rafmacalaba/datause-encoder-data huggingface dataset:
least government intervention or regulatory burden.<sup>24</sup> We regress our measures of employment-intensive growth in manufacturing and the secondary sector and of agricultural productivity-intensive growth against the overall score and the 10 indexes. Table 10 presents the results.

Most of the results are contrary to what could be expected. The overall score is significant only for employment-intensive growth in the secondary sector, with more “freedom” being damaging for employment growth. Business regulation affects mostly productivity-intensive growth in agriculture, with a negative effect. The results suggest that openness to trade as measured by import and export taxes limits employmentintensive growth in the secondary sector and in manufacturing. Macroeconomic stability and lack of price controls appear to promote employment-intensive growth in manufacturing but to hamper productivity growth in agriculture. Financial freedom is harmful to employment-intensive growth in manufacturing and the secondary sector, and the index of property rights is negatively correlated with agricultural productivity growth. Therefore, aside from monetary stability, the effects of a lower regulatory burden are contrary to the general wisdom, as they do not reduce poverty. The same results hold if each of these variables is regressed separately (rather than jointly) on our measures of employment and productivity growth.

In addition to the above-mentioned variables we use a more traditional measure of trade: the share of imports and exports in value added (openness). We also asses whether the situation of being landlocked, foreign direct investment, the terms of trade or the real effective exchange rate affect the growth pattern.<sup>25</sup> The measures come from Sinnott and Lopez (2003).

> 24 The 10 freedoms measured are the following: Business Freedom measures the costs and the time it takes to open and close a business; the Trade Freedom index measures taxes on imports and exports; Fiscal Freedom measures the tax burden on individuals and corporations and as a percent of GDP; Freedom from Government measures expenditures as a percent of GDP and revenue from state-owned enterprises; Monetary Freedom measures price stability and price controls;  Investment Freedom measures the regulatory bias against foreign direct investment; Financial Freedom measures government intervention in the financial system, including ownership and regulation;  Property Freedom measures the ability and guaranties to accumulate private property; the Freedom from Corruption index measures the perception of government corruption; and Labor Freedom measures hiring and firing restrictions as well as non-wage labor costs (only available since 2007).

25 Terms of trade are measured as the price index of exports over the price index of imports; the real effective exchange rate index accounts for the existence of distortions between the tradable and nontradable sector and is defined at the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs.

28
# Table 1

# Probability of Being Seen by Different Categories of Physicians and Nurses at Aybar Hospital by Level of Training and Setting

|Emerge|ncy|Inpatient|Outpa|tient|
|---|---|---|---|---|
|Category<br>of<br>N!Uber ot P<br>Medical Staff<br>Contact|atient<br>sa<br>%|Number of Patient<br>Contactsa %|Number of P<br>Contact|atient<br>sa<br>%|
|Chief of Staff/<br>Staff Physician<br>98|3.3|6<br>4.9|1<sup>,</sup>336<sup>b</sup>|86.2|
|Resident III-IV<br>216|7.2|40<br>32.5|87|5.6|
|Resident I-II<br>1006|33.7|54<br>43.9|121|7.8|
|Intern<br>1669|55.8|23<br>18.7|206|13.3|
|Graduate<br>Nurse<br>62|8.9|137<br>17.9|1|1.0|
|Auxilliary<br>Nurse<br>634|91.1|629<br>82.1|110|99.0|

Source: The Urban Institute

- a. A contact does not necessarily represent the amount of time a physician or nurse spent with a patient, but the total number of staff contacts with patient(s). Thus one chief of staff physician could have contact with 6 inpatients, six chiefs of staff could have treated one patient or some multiple that produces six contacts between inpatient(s) and chiefs of staffs.

- b. Nineteen general practitioners represent twenty-five percent of staff physicians and provide about forty percent of outpatient consultations.
Comparing the percentage of recent youth migrants working in certain occupations to the percentage of older migrants and the percentage of native youth working in these jobs shows that young migrants tend to be more clustered in certain occupations. For example, in Spain, 22% of young male migrants are agricultural laborers, compared to 14% of 3050 year old migrants and less than 3 percent of native youth workers. Other jobs tend to be similar across immigrant age groups, but different from youth (e.g. in Argentina, 69% of young migrant women are in domestic service, compared to 68% of 30-50 year old migrant women, and only 20% of native young women). Yet others seem to be common for native and immigrant youth, but less common for older migrants, such as sales and cashier jobs for women.

In order to formally measure whether recent youth migrants are clustered in just a few jobs, I calculate a Herfindahl Index to measure the degree of occupational concentration. This is the sum of the squared proportions of workers in each occupation category. The index takes values between 0 and 1, with 0 being the least concentrated and 1 indicating complete concentration, with all workers working in the same occupation. This can only be done accurately for surveys with large samples and rich occupational coding, restricting the analysis to Spain, South Africa and the U.S. Calculating this index over all 475 occupation categories listed in the 2000 U.S. Census gives a Herfindahl index for recent male youth of 0.028, which is higher than the indices for older migrants and native youth (Table 9). We also see male migrant youth to be more concentrated in just a few jobs in Spain and South Africa. The results for females are more mixed, but in two out of

- 14 -
# **3. Calculating the AVE of Time in Importing and Exporting for the GTAP sectors and countries.**

Following Hummels et al. (2007), Minor combines the above data set with the World Bank’s _Doing Business_ data set for 2012. The _Doing Business_ data set shows the number of days in transit in each country for importing and for exporting goods. The _Doing Business_ data set is not distinguished by product, so it is assumed the same number of days applies to all products. Combining the Doing Business data set with the Minor data set mentioned in step 2 above, yields the AVE equivalents of the cost of time to export or import by product and country in the GTAP data set for imports and exports, where the AVEs are bilateral depending on the partner country.

# **4. Aggregating the AVE of the time in trade to the products and regions of our model.**

We start with the estimates of Minor (2013) described in step 3 of the bilateral ad valorem equivalents (AVEs) of the time in trade for exports and imports. We then aggregate these estimates to the products and regions of our model. The mapping of the GTAP sectors and regions to the sectors and regions of our model is described in Appendix A. The weights we use for the aggregation are bilateral trade weights, taken from the GTAP 8.1 data set, which is based on 2007 trade weights. There are four steps in the aggregation, which we describe in both words and mathematics. We specify the mathematics for exporting; the importing aggregation is fully symmetric and is omitted.

(i) **Total time costs of exporting product k from region r to region s** . We calculate the value of the total time costs of exporting product k from region r to region s by multiplying the AVEs of time costs of exporting product k from region r to region s by the bilateral exports if product k from region r to region s. The trade flow data are taken from GTAP 8.1 with the base year 2007.

Let = the value of exports of product k from region r to region s from the GTAP data set.

Let<sup>= the bilateral trade weighted ad valorem equivalent of the time in trade in exporting</sup> product k from region r to region s (from step 3 above).

96
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-10-

As in the Netherlands since 1974, so called "employer contributions" do not exist in Chile since 1981 in order to maximize worker awareness of the fact that funds contributed are their own individual property. As always, employers pay labor costs inclusive of "employee contributions".

Chilean contributors receive two types of benefits in exchange of contributions, cash benefits and other services, in exchange for fees. We discuss cash benefits first. In a pension system with a given contribution rate, benefits are determined by the rate of return obtained by the pension funds. In the Chilean -case, these have been much higher than expected, as shown in the next table, suggesting that the quality of service was very high.

Table 2 _Real Razes of '7eturn in Chilean Pension Fund_ ( % per year. above CPI inflation)

|Year|Return|Year|Return|Year|Return.|
|---|---|---|---|---|---|
|1981*<br>|12.9 %|1985|13.4 %|1989|6.9 %|
|1982<br>|28.5 %<br>|1986|12.3 %|1990|15.6 %|
|1983<br>|21.2 %|1987|_5.4_%|1991|29.7 %|
|19B4|3.6 %|1988|6.5 %|1992|_5.2_%|

: Only July to December. because AFPs started operations in may 1. 1981. Source: Boletin Superintendencia AFPs.

However, these high returns were due mostly to high returns generally in Chile. It is more meaningful to measure the quality of investment services by the difference between actual returns and potential returns at the same level of risk. In the Chilean case this difference has been remarkably small, as shown in econometric work by Walker (1991). In other words, Chilean workers were able to sbare in booming bond and stock markets in Chile during the 1980's. This is much better than the large negative differences observed in many state-managed pension systems.

We discuss now other services. The five main services provided by AFPs to. active workers a..bnle are.<sup>3</sup> a) Collection Services. This means receiving contributions every month, crediting it to the correct individual account, pursuing mismatches and 3 The services provided by insurance companies and insurance brokers are discussed below.
bureaucracy quality, democratic accountability, government effectiveness, and burden of government regulation are included in the final benchmark regression specification. Due to the presence of high correlation among variables, government effectiveness and burden of government regulation are included alone in regression specifications.

Two variables are included to control **_judicial determinants of corruption_** in our regression analyses: “law and order” from International Country Risk Guide Database and “rule of law” from the World Bank Institute’s Worldwide Governance Indicators Database. In the literature, several studies find a negative link between corruption and judicial determinants.<sup>17</sup> Since these variables are close substitutes, they are included one at a time in the initial regression specification. In our regression outcomes, given that higher values of these indexes indicate an improvement, both variables have the expected negative sign. But only the “rule of law” index has a statistically significant coefficient. Given that these two variables are close substitutes, only “rule of law” is included in the benchmark specification.

In the last set of control variables, **_geographical determinants of tax corruption_** are considered. In our regression analysis the variable included in this group is total natural resources rents (% of GDP) from The World Bank’s World Development Indicators. The link between corruption and natural resources has not been extensively researched. In one example, Leite and Weidmann (1997) present a negative relationship between corruption and the share of natural resources in GDP. In our regression results, the variable has an expected positive sign but its estimated coefficient is not statistically significant. Because the estimated coefficients of the tax simplification variables are robust to the inclusion or exclusion of the variable which captures natural resources rents, they are excluded in the benchmark regression specifications. The estimated coefficients are reported in column (12) of Table A2 in Annex.

As pointed out in the previous section, the value of tax corruption changes significantly across countries, even if they take place in the same income groups. Thus, country fixed effects

> 17 Iwasakia and Suzukib (2012), Revier and Elbahnasawy (2012), Evrensel (2010),  Tanzi (1998), Damania, Fredriksson, and Mani (2004), Herzfeld and Weiss (2003), Broadman and Recanatini (2000), and Ades and di Tella (1997).

21
**_Africa relative to other regions._** Table 13 puts Sub-Saharan Africa in perspective and compares key economic indicators with those of other developing regions in 1995– 2005. Inflation was single digit and only slightly below that of other regions. On aggregate, FDI and trade compare well with such indicators in other regions (there are issues, however, as discussed next). The growth rate was still behind that of the low- and middle-income countries but comparable to that of Latin America and the Caribbean. Savings and investments were still well below those of other regions. Both private consumption and government consumption were higher than those of other regions, reflecting the low income of African countries.

<!-- Start of picture text --> Figure 15: Non-Oil Exports as Percent of GDP Figure 16: Africa’s Share of World Trade<br>(SSA versus other regions)<br>50 8%<br>1983-85 1993-95 2003-05<br>45 i i i 7%<br>40<br>6%<br>35<br>30 5%<br>25 4%<br>20<br>3%<br>15<br>2%<br>10<br>5 1%<br>0 0%<br>East Asia and Eastern Europe Latin America Middle East South Asia Sub-Sahara 1948 1953 1963 1973 1983 1993 2003 2004 20<br>Pacific and Formal & Caribbean and North Africa<br>Soviet Union Africa Source: IMF and World Bank database<br>Note: Export shares are unweighted averages.<br>Source: IMF World Economic Outlook<br>Figure 17: Concentration and Diversification of Figure 18: Net FDI as % of GDP by region<br>Export: 2000-2004 Average (SSA versus other regions)<br>90<br>80<br>6<br>70<br>East Asia & Pacific<br>60 5<br>50 Latin America & Carib<br>4<br>40<br>Europe & Central Asi<br>30 3<br>20 Sub-Saharan Africa<br>2<br>10<br>South Asia<br>0<br>1<br>Europe and Central East Asia- Pacific South Asia Latin America and Middle East and Sub Saharian Africa<br>Asia Caribbean North Africa Middle East & North<br>0<br>Export Concentration Index (0-100) Share of Top 5 Products in Total Exports (%) No. of Exported Product Categories<br>left axis left axis right axis<br>Source: World Bank various years h. Source: World Bank various years g.<br>1990199119921993199419951996199719981999200020012002200320042005<br>A fric a 's s h a re o f w o rld e x p o rts<br>N o no il e x p o r ts a s sh a r e o f G D P (p e r c e n t<br>F D I a s % o f G D P<br><!-- End of picture text --> 36
# **Eyes in the Sky, Boots on the Ground**

# **Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis in Uganda**<sup>**1**</sup>

David B. Lobell<sup>a</sup> , George Azzari<sup>b</sup> , Marshall Burke<sup>c</sup> , Sydney Gourlay<sup>d</sup> , Zhenong Jin<sup>e</sup> , Talip Kilic<sup>f</sup> , and Siobhan Murray<sup>g</sup>

# **JEL Codes:** C83, Q12.

**Keywords:** Maize, Crop Yield Estimation, Crop Cutting, Remote Sensing, Uganda.

1 Following the lead author, the co‐authors are listed alphabetically. a Corresponding author. Professor, Department of Earth System Science, and the Center on Food Security and the Environment (FSE), Stanford University, Stanford, CA. <u>dlobell@stanford.edu.</u><sup>b</sup> Research Associate, Department of Earth System Science and the FSE, Stanford University, Stanford, CA. gazzari@stanford.edu.<sup>c</sup> Assistant Professor, Department of Earth System Science and the FSE, Stanford University, Stanford, CA. mburke@stanford.edu.<sup>d</sup> Survey Specialist, Living Standards Measurement Study (LSMS), Development Data Group (DECDG), Rome, Italy. <u>sgourlay@worldbank.org.</u><sup>e</sup> Postdoctoral Scholar, Department of Earth System Science and the FSE, Stanford University, Stanford, CA. jinzn@stanford.edu.<sup>f</sup> Senior Economist, LSMS, DECDG, World Bank, Rome, Italy. <u>tkilic@worldbank.org.</u><sup>g</sup> Technical Specialist, LSMS, DECDG, World Bank, Washington, DC. <u>smurray@worldbank.org. The lead principal investigators were Lobell and Kilic on the</u> Stanford University and the World Bank front, respectively.
Table A10: Impact on Indexes calculated with principal component method

|_Dep. Var. (Yt):_|_HIV_|_knowledge_||_HI_|_V attitudes_||_H_|_IV testing_||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||_Full Sample_|_Females_|_Males_|_Full Sample_|_Females_|_Males_|_Full Sample_|_Females_|_Males_|
|Treated|0.184***|0.302***|0.089|0.177***|0.156***|0.207***|0.146**|0.161*|0.131|
||(0.055)|(0.079)|(0.083)|(0.042)|(0.059)|(0.055)|(0.057)|(0.085)|(0.083)|
||[0.002]|[0.001]|[0.288]|[0.000]|[0.021]|[0.001]|[0.011]|[0.069]|[0.225]|
|Treated*Y_t−_1|-0.066*|-0.016|-0.114**|-0.011|-0.006|-0.014|-0.028|-0.039|-0.035|
||(0.039)|(0.049)|(0.045)|(0.040)|(0.051)|(0.053)|(0.033)|(0.042)|(0.050)|
||[0.257]|[0.932]|[0.043]|[0.787]|[0.932]|[0.798]|[0.630]|[0.725]|[0.731]|
|_Yt−_1|0.433***|0.420***|0.442***|0.436***|0.487***|0.391***|0.543***|0.591***|0.492***|
||(0.031)|(0.041)|(0.033)|(0.032)|(0.040)|(0.046)|(0.026)|(0.034)|(0.038)|
|Observations|4,986|2,323|2,663|4,986|2,323|2,663|4,971|2,320|2,651|
|R-squared|0.208|0.240|0.204|0.188|0.247|0.157|0.279|0.378|0.195|
|Controls<sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep var (Control)|0.189|0.177|0.199|0.119|0.188|0.0594|0.0895|0.236|-0.0391|
|P-value test joint sig|0.000|0.000|0.138|0.000|0.012|0.001|0.008|0.064|0.059|
|_Dep. Var. (Yt):_|_Attitudes tow_|_ards risky _|_sex-_|_Risky sexual _|_behavior (_|_for sexu-_||||
||_ual behavior_|||_ally active)_||||||
||(1)|(2)|(3)|(4)|(5)|(6)||||
||_Full Sample_|_Females_|_Males_|_Full Sample_|_Females_|_Males_||||
|Treated|0.042|-0.025|0.112**|0.060|0.151|0.029||||
||(0.036)|(0.056)|(0.056)|(0.075)|(0.166)|(0.140)||||
||[0.432]|[0.661]|[0.096]|[0.432]|[0.622]|[0.836]||||
|Treated*Y_t−_1|0.017|0.007|0.022|-0.166**|-0.263*|-0.150**||||
||(0.031)|(0.046)|(0.042)|(0.064)|(0.154)|(0.075)||||
||[0.571]|[0.887]|[0.607]|[0.026]|[0.236]|[0.098]||||
|Y_t−_1|0.278***|0.295***|0.260***|0.396***|0.386***|0.405***||||
||(0.024)|(0.037)|(0.036)|(0.054)|(0.145)|(0.064)||||
|Observations|4,973|2,320|2,653|1,682|760|922||||
|R-squared|0.113|0.147|0.101|0.265|0.146|0.125||||
|Controls <sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|Yes||||
|Mean Dep. Var. (Control)|0.0858|0.144|0.0346|-0.0488|0.837|-0.753||||
|P-value test joint sig|0.254|0.647|0.0418|0.370|0.509|0.339||||

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. P-values in square brackets corrected for multiple hypothesis testing using FWER. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. (a) Controls in each regression include: female, age, currently attending school, years of education, english spoken, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

xvii
<!-- Start of picture text --> 12 72.5<br>11.5 72<br>11 71.5<br>10.5 71<br>10 70.5<br>9.5 70<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual<br>accumulated consumption accumulated consumption<br>-41 18.5<br>18<br>-42<br>17.5<br>17<br>-43<br>16.5<br>-44<br>16<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual<br>accumulated consumption accumulated consumption<br>(period 1)<br>Date of final measurement<br>(period 0)<br>Date of bill issuance<br><!-- End of picture text --> Figure 1 Dates by Annual Accumulated Consumption _Notes:_ Dates are normalized so that May 1st, 2009 corresponds to day 1. Scatter points correspond to local averages computed by bins with a bandwidth of 1 unit of normalized AAC. Solid lines correspond to parametric fits generated from regressions that include a constant, a treatment dummy and a linear term for normalized AAC. Dashed lines are the 95% confidence interval.

19
<!-- Start of picture text --> Household Savings Rate<br>je a—~TM~.\<br>- OUN<br>geoe) VAfepentreo,Sy\\\\\\\\<br>a /<br>+ o/ y<br>N =o°FLZ \S‘<br>we ESwe- \NS<br>a NS<br>15 1 -5 ) 5 1<br>Household Savings Rate<br>--— == 2004 ------- 2006 ——— 2008 ——— 2010<br><!-- End of picture text -->