rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A19: Identifying the $1.90 poor based on covariates of poverty**

_Colombia 2017_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.000***|-0.002***|0|-0.003***|-0.005***|0|
|Asset ownership|-0.005***|-0.017***|0|-0.042***|-0.053***|0|
|Literacy|-0.010***|-0.035***|0|-0.056***|-0.080***|0|
|Not employed in the<br>agricultural sector|-0.007***|-0.014***|0.0001|-0.045***|-0.051***|0.0001|
|Access to electricity|-0.015***|-0.044***|0.0001|-0.118***|-0.147***|0.0001|
|Piped drinking water|-0.004**|-0.021***|0.0017|-0.052***|-0.069***|0.0017|
|Improved sanitation|-0.007***|-0.018***|0|-0.056***|-0.067***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A20: Identifying the $3.20 poor based on covariates of poverty**

|||_Colombia_<br>|_2017_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.001***|-0.005***|0|-0.009***|-0.013***|0|
|Asset ownership|-0.011***|-0.040***|0|-0.114***|-0.142***|0|
|Literacy|-0.013***|-0.059***|0|-0.136***|-0.182***|0|
|Not employed in the<br>agricultural sector|-0.011***|-0.032***|0|-0.128***|-0.150***|0|
|Access to electricity|-0.013***|-0.075***|0|-0.232***|-0.293***|0|
|Piped drinking water|-0.016***|-0.043***|0.0013|-0.125***|-0.151***|0.0013|
|Improved sanitation|-0.013***|-0.038***|0|-0.131***|-0.156***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 57
Figure 4 Central Bank of Yugoslavia _Real_ Dafatce Sheet _(Base_ = 1980)

|||I-e<br>I|--Credt|<br>--<br>_30_|Money--|*--NetFo<br>lJUa|w<br>--<br>Nt W<br>bUltt|orlh|
|---|---|---|---|---|---|---|---|---|
|1|Ra|Ce<br>to of<br>and S|1962<br>1<br>ntral<br>Domes<br>electe|<br>o<br>184<br>1<br>Table 2<br>Bank of Y<br>tic Assets to F<br>d Interest Rat|5<br>1<br>ugo<br>oreig<br>es, 19|9<br>19<br>slavia<br>n Liabil<br>80.198|67<br>1966<br>19<br> <br>ities<br>9||
|Net Dom<br>Asset/Net For.|1966<br>1.74|1961<br>1.67|1982<br>1.31|1983<br>1984<br>0.76<br>0.63|1985<br>_0.55_|<br>1986<br> <br>0.50|1987<br>1988<br>0.29<br>0.17|1989<br>0.12|
|Limb.<br>Average Interest<br>Rate on DomesUc<br>|3.5|3.5|3.5|_6.5_<br>20|38|48|61<br>100|1000|
|Assets<br>Interest on<br>Forign<br>Liabilltes (DM)<br>Domesllc|8.1<br>37.5|8.9<br>35.7|10.3<br>32.7|6<br>6.4<br>60.0<br>53.0|5.6<br>75.0|4.8<br>92.0|4.8<br>3.8<br>169.0<br>240.0|7<br>2685.0|
|Inflation(Dec.)<br>Domestic<br>Idflatdon (Avg.)|30.9|40.0|31.5|40.2<br>54.7|72.3|89.8|120.8<br>194.0|1240.0|

38
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indexed bonds and floating rate notes in local currency issuance policies. Therefore, the decision horizon was extended to five years in order to analyze the cost-risk characteristics of these instruments more precisely.

In cost-risk modeling of public debt, many developed debt management offices use longer decision horizons to get a better picture of the medium- and long-term policy responses. Although, the flexible nature of the model enables us to easily extend the decision horizon, volatile historical data regarding macroeconomic and financial variables, mostly attributable to financial crises and regime changes before 2002, and the limited extent of future projections for fiscal data make simulations for horizons greater than 5 years difficult.

The granularity of the model was modified from monthly to quarterly in order to simplify cash-flow modeling. Although a monthly frequency to calculate cash flows is very useful for a highly detailed understanding of government’s financing strategy, i.e., annual financing plan, from a longer-term strategic perspective, such a high degree of model granularity leads to a higher degree of complexity and potentially unwieldy calculations.

Typically, debt managers focus on annual cost and risk figures and combine annual observations with averages across years. With the adjusted cost and risk metrics, annual figures are taken into account, instead of using cumulative (or average) figures for the decision horizon. This prevents the possible information loss that may arise from the aggregation approach. Although it may be reasonable for debt management offices that have a long decision horizon to focus on averages, in the Turkish case the relatively short length of the decision horizon creates an incentive to analyze each year’s expected debt dynamics separately in a more detailed manner. This helps identify possible concentrations in cost and repayment profiles in specific years.

# **3.3 Debt Structure Metrics**

A main objective of cost-risk modeling is to provide guidance for the formulation of strategic benchmarks. Strategic benchmarks describe the policies designed to reach the desired debt structure and they are typically expressed as numerical targets for key portfolio indicators (IMF and the World Bank, 2001). In other words, strategic benchmarks are generally structured and communicated through the use of key portfolio indicators (debt structure metrics). Although the most common debt structure metrics are the average term-to-maturity, duration and share of maturing debt, countries use a variety of metrics based on their market 14
Peri, Giovanni (2012). “The Effect of Immigration on Productivity: Evidence from U.S. States.” _Review of Economics and Statistics_ 94(1): 348–358.

USAID (2014). “(Re)Assessing The Relationship Between Food Aid and Armed Conflict.” USAID Technical Brief.

Willis, Brandon and Doug O’Brien. “Summary and Evolution of U.S. Farm Bill Commodity Titles.” National Agriculture Law Center. Accessed 26 January 2015. http://nationalaglawcenter.org/farmbills/commodity/ 25
1 See e.g. Thomas, 1990, Haddad, Hoddinott and Alderman, 1997, Duflo, 2000, Quisumbing and Maluccio, 2000, Attanasio and Lechene, 2002, Katz and Chamorro, 2002, Schady and Rosero, 2007.

> 2 The World Bank defines the investment climate as _the set of location-specific factors shaping the opportunities and incentives for firms to invest productively, create jobs and expand_ (World Bank, 2005, p19). De facto, any factor that affects firm performance and decision making can be considered part of the investment climate. This has led some (e.g. Easterly, 2002) to criticize the concept as being devoid of any meaning. Instead, we take the view that it is important to clearly specify which aspects of the investment climate we are considering.

3 For example, in the Amhara region in Ethiopia, where the Ethiopian Rural Investment Climate Survey data used in this paper were collected, the belief that the harvest will be bad if women work on the farm is prevalent (Zwede and Associates, 2002, Bardasi and Getahun, 2007).

4 Gender differences in the performance of male and female entrepreneurs in developed countries are relatively well documented, but the evidence is mixed. Some studies report evidence of female underperformance, whereas other do not find gender differences (see e.g. the literature reviews in Parker, 2009 and Sabbarwal and Terrel, 2008).

5 In addition, Sabbarwal and Terrel (2008) find some evidence that female firms in Eastern Europe and Central Asia have higher returns to scale, but the differences in returns to scale between men and women are small, and could be an artifact of a rather restrictive production function specification. For example, differences in technology between sectors are modeled as being additive in TFP, rather than as interacting with the returns to capital, labor and material inputs.

6 Some studies suggest women indeed have more difficulty accessing finance than men (e.g. Brush 1992, Carter, 2000), while others detect no gender differences (Blanchflower et al., 2003, Storey, 2004, Cavalluzzo and Wolken, 2005).

7 Thus, in many cases the term “activity” might have been more appropriate than the term enterprise.

8 Loening et al (2008) demonstrate that the rural non-farm sector in the Amhara region is very similar to the rural nonfarm sector in other regions, although its composition is slightly skewed towards manufacturing activities, whereas in other regions of Ethiopia trade and services enterprises dominate.

9 Only three enterprises in the Ethiopian dataset employ more than 10 workers. Since the enterprises in this dataset are all household-based we might miss out on fully commercial enterprises owned or managed by individuals not living in these communities. However, from the community level dataset one can infer that there are not more than a dozen firms with more than 20 employees in a radius of one hour commuting distance from the 179 surveyed communities. It 30
and spending levels. Second, because the schooling institutions can vary in a range of ways, it is difficult to ensure that other influences on achievement are properly accounted for in the analysis. For these reasons, we turn to data generated within separate countries.

# **_6.2 Within-country Evidence – Developed Countries_**

For policy deliberations, information on resources from within individual countries is perhaps more appropriate than cross-country information. Most importantly, it generally allows for much more detailed analyses that can more reliably isolate factors subject to policy decisions. The open question, to which we return below, is whether information gleaned from the schools in one country translates into reliable advice in other countries. We begin by reviewing evidence from developed countries and move to developing countries in the next section.

Researchers have studied the determinants of student achievement for more than 40 years. The work was begun in the United States in the “Coleman Report” (Coleman et al. (1966)), which introduced the idea of using statistical analysis to relate various inputs of schools to student outcomes. This work also underscored the importance of including nonschool factors by demonstrating that family differences were very important in explaining variations in achievement across students. While this original study has been subjected to considerable criticism, it led to an extensive line of research.

The general picture about school resources in developed countries is now well known and has been reviewed in a variety of places (see Hanushek (2002b), Hanushek (2003), Wößmann (2005b)). Here we simply review the highlights and point to the key open issues.

The available studies concentrate on various common inputs to schools such as teacher experience or class size. These factors are both readily available in both administrative and survey data sets and frequently identified as the focus of policy. The available econometric evidence now includes literally hundreds of separate estimates within the U.S. and other developed countries. Quite uniformly, however, there is little strong evidence that _any_ of the following factors has a consistent impact on achievement: the level of teacher education, the pupil-teacher ratio, the characteristics of administration, or the facilities of the school. Specifically, aggregating results across studies, a minority of estimates are statistically different from zero (at the 5 percent level or better), and the studies do not even uniformly indicate improvements in performance with increased resources.

The two factors that more frequently appear to positively influence student outcomes are teacher experience and measures of teacher achievement tests. We discuss these below under the assessment of the quality of studies.

63
**Table 1: Summary Statistics for Households in Rural Java (1999/2000 IFLS)**

|**Variables**|**Mean**|**Std. Err.**|
|---|---|---|
|totalpce(Rupiahper capitaper month)|257273|7660|
|foodpce(Rupiahper capitaper month)|154389|4332|
|nonfoodpce(Rupiahper capitaper month)|102885|4745|
|household size|3.06|0.09|
|age of head|48.41|0.45|
|married head|0.84|0.01|
|female head|0.18|0.01|
|highest education of head: elementary|0.58|0.02|
|highest education of head:jr. high school|0.07|0.01|
|highest education of head: high school|0.05|0.01|
|highest education of head: university|0.08|0.01|
|hh utilizes electricity|0.90|0.03|
|hh owns farmland|0.50|0.03|
|hh non-farm business|0.44|0.03|
|hh farm business - rice most valuable crop|0.38|0.03|
|hh farm business - other cropmost valuable|0.22|0.03|
|shock: delayof monsoon onset(>1 sd)|0.34|0.06|
|shock: delayof monsoon onset(>2 sd)|0.16|0.04|
|shock:post-onset low rainfall(<-1 sd)|0.57|0.06|
|shock:post-onset low rainfall(<-2 sd)|0.45|0.06|
|N=2159|||

# **4. Empirical Results**

We present our findings on (i) the impact of rainfall shocks on per capita household consumption levels and (ii) the role that various social programs may have played in mitigating the negative welfare impacts of the rainfall shocks. For the first part, we used regression analysis to quantify the average reduction in household welfare levels for those exposed to low rainfall shocks. For the second part, we used propensity score matching to estimate the extent of moderating effects offered by the various community-based programs.

## _<u>Welfare Impacts of Rainfall Shocks</u>_

Given the importance of rain-fed agriculture, in particular rice farming, to rural livelihoods in Indonesia, we study the potential impact of rainfall shocks on per capita total household expenditure, and its food and nonfood components. We focus on rural Java, the 10
Figure 1: Product churning at the firm level <!-- Start of picture text --> 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Export Products Introduced Export Products Dropped<br>total number of exported products<br>Note: Based on unbalanced panel<br>Source: EIM (INEGI, Aguascalientes)<br>4,000<br>3,000<br>2,000<br>Number of Products<br>1,000<br>0<br><!-- End of picture text --> Figure 2: Product churning at the firm level <!-- Start of picture text --> 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Introducing export products Dropping export products<br>Simultaneously introducing and dropping export products<br>Note: Based on unbalanced panel<br>Source: EIM (INEGI, Aguascalientes)<br>400<br>300<br>200<br>Number of Firms<br>100<br>0<br><!-- End of picture text --> 11
# **2. The idea in theory**

With adequate enforcement, a competitive market for WPs will drive out illegal migration. This does not require full enforcement, defined as a situation in which there is no chance that an illegal migrant will escape deportation by the authorities in the host country. <u>Lokshin and Ravallion (2021)</u> show that adequate partial enforcement, as measured by the probability of being caught and sent home, is sufficient to make legal migration the preferred choice. (The Wall might not be needed.) That is what we assume here.

The costs of migrating include the foregone earnings at the origin, the extra living costs incurred in the destination, out-of-pocket costs of moving, taxes imposed by the U.S. and any remittance demands made by the family and friends back in Mexico that would not have been made if the migrant had stayed home. The key implication of these costs of migration is that the distribution of labor earnings, net of the costs of migrating, facing potential Mexican migrants is not the same as the existing distribution of earnings in the high-wage country. Specifically, we assume that potential migrants expect to receive net earnings in the U.S. with a continuous cumulative distribution , with strictly increasing as usual. Given the costs of moving, the migrant's net earnings distribution can be taken to be unambiguously “poorer” than the GG(ww) GG(. ) distribution found in the U.S., denoted , which is also continuous in ] with strictly also increasing.<sup>7</sup> In other words, we assume that FF(ww) ww∈<sup>mmmm</sup> for all ]. Demand for the WPs is M where M is the total [ww<sup>m</sup> , ww<sup>mmm</sup> FF(. )<sup>mm</sup> GG(ww) > FF(ww) workforce in Mexico, while U is the U.S. workforce, with M U . We impose two ww∈[ww<sup>m</sup> , ww<sup>m</sup> [1 −GG(pp)]nn nn restrictions on the distribution, namely that M , and ) = 1, which nn nn + nn = nn<sup>mmmm</sup> imply positive excess demand at and an excess supply at . Invoking continuity and GG(. )<sup>mm</sup> GG�ww<sup>m</sup> �< nn /nn<sup>mm</sup> GG(ww<sup>m</sup> monotonicity, a (unique) equilibrium is given by: ww<sup>m</sup> ww<sup>m</sup> M ) (1) where U M is the weighted mean of the two distributions. pp= HH<sup>−1</sup> (nn /nn We can now introduce a tax on the WP as a policy instrument for managing the HH(ww) ≡[FF(ww)nn + GG(ww)nn ]/nn We can now introduce a tax on the WP as a policy instrument for managing the scheme—for determining the desired basic income and/or controlling migration flows. The tax is

> 7 In the empirical work we allow for a replacement rate less than unity, such as due to disutility of labor.

5
farmer, in turn, depends on the perception of risk and training received on safe handling of pesticides. In our survey, only 4% farmers reported receiving basic training on the use of pesticides and safe handling<sup>7</sup> , while 87% openly admitted that they do not take any protective measures during the handling of pesticides.

# _Health effects_

A detailed medical examination of sample farmers was beyond the scope of the study. Instead our analysis relied solely on self-assessed/ reported health effects, where farmers were questioned if they experienced any health impairment after mixing and spraying pesticides.<sup>8</sup> Over 49% of farmers experienced at least one symptom, with the most commonly reported as neurological (headaches: 27%, dizziness: 8%), eye (irritation: 26%), dermal (skin: 13%), and gastrointestinal (vomiting: 9%). The interviews further revealed that 26% of the respondents experienced multiple health effects, with an average of 3 and a maximum of 5. Upon asking sick farmers whether they believed that these symptoms were related to pesticide use, 82% believed this to be true. The health effects variable, “sick” was defined as whether a farmer experienced at least one symptom (=1) or not (=0).

# **5. Determinants of Farmer’s Pesticide Overuse**

# _The model_

To model pesticide overuse, we used a trivariate probit framework with farmers’ misperception of risk and health effects as endogenous dummy variables. The structure of the model follows a three-equation form where the estimation outcome of the first equation is used as a regressor in the second equation, and the second outcome in the third as follows:

> 7 The primary sources of information for pesticide use and handling were: pesticide suppliers or companies (67.27%), the Agricultural Ministry (19.09%) and “personal knowledge” (10.91%).

> 8 Are self-reported health effects a credible measure?  Detailed information for Bangladeshi farmers is virtually non-existent, however, medical tests of the farming population in other Asian countries may be indicative.  Several clinical studies conducted on rice and vegetable farmers in Indonesia, Philippines, and Vietnam revealed 58% - 99% of the farmers exposed to pesticides had at least one health effect (Xuyen _et al_ ., 1998; Kishi _et al_ ., 1995; Antle and Pingali, 1994; Rola and Pingali, 1993).  This evidence suggests that the degree of upward bias, if at all in a self-assessment of health effects in Bangladesh may not be large.

9
# **Appendix 2: Data Definition and Sources**

## **Definition**

**Provisions/Assets =** Loan Loss Provisions / Total Assets

**Earnings before Provisions / Assets** = (Profit before Tax + Loan Loss Provisions) / Total Assets

**Loans / Assets** = Total Loans / Total Assets

**Loans in Real Terms** = Total Loans / Consumer Price Index

**Loans Growth Rate** = [Loans in Real Terms (t-1) - Loans in Real Terms(t)] / Loans in Real Terms (t-1)

**GDP per Capita (in 1995 US Dollars)** = GDP at constant 1995 US dollar market prices / Total population

# **II. Sources**

# **Income Statement and Balance Sheet Items taken from Bank Scope**

Loan Loss Provisions: - Bank Scope, summary code No: 2095 Profit before Tax - Bank Scope, summary code No: 2105 Total Loans - Bank Scope, summary code No: 2000 Total Assets - Bank Scope, summary code No: 2050

# **Series from the IMF and the World Bank**

CPI (1995) = 100 - IFS line 64, IMF GDP at market prices (constant 1995 US Dollars) - World Development Indicators, World Bank Population, Total - World Development Indicators, World Bank Gross Domestic Product - IFS line 99b, 1MF 20