rafmacalaba/datause-encoder-data huggingface dataset:
yield) is similar across village and small-town samples but its effect on wage is larger in the village sample compared with the small-town sample. A positive rainfall shock increases employment in the production of informal village good (small-scale manufacturing and services) but has no statistically significant effect on the labor allocated to large-scale manufacturing employment in the rural sample. A positive productivity shock has large and positive impacts on employment in construction and transport, education, health and other services, and manufacturing employment in larger scale enterprises located in small towns. When interpreted as instrumental variables estimates of the effects of productivity increase, the empirical results suggest that agricultural productivity induces structural transformation within the services sector with employment in skilled services provided by larger firms growing at a much faster pace compared with services provided by smaller firms/individuals.

Our analysis brings to focus a methodological point about the sample used in the empirical analysis. Most of the existing analysis of the effects of agricultural productivity relies on a sample of villages where all of the households are classified as rural. Our analysis shows that it is important to use the small towns in addition to the “village sample”.

The rest of the paper is organized as follows. Section 2 provides a simple general equilibrium model to highlight the different channels of interactions between farm and non-farm sectors located in rural areas and towns. Section 3 describes the econometric strategy followed by a description of data used in empirical analysis in section 4. Section 5 organized in different sub-sections presents the main empirical results. Section 6 concludes the paper.

# **2. The Model**

We develop a simple general equilibrium model of a small-town economy (STE, for short) composed of both rural and urban households. The model is intended to capture the local economy at the upazila level as our empirical analysis is based on an upazila level panel data set. The representative rural household lives in a village, is endowed with _A_ amount of agricultural land, and is engaged in the production of food ( _f_ ) and a composite village good ( _S_ ) (low quality manufacturing or services produced by small family enterprises).

4
In the simplest terms, a poverty line is a threshold expressed in monetary terms below which individuals are deemed poor. This threshold should pertain to the minimum amount of Nepali rupees to satisfy minimum caloric requirements and basic needs for nonfood goods and services. In 1995, the official poverty rate of 42 percent was based on the poverty line of Rs. 5,089 per person per year. In 2003, the poverty line of Rs. 7,696 was used after adjusting for inflation between the two survey rounds.<sup>11</sup> This is to ensure that the same standard of living is used to identify the poor across the two survey rounds so that Rs. 7,696 in 2003 should afford the same basket of goods and services Rs. 5,089 afforded in 1995.

If the original poverty line was used to estimate poverty in 2010 after inflation adjustments, the poverty line would have been Rs. 14,316 and the resulting poverty rate would have been as low as 12.5 percent. This, however, was not adopted as the official poverty rate because of the growing perception and mounting evidence about improving living standards in Nepal. Overall economic well-being in Nepal had improved so much that the definition of what it meant to be poor in 1995 became obsolete by 2010. Some of what used to be luxury became necessity. For example, in 2010, Nepalis on average consumed more expensive food items such as meat and eggs and less on cereals than 15 years ago. They also spent more on nonfood goods and services than they did in 1995.

In order to reflect the evolving way of life and the current consumption patterns in Nepal, a new poverty line was estimated using consumption data from the 2010 NLSS.<sup>12</sup> The new poverty line was set at Rs. 19,262, an increase in real value by 35 percent compared to the original poverty line in 1995/96. Even with the revised poverty line, the poverty rate in 2010 was estimated at 25 percent (World Bank 2013).<sup>13</sup>

**Table 2: Poverty Rates and Pace of Poverty Reduction in Nepal**

|Survey Year|1995 Poverty Line|2010 Poverty Line|Official Poverty<br>Estimate in 2010|
|---|---|---|---|
||Poverty|HeadcountRate (% of Pop|ulation)|
|1995|41.8%|63.8%||
|2003|30.8%|49.4%||
|2010|12.5%|30.8%|25.2%|
|Period|Pace of Poverty|Reduction(annualpercenta|ge point change)|
|95-03|1.36%|1.80%||
|03- 10|2.62%|2.66%||

Source: World Bank (2013) and Estimates by the Central Bureau of Statistics and the World Bank

> 11 Another adjustment was made to reflect changes in demographic composition of an “average household” to revise the minimum caloric threshold.

> 12 See World Bank (World Bank 2013) and Annex I for more details.

> 13 See Appendix I for more detailed technical issues on calculating comparable poverty trends in Nepal.

7
household heads only with primary education ( **Table C.3** ), differences are statistically significant for most cases when comparing pairs of surveys with more than one year of separation. In sum, the results show the differences in education levels of the heads of households across different years are significant, suggesting caution and providing evidence to be conservative when relying in the assumption of time invariability of the characteristics across periods. Strictly speaking, the results of the means tests suggest that for Colombia the time interval between the cross-section surveys should not be more than two years apart from each other.

# **4. Vulnerability lines**

Identifying the vulnerable group usually relies on estimating “appropriate” lines that allow us to classify the population in different categories, similarly to the definition of poverty. However, in contrast with poverty, where typically one threshold (i.e. poverty line) is enough to split the population in poor and nonpoor, in the case of vulnerability two lines might be necessary. First, we need to define the cutoff point that represents the lower bound of the vulnerable group, which in practice usually coincides with the poverty line, meaning that people or households who graduate from poverty (i.e. achieve incomes above the poverty line) do not immediately become part of the middle class but instead remain in a state of vulnerability. Second, probably the most relevant line is the upper bound of the vulnerable group that we call “vulnerability line”, and typically represents the lower bound of the middle class.

Once poverty and vulnerability lines are defined, individuals or households could be classified either as poor when their incomes are below the poverty line, or as vulnerable when their incomes are above the poverty line but below the vulnerability line, or as middle class when their incomes are above the vulnerability line (and implicitly above the poverty line, since the value of the vulnerability line is higher than the poverty line). Notice, the economic literature (Atkinson and Brandolini, 2013; Lopez-Calva and Ortiz-Juarez, 2014) usually considers the upper bound line of the vulnerable group as the lower bound of the middle class. In addition, any proposal to empirically estimate a vulnerable group requires accepting the implicit assumption that it is possible not only to formulate a relevant concept for class but also to identify these categories from empirical methods (Lopez-Calva and Ortiz-Juarez, 2014).

Although there is currently no consensus in the literature on the best methodology to estimate the vulnerability line, the Government of Colombia has been exploring the use of the economic security approach, based on the criterion of vulnerability to poverty, to identify the upper limit of the vulnerable group or the lower bound of the middle class based on the Lopez-Calva and Ortiz-Juarez approach (see 11
**Table 1. Logit models of perceptions of corruption as an obstacle and of likelihood of paying tax-related bribe (country and sector controls omitted form output)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Dependent|variable: Corr|uption-not a|n obstacle to|current oper|ations (binary)|||
|Bribe tax (all firms)|**-0.145*****<br>(1.02e-06)|**-0.142*****<br>(1.44e-06)|||**0.0734*****<br>(0.00354)|**-0.104*****<br>(0.000220<br>)|**-0.117*****<br>(0.00265)||
|Number of tax||**-0.0284****|**-0.0241***|-0.00929||<br>**-0.0237***||-0.0219|
|inspections (all firms)||(0.0465)|(0.0665)|(0.831)||(0.0926)||(0.108)|
|Bribe tax (incidence -<br>% firms reporting<br>non-zero payment)|||**-1.827*****<br>(0)|||||**-1.522*****<br>(0)|
|Firm was<br>visited/inspected by a<br>tax official in the last<br>year|-0.110<br>(0.256)||||||||
|<br>Number of tax|||||**0.0238*****||-0.0170||
|inspections|||||(0.00920)||(0.171)||
|(inspected firms only)|||||||||
|<br>Bribe was expected<br>during tax inspection|||||||**-1.243*****<br>(0.000625<br>)||
|Bribe tax (firms<br>reporting non-zero<br>percentage)||||0.0143<br>(0.341)|||||
|Bribery is frequent<br>in tax administration||||||**-0.417*****<br>(1.18e-05)|**-0.284****<br>(0.0453)|**-0.363*****<br>(0.000285<br>)|
|Bribery is frequent<br>in customs||||||-0.0977<br>(0.424)|**-0.323****<br>(0.0187)|<br>-0.139<br>(0.269)|
|Bribery is frequent<br>in courts||||||-0.0883<br>(0.354)|-0.152<br>(0.269)|-0.0870<br>(0.368)|
|Firm size: medium|-0.173|-0.164|-0.165|-0.301|-0.0965|-0.172|-0.131|-0.179|
|(20-99 employees)|(0.103)|(0.123)|(0.123)|(0.399)|(0.733)|(0.140)|(0.435)|(0.128)|
|<br>Firm size: large|<br>-0.145|<br>-0.143|<br>-0.159|<br>-0.380|<br>0.337|<br>-0.0250|<br>0.168|<br>-0.0402|
|(>100 employees)|(0.379)|(0.394)|(0.340)|(0.439)|(0.316)|(0.893)|(0.473)|(0.826)|
|Constant|-0.131|-0.155|-0.0543|-<br>2.993**<br>*|-<br>3.452***|0.963***|1.314***|1.019***|
||(0.506)|(0.429)|(0.777)|(9.28e-<br>09)|(0)|(7.48e-<br>06)|(8.25e-<br>05)|(1.78e-<br>06)|
|Observations|10,292|10,080|10,166|1,234|5,133|8,959|4,778|9,043|

Models 6 through 8 add information on the perception of bribe frequency in tax authorities, courts, and customs. Perceptions of bribery in courts and customs have no apparent effect on overall perceptions of corruption as an obstacle (except for firms that paid a tax-related bribe and for which perception of corruption in customs is associated with general perceptions of corruption – Model 7). However, perceptions of a corrupt tax system significantly and 10 | P a g e
**Table 5. Scorecard information balance tests**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Project Funds|Project start<br>date|High<br>reported<br>corruption|Male|Can write|Can read|Distance to<br>s/c<br>headquarter|
|Training|60,239|0.380|0.017|0.021|0.041|0.027|32.033|
||[59,693]|[0.484]|[0.058]|[0.025]|[0.027]|[0.028]|[25.551]|
|Scorecard|78,426|0.277|0.043|-0.030|0.004|-0.002|21.557|
||[71,073]|[0.574]|[0.067]|[0.028]|[0.028]|[0.029]|[15.474]|
|Training x scorecard|-8,340|-0.060|-0.026|0.025|0.013|-0.005|-42.160|
||[86,583]|[0.701]|[0.083]|[0.036]|[0.036]|[0.037]|[28.271]|
|Training + Scorecard +|0.028|0.213|0.560|0.478|0.023|0.437|0.415|
|interaction=0||||||||
|Training=Scorecard|0.770|0.838|0.661|0.051|0.156|0.282|0.396|
|Control means|11,700,000|38.16|0.271|0.441|0.615|0.622|87.804|
|N|574|542|528|3,853|3,851|3,853|3,797|
|R-squared|0.764|0.316|0.001|0.075|0.075|0.065|0.059|

Notes: This table reports a balance test for project and individual-level characteristics. Columns 1 to 4 are measured at the project level during the project audit. Columns 5 to 8 are from the endline household survey and represent participant characteristics that were not expected to change due to the treatment. Standard errors are reported in brackets below the coefficients. *** p< 0.01, ** p< 0.05, *p< 0.1.

41
18 2011—80,000 more than in 2010, with many families cutting down basic necessities such as food and reducing the quality and the amount of food intake. The well-being of children in the Czech Republic, Poland, Greece, and the United States also seems to have deteriorated. Similarly, the UN composite Human Development Index, computed as a function of measures of life expectancy at birth, access to knowledge, and a decent standard of living, declined between 2007 and 2012 for a number of middle and high income countries in the Middle East and Europe (the United Arab Emirates, Bahrain, Greece, Luxembourg, Ireland, and Iceland) as well as for small island states. Advanced countries and regions closely linked to them registered the smallest improvement in the index (figure 12).

**Figure 9. Development indicators before and after the crisis by income group**

<!-- Start of picture text --> Ratio of girls to boys in primary and secondary<br>Malnutrition prevalence (% of children under 5)<br>education (%)<br>25 105<br>100<br>20<br>95<br>15<br>90<br>10<br>85<br>5<br>80<br>0 75<br>LIC LMIC UMIC HIC LIC LMIC UMIC HIC<br>precrisis postcrisis precrisis postcrisis<br>Mortality rate, under-5 (per 1,000) Maternal mortality ratio (modeled estimate, per<br>120 100,000 live births) by income groups<br>600<br>100<br>500<br>80<br>400<br>60<br>300<br>40 200<br>20 100<br>0 0<br>LIC LMIC UMIC HIC LIC LMIC UMIC HIC<br>precrisis postcrisis 2005 2010<br><!-- End of picture text --> _Source_ : Authors’ computations, based on World Bank’s World Development Indicators database.
||(0.250)|(0.275)|(0.548)|(0.491)|(0.394)|(0.402)|(0.415)|(0.528)|
|---|---|---|---|---|---|---|---|---|
|Started or increased delivery of<br>goods, services or carryout|0.260*|0.447|-0.429|-0.417|0.518|0.080|1.410**|1.369**|
||(0.146)|(0.281)|(0.335)|(0.424)|(0.483)|(0.490)|(0.587)|(0.628)|
|Started or increased remote work|0.164**|0.091|0.309|0.312|-0.977***|-1.002***|-0.260|-0.228|
||(0.078)|(0.164)|(0.257)|(0.323)|(0.336)|(0.362)|(0.323)|(0.346)|
|Exporting directly (at least 10% of<br>sales)|0.142|0.250|0.162|0.497|0.274|0.152|0.826|1.079|
||(0.101)|(0.164)|(0.443)|(0.523)|(0.577)|(0.662)|(0.750)|(0.791)|
|Constant|0.279***|0.286|-1.942***|-2.562***|-1.966***|-1.427***|-3.278***|-3.284***|
||(0.097)|(0.232)|(0.535)|(0.459)|(0.357)|(0.354)|(0.479)|(0.500)|
|Number of observations|619|469|630|473|679|522|554|427|
|r2_w|0.215|0.334|||||||
|r2_b|0.111|0.079|||||||
|r2_o|0.097|0.077|||||||
|rho|0.620|0.677|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

47
**Figure 1:** _Average HIPC Debt as a Share of Exports and GNI._ Average debt is normalized at Decision Point (grey line) and Completion Point (black line). The vertical lines mark the years of Decision Point and Completion Point, respectively. Source: World Bank (2015).

<!-- Start of picture text --> Years from Completion Point<br>-40 -35 -30 -25 -20 -15 -10 -5 0 5<br>-30 -25 -20 -15 -10 -5 0 5 10 15<br>Years from Decision Point<br>Debt (% of Exports) (Average, Normalized DP)<br>Debt (% of Exports) (Average, Normalized CP)<br>(a) Debt (% of Exports).<br>Years from Completion Point<br>-40 -35 -30 -25 -20 -15 -10 -5 0 5<br>-30 -25 -20 -15 -10 -5 0 5 10 15<br>Years from Decision Point<br>Debt (% of GNI) (Average, Normalized DP)<br>Debt (% of GNI) (Average, Normalized CP)<br>900<br>750<br>600<br>450<br>300<br>150<br>Average Debt (% of Exports) in HIPCs 0<br>250<br>200<br>150<br>100<br>50<br>Average Debt (% of GNI) in HIPCs<br>0<br><!-- End of picture text --> (b) _Debt (% of GNI)._ 21
**Table 14: Average Treatment Effect on 4**<sup>**th**</sup> **and 6th-Graders – Three to four years into the intervention – Controlling for variables at baseline**

||Math|English|
|---|---|---|
|WSD|-0.06|0.00|
||(0.07)|(0.06)|
|Grant|-0.07|-0.06|
||(0.06)|(0.06)|
|Baseline PTA Meetings|-0.01|-0.01|
||(0.01)|(0.01)|
|Baseline Test Scores|0.34 ***|0.46***|
||(0.05)|(0.06)|
|4th Grade Dummy|-0.68***|-0.73***|
||(0.03)|(0.03)|
|Constant|-0.21***|-0.27***|
||(0.07)|(0.07)|
|P-value WSD = Grant|0.92|0.30|
|Observations|4716|4716|

Notes: Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level.

# **Table 15: Effect of the Interventions on Student and Teacher Absenteeism, and on Enrollment**

||Abse|nteeism|LogFirst-Grade|
|---|---|---|---|
||Students|Teachers|Enrollment|
|WSD|-4.94**|-3.11*|-0.01|
||(2.24)|(1.75)|(0.1)|
|Grant|-2.61|-0.22|0.03|
||(2.24)|(1.76)|(0.1)|
|Constant|23.35***|13.31***|4.16***|
||(1.72)|(0.01)|(1.26)|
|P-value WSD = Grant|0.25|0.11|0.62|
|Observations|407|274|274|

Notes: Robust standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. The dependent variable in the first column is the percentage of student absent on the day of survey (scale of 0-100). The dependent variable in the second column is percentage of teachers absent (scale of 0 - 100). The dependent variable in the third column is the log enrollment of firstgraders. The unit of observation in the first column is the classroom. The unit of 34
- 8 -

on the left side; the majority of PPP water transactions have no competition. This phenomenon is characteristic of the LAC region.

Figure 2. Number of Bidders in Water-Sector PPP Auctions by Region <!-- Start of picture text --> (Latin America and the Caribbean) (East Asia and Pacific)<br>0.4 0.4<br>Avg. 2.23 Avg. 6.13<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 11 12 13~ 1 2 3 4 5 6 7 8 9 10 11 12 13~<br>Number of bidders Number of bidders<br>(Europe and Central Asia, and Middle East and North Africa) (Sub-Saharan Africa)<br>0.4 0.4<br>Avg. 3.23 Avg. 4.40<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 11 12 13~ 1 2 3 4 5 6 7 8 9 10 11 12 13~<br>Number of bidders Number of bidders<br>Density Density<br>Density Density<br><!-- End of picture text --> Source: PPI Database.

“Overspecification” is one of the alleged reasons for the limited competition in water-sector PPP transactions. In general, infrastructure construction and its operation and maintenance require a considerable amount of technical, experiential and managerial resources. On top of that, there are a few active players in the private water sector, as mentioned above. As a result, there is always a great risk that too restrictive qualification conditions could easily exclude most of potential firms that might be interested in bidding on the transactions.

The Private Participation in Infrastructure Services (PPIS) Database, which covers most private and public water utilities over the world, reveals that the past PPP transactions in the water and sewerage sector seem to have been relatively small (Figure 3). For example, about 90 percent of water contracts aim to deal with utilities producing—more precisely selling—
###

Figure 3: Change in firm trade outcomes for GVC firms and non-GVC firms

## (a) Customs data: Change in value of exports and imports

<!-- Start of picture text --> 10 10<br>4<br>0 0<br>-10 -4 -1 - 11 -3 -2 -10 -14 -6 -12 -9<br>-20 -16 -20<br>-24<br>-30 -30 -28<br>-33<br>-40 -37 -40<br>- 42<br>-50 -50<br>Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>Quarter of 2020 Quarter of 2020<br>Non-GVC Firms GVC Firms Non-GVC Firms GVC Firms<br>(b) BPS data: Change in share of exports and imports<br>1<br>0 0<br>-10 -8 -10<br>-11<br>-20 -20<br>-19<br>-30 -30<br>Non GVC firm GVC firm Non GVC firm GVC firm<br>P-value of test that GVC firms are different than non-GVC firms: 0.035 P-value of test that GVC firms are different than non-GVC firms: 0.088<br>Change in firm exports Change in firm imports<br>Change in share of exports over sales (p.p)<br>Change in share of imports over inputs costs (p.p)<br><!-- End of picture text --> _Notes:_ The sample includes in panel a 153,038 firm-quarter observations in the left plot and 579,123 firm-quarter observations in the right plot in 20 countries and in panel b 617 exporting firms in the left plot and 1,024 importing firms in the right plot in 13 countries.

The BPS data neither covers the universe of globally engaged firms nor is stratified by the degree of global engagement of firms. Yet the fact that the results based on BPS data are consistent with those obtained for the universe of globally engaged firms in the customs data gives us confidence that BPS data provides a credible understanding of the impact of the pandemic on globally engaged firms.

# **5.2 Resilience and Recovery in Firm Performance**

Business recovery from the pandemic is shown based on the latest round of the BPS data by (Cirera et al., 2021a), but the extent to which such recovery is felt by firms with global engagement is not known. The following key patterns emerge when comparing recovery for globally engaged firms with that of other firms. _First_ , the immediate impact of the pandemic on total sales of globally engaged firms is not significantly different from that on other firms, but globally engaged firms are certainly recovering faster. For example, while non-exporters experienced a decrease in total sales by 14