rafmacalaba/datause-encoder-data huggingface dataset:
37 expectations art. assumed to be based linearly on the previous perlod's price. A priori, a positive change will provide an Incentive to raise the rate of new planting and a negative change to reduce it.

Using non-nested tests, the specifications based on AREVPROD and either NEWCAPR2 or KNPLR2 , which were generally very similar, twere compared with the results obtained with PRICOCO(-1) and PRYCOCO(-2) (Table 7) replacing AREVPROD(-1) and AREVPROD(-2), respectively. The tests given in Table 8 favor the model based on PRICOCO.

Whereas the variables AREVPROD and PRICOCO are highly correlated, the regression of AREVPROD on PRICOCO leaves a substantial unexplained variance essentially because the former depends by definition on the average yield of OLDARBA, which has shown very substantial variation. If, however, the relevant variable is the expected yield on new area, which **may** differ from that on OLDAREA. then the final choice of equation can be rationalized.

The results presented in Table 7 show that all explanatory variables have t-ratios of about 2.0 or greater. The price and the interest rate effects are precisely measured and have the a priori expected signs. The R- bar-squared statistic has a value of .9522 and the diagnostic tests presented in the lower part of Table 7 are all satisfactory. The CUSUM and CUSITMSQ plots were examined ; the former deviates markedly from the zero axis suggesting that at least some of the estimated coefficients **may** not be robust. The recursively estimated coefficients plotted in Figures 14 through 18 and these suggest that the coefficients of NEWCAPR2(-1) and CHPRI are
# **Table 9 Determinants of the Sharpe Ratio**

_ROE_ The dependant variable is Sharpe ratio constructed as where ROE is return on equity and _SROE_ SROE represents standard deviation of return on equity. _Assets_ is the natural logarithm of total assets in US $. e _quity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflationadjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the squares of fee income and non-deposit funding, respectively. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. Both the dependent and independent variables are mean values over years. We obtain bank level data from Bankscope and macroeconomic data from the World Development Indicators (WDI) of World Bank. We estimate all regressions with mean data and clustering at country level. Standard errors are in parentheses. *, ** and *** denote significance at 10%, 5% <u>and 1%, respectively.</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|-0.076|-0.219|-0.153|-0.218|
||(0.122)|(0.164)|(0.145)|(0.166)|
|Equity|0.578|-0.943|0.344|1.150|
||(1.245)|(1.265)|(1.162)|(0.977)|
|Assets growth|1.825|1.234|1.480|1.511|
||(2.451)|(2.253)|(2.493)|(2.378)|
|Overhead|-5.221**|-8.047**|-7.624***|-5.703*|
||(2.439)|(3.840)|(2.537)|(3.205)|
|Fee income|-3.267***||-4.140***|1.652|
||(0.861)||(1.138)|(2.483)|
|Non-deposit funding||1.256|3.629**|8.886*|
|||(0.916)|(1.522)|(5.052)|
|Fee income sq||||-6.338***<br>(2.128)|
|Non-deposit funding sq||||-6.652<br>(5.736)|
|Inflation|-3.507|-1.668|-0.696|0.412|
||(4.100)|(4.235)|(3.938)|(3.995)|
|GDP growth|-22.007**|-19.071**|-16.882**|-12.466*|
||(9.544)|(9.334)|(8.063)|(7.404)|
|GDP per capita|0.056**|0.062**|0.052**|0.054**|
||(0.023)|(0.024)|(0.023)|(0.024)|
|N|918|918|905|905|
|R-sq|0.099|0.072|0.110|0.120|
|Country fixed effects|N|N|N|N|
|Time fixed effects|N|N|N|N|
|Clusteringlevel|Country|Country|Country|Country|

47
# **Insurance, Credit, and Technology Adoption: Field Experimental Evidence from Malawi**<sup>∗</sup>

## Xavier Giné

Development Economics Research Group, World Bank and Bureau for Research and Economic Analysis of Development (BREAD) Dean Yang Ford School of Public Policy and Department of Economics, University of Michigan; Bureau for Research and Economic Analysis of Development (BREAD); and National Bureau of Economic Research (NBER)

**Keywords** : risk-sharing, insurance, credit, microfinance, technology adoption

> ∗ Gine: <u>xgine@worldbank.org. Yang: deanyang@umich.edu. We gratefully acknowledge financial support</u> from CRMG (World Bank) and in particular the support of Erin Bryla, Shadreck Mapfumo and Frank Masanka. We also thank Ephraim Chirwa and the Wadonda team for their efforts in collecting the data. We received valuable feedback and suggestions from Chris Ahlin, Steve Boucher, Adriana Lleras-Muney, Eldar Shafir, and participants in several seminars. Paola de Baldomero Zazo, Jessica Goldberg and Cheryl Raleigh provided superb research assistance.
# <u>Table 3: Compliance with Assignment to Entrepreneurship Track (Marginal Effects)</u>

||<br>**Training c**|<br>**ompletion**|<br>**Training**<br>**comp**|<br>**& coaching**<br>**letion**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Unemployment rate in governorate|0.01*|0.00|0.01*|-0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Subject: Economics, Finance, Business|-0.16*|-0.20*|-0.18*|-0.18*|
||(0.04)|(0.05)|(0.04)|(0.05)|
|Male|-0.09**|-0.08**|-0.11*|-0.10**|
||(0.04)|(0.04)|(0.04)|(0.04)|
|Applied in pair|0.12*|0.12*|0.06|0.06|
||(0.04)|(0.04)|(0.04)|(0.04)|
|Had a project idea|0.09***|0.12**|0.05|0.09***|
||(0.05)|(0.05)|(0.05)|(0.05)|
|Family can provide financial support|0.13*|0.06|0.17*|0.01|
||(0.04)|(0.04)|(0.04)|(0.05)|
|Is willing to take risk|0.01|0.03|0.01|0.03|
||(0.07)|(0.07)|(0.07)|(0.08)|
|Preference for stable salary|-0.01|-0.00|-0.01|0.02|
||(0.04)|(0.04)|(0.04)|(0.05)|
|Household income 301-500 TND (ref = <300|0.06|0.07|0.03|0.04|
||(0.04)|(0.04)|(0.05)|(0.05)|
|Household income 501-800 TND|0.07|0.09***|0.05|0.06|
||(0.05)|(0.05)|(0.05)|(0.06)|
|Household income> 800 TND|-0.02|0.02|-0.06|-0.02|
||(0.05)|(0.05)|(0.05)|(0.05)|
|University of Sousse (ref = Tunis)||0.02||0.20*|
|||(0.07)||(0.06)|
|University of Monastir||-0.01||0.20*|
|||(0.06)||(0.05)|
|University of Kairouan||0.11***||0.25*|
|||(0.06)||(0.05)|
|University of Sfax||-0.22*||-0.19*|
|||(0.07)||(0.07)|
|University of Gafsa||0.12***||0.29*|
|||(0.07)||(0.06)|
|University of Gabes||-0.09||-0.07|
|||(0.09)||(0.09)|
|University of Jendouba||0.15**||0.15**|
|||(0.06)||(0.07)|
|Number of observations|856|856|856|856|
|R2|0.063|0.093|0.0611|0.1184|

Notes: Results shown are marginal effects from a probit model on determinants of compliance (defined as "training completion" in specification (1) and (2), and "training and coaching completion" in specifications (3) and (4)) among sutdents assigned to the entrepreneurship track. 67% of selected students completed the training, 59% the training and coaching. * p<0.01, ** p<0.05, *** p<0.1 33
**Table 2: Key household** **<u>characteristics</u>**

||**Total**||**Quartile of as**<br>|**set distributi**<br>|**on**|
|---|---|---|---|---|---|
|||Bottom|2<sup>nd</sup>|3<sup>rd</sup>|Top|
|Head's age (years)|45.4|42.2|43.9|46.3|49.2|
|Head's education (years)|4.7|2.6|4.1|4.9|7.2|
|Female headed household (%)|17.2|21.7|18.7|14.4|14.0|
|Household size|6.3|4.5|6.0|6.9|7.8|
|Knowledge of land law at the household level (%)|40.7|26.1|46.1|43.1|47.6|
|Value of household assets now (US $)|1194.2|33.8|141.5|352.6|4257.7|
|Value of livestock assets now (US $)|197.6|8.5|43.5|186.4|553.2|
|Value of all non-land assets now (US $)|1408.9|46.7|191.5|552.2|4854.9|
|Value of land owned (US $)|1521.0|444.8|701.8|1135.4|3813.1|
|Value of land occupied (US $)<sup>1</sup>|783.4|116.7|195.5|677.3|2126.1|
|Total value of land (US $)|2304.5|561.5|897.4|1812.6|5939.2|
|Value of land and non-land assets (US $)|3760.2|611.9|1101.7|2388.7|10943.2|
|Number of parcels owned|1.6|1.7|1.4|1.5|1.6|
|Number of parcels occupied|0.4|0.2|0.4|0.4|0.8|
|Total Number of parcels|2.0|2.0|1.7|1.9|2.4|
|Area of land owned in acres|4.8|3.4|2.5|3.3|9.9|
|Area of land occupied in acres|0.7|0.2|0.5|1.0|1.0|
|Number of parcels|2.3|2.3|2.1|2.2|2.7|
|Engaged in crop production (%)|93.7|92.2|98.8|95.1|88.8|
|Engaged in wage employment (%)|15.9|6.6|11.6|12.8|32.6|
|Engaged in business activities (%)|32.6|24.6|33.6|36.6|35.5|
|Engaged in selling own livestock products (%)|52.6|28.3|57.7|63.0|61.6|
|Crop income (US $)|247.1|127.9|192.6|381.2|287.0|
|Wage income (US $)|167.8|28.9|44.2|70.7|528.3|
|Business income (US $)|110.4|40.8|68.1|88.0|245.1|
|Livestock products income (US $)|43.8|6.9|4.4|18.5|145.8|
|Total income (US $)|569.2|204.6|309.3|558.5|1206.2|
|Share of crop income in total (%)|43.4|62.5|62.3|68.3|23.8|
|Share of wage income in total (%)|29.5|14.1|14.3|12.7|43.8|
|Share of business income in total (%)|19.4|20.0|22.0|15.8|20.3|
|Share of livestockproducts in total(%)|7.7|3.4|1.4|3.3|12.1|
|Number of observations|970|244|241|243|242|

<u>1 Occupied parcels are evaluated using the median prices corresponding to the respective communities. Rented-in (both under fixed</u> and sharecropping contracts) and borrowed-in parcels are excluded while computing the value of land occupied by the household. _Source:_ Own computation from 2004 WB/Makerere/FASID land tenure survey.

23
**Table 8. Impact of out-migration**

||(1)|(2)|
|---|---|---|
|||Second stage|
|Variables|OLS-IHHFE|2SLS-IHHFE|
|Migrated across survey waves (1=mover, 0=stayer)|0.194***|0.582***|
||(0.058)|(0.142)|
|Male|0.002|0.003|
||(0.003)|(0.004)|
|Unmarried|0.009|0.001|
||(0.005)|(0.007)|
|Unmarried male|0.000|-0.000|
||(0.006)|(0.006)|
|Age category (reference: aged 10-14)|||
|Aged 15-24|0.006|-0.004|
||(0.005)|(0.006)|
|Aged 25-34|0.004|-0.006|
||(0.006)|(0.007)|
|Aged 35-49|0.002|-0.001|
||(0.006)|(0.006)|
|Aged 50-65|0.011*|0.013*|
||(0.006)|(0.007)|
|Aged 66 plus|0.008|0.015*|
||(0.008)|(0.009)|
|Number of effective years of schooling completed|0.002***|0.001*|
||(0.001)|(0.001)|
|Constant|-0.032***||
||(0.009)||
|Cragg-Donald Wald F statistic||7.854|
|Sargan's statistic||8.431|
|Sargan p-value||0.208|
|Endogeneity test of mover (Hausman Chi-square)||6.042|
|Endogeneity test of mover (Hausman p-value)||0.0140|
|Observations|11,593|11,338|
|Number of households|2,602|2,400|
|Initial Household Fixed Effects|||
|Robust standard errors in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|Source: Authors’ calculations with UNPS.|||

27
Table 2: Actual & Implied Employment Share Gap

||Industry<br>Manufacturing|Industry|Manufacturing|
|---|---|---|---|
||All Comparator<br>Countries<br><br>|A<br>|sia Only<br>|
||(1)<br>(2)|(3)|(4)|
|Actual Gap|11.1<br>9.9|11.6|10.1|
|Final Consumption|5.2<br>7.1|3.8|6.5|
|Trade|9.9<br>8.1|10.0|8.0|
|Sectoral Linkages|7.5<br>6.7|7.4|6.1|
|Wage Gap|6.2<br>7.3|8.4|8.7|
|Labor Intensity|14.2<br>10.8|15.2|11.4|

The first row reports the actual gap in industrial/manufacturing employment share. The following rows report the implied gap remaining when the variables pertaining to the listed factor is counterfactually changed to a comparator country, keeping other variables unchanged. All figures are averages across the Latin American and the comparator set countries. All figures in percentage points.

variables of the Latin American countries with those of the comparator set. The lower the values in these rows, the higher is the contribution of the factor in explaining the aggregate gap.

On average, the share of employment in the broad industrial sector in a Latin American economy is 11 percentage points lower compared to the comparator set. The counterfactual exercise reveals that more than half of this gap is accounted for by differences in final consumption shares. Cross-country differences in sectoral wage gaps and linkages are also significant. Differences in wage gaps account for roughly 5 percentage points while variations in sectoral linkages explain a little over 3.5 percentage points which represent a third of the total gap. Differences in trade shares, on the other hand, play a muted role and can account for only a tenth of the gap in industrial employment shares. Finally, the implied gap across the Latin American economies and the comparator set increases when the value-added shares in the former are counterfactually brought to the levels present in the latter. This implies that the intensity of intermediate use in the industrial sector in Latin America relative to other sectors of the economy is lower compared to the comparator countries.

Column (2) reports the result for the manufacturing sub-sector which constitutes the bulk of the gap. The findings for the sub-sector are somewhat different compared to the aggregate industrial sector. Though differences in final consumption and wage gaps are still significant, they can only account for a quarter of the manufacturing gap. The relevance of differences in trade patterns in explaining the employment share gap is more pronounced in manufacturing. The evidence justifies the importance of trade in manufacturing compared to the other industrial sub-sectors which are arguably less tradable. Nonetheless, the influence of trade in explaining the manufacturing gap wanes compared to what can be explained by differences in final 9
|**Panel C:**|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Electronics**||Electr|onics||Inter|mediate electr|onics||Final electro|nics|||
|(Selection|||||||||||||
|intermediate<br>imports)|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|
|(Dummy share|||||||||||||
|2004-2010 > 15)|-0.413***|||-0.127*|-0.238**|||-0.113|-0.491***|||-0.139*|
||(0.058)|||(0.068)|(0.104)|||(0.127)|(0.069)|||(0.079)|
|(Dummy share|||||||||||||
|<br>2004-2010 > 10)||-0.500***||-0.241***||-0.262***||-0.062||-0.598***||-0.308***|
|||(0.051)||(0.065)||(0.093)||(0.119)||(0.061)||(0.077)|
|(Dummy share|||||||||||||
|2004-2010 > 5)|||-0.536***|-0.394***|||-0.380***|-0.326***|||-0.598***|-0.420***|
||||(0.050)|(0.054)|||(0.099)|(0.104)|||(0.058)|(0.063)|
|Observations|49,933|49,933|49,933|49,933|13,836|13,836|13,836|13,836|36,097|36,097|36,097|36,097|
|R-squared|0.910|0.910|0.910|0.911|0.922|0.922|0.922|0.922|0.903|0.903|0.903|0.904|
|Treated|789|1158|1838|1838|241|358|583|583|548|800|1255|1255|
|Total country-|||||||||||||
|products|3970|3970|3970|3970|1033|1033|1033|1033|2937|2937|2937|2937|

Note: Robust standard errors clustered at the cluster importer-product level are in parentheses. All specifications include importer-year, importer-product, and product-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan. *** p<0.01, ** p<0.05, * p<0.1 25
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36
**Table 11: Resource requirements for full achievement of MDGs fulfilled with foreign grants**

||Accelerate<br>growth|d|Baseline|Accelerate<br>growth|d|Baseline|
|---|---|---|---|---|---|---|
||billio|ns of|USD|%|of G|DP|
|_2005_|0.6||0.7|5.8||6.5|
|_2006_|0.4||0.5|3.6||4.4|
|_2007_|0.5||0.6|4.3||5.2|
|_2008_|0.7||0.8|5.3||6.2|
|_2009_|0.8||0.9|5.7||6.7|
|_2010_|1.0||1.2|6.8||7.9|
|_2011_|1.1||1.3|6.7||7.9|
|_2012_|1.5||1.7|8.1||9.5|
|_2013_|1.7||2.0|8.5||10.0|
|_2014_|2.2||2.6|10.2||12.0|
|_2015_<br>|2.6||3.1|10.9||12.8|
|_cumulative_|||||||
|_2005-2015_|13.0||15.4||||

_Note_ : The 'accelerated growth' column shows the resource requirements needed to reach the full set of MDGs under foreign grant financing when the initial point is the accelerated growth scenario. The 'baseline' column shows the same resource requirements when the initial point is the baseline scenario.

24
# **Table 8. The Effect of PMT on Height-for-Age of Children 6-60 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poo<br>OL<br>|led<br>S<br>|Comm<br>Fixed-<br>|unity<br>Effects<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**_A. 6-12 Months_**|||||
|Year Dummy (2000=1)|0.021|0.087|-0.047|-0.064|
||(0.170)|(0.170)|(0.341)|(0.356)|
|_Exposed Share_*Year|-0.044|-0.122|0.220|0.241|
||(0.187)|(0.190)|(0.476)|(0.500)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.14|0.16|0.57|0.58|
|**_B. 12-24 Months_**|||||
|Year Dummy (2000=1)|-0.121|-0.116|-0.208|-0.221|
||(0.104)|(0.105)|(0.144)|(0.151)|
|_Exposed Share_*Year|0.042|0.042|0.464**|0.528**|
||(0.125)|(0.126)|(0.229)|(0.237)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.08|0.09|0.40|0.41|
|**_C. 24-60 Months_**|||||
|Year Dummy (2000=1)|-0.055|-0.043|-0.022|-0.028|
||(0.058)|(0.058)|(0.071)|(0.073)|
|_Exposed Share_*Year|-0.107|-0.127|-0.079|-0.064|
||(0.117)|(0.118)|(0.172)|(0.175)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.15|0.15|0.26|0.26|

Note: Dependent variable is child height for age z-score. _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. Other covariates (with coefficients reported in Appendix Tables A8.1, A8.2 and A8.3) include: gender (boy=1), age (months), father and mother’s education and height, male head of household, farm household, household access to private toilet, sanitation and free health services, distance of village to bus station, community’s access to sewerage and piped water, number of village midwives, presence of a _posyandu_ in the community, and an indicator for whether the community is urban. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5% and 1%.

34
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. Various years(a). Poland: Enterprise and Financial Sector Adjustment Loan Project Files.

. Various years(b). Hungary: Enterprise and Financial Sector Adjustrnent Loan Project Files.
realized production and any crop losses incurred. In the case of Ethiopia, we furthermore have crop cut data for a sub-sample of plots.<sup>19</sup> The data are available publicly in the World Bank Microdata Library<sup>20</sup> and cover almost 20,000 plots across over 8,000 farms. We complement this data with data from a crop cut and survey experiment, the _Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety_ (MAPS) in Uganda. Table 1 summarizes our data sources.

The main unit of analysis for this paper is the plot with separate information for each crop cultivated on the plot. This high level of disaggregation is a feature of our data compared to other approaches that typically analyze losses at the farm level or higher.<sup>21</sup> In Ethiopia, the analysis focuses on two of the major staple crops in the country, maize and sorghum, while in Malawi the paper analyzes maize as the country’s foremost crop cultivated. Furthermore, we also build a plotlevel aggregate of total harvest value on the plot (i.e. across all crops cultivated for every plot of a farm) in local currency units in Malawi to verify the robustness of our analysis to the inclusion of all crop types. The analysis centers around the main agricultural season in each of the two countries, the _Meher_ season in Ethiopia and the rainy season in Malawi. Where applicable, the paper expresses production quantities in total kilogram terms as well as in per hectare terms. The plot sizes used are measured by GPS if available or for small plots with rope and compass in the case of Ethiopia. To correct for outliers in self-reported harvest and input variables as well as our crop losses estimates, per hectare variables are winsorized at the 0.01 percentile.

_Table 1:_ Data sources and sample sizes.

|**Country**|**Data Source**|**Sample Size**|**Survey Rounds**|
|---|---|---|---|
|Ethiopia|Ethiopian Socioeconomic Survey (ESS)|13,570plots|2013/14, 2015/16,<br>2018/19|
||||2010/11, 2013/14,|
|Malawi|Integrated Household Panel Survey (IHPS)|5,998plots|2016/17,2019/20|
|Uganda|Methodological Experiment on Measuring Maize<br>Productivity, Soil Fertility and Variety (MAPS2)|213 plots|2016|

> 19 Crop cuts have been termed the “gold standard” to obtain an objective measure of crop yield (Carletto, Gourlay and Winters, 2015).

> 20 https://microdata.worldbank.org/index.php/catalog/lsms

> 21 However, it is important to note that in case of urgent need of swift policy action in response to a sudden disaster, more aggregate approaches to measuring crop losses retain validity for the relative speed at which they can be conducted.

10