rafmacalaba/datause-encoder-data huggingface dataset:
end of the income range (i.e., as twice the poverty line or 966= 483*2) that defines vulnerability as proposed in India (NCEUS, 2007).

Estimation results provided in in Table 8 show strong welfare improvement for both the poor category and the middle class, while the vulnerable category remains almost the same and accounts for less than 50 percent of the population. In particular, the poverty category decreases by 14 percent and the middle class increases by 19 percent in this period. The population as a whole are rather mobile, with 23 percent of the population experiencing upward mobility while 15 percent experience downward mobility. Compared to the US and Vietnam at a similar vulnerability index of 20 percent over a roughly similar period (2004-2008), it can be calculated that India has the most mobility with 38 percent of the population churning their consumption levels, which is then followed by Vietnam (34 percent) and the US (18 percent).<sup>26</sup>

# **3.3. Profiling of Mobility for All Three Countries**<sup>**27**</sup>

This vulnerability framework is also amenable to analysis of welfare transitions at a more disaggregated population level such as the relationship between these transitions and household characteristics. This analysis can be implemented using either true or synthetic panel data. As an example, a graphical presentation is provided in Figure 2 of the relationship between education and welfare mobility for the US (Panel A), Vietnam (Panel B), and India (Panel C), where the data for the first two countries are true panel data while those for India are synthetic panel data. For illustration purpose, we set the vulnerability index at 10 percent for the US and Vietnam, but

> 26 The vulnerability index for the US is 19 percent instead since this is the largest index available for this country. These calculations for the US and Vietnam are available upon request. For a more detailed discussion of income mobility for India during this period and over a longer period dating back to 1993, see World Bank (forthcoming). 27 Given the scope of this paper, we only provide illustration of the vulnerability framework that measures mobility with positional movement. We leave other mobility measures for future research. For other definitions of income mobility, see, for example, Fields (2008); see also Jantti and Jenkins (2013) for a recent review of the literature on income mobility.

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<!-- Start of picture text --> Figure 13a: Fraction of firms in micro tax regime Figure 13b: Fraction of firms in micro tax regime<br>around GEL 30,000 cutoff – 2011 data (post-reform) around GEL 30,000 cutoff – 2012 data (post-reform)<br>Local Avg. + Polynomial in GEL 100 Intervals from Cutoff Local Avg. + Polynomial in GEL 100 Intervals from Cutoff<br>10000 20000 30000 40000 50000 10000 20000 30000 40000 50000<br>2011 Declared Revenue 2012 Declared Revenue<br>.1 .15<br>.08<br>.1<br>.06<br>.04<br>.05<br>.02<br>Fraction of firms in micro tax regime Fraction of firms in micro tax regime<br>0 0<br><!-- End of picture text -->

**Figure 13a: Fraction of firms in micro tax regime around GEL 30,000 cutoff – 2011 data (post-reform)**

<!-- Start of picture text --> Figure 14a: Fraction of firms in small tax regime Figure 14b: Fraction of firms in small tax regime<br>around GEL 100,000 cutoff – 2011 data (post-reform) around GEL 100,000 cutoff – 2012 data (post-reform)<br>Local Avg. + Polynomial in GEL 500 Intervals from Cutoff Local Avg. + Polynomial in GEL 500 Intervals from Cutoff<br>50000 100000 150000 50000 100000 150000<br>2011 Declared Revenue 2012 Declared Revenue<br>.2<br>.1<br>.08<br>.15<br>.06<br>.1<br>.04<br>.05<br>.02<br>Fraction of firms in small tax regime Fraction of firms in small tax regime<br>0 0<br><!-- End of picture text -->

**Figure 14a: Fraction of firms in small tax regime around GEL 100,000 cutoff – 2011 data (post-reform)**

# _5.2 Impact of the reform on formal firm creation_

This section uses 2010 through 2012 data to estimate the impact of the reform on formal firm creation. It is important to point out the following measurement issue here. The eligibility thresholds are based on annual revenue. For a firm that registers in one year, for example 2010, we only have data on declared annual revenue for the following year, in this case 2011. This is because the firm declares revenue for less than a full year in the year when it was created, simply because it has not yet been 23
45 offices (as it does not depend in the example of Poland quoted above) which also may be expected to be better in richer countries.

High percentage of targeting errors means that the costs of poverty alleviation will be high. As discussed in Section 1, one cannot assume that the costs of poverty elimination based on perfect targeting (i.e. assuming that all of the money goes only to the poor and in the exactly required amounts) are anything to go by. These costs, which range, for realistic poverty lines, between 2 and 3 percent of GDP (see Milanovic, 1994) must not be increased only by a factor of about 2—a number derived from Western experience, but by more. The formula is: where PD*/GDP is the "effective" poverty deficit (PD) as a percent of GDP. This is equal to the headcount (HC) times the average shortfall of the poor below the poverty line (sh) divided by GDP—that is the PD/GDP "calculated" under the assumption of perfect targeting— "grossed up” by an error factor which is a positive function of the poverty headcount (HC). In Poland, a country with a reasonably developed social assistance system, the "gross up" factor amounted in 1993 to 2.3. About 44 percent of all social assistance was paid to the poor and was not in excess of the poverty line; 15 percent was the "spillover", and 41 percent was paid to the non-poor,” Since ITS system relies on more restrictive conditions than the MIG (low income plus some dysfunctionality—most commonly zero elasticity of supply of labor), the numbers of potential claimants will be less. This is because fewer people will expect to get social aid and fewer will apply. Fewer applicants in turn implies that both the "failure" and "excess" errors will be less frequent, (not only in actual numbers, which is obvious because the number of applicants under ITS will be less than under MIG) but in proportion to the applicants. *° Fewer number of applicants and less frequent errors will both tend to reduce costs of poverty alleviation under the ITS system. Smaller HC will tend to reduce the value in equation (1) through both channels: lower "calculated" PD/GDP (under perfect targeting), and lower error factor.

> See World Bank (1994, Chapter 4).

> This follows from the assumption that the percentage of errors is a function of the number of potential claimants. Although the number of targeting errors (individuals who are wrongly excluded or included) will be the same, with the given number of applicants, under ITS or MIG system, the expected greater number of applicants under the MIG system will raise the expected proportion of errors under MIG.
The core instrument with the same format as in the baseline was administered to 24 households within each cluster. The large sample of 10,700 households enables estimation of the malnutrition rate at the district level. Half of the selected households per village, (total of 5350 households nationally) were in addition administered a more in-depth questionnaire. The core questionnaire was expanded to include a female module with in-depth questions on knowledge and practices. The questions cover pregnancy and child care adapted from the Demographic and Health Survey (DHS) in order to capture intermediate indicators that are likely to be affected by the program. Females and the community nutrition worker in participating communities received separate survey instruments with specific questions about the program.

We complement the anthropometric surveys with two additional sources of census-based data. The first one is the Commune Census data, conducted in 2001 under a joint collaboration between Cornell University, the National Statistical Institute (INSTAT) and the agricultural research institute within the Ministry of Scientific Research (FOFIFA). The census contains detailed information on demographic and socio-economic characteristics of all communes in the country, such as remoteness, main economic activities, local infrastructure, and a detailed history of weather shocks<sup>14</sup> . Second, we use commune level estimates<sup>15</sup> of poverty from the poverty map developed by Mistiaen et al (2002) by combining the 1993 household survey with the 1993 population census. The technique allows estimating consumption-based measures of poverty and inequality at very low level of geographic disaggregation. Mistiaen et al (2002) document a considerable degree of spatial heterogeneity in poverty across administrative units within provinces in particular across districts.<sup>16</sup> _3.1. Nutritional outcomes_ A summary description of nutrition status at both baseline (1997/98) and follow-up (2004) is presented in Table 1, and summarized in Figure 1 and Figure 2. The two graphs reproduce non-parametric age profiles of weight for age (Figure 1) and height for age (Figure 2), as in Shrimpton et al (2001). The shapes of the graphs confirm the international evidence.

> 14 The Census covers 1385 (out of a total of 1394) communes in the country in 2001. The missing communes could not be reached because of the local security reasons. The questionnaire was administered to a focus group composed of residents of the commune. 15 The Communes were introduces in 1995, replacing Firaisanas as the smallest administrative unit. In order to create communes, some of the Firaisanas were subsequently split or changed some of the boundaries. Our unit of analysis is the community: we are able to assign all communities to old Firaisanas/new Communes with the help of a geographic mapping provided to us by the Statistical Institute.

> 16 The fact that the poverty map dates back to 1993, might raise the concerns of its relevance to the current study. However, the national rankings of communes did not change substantially over this period.

8
<mark>proportion of households that state they have some or complete trust in other people.</mark>

- <mark>Quality of public services such as police, courts, education, health, and social assistance contribute toward adaptive capacity. Areas with good services will find it easier to respond to climate risks and to craft the public-private collaboration required to prepare for climate change. We measure this variable using LiTS data on the average number of public services (out of 8 max) for which households declare that they are either satisfied or very satisfied. If all households in a region were satisfied with the quality of all 8 public service areas, the value becomes 8. If no household is satisfied with any of the services, the value becomes zero.</mark>

- <mark>Corruption in daily interactions measures a key hindrance for adaptive capacity and, vice versa, absence of corruption makes adaptation easier and proxies institutional strength. We measure corruption using LiTS data on the average number of public services (out of 8 max) for which households declare that they never or seldom find it necessary to pay bribes in order to obtain the service in question (the services are the same as above, namely police, courts, education, health, and social assistance services).</mark>

<mark>Compared to adaptive capacity, constructing indices of exposure and sensitivity proved relatively straightforward. We construct the exposure index from variables measuring temperature and precipitation variability and natural disaster frequency. We construct the sensitivity index from variables measuring agricultural, demographic, health, poverty, and disaster-related sensitivity to climate variability. This is described in the following section.</mark>

# **<mark>Variables in the index</mark>**

<mark>The exposure sub-index is comprised of the following six variables measuring exposure to variability and extreme values of temperature and precipitation as well as to natural disasters: (i) Standard deviation of the average monthly temperature 1950-90 (see formulas in Appendix 1); (ii) the range between maximum and minimum average monthly temperature; (iii) the frequency of extremely hot or cold months, defined as the frequency of months in which the average temperature exceeded 30 C or fell below - 10 C; (iv) the frequency of extremely dry months in the spring (less than 5 ml total precipitation per month) and summer (0 ml total precipitation per month); (v) the standard deviation of monthly total precipitation; and (vi) the frequency of weather related disasters between 1998-2009.</mark> <mark>The sensitivity sub-index is comprised of five variables measuring agricultural, demographic, health, poverty, and disaster-related sensitivity to climate change and variability. Sensitivity of agriculture to impacts of climate change and variability is measured as the average of three variables: Area of irrigated land per capita, the degree of diversification of crop land-use measured by the Herfindahl index, and the share of</mark> <mark>7</mark>
While all three sectors experienced declines in their high rates of growth after mid-2007, manufacturing, where exports are concentrated, experienced a sharp drop at the time of the crisis, and some of the effects of this shock likely spilled over into the services sector as well.

# 3. **The Financial Crisis and Shocks to Employment**

Anecdotal reports started showing up in both the Chinese and the international press that sparked early concern that the global financial crisis would have a serious impact on employment. For example, in October 2008, the owner of a textile factory in Shaoxing, a city 100 miles from Shanghai, fled in the middle of the night and left 4,000 workers unemployed and US$200 million in unpaid bills. When employees showed up to work in the morning and found the factory gates bolted, they erupted in protest (Xiao 2008). In addition, there was frequent documentation of laid-off migrant workers returning home to villages early for the spring festival in 2009 (see, for example, Johnson and Batson 2009) and in some cases creating conflict with those renting their land (Yang 2008).

Surveying firms, households, or individuals yields some perspectives on the employment impact of the crisis. Surveys at each level have both advantages and disadvantages. A firm survey that enumerates employment and vacancies is more likely to capture changes in demand for labor at the firm level. Since firms in export sectors can then be separated from other types of firms, this approach promises to provide a direct link to export shocks. At the same time, however, firm surveys typically oversample larger firms and may thus miss the effect of shocks on smaller firms, which are less likely to be in the formal sector. In addition, firm surveys—especially if they are repeated cross-sectional surveys—may understate the effects of labor market shocks because they do not capture shocks to employment resulting from firm bankruptcies **.** Alternatively, firm surveys may overstate labor market shocks if all firm attrition from a panel survey is attributed to bankruptcy or restructuring as a result of the crisis.

# **3A. Evidence from Firm Surveys**

5
# **Introduction**

This paper is a study of the economic impact of non-ocular (or non-blinding) onchocerciasis. Onchocerciasis, caused by infection with the nematode worm _Onchocerca volvulus,_ is a serious public health problem in much of tropical Africa where an estimated 17.5 million people are infected.<sup>3</sup> Oncho-infected individuals exhibit a myriad of symptoms ranging from intense itching, skin depigmentation and atrophy, muscoskeletal pains, and blindness. The ocular pathology of onchocerciasis varies from region to region. In West Africa, for instance, prevalence of ocular damage is largely associated with the savanna strain of the worm. In East Africa, on the other hand, ocular damage is rare among both the savanna and forest strains. The devastating impact of blinding caused by onchocerciasis is relatively well known and documented.<sup>4</sup> The successful operation of the Onchocerciasis Control Programme (OCP) has virtually eliminated onchocercal blindness in 11 participating countries in West Africa.

Recent studies have shown that the non-blinding manifestation of onchocerciasis also causes significant adverse socioeconomic consequences.<sup>5</sup> Approximately 9 million infected people live in areas where oncho-related blindness is not common. Rather, skin-related symptoms are predominant, leading to intense itching, fatigue, and insomnia. This has prompted the initiation of a multi-country program, namely the African Programme for Onchocerciasis Control (APOC). This program, supported by a coalition of international donors (including the World Bank), national governments, as well as non-governmental organizations, is primarily oriented towards the large-scale distribution of ivermectin -- a drug that must be taken at least once per year for an extended period of time in order to kill skin microfilaria.6 In this study, we investigate the economic impact of the non-blinding strain of onchocerciasis [henceforth referred to as onchocercal skin disease (OSD)]. We examine data collected from a sample of workers at a coffee plantation in southwest Ethiopia: an area known for its high prevalence of OSD. Using regression and other data analysis techniques, we test a variety of hypotheses concerning the incidence and severity of OSD and its impact on economic productivity.

# **Study Area and Methodology**

Our study site is the Teppi coffee plantation, which is situated 575 kms southwest of Addis Ababa, Ethiopia. The site was chosen for its high incidence of OSD, ready availability of

- 3 World Health Organization (1995a).

- See Prost and Prescott (1984), Kim and Benton (1995), and Kimn _et al._ (1996).

- _5_ See, for example, Oladepo _et al._ (1996), Workneh _et al._ (1993), and World Health Organization (1995b).

- 6 Ivermectin does not kill the adult worm.

2
Appendix 6. Country-case study sources

|Episode|Main sources|
|---|---|
|Argentina|Hornbeck 2013; IMF 1989, 2001a, 2016b; Kaufman 1989; Kawai, Newfarmer<br>and Schmukler 2005|
|Bangladesh|IMF 1974|
|Brazil|Boughton 2001; IMF 1981a, 2003a|
|Bolivia|Boughton 2001; IMF 1978a; Kaufmann, Mastruzzi, and Zavaleta 2003; Sachs<br>1988b; Morales and Sachs 1999;|
|Cameroon|Daumont, Le Gall, and Leroux 2004; IMF 1998a, 2004b|
|Chile|Boughton 2001; IMF 1978b, 1982, 1985|
|Colombia|World Bank 1996|
|Indonesia|Boughton 2012; IMF 2001b; 2003a|
|Korea, Rep.|Boughton 2012; IMF 2001b; 2003a|
|Malaysia|Boughton 2012; Radelet et al. 1998|
|Mexico|Boughton 2001, 2012|
|Nepal|IMF 1984a|
|Niger|IMF 1998a; 1998b; World Bank 1994|
|Nigeria|Daumont, Le Gall, and Leroux 2004; IMF 1999b, 2012|
|Paraguay|IMF 1984b|
|Peru|Boughton 2001; Sachs 1985|
|Philippines|IMF 1997; 1999c; Kawai, Newfarmer and Schmukler 2005|
|Thailand|Boughton 2012; IMF 2001b; Radelet et al. 1998|
|Uruguay|IMF 1978c, 1981b, 1995, 2001c, 2003b|
|Venezuela|Boughton 2001, 2012; IMF 1978d|
|Zimbabwe|Boughton 2012; IMF 1999d, 2001d|

Note: Unless otherwise specified, IMF references refer to Article IV staff reports.

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**Table A23: Excess Interaction and Tax Revenue Losses II**

|During delegated randomizat<br>|ion of inspe<br>|ctor assignm<br>|ent<br>|
|---|---|---|---|
|Dependent variable:|**Hypot**|**hetical rev**|**enue loss**|
|reference price|Importer|Exporter|Third-party|
||(1)|(2)|(3)|
|Excess interaction share|0.146***|0.267***|0.024*|
||(0.028)|(0.068)|(0.011)|
|Withheld from randomization (WFR)|0.043|0.021|0.184|
||(0.160)|(0.269)|(0.162)|
|WFR*Excess interaction share|1.279***<br>(0.327)|3.328***<br>(0.638)|0.479**<br>(0.206)|
|Exogenous declaration characteristics|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|
|Month-year fxed efects|Yes|Yes|Yes|
|Observations|17,726|12,753|1,508|
|R-squared|0.149|0.544|0.436|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by the official assignment rules, calculated using calibration (as explained in Section 4). Declaration characteristics include the tax rate, the share of value accounted for by differentiated products, a dummy indicating whether the declaration was mixed, and a dummy indicating whether valuation advice was issued. Exogenous declarations characteristics include the tax rate, the share of value accounted for by differentiated products, and a dummy indicating whether the declaration was mixed. "Importer", "Exporter" and "Third-party" refer, respectively, to median unit import prices, prices reported by countries exporting to Madagascar, and transaction-specific valuation advice provided by the thirdparty GasyNet based on its own proprietary data. "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period November 18, 2017 to November 17, 2018.

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particularly in high potential zones, while water conservation, irrigation and shading/sheltering of crops are the main adaptation measures in drier regions. The analysis shows, however, that credit constraints, poverty and lack of information hinder households from taking the most important adaptive measures, such as water management.

The key policy lesson from this study is that global warming will have adverse effects on agriculture in Kenya. Given the difficulties of averting global warming, adaptation to climate change is essential to counter the expected impacts of long-term climate change. We argue that the government must play a critical role in encouraging adaptations to climate change if farmers are to counter the expected impact of global warming. Critical interventions would be monitoring climate change and disseminating information to farmers through agricultural extension, to encourage both short- and long-term adaptations. Improved management and conservation of available water resources, water harvesting and recycling of waste water could generate more water for irrigation, which is especially important in the arid and semiarid areas. This would help to lessen the expected repercussions of global warming. Policies for credit provision and improved household welfare are also a priority for both short- and long-term adaptation measures.

One limitation of this study is that it is based on general crop agriculture and does not model the impact of climate change on individual crops and livestock, which would be important for assessing the full impact of climate change on arid and semi-arid areas. The study also does not model the impact of adaptations that farmers make to counter the repercussions of climate change. We recommend future research in these areas. Future research that uses panel//time series data may also be expected to provide better estimates of the impact of climate change on Kenyan agriculture.

4