rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

Some villages have central seed storehouses that members of all compounds may use for groundnuts, coarse grains, and other seed. In these, however, sacks or bundles of seeds are usually individualized and may be labelled. The stores are often kept locked, and two or more keyholders may be required to open them. An advantage of this form of storage is the economy of scale in strong construction.16 A cement house with an iron roof, beyond the means of many individual farmers, cuts down rodent damage. Centralization also makes treatment with storage pesticides easy and inexpensive. Another advantage is that the system adds disincentives to consume one's seed before planting, since doing so requires arrangement with the keyholder(s). A disadvantage is that farmers must let others know how much they possess, something they usually try not to do in the case of personal or family stores.

# Storage Facility Projects

Village seed stores appear to have been an effective form of development intervention by various governmental and non-governmental agencies (most notably the Freedom from Hunger Campaign). Although there have been several projects for building and managing seed stores, many villages remain without these. People in these villages are clamoring to get them. Gambian farmers are losing their own resources to rodents, birds, smaller pests, and molds while they continue to accrue new and expensive debts for food and seed. This unfortunate cycle should be broken.

No one appears to have comprehensive knowledge of what storage facilities exist in the Gambian countryside--a nationwide survey would be welcome--but it
14

# IV. **How Much Data Does a Regulator Really Need?**

## **Data Requirements**

A main concern in most reforming developing countries is the limited access to data. This implies that a careful assessment of the data capacity of a regulator is probably the first stage in the development of a toolkit for any regulator. The following table'<sup>2</sup> shows the input requirements of the HCPM, compared to those in the accounting approach.

|**Data**<br>**Requirements**|**Accounting**<br>**Approach**|**HCPM**|
|---|---|---|
|Customer data|Only aggregate<br>historical data used.|Uses and processes<br>data at any level of<br>aggregation.|
|Input Prices|Highly aggregated;<br>not forward looking;<br>accounting<br>categories not<br>relevant to economic<br>issues.|Highly<br>disaggregated input<br>data possible.|
|Engineering|Engineering inputs<br>other than historical<br>experience not<br>possible.|Based on network<br>optimization<br>principles; can<br>design to variety of<br>engineering<br>standards.|
|Time to<br>implement|Accounting data<br>available now in<br>many countries, but<br>not in others.|Logic available<br>now; inputs may<br>require six months<br>or more.|

Most typically, regulators of privatized utilities will start from scratch and initially have only a very modest source of information to work with. As regulators build on this information, they will need to consider the policy issues that they will ultimately have to address. These issues will determine the form of the best cost model for regulatory purposes and therefore should determine the optimal degree of disaggregation to impose on the data collection process. A significant advantage of a flexible model such as the HCPM model is its ability to accommodate various levels of input data disaggregation.

12 Reproduced from Sharkey et al. (1999).
deviation increase in early cognitive development predicts substantially improved school outcomes in adolescence, as measured by test scores, grades attained and dropout behavior (e.g., 0.71 additional grade by age 18 in Brazil).

Looking beyond childhood, Garces et al. (2002) find evidence from the U.S. Head Start program that links preschool attendance not only to higher educational attainment but also to higher earnings and better adult social outcomes. Using longitudinal data from the Panel Study of Income Dynamics, they conclude that, controlling for the participants’ disadvantaged background, exposure to Head Start for whites is associated with significantly lower dropout rates in the short run, and in the long run 30 percent greater probability of high school completion and 28 percent higher likelihood of attending college, as well as higher earnings in their early twenties. For African-Americans, participation in Head Start is associated with a 12percentage-points lower probability of being booked for or charged with a crime.

Another example of an unintended long-run outcome is provided by Angrist et al.’s (2004) evaluation of Colombia’s school voucher program at the secondary level (PACES or Programa de Ampliación de Cobertura de la Educación Secundaria), which finds longer-run outcomes beyond the original program goal of increasing the secondary school enrollment rate of the poorest youths in urban areas. Using administrative records, the follow-up study finds that the program increased also high school graduation rates of voucher students in Bogota by 5-7 percentage points, consistent with the earlier outcome of a 10-percentage point increase in eighth-grade completion rates (Angrist et al. 2002), and correcting for the greater percentage of lottery winners taking college admissions tests, the program increased test scores by two-tenths of a standard deviation in the distribution of potential test scores.

In their evaluation of a rural roads project in Vietnam, Mu and van de Walle (2007) find 14
**Figure 17. Simulated change in earnings as a result of the minimum wage increase assuming perfectly elastic labor demand**

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-23.5<br>Total Gender Age Education<br>Male Female 15-19 20-24 25-29 30-34 35-54 55-64 Primary or less Secondary Post-secondary<br><!-- End of picture text -->

_Source: Own estimates using the 2018 Kosovo LFS._ <mark>Sensitivity checks: Wage imputations and misreporting</mark>

**<mark>The main challenge in evaluating the potential effect of the minimum wage policy using the LFS data is that the wages are reported in brackets</mark>** <mark>. Therefore, wage imputation is required, and in the main analysis we have applied a simple imputation technique assuming that wages are uniformly distributed on the reported interval. Alternatively, wages can be imputed conditional on observable characteristics from the 2017 HBS in which exact wages are reported.</mark><sup>32</sup> <mark>The difficulties with this approach are:</mark>

<mark>1. There is underreporting of labor income in HBS (compared to LFS). HBS wage distribution is shifted to the left.</mark> <mark>2. There is uncommon support</mark><sup>33</sup> <mark>in the distributions (difference in observable characteristics between LFS and HBS).</mark>

**<mark>We apply the following algorithm, based on Nopo (2008):</mark>**

<mark>Step 1: Select one individual (without replacement) from the LFS.</mark><sup>34</sup> <mark>Step 2: Select all individuals who have the same characteristics as the individual selected in step 1 from HBS.</mark><sup>35</sup> <mark>Step 3: Construct a “synthetic” individual whose synthetic earnings are equal to the average of all individuals selected in step 2 (from HBS) and match him or her to the original individual selected in the LFS.</mark>

> 32 Because of data availability, we match the 2017 LFS with the 2017 HBS survey. In the main analysis, we use the 2018 LFS.

> 33 <mark>In probability theory, the support of a probability distribution can be loosely thought of as the closure of the set of possible values of a random variable having that distribution.</mark>

> 34 We match only employees with non-missing wages.

> 35 Specifically, we match individuals by the following characteristics: gender, age, urban status, education level, marital status, relationship to the head of the household, and occupation.

29
# **C Additional Tables**

Table S1: Estimation Results for Logit Demand Model

||OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|
|Price - Incentive (1,000 USD)|-0.007<sup>_∗∗∗_</sup>|-0.019<sup>_∗∗∗_</sup>|-0.019<sup>_∗∗∗_</sup>|-0.027<sup>_∗∗∗_</sup>|-0.029<sup>_∗∗∗_</sup>|
||(0.002)|(0.002)|(0.002)|(0.005)|(0.005)|
|Log Charging Ports|-0.393<sup>_∗∗∗_</sup>|-0.226<sup>_∗_</sup>|0.261<sup>_∗_</sup>|0.233|0.790<sup>_∗_</sup>|
||(0.125)|(0.114)|(0.152)|(0.150)|(0.460)|
|Range (miles)|0.005<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|
||(0.001)|(0.003)|(0.001)|(0.001)|(0.001)|
|1(PHEV) _×_ Range|0.019<sup>_∗∗∗_</sup>|-0.006|0.006|0.003|0.003|
||(0.005)|(0.007)|(0.006)|(0.007)|(0.007)|
|Vehicle size (m3)|0.002|0.018|-0.018|0.007|0.013|
||(0.017)|(0.019)|(0.014)|(0.021)|(0.021)|
|Indicator of Non-fnancial Incentives|0.378|0.219|0.177|0.173|0.043|
||(0.285)|(0.267)|(0.148)|(0.149)|(0.182)|
|Brand FE||✓|✓|✓|✓|
|Fuel Type FE||✓|✓|✓|✓|
|Country FE|||✓|✓|✓|
|Year FE|||✓|✓|✓|
|First Stage F-stats for Price||||54.67|77.00|
|First Stage F-stats for Charging Station|||||36.34|
|Underidentifcation Test||||62.60|77.22|
|Weak Identifcation Test||||31.85|22.47|
|Overidentifcation Test||||55.16|62.93|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is the logit share in logit demand model. _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 S6
Notes: trade gap = ln(exports reported by Germany)pt – ln(imports reported by the importing country)pt where p stands for a 6-digit HS product and t for year. The median tariff values are calculated for each country and each year.

To shed some light on these questions, we start by presenting simple summary statistics of the trade gap for each country in our sample. In each country, we split the products into those with the tariff above the median rate and those with the tariff below the median (Table 2). In all countries, except for Poland, the trade gap is higher for products whose tariffs are above the median. For instance, while in Bulgaria there is no trade gap for products with low protection, in the case of goods with above median tariff rate the discrepancy increases to 26%. In Hungary, the value of exports of products with a below median tariff rate is 3% lower than the value of imports, but in the case of above median tariff rates, exports are underreported by 16%. These summary statistics are consistent with the idea that the gap value is a proxy for tariff evasion. We obtain similar results when we split the sample between products with the top 25% tariff rates versus the rest. The puzzling result regarding Poland may be explained by the high percentage of products subject to zero tariffs. The percentage of products exempt from tariffs increased from 12% in 1998 to 89% in 1999 and remained well above 90% in the following years.

Next we estimate a simple model of the trade gap as a function of the tariff rate and year fixed effects. We do so for each country _c_ in the sample separately.

ln _Export_ valueGermany_ , _cpt_ − ln Im _port_ valuecpt_ = _trade_ gapcpt_ = α + β _tariff cpt_ +α _t_ + ε _cpt_ where _p_ stands for a 6-digit product and _t_ for year. Our prior is that if the gap value is a good proxy for tariff evasion then the estimated coefficient of the tariff rate should be positive and significant. The results, reported in Table 3, are consistent with the summary statistics presented earlier. The estimated coefficient on the tariff rate is positive and significant at the 1% level for all the countries but Slovenia and Poland. The higher the tariff rate, the lower the value of imports reported by the importing country relative to the reported exports (i.e, the higher the trade gap). A one-percentage-point increase in the tariff level is associated with a 4.4% increase in the trade gap in Ukraine, 3.2% increase in the Russian Federation and 0.8% increase in Hungary. These results are in line with Fisman and Wei’s study which finds a 3% increase.<sup>9</sup> It is interesting to note that Ukraine, the country with the highest estimated elasticity, has the second highest prevalence of corruption in customs as reported in the BEEPS survey. Slovenia, a country for which there is no statistically significant relationship, is ranked as the cleanest country in terms of corruption in customs according to BEEPS. See Appendix II for more details. The insignificant

> 9 Note that these calculations do not take into account the direct effect an increase in a tariff rate may have on the volume of imports.

9
rule, however, is a mechanism to delay, but not to prevent such exits. We only consider the first exit of a family since we do not want to estimate any effects on re-entry.<sup>14</sup> If the head leaves the social registry CadUnico, but a different person from the same family receives the benefits and thus _converts_ to being the head, we classify this family as still _in BF_ .

**Formal labor market participation** We identify formal labor market participation by merging information from CadUnico and RAIS. We classify families as having at least one member in formal dependent employment if any adult family member can be found in RAIS in a given semester.

**Municipality-level employment growth** We calculate employment growth at the municipality level using RAIS data. RAIS reports the number of job links, which slightly differs from the number of workers since a small share of individuals holds more than one formal job. To calculate the current stock of employment, we use job link data for the private sector, additionally excluding public firms within it, as hiring policies in the public sector are expected to follow different mechanisms. Employment growth is measured as the difference between the current period’s number of private-sector employment links and the previous period’s number of private-sector employment links, divided by the number of private-sector employment links in the previous period.

# **3 Descriptive Statistics**

This section presents descriptive statistics of our sample. We start by laying out some characteristics of the individual data before describing some of the exit dynamics using the family data. In December 2012, the average age of individuals was 35. Individuals between 25 and 34 years old are the prevalent age group (32%), followed by individuals between 35 and 44 years old. In addition, 66% of individuals were female and 34% male, consistent with the targeting approach of the program (which privileges female household heads). The mean family size in 2012 was 3.6, and most families had one or two children. Most individuals are Afro descendent and have a middle school education (Annex Table A.1).

When registering in CadUnico, all adult members are also asked to report basic information regarding their employment status. Out of all individuals, 43% reported that they worked within the last 12 months. Most of them worked as self-employed, followed by formal dependent

> 14BF has a waiting list. Hence, not everyone eligible will get the benefits. These circumstances make estimation on entry difficult.

10
**Table 5: Time trends for FUAs, 2014 – 2021**<sup>*****</sup> Dependent variable:  Local CO2 Anomaly (ppb)

||Tre|nd||
|---|---|---|---|
|Significance Level|Decrease|Increase|Total|
|Not Significant|804|615|1,419|
|.05|151|72|223|
|.01|87|27|114|
|.001|34|9|43|
|Total Significant|272|108|380|
|Total|1,076|723|1,799|

*  FUAs with degrees of freedom ≥ 30

**Table 6:  Regional distribution of FUA anomaly trends, 2014 – 2021 *** [Count / Column Pct]

|Region|Decrease|Increase|Total|
|---|---|---|---|
|Africa|32|9|41|
||(11.8)|(8.3)|(10.8)|
|Americas|72|21|93|
||(26.5)|(19.4)|(24.5)|
|Asia|86|58|144|
||(31.6)|(53.7)|(37.9)|
|Europe|75|20|95|
||(27.6)|(18.5)|(25.0)|
|Oceania|7|0|7|
||(2.6)|(0.0)|(1.8)|
|Total|272|108|380|

* Column percents in parentheses

15
Table 15 reports four basic needs indicators derived from the CILSS, disaggregated by region.7/ The H-S weights make very little difference to the indicators. For the country as a whole, the indicators are marginally reduced by the weights. That there is little adjustment arising from the H-S weights suggests that the basic needs indicators are not closely related to household size. An examination of the data confirms this. 8/ An interesting feature of the results is the difference in the direction of change between Abidjan and the other regions. For Abidjan, most indicators are adjusted _upward_ by the H-S weights, whereas for the other regions the adjustment tends to be downward. Although most of these adjustments are small, they suggest that larger households in Abidjan have lower basic-needs fulfillment compared with smaller households, so that reducing their weight in the sample increases the indicator. The opposite seems to apply to the other regions -- larger households enjoy on balance slightly higher levels of basic needs fulfillment.

Whilst the H-S weights make very little difference to the mean value of these indicators, the correction of the 1985 and 1986 data sets is nevertheless important for basic needs analysis. Table 16 disaggregates the indicators by poverty status, showing how they are affected by the H-S weights for the very poor, the mid-poor and the non-poor. Whilst the weights make little difference to the measures for the non-poor, they are seen to have a far more significant effect on the basic needs indicators of the poor, especially the very poor. For example, literacy rates among the very poor are adjusted downward by 12 percent in 1985 and 10 percent in 1986. Similarly, primary school enrollment rates for the very poor are adjusted downward by 10 percent in 1985 and 5.3 percent in 1986.

- 7/ For basic needs indicators, the disaggregation by region is particularly important given the role played by the supply of basic-needs services (schooling, health clinics, etc), which varies tremendously between regions in CMte d'Ivoire.

- 8/ For example, literacy rates in other cities in 1986 varied from 44.2 for one or two member households to 48.1 for households of between 10 and 17 members, and to 37.3 for households of size 18 and over. As an example of a rural area, comparable figures for East Forest are 18.9, 28.0 and 28.5 respectively.

31
Policy Research Working Paper 9485

# **Abstract**

This paper estimates the impact of a differential relaxation of COVID-19 containment policies on aggregate economic activity in India. Following a uniform national lockdown, the Government of India classified all districts into three zones with varying containment measures in May 2020. Using a difference-in-differences approach, the paper estimates the impact of these restrictions on nighttime light intensity, a standard high-frequency proxy for economic activity. To conduct this analysis, pandemic-era, district-level data from a range of novel sources are combined —monthly nighttime lights from global satellites; Facebook’s mobility data from individual smartphone locations; and high-frequency, household-level survey data on income and consumption, supplemented with data from the Indian Census and the Reserve Bank of India. The analysis finds that nighttime light intensity in May was 12.4 percent lower for districts with the most severe restrictions and 1.7 percent lower for districts with intermediate restrictions, compared with districts with the least restrictions. The differences were largest in May, when the different policies were in place, and slowly tapered in June and July. Restricted mobility and lower household income are plausible channels for these results. Stricter containment measures had larger impacts in districts with greater population density of older residents, as well as more services employment and bank credit.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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 rcmbeyer@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
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