rafmacalaba/datause-encoder-data huggingface dataset:
relief amount per beneficiary in the sample frame, Rs 33,498 (US$ 740), is higher than the state average of Rs 24,275 (US$ 540). There are sevaral reasons for this. First, landholdings in the (non-drought affected) survey district are slightly larger, the bulk of qualifying farmers in the state, and are more likely to be irrigated than landholdings in the rest of the state. Since there is a positive relationship between landholding, crop value and loan size and also between loan size and relief amount, larger landowners will tend to get more relief. Second, some banks not included in the sample frame, such as rural regional banks and smaller credit cooperatives, are likely to issue smaller loans on average than the larger banks included in the sample frame.

# **5.2 The Debt Relief Survey**

In total, 2,897 households were surveyed in five rural districts of the northwestern Indian state of Gujarat between October and December 2009, approximately one and a-half years after the program.<sup>16</sup> The four sample districts, Mehsana, Gandhinagar, Kheda and Anand<sup>17</sup> form a contiguous band in the central and northwestern part of the state. These districts include relatively rich agricultural land and are slightly more rural than Gujarat as a whole, with 64-80% of households residing in rural areas. Like any of India’s 28 states, Gujarat is unique in some ways and ordinary in others. It is richer than average, with a per-capita income about 26% above the all-India average. It is also more urban than the rest of the country, with 37% of its population living in urban areas versus 28% for India overall. Agriculture makes up about the same share of Gujarat’s economy, however, as for India overall. In terms of banking, Gujarat enjoys slightly higher than average commercial bank coverage, with one commercial bank per 14,220 inhabitants, versus 15,601 for India overall (Government of Gujarat, 2008a,b; Government of India, 2001a,b). Nearly one million Gujarat farmers qualified for debt relief under the 2008 scheme, with average relief of Rs 24,275 (US$ 540). This was 37% higher than the all-India average relief of Rs 17,712 (US$ 392). However, because Gujarat is more urban and therefore had fewer beneficiaries, the state received slightly below-average relief on a per-capita basis (Government of India, 2008).

Households were visited by survey teams between October and December 2009 and asked to par-

> 16 Conducting a baseline survey was not feasible, as the program was enacted immediately after its announcement to minimize manipulation. Comprehensive lists of beneficiaries were therefore not available sufficiently ahead of time.

> 17Bank branches from which survey respondents were drawn were located in these four districts, however some clients resided in Ahmedabad district, which surrounds the state’s largest city and is wealthier and more urbanized.

16
MONGOLIA'S INFORMAL SECTOR Appendix B: Methodologies and **Descriptive** Statistics

**41**

# Kiosk Sample and Population

117. A survey of 245 kiosks<sup>9 3</sup> was conducted in March, 1997. As with the survey of taxicabs, the purposes of conducting the survey of kiosks included the desire to assess the impact of the growing number of kiosks on the economy of Ulaanbaatar in terms of aggregate contribution to value added, employment and income. The analysis of kiosks is easier than that of taxicabs in one important respect. The population of kiosks is easily obtained, and the sample selection problem encountered with taxicabs is not a problem with kiosks.

118. The sample of kiosks was selected to conform roughly in proportion to the number of kiosks located in the six districts of Ulaanbaatar.9 Roughly 11% of the kiosks in Ulaanbaatar were surveyed. The number of kiosks in each district in both the population and the sample are depicted in Table B2, below.

Table B2. Kiosk Sampling

|**District**|**Kiosks in District**|**Kiosks Sampled**|
|---|---|---|
|Bayangol|283<br>12.7%|32<br>13.1%|
|Bayanzurh|551|70|
||24.7%|28.6%|
|Chingeltei|185<br>8.3%|20<br>8.2%|
|Han-Uul|158|21|
||7.1%|8.6%|
|Songino-Hairhan|802|102|
||36.0%|41.6%|
|Suhbaatar|250|0 (see notes)|
||11.2%|0%|

_Source:_ Ulaanbaatar City Administration, and the survey of kiosks conducted by the author.

## **Value Added from Kiosks in Ulaanbaatar**

119. The estimates of value added were compiled in a straightforward manner, using the responses to survey questions and extrapolating for the population of kiosks in Ulaanbaatar. Each kiosk in the survey provided the average earnings after subtracting the cost of inventories, transportation, and rent, for both the average weekday and for the average weekend day. These daily averages were then weighted for each kiosks for the number of weekdays and weekend days that the kiosk is usually open for business to arrive at an average weekly value added for each kiosk. The sample mean for average weekly value added is 47,900 togrogs. Extrapolated to a year for 2,229 kiosks in Ulaanbaatar yields an estimate of value added of approximately 5.55 billion togrogs per year at current (3/97) prices.
**Table 4: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (By Cohorts)**

|**Country**|**1970**|**1980**|**1990**|**Country**|**1970**|**1980**|**1990**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.731|0.696|0.659|Benin|0.867|0.840|0.809|
|Indonesia|0.787|0.791|0.774|Burkina Faso|0.844|0.688|0.727|
|Philippines|0.816|0.830|0.810|Burundi|n.a|0.740|0.639|
|Vietnam|0.829|0.867|n.a|Cameroon|0.742|0.841|0.871|
|||||Chad|0.735|0.753|0.677|
|Europe & Central Asia||||Congo, Rep.|0.926|0.863|0.728|
|Albania|n.a|0.858|0.762|Cote d'Ivoire|0.737|0.845|0.732|
|Armenia|0.940|0.746|0.555|Ethiopia|0.749|0.781|0.679|
|Kyrgyz Republic|0.496|0.786|0.501|Gabon|0.890|0.672|0.775|
|Tajikistan|n.a|0.700|0.587|Ghana|0.853|0.781|0.796|
|Turkey|0.819|0.767|0.701|Guinea|0.452|0.787|0.753|
|||||Kenya|0.656|0.746|0.877|
|Latin America & Carib|bean|||Lesotho|0.716|0.727|0.817|
|Bolivia|0.761|0.709|n.a|Liberia|n.a|0.680|0.699|
|Brazil|0.758|n.a|n.a|Madagascar|0.859|0.816|n.a|
|Colombia<br>|0.778|0.755|0.706|Malawi|0.764|0.767|0.745|
|Dominican||||||||
|Republic|0.701|0.727|0.838|Mali|0.855|0.745|0.699|
|Guatemala|0.759|0.796|0.722|Mozambique|0.914|0.765|0.759|
|Haiti|0.709|0.644|0.629|Namibia|0.697|0.760|0.823|
|Peru|0.771|0.771|0.738|Niger|0.767|0.704|0.705|
|||||Nigeria|0.707|0.700|0.693|
|Middle East & North A|frica|||Rwanda|0.727|0.753|0.715|
|Egypt, Arab Rep.|0.781|0.746|0.656|Senegal|0.820|0.810|0.792|
|Jordan|0.769|0.662|0.543|Sierra Leone|n.a|0.764|0.597|
|||||South Africa|0.877|0.836|0.592|
|South Asia||||Tanzania|0.581|0.672|0.711|
|Afghanistan|n.a|0.725|0.561|Togo|0.832|0.839|0.837|
|Bangladesh|0.854|0.861|0.718|Uganda|0.784|0.755|0.704|
|India|0.860|0.759|0.686|Zambia|0.726|0.777|0.741|
|Nepal|0.680|0.684|0.640|Zimbabwe|0.688|0.687|0.721|
|Pakistan|0.823|0.814|0.762|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) disaggregated by different decades of birth cohorts (the 1970s, 1980s, and 1990s).

38
different stages. Furthermore, as long as the inflated dropout rates are driven by classical (random) measurement error, this would just lower the accuracy of our model. In that scenario, our estimates give us a lower bound of the accuracy levels of the model, and hence we would expect a better performance as the quality of the panel improves over time. Indeed, as we will show in the next section, the models work better in Guatemala, where the measurement error of dropout is much lower.

# **4. Who will drop out: Empirical predictions**

# **_4.1 Results_**

In our analysis, we focus on three periods during which both Guatemala and Honduras lose the majority of students to dropout: the transition between primary and lower secondary school, within lower secondary, and the transition from lower secondary to upper secondary. For ease of exposition, below we present the results of using data available in year t to predict who will drop out in the transition from primary to lower secondary in year t+1. Additional results on predicting dropouts within lower secondary and in the transition from lower to upper secondary, are presented in the Appendix.

We follow the method of Knowles (2015) and Stuit et al (2016) in estimating dropout models in three steps. These prediction models are based on the conceptual framing of dropout as a decision that can be affected by a broad range of underlying factors discussed in Section 1, and include all of the available covariates that could reasonably capture one of these factors. In this way, prediction modeling differs from other exercises that estimate the correlational or causal relationships between specific factors and dropout. First, we estimate linear probability models with dropout as a binary outcome, using the individual/household, school, and municipality/department-level covariates described in the previous section. However, for Guatemala, given the large number of variables available to include in the models we add a ‘zero step’: we combine the information from several highly correlated variables that measure similar characteristics and construct indices using a Principal Component Analysis (Pearson 1901; Hotelling 1933; Jolliffe 2002). These indices were obtained as component scores for the first 13
29 We can also adjust the years of experience for female workers to account for the number of children they had. This is achieved by reducing experience by 1 year per child, or even 2 years (Web Appx. Table A.6). While that seems high, it may take into account the time cost of pregnancy as well as any negative effects on the career of mothers, due to discrimination or self-sorting into lower intensity lower-returns jobs.<sup>51</sup> Finally, we verify that most of the returns in terms of monthly wages are driven by differences in hourly wages. While the number of hours worked increases with both education and experience, the corresponding returns are small compared to the returns in terms of hourly wages (not shown, but available upon request).

**7.2. Robustness Checks and Other Issues for the Aggregate Impact**

In Section 5.2., we identify the likely scenario for each country group, and compute the costs of the COVID shock to human capital using the baseline set of parameters. The HICs suffer more than the MICs and LICs, and therefore the global economic impact of COVID-19 is mainly felt by the HICs. This is largely as a result of the employment / experience shock. After ten years, once the direct unemployment effect has worn off, the HICs and MICs suffer more than the LICs. This is due to the education shock exceeding the experience shock in severity except in the MICs. In the HICs the losses after 10 years remain largely due to the experience shock as the returns to experience are high. It is important to establish whether these findings are sensitive to the choice of parameters. We thus repeat the analysis for a variety of different parameterizations. In all cases results hold. Indeed, the returns are similar despite various alterations (Web Appx. Table A.7) and, as a result, so are the quantitative findings (Web Appx. Table A.8). **Child Labor.** In the baseline estimation we assume that agents start accumulating work experience from age 18 at the earliest. Thus, even if agents were to work before 18, the associated experience is irrelevant later on. We repeat the estimation assuming work experience is accumulated from age 15, 13 or even 6, thus allowing for child labor.<sup>52</sup> Indeed, pandemics may increase child labor. However, in Web Appx. Section I we discuss how substitution effects due to worsening labor market conditions might compensate for income effects leading to reduced school enrolment, thus precluding 51Note that we only know the number of children still in the household and for household heads and their spouse only. We thus only consider these workers for this robustness analysis. 52If we use age 15 (13), for individuals with at least 9 (7) years of education, experience is age - years of education - 9 (7). For individuals with less than 9 (7) years of education, experience is age - 15 (13).
||Append|ix Table A1 -|Test of Rando|mization for|Baseline Hou<br>|sehold Outc<br>|omes<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
||Migrant-Only<br>Training (A)|Family-Only<br>Training (B)|Migrant and<br>Family Training<br>(C)|Control Group<br>(D)|<br>P-value A=B|P-value A=C|P-value A=D|P-value B=C|<br>P-value B=D|P-value C=D|P-value<br>A=B=C=D|
|Has a Business|0.57|0.63|0.61|0.58|0.429|0.586|0.969|0.807|0.448|0.61|0.822|
|Average Monthly Income (USD)|167.94|144.26|135.6|158.69|0.414|0.264|0.784|0.656|0.604|0.406|0.642|
|Average Monthly Consumption (USD)|133.21|158.99|140.32|137.67|0.139|0.628|0.778|0.291|0.249|0.868|0.525|
|Records Income and Expenditures|0.04|0.02|0.04|0.02|0.478|0.906|0.389|0.417|0.902|0.335|0.697|
|Has Savings|0.44|0.53|0.51|0.5|0.18|0.333|0.358|0.713|0.65|0.94|0.576|
|Has Had a Savings Account|0.54|0.58|0.55|0.52|0.661|0.958|0.718|0.706|0.428|0.684|0.888|
|Has Received Remittance|0.38|0.33|0.34|0.38|0.469|0.643|0.985|0.797|0.477|0.654|0.859|
|Discusses Financial Issues with Family|0.87|0.95|0.96|0.89|0.077 *|0.035 **|0.612|0.722|0.191|0.098 *|0.09 *|
|F-Test for Joint Significance (Probit/Multi-Logit)|||||0.258|0.477|0.917|0.961|0.603|0.615|0.864|

This table presents demographic summary statistics for respondents at baseline by treatment status -- Columns (1) - (4); as well as p-values for equality of means tests across treatments -- Columns (5) - (11). Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
sharing of this entered information electronically by the Government, thereby (a) eliminating the need for multiple, often duplicative requests for information and the associated time and resources; (b) increasing the speed and accuracy of the disposition of regulatory functions, and (c) reducing opportunities for unofficial charges.

The resultant electronic “single window” for all border processing and clearance related formalities will make a significant contribution to the achievement o f the government’s trade facilitation objectives by lowering transaction costs and reducing clearance times. Ensuring appropriate interconnectivity between core government agencies and private sector traders (to allow direct trader input of declarations and associated documentation) and banks (to facilitate the payment of duties and taxes) is an essential element.

**UNCTAD’s ASYCUDA automated Customs processing system.** The system will provide the IT support necessary to introduce a range of modem approaches to Customs administration including risk management and post clearance audit. The ASYCUDA system has been deployed in a number of countries throughout the world and the latest version of the product is designed to support selective examinations, trader compliance incentives, effective management of Customs clearance, payment schemes to facilitate trade and secure duty and tax collection, and the production of more accurate and timely trade statistics.

The component will finance the procurement and effective implementation of the ASYCUDA system in Customs including the provision of appropriate technical assistance to ensure effective implementation of the system and its seamless integration with non-automated system and procedures. The component will also finance the development and implementation of a continuity network to provide an electronic Single window to ensure traders are able to discharge all their regulatory responsibilities through one gateway. As a part of this development, a number of small agency-specific systems will need to be developed and implemented to ensure all key import/export related processes are able to be undertaken electronically.

Source: The World Bank, Project Appraisal Document

# **Tunisia Export Development Project (1999- Present)**

The Government, with World Bank support through the Export Development Project, initiated new measures in 1999 to systematically tackle trade facilitation issues, starting with the simplification and automated processing of trade documents. The project focused on improving the information exchange associated with cargo handling and clearance activities, through the use of information and communications technologies, and initiating measures in view of simplification of customs and inspection procedures.

**Document simplification and standardization** . Providing a foundation for the reforms were the simplification of and adoption of international standards for

36
_ESTIMATING FOOD PRICE INFLATION_ _25_

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**_Box 2: Real Exchange Rate in franc zone._** Countries with fixed exchange rate regimes are mostly in the franc zone, where the CFA franc is tied to the euro.  There is however an increasing divergence between the eastern and western part of the zone.  The CEMAC countries benefit from large oil inflows and their growth performance is better in the 1990s and since 2000. The WAEMU countries did not adjust as well to higher oil prices, partly because their exports were hurt by an exchange rate tied to a strong euro. The growth of the latter decelerated to 2.7 percent since 2000 with some improvement during 2004-06 at 3.5 percent a year.  Real exchange rate changes for selected countries indicate appreciations during 2000-06 of: 17 percent in Benin, 9 percent in Burkina Faso, 18 percent in Chad, 13 percent in Côte d’Ivoire, 8 percent in Togo and 1 percent in Mali.  The country level appreciations (with the exception of Mali) are sufficiently large to be worrisome from the point of view of export competitiveness, although whether an adjustment of the CFA franc is necessary depends (among other things) on whether the strength of the euro relative to the dollar will persist.

**_The Case of Cotton_ .** The structure of CFA zone economies is such that an appreciation of the exchange rate will reduce the real price of exports much more rapidly than the unit cost of labor with negative consequences for export competitiveness; but the impact will vary considerably at the sector level in each country. The case in point is the cotton sector.  Because cotton prices are quoted in US dollars, cotton producers in the CFA franc zone are increasingly confronted with export prices that are significantly lower in euro/CFA franc than the price in dollars (see Figure 14).  This disparity puts downward pressure on farm gate prices and will add to the potential fiscal cost of any scheme to stabilize farm prices or provide financial assistance to poor farmers. In the case of the cotton sector, the issue goes beyond the exchange rate, however, and reflects declining relative cotton prices driven by new technologies employed by new competitors such as Brazil, China and India.

<!-- Start of picture text --> Figure 13: Real Effective Exchange Rate Figure 14: Monthly Price of Cotton (AINDEX) in US<br>in Franc Zone Countries Dollar and in Euro<br>135<br>180<br>130<br>160<br>125 Chad<br>140 Dollar price<br>Benin<br>120<br>Côte d'Ivoire 120<br>115 Burkina Faso<br>Togo 100<br>110<br>Mali 80 Euro price using official<br>105 exchange rate<br>60<br>100<br>1997- 2002 2003 2004 2005 2006<br>2001<br>REER 2000=100<br>C o tto n p rices, cu rren t $ US o r E UR O<br>Jan -9 9 Ju l-9 9 Jan -0 0 Ju l-0 0 Jan -0 1 Ju l-0 1 Jan -0 2 Ju l-0 2 Jan -0 3 Ju l-0 3 Jan -0 4 Ju l-0 4 Jan -0 5 Ju l-0 5 Jan -0 6 Ju l-0 6 Jan -0 7<br><!-- End of picture text --> Source: AFRCE various years.

31
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- 14 -<br>GRAPH 5  LOGS: TEAK<br>TH}AIAND<br>5DD - _<br>450  -<br>SDD  35D  -<br>S~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>0  0<br>0<br>ZDD  5a-<br>1 5D -<br>1DD - 150  E<br>IDD -<br>1965  197D  1975  19SD  1985<br>YBAR<br><!-- End of picture text -->