rafmacalaba/datause-encoder-data huggingface dataset:
poverty rate. This negative correlation between the overall rationing rate and the share of work going to women implies that women are more likely to be rationed than men.

In Dutta et al. (2012) we are able to directly measure rationing at the individual level in Bihar, using our specially designed surveys. Then we find direct evidence that the rationing rate is higher for women than men. In the first round of our survey (2008/09) the rationing rate ( _R_ ) was 85% for women in Bihar, versus 65% for men. This had narrowed somewhat by the second round (2009/10) to 85% versus 74%, but only because of greater rationing of men.

Gender differences in the opportunities available in the casual labor market can also be expected to influence demand for work. We find that the female market wage rate has a significant negative effect on women‘s share of the work provided, while the male wage rate has the opposite effect (Table 7).<sup>29</sup> The female wage relative to the male wage is the relevant variable. This suggests that there is an intra-household substitution effect; for example, when casual labor market opportunities are good for men but bad for women this makes it easier for women to get the (limited) number of jobs available on the scheme.<sup>30</sup> The wage effect is strong statistically, and greatly increases the explanatory power.<sup>31</sup> The negative effect of rationing on women‘s access to the scheme also persists when we control for differences in the wages received for private (non-PW) casual work. The final column gives our preferred regression in which the share of MGNREGS work going to women depends on the overall rationing rate— implying that women are more rationed than men—and the female market wage relative to the male wage.

> 29 We tested an encompassing specification in which the log of the male wage rate, log of the female wage rate and log of the MGNREGS wage rates entered separately. The homogeneity restriction that the sum of the coefficients equals zero could not be rejected (F(1,14)=0.41; prob.=0.53), but nor could we reject the null that it was the log of the female wage relative to the male wage that mattered, with the MGNREGS wage having no affect (F=1.49; prob.=0.26). Also, the MGNREGS wage rate on its own was not significant. So we opted for a specification in which it is the log of the relative wage that is the regressor as in Table 7. Table 7 also gives a specification with male and female wages entering separately.

> 30 A similar result was found by Datt and Ravallion (1994) in studying time allocation within households in response to the availability of work under Maharashtra‘s Employment Guarantee Scheme.

> 31 We also tested for an effect of the female share of other (non-public works) casual labor. This is endogenous but it allows us to control for local social norms that influence the propensity for women to do any casual wage labor. The new variable was not, however, significant and the coefficients on the other variables were affected little.

17
Robustness checks have also been undertaken, including additional control variables. It is also worth noting that city density is negatively associated to lower level of congestion, confirming that denser cities are more congested.

**Table 4**

**Regression Results of Low Congestion with overall restriction (country fe)**

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|<br>**PANDEMIC & LOCKDOWN EVOLUT**<br>***|<br>**ION**<br>***|<br>***|
|**Covid death (per Million)**<br>0.053<br>(0.003)|0.048<br>(0.003)|0.090<br>(0.0036)|
|**Stringency Index**<br>0.040***<br>(0.0004)|0.040***<br>(0.0005)|0.077***<br>(0.001)|
|**CONTROLS**|||
||1617***|3018***|
|**Working days**|-.<br>(0.030)|-.<br>(0.060)|
|**Min temp**|0.0007<br>(0.003)|0.002<br>(0.005)|
|**Avg temp**|-0.012***<br>(0.002)|-0.022***<br>(0.005)|
|**Precip**|-0.048***<br>(0.010)|-0.098***<br>(0.020)|
|**City density**||-0.003**<br>(0.001)|
|**COUNTRY FE**|||
|**Country FE**<br>Yes|Yes|Yes|
|**Time dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-1.541***<br>(0.385)|-0.859**<br>(0.427)|-3.472***<br>(0.877)|
|**N**<br>68076|68076|68076|
|**N cities**<br>186|186|186|
|**Wald χ2**<br>16227.92***|16319.18***|12899.60***|
|**Log likelihood**<br>-19162.402|-17563.367|-17343.466|
|**LR (chi2)**<br>1996.67***|2349.993***|2361.97***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

16
20 percentages are highest at the lowest levels of income and among the heaviest gas users. What is striking is that in half of the income-usage cells virtually everyone would be classified as fuel poor. Panel B presents the same shares once the HUS is received. In the first quartile of the distribution of income, for example, the HUS reduced the rate of fuel poverty by half. Massive improvements are also observed in the top income quartile, especially at high consumption levels.

Table 14 displays fuel poverty over time—without and with the HUS. Clearly, the HUS reduces fuel poverty by half. Yet, a good one-third of the households in Ukraine meets the definition of fuel poor, even when the HUS is in place.

# _5.B. Estimating Demand_

What would be the welfare effects of removing the HUS completely? We answer this question using two possible approaches. The first fits a gas demand function to households that use gas for space heating, assuming that the HUS serves as a subsidy on the price of gas per cubic meter. As shown in table 10 and figure 3, treating the HUS as a price subsidy increases substantially the variation in the effective price of gas, making it possible to fit a proper demand function.

The estimation results are displayed in col. (1) of table A.1. in the Appendix.<sup>21</sup> They show that the price elasticity of residential gas demand is -0.17, a figure that is very close to that (approximately -0.16) in Alberini et al. (2020) based on a sample from Transcarpathia. The

> 21 We remind the reader that the sample used for the regression of Appendix A includes only the HBS households that use natural gas for space heating, and only the 2017 and 2018 HBS data. This is because the one-part tariff with stable, uniform prices make it possible for us to compute the quantity consumed from the expenditure and HUS information. The tariff structure remained in place in 2019, but we are not satisfied with the quality of the data from the 2019 HBS at our disposal (the first release by Ukrstat), and for this reason we do not include the 2019 HBS data in our estimation effort.
**Figure 5-1: Simulated changes in the value of agricultural exports due to implementation of final accession commitments (Scenarios 2a and 2b) in million US$**

<!-- Start of picture text --> Paddy rice<br>Wheat<br>Cereal grains nec<br>Vegetables, fruit, nuts<br>Oil seeds<br>Sugar cane, sugar beet<br>Plant-based fibers<br>Crops nec Change due to final WTO implementation and +7%<br>Cattle,sheep,goats,horses productivity change primary agriculture<br>Animal products nec<br>Raw milk Change due to final WTO implementation<br>Wool, silk-worm cocoons<br>Forestry<br>Fishing<br>Meat: cattle,sheep,goats,horse<br>Meat products nec<br>Vegetable oils and fats<br>Dairy products<br>Processed rice<br>Sugar<br>Food products nec<br>Beverages and tobacco products<br>0 50 100 150 200<br>Change in exports in US$ mill.<br><!-- End of picture text --> <!-- Start of picture text --> Source: Own calculations.<br><!-- End of picture text -->

**Figure 5-2: Simulated changes in Ukrainian net agricultural trade due to implementation of final accession commitments (Scenarios 2a and 2b) in million US$**

<!-- Start of picture text --> Paddy rice<br>Wheat<br>Cereal grains nec<br>Vegetables, fruit, nuts<br>Oil seeds<br>Sugar cane, sugar beet<br>Plant-based fibers<br>Crops nec<br>Cattle,sheep,goats,horses<br>Change due to final WTO implementation<br>Animal products nec and +7% productivity change primary<br>Raw milk agriculture<br>Wool, silk-worm cocoons<br>Forestry Change due to final WTO implementation<br>Fishing<br>Meat: cattle,sheep,goats,horse<br>Meat products nec<br>Vegetable oils and fats<br>Dairy products<br>Processed rice<br>Sugar<br>Food products nec<br>Beverages and tobacco products<br>-50 0 50 100 150 200<br>Change in net trade (change in exports - change in imports) in US$ mill.<br><!-- End of picture text --> _Source: Own calculations._ 40
# **1. Introduction**

Constituents arguably consider a national leader’s ability to deliver economic growth to be one of his or her most important attributes. Rewarding good economic outcomes while a leader is in office is a natural consequence of a principal-agent framework, where it is hard for voters or other constituents to directly observe the leader’s abilities or to assess the quality of his or her actions (Wolfers 2007). Although previous literature addresses the importance of leaders to growth in general, we do not know how to quantitatively attribute growth to particular leaders—as constituents must—and there are no estimates of the growth contributions of individual national leaders.<sup>3</sup> This paper aims to fill these gaps.

Perceptions of economic competence are possibly even more important for leaders in nondemocracies/autocracies (which we define using Polity data, see Section 3). These leaders have fewer constraints on their power, which arguably makes economic performance more sensitive to their intentions and abilities. Although they do not face competitive elections, leaders of nondemocratic countries usually rely on the support of different constituents, who must assess if the leader is a “benevolent autocrat” or a “bad emperor.” Fast economic growth forms part of the narrative justifying the rule of famous leaders such as Deng Xiaoping (China), Augusto Pinochet (Chile), Lee Kuan Yew (Singapore), and Paul Kagame (Rwanda), often to an audience well beyond their borders: media, foreign powers, foreign aid donors, and other policy makers.<sup>4</sup> The modern leaders-and-growth literature starts with Jones and Olken (2005), who used random leader deaths in office (due to illness or accident) to investigate the effect of leaders on growth. They found that leaders have a significant effect on growth in both directions: specifically,

> 3 Aruoba et al. (2019) estimate the probability that US state governors are good, based on job approval ratings.

> 4 For example, Deng’s _New York Times_ obituary states: “In the 18 years since he became China's undisputed leader, Mr. Deng nourished an economic boom that radically improved the lives of China's 1.2 billion citizens.” [link]

2
consumers from costlessly shifting demand from one producer to another. As a result, “more efficient (lower cost) plants cannot lure away all demand from their less efficient industry rivals simply with lower prices” (p. 534). This enables lower productivity plants to survive, even in long run equilibrium, despite their lower productivity. Similarly, an implication of the Melitz‐ Ottaviano (2008) model is that high transport costs can reduce the scale of market selection via firm entry and exit.

As outlined by Melitz‐Ottaviano (2008), the more differentiated a product is (in the sense of lower product substitutability), the lower is its industry (average and minimum) productivity and the greater is the within‐industry dispersion of productivity. Any exogenous increase in market size would therefore lead to growth in the average (or expected) plant‐level productivity and alter its dispersion relative to the industry average. The lower the degree of product differentiation, the larger these changes will be because they are the outcome of market expansion that speeds up the reallocation of market shares and factor inputs (both within the industry itself and between the industry and the rest of the economy) by raising producers’ entry and exit rates to and from that industry.

We test these predictions using a 10‐year panel of manufacturing firms from Ethiopia.<sup>1</sup> This panel contains annual production data for the period 2000 to 2010 and covers all manufacturing firms employing 10 or more employees.<sup>2</sup> It is arguably the most comprehensive longitudinal data set on manufacturing firms in Sub‐Saharan Africa at this moment. Industries are classified in the dataset at the 4‐digit ISIC level. Importantly, the Ethiopian data include producers’ physical outputs, q� , along with their respective prices, p� . This allows us to distinguish between revenue‐ based measures of total factor productivity (TFPR) and those based on physical outputs (TFPQ). As is standard in the literature, we define TFPR as the value of revenue �p�q�� per input unit �x�� and TFPQ as the number of physical units produced per unit of output �q�/x��. TFPQ measures the technical efficiency of a plant.

> 1 This panel is compiled from 10 years of the Ethiopian Survey of Large and Medium Scale Manufacturing Industries which, despite its name, is a census of all manufacturing firms with 10+ employees.

> 2 Data from 2005 are dropped because a survey was conducted during that year rather than a census.

3
coefficients of dry shocks monotonically increase in magnitude as the value of the threshold rises. Depending on the threshold used, GDP growth decreases between 0.38 to 0.72 percentage points, on average, indicating that more severe dry shocks have more damaging consequences. Wet shocks remain beneficial to growth, but the impacts are more muted, and are not always significant.

## **Table 2 Impact of drought severity on GDP per capita growth, 2 bins with various thresholds, Low- and Middle-Income Countries Sample**

|Dep. Var.<br>Δlog(GDPp.c.)|(1)|(2)|(3)|(7)|(8)|
|---|---|---|---|---|---|
|Threshold X:|1.6|1.3|1|0.8|0.5|
|||Impacts are|relative to categ|ory (-X<z<X)||
|Extreme(z<-1.6)|-0.7204***<br>(0.188)|||||
|Extreme(z>1.6)|0.1554<br>(0.197)|||||
|Dry (z<-1.3)||-0.6196***<br>(0.151)||||
|Wet(z>1.3)||0.2194<br>(0.159)||||
|Dry(z<-1)|||-0.5429***<br>(0.125)|||
|Wet(z>1)|||0.2721*<br>(0.137)|||
|Dry(z<-0.8)||||-0.5060***<br>(0.109)||
|Wet(z>0.8)||||0.2531*<br>(0.128)||
|Dry(z<-0.5)|||||-0.3864***<br>(0.099)|
|Wet(z>0.5)|||||0.1295<br>(0.115)|
|Avg Temp(C)|0.7697*|0.7742*|0.7802*|0.7818*|0.7770*|
||(0.340)|(0.339)|(0.340)|(0.340)|(0.340)|
|Avg Temp Sq.|-0.0263**<br>(0.009)|-0.0257**<br>(0.008)|-0.0250**<br>(0.008)|-0.0247**<br>(0.008)|-0.0250**<br>(0.008)|
|N|667981|667981|667981|667981|667981|
|Cell FE|y|Y|y|y|Y|
|Year FE|y|y|y|y|Y|
|Country Trends|y|y|y|y|Y|
|R-sq|0.304|0.304|0.305|0.305|0.304|

_Note: Dependent variable is change in gridcell log(GDP p.c.). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._

# 4.2 Impacts along the distribution of drought severity

What is clear from the previous section is that rainfall deficits that are 1 standard deviation below normal levels have adverse impacts on GDP per capita growth rates, especially in the developing country sample. To investigate how the impacts can vary based on different sizes of shocks, this section unpacks the baseline results to assess impacts across the full distribution of shocks that range from mild to extreme. To assess how the impacts differ across the entire distribution of drought severity, Table 3 assesses equation
Policy Research Working Paper 9609

# **Abstract**

With the surge in publicly available high-resolution satellite imagery, satellite-based monitoring of smallholder agricultural outcomes is gaining momentum. This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and that are integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-meter resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75 percent accuracy. However, the predictive accuracy varies with the approach to georeferencing plot locations and the number of observations in the training data. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60 percent of the training data. Seemingly small erosion in accuracy under less preferable approaches to georeferencing plots results in total area under maize cultivation being overestimated by 0.16 to 0.47 million hectares (8 to 24 percent) in Malawi.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors 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
the job separation rate was 2.3 percent in 1999 (World Bank 2001c), that is considerably lower than in Lithuania. It seems that only in Russia, and-to a smaller extent-Bulgaria the inflows from employment into unemployment were of similarly large magnitude as in Lithuania (5.6 percent in Russia 1995/96 and 4.3 percent in Bulgaria in 1996/96).'5

**Table 13 Transition Probabilities Across Labor Force States**

**Panel A**

|||May,2001||
|---|---|---|---|
|May, 2000|Employed|Unemployed|Outofthelaborforce|
||Samejob<br>Newjob|||
|Employed|82.1<br>5.6|5.7|6.6|
|Unemployed|x<br>24.0|60.4|15.6|
|Outofthelaborforce|x<br>5.2|4.8|89.9|
|PanelB||May,2001||
|November,2000|Employed|Unemployed|Outofthelaborforce|
||Samejob<br>Newjob|||
|Employed|89.4<br>3.7|3.2|3.7|
|Unemployed|x<br>30.2|57.5|12.3|
|Outofthelaborforce|x<br>3.5|3.0|93.5|

_Source Lithuanian Labor Force Survey, Bank staff calculations._ Expectedly, most of the jobs are terminated by employers, so job separations are largely involuntary. Lay-offs are the main reason for unemployment among previously employed worker (46 percent), followed by terminations of temporary jobs (22 percent). Still, voluntary quits account for a sizeable proportion-almost one-third-of unemployment among the previously employed (Table 14). This indicates, that a relatively large group of workers accepts temporary spells of unemployment as a means of changing-presumably improving-their labor market status.

> 5 Data for Russia are reported m Bell (2001), data for Bulgaria taken from World Bank (2001a).

43
compliant investments. Table II offers an overview of such regulatory institutions in key jurisdictions where Islamic finance services are offered.

||Table II - External_Shariah_CG i<br>|nstitutions by country*<br>|
|---|---|---|
|Country|Centralized SSB or High_Shariah_<br>Authority or Fatwa Board|Islamic Rating Agency|
|**_Jordan_**|No|No|
|**_Malaysia_**|�|�|
|**_Sudan_**|�|No|
|**_Bahrain_**|No†|No†|
|**_Kuwait_**|�|No|
|**_Pakistan_**|�|No|
|**_UAE_**|�|No|
|**_Indonesia_**|�|No|
|* See Annex III fo<br>† Bahrain is the se<br>an international sc|r the names and powers of these departments/authorities.<br>at of the IIFM and the IIRA that respectively set standar<br>ale.|ds for Islamic jurisprudence and rate Islamic instruments on|
|_Source: Official G_|_overnment websites and Central Banks’ Annual Reports._||

One of the distinctive goals of these bodies is the standardization of _Shariah_ practices within their jurisdictions. Countries such as Kuwait, Malaysia or Pakistan have taken significant actions in this respect, while others have not followed this route. The standardization of Islamic instruments may be a major determinant in ensuring the enforceability of Islamic financial contracts in disputes brought before civil courts that are not legally bound by the _Shariah_ . Accordingly, standardization of practices would support property rights of involved stakeholders as well as sustain the development of IIFS in non-Islamic countries. However, the practice of centralized SSBs creates the possibility that one IIFS group operating in different jurisdictions may have products deemed _Shariah_ compliant in one place and not in another. In addition, regulators in non-
**Table 1: The tiered formula for cuts in agricultural tariff bindings**

||**Developed**||**Developing**||
|---|---|---|---|---|
|_Band_|_Range, %_|_% Cut_|_Range, %_|_% Cut_|
|A|t0 ≤20|50|t0≤ 30|33.3|
|B|20 < t0≤50|57|30 < t0≤80|38|
|C|50< t0≤ 75|64|80< t0≤130|42.7|
|D|t0>75|70|t0>130|46.7|
|**Average cut**|**Min**|**54%**|**Max **|**36%**|

Source: Authors’ summary based on WTO 2008a Unlike the Swiss formula used in the non-agricultural market access negotiations, this formula does not provide a smooth mapping from initial to final tariffs. The larger cuts applying to tariffs in the higher bands mean that tariffs just above the boundaries between the bands end up somewhat lower than some tariffs in the lower bands, resulting in the surprising saw-tooth tariff mapping depicted in Figure 1.

**Figure 1: The pattern of tariff cuts under the tiered formula**

<!-- Start of picture text --> 90% 80%<br>80% 70%<br>70%<br>60%<br>60%<br>50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10% 10%<br>0% 0%<br>0% 20% 40% 60% 80% 100% 120% 140%<br>Initial Tariff<br>Developed countries: Post Formula Tariffs<br>Developing countries: Post Formula Tariffs<br>Developed countries: Tariff Cut Equivalent<br>Developing countries: Tariff Cut Equivalent<br>Post Formula Tariff Tariff Cut Equivalent<br><!-- End of picture text --> Source: Authors’ summary based on WTO 2008a As shown in Table 1, the Doha tariff-cutting formulas have the economically desirable feature of making larger cuts in the higher—and hence more economically costly—tariffs. In line with 5