perm full-ti<br>since D|change of<br>me workers<br>ec 2019|Decreased t<br>of temp w|ot number<br>orkers|Decreased to<br>opera|tal hours of<br>tions|Anticipate<br>arrears on<br>liabi|falling in<br>outstanding<br>lities|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Received national or local|0.034|0.008|-0.090|0.049|0.054|-0.449|-0.473**|-0.197|
|government support|(0.147)|(0.111)|(0.309)|(0.416)|(0.364)|(0.338)|(0.235)|(0.248)|
|Average change in monthly sales<br>compared to one year ago|0.111|0.080|-1.181*|-1.602**|-2.556***|-2.857***|-2.972***|-2.574***|
||(0.176)|(0.206)|(0.653)|(0.719)|(0.652)|(0.749)|(0.621)|(0.731)|
|Decreased demand for products or<br>services compar to one year ago|0.018|0.170*|0.710**|0.739|1.459**|0.924*|1.241*|1.545*|
||(0.071)|(0.087)|(0.357)|(0.628)|(0.580)|(0.544)|(0.662)|(0.916)|
|Decreased supply of inputs<br>compared to one year ago|-0.241**|-0.448***|0.052|0.350|1.135***|1.245***|-0.712|-0.693|
||(0.114)|(0.167)|(0.338)|(0.390)|(0.358)|(0.345)|(0.453)|(0.543)|
|Decreased liqdt or cash flow avail.<br>since COVID-19 began|-0.062|-0.258|-0.170|0.292|-0.598*|-0.525|0.850|0.459|
||(0.086)|(0.174)|(0.555)|(0.606)|(0.348)|(0.423)|(0.739)|(0.893)|
|Overdue on obligations to financial<br>institutions|-0.034|0.118|0.696|0.632|0.134|0.499|1.535***|1.044*|
||(0.058)|(0.101)|(0.488)|(0.510)|(0.507)|(0.357)|(0.455)|(0.633)|
|Ever temporarily closed during the<br>COVID-19 outbreak|-0.567***|-0.410|0.104|-0.302|0.354|0.450|-0.131|-0.097|
||(0.086)|(0.251)|(0.359)|(0.344)|(0.306)|(0.274)|(0.440)|(0.558)|
|Adjusting or converting their<br>production or services|0.119|0.325**|-0.137|-0.600**|0.736**|1.149**|0.335|0.422|
||(0.113)|(0.144)|(0.280)|(0.258)|(0.314)|(0.480)|(0.309)|(0.407)|
|Started or increased online business<br>activity|-0.402|-0.596**|0.202|0.439|-0.629|-0.496|-0.776*|-0.810|

46