rafmacalaba/datause-encoder-data huggingface dataset:
particularly in the case of India. Consequently, the true or “inherent” comparative advantage of South Asian countries may somewhat, are different than the following empirical results suggest.<sup>24</sup> Table 8 provides, for each of the South Asian countries, their import structure<sup>25</sup> and the corresponding RCAs of South Asian partners. A review of complementarity by each SAC’s major imports and partner’s comparative advantage does not seem encouraging for the potential success of the FTA. For example, India, the major market in the region, shows that its major imports can be identified mostly in semi-manufacturing (which is a consequence of its import policies) for which the rest of the region largely shows neither the capacity nor comparative advantage. This is reflected in the meager share of imports from the regional members on each item and the corresponding RCAs. India’s sugar imports (SITC 0612) reported a substantial (70 percent) share of its total imports from the region. However, sugar represents a mere 0.4 percent of India’s global imports. The prospects of the rest of the SACs substantially increasing their current low share of exports to India seems less promising based on their current trade structures. However, with sufficient reduction in India’s existing trade barriers—the new incentives bringing about an adjustment of resources among competing industries between India and its neighbors, enhancing current, as well as new capacities, its neighbors could make progress. In the meantime, prospects that may exist are confined to what are often termed “niche” markets (identified at HS 6-disaggregated level), which are unlikely to have a substantial impact on transforming their meager export shares with India unless quality and product features are compatible with those imported from elsewhere by India.

The statistics of the second largest economy, Pakistan, suggest a similar scenario. The region, which barely accounts for 2 percent share of Pakistan’s imports, overall demonstrates a negligible capacity to fulfill its import needs. The RCA indices show prospects that a limited portion of Pakistan’s import requirements

> 24 Ideally, comparative advantage should be measured in an environment in which neither external trade constraints (like tariff and non-tariff barriers, or preferential access to export markets) distort an individual country’s “real” export structure, or where domestic market interventions (such as subsidies) in the exporting country have sector-specific effects or a general anti-export bias.

> 25 The major 50 imports at SITC 4-digit level represent between 66 and 96 percent of total imports of South Asian countries. The product mix in 1998 is largely representative of latter 1990s.  Readers must also note that the product differentiation within each of the SITC 4-digit classification (identified at Harmonized Classification 6-digit level) may provide part of an explanation for the apparent discrepancy between similar products that have successful global exports, but have no effectual intra-regional trade.

29
<!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10<br>Years to management by African Parks<br>probability any iNaturalist visits<br>D<br><!-- End of picture text -->

**Fig. S9** : **Effect of AP management on probability of positive iNaturalist visits, excluding potential protected area staff** . We exclude potential protected area staff from iNaturalist data, then repeat the procedure which produced Figure S8 (SI A.3). Points are regression coefficients, bars are 95% confidence intervals, standard errors are clustered at the protected area level, the unit of observation is the area-year, the number of observations is 3,625, and the control variables are area fixed effects, year fixed effects, a third-order polynomial in precipitation in m, and 12 degree day bins (Section 4). The ATT is 0.175 (standard error = 0.040).

<!-- Start of picture text --> 0.50<br>0.25<br>0.00<br>-0.25<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10<br>Change in probability of positive eBird v Years to management by African Parks<br><!-- End of picture text -->

**Fig. S10** : **AP management increases the probability of positive eBird visits.** Points are regression coefficients, bars are 95% confidence intervals, and standard errors are clustered at the protected area level. The unit of observation is an areayear and the control variables are area fixed effects, year fixed effects, a third-order polynomial in precipitation in m, and 12 degree day bins (Section 4). The dependent variable is an indicator for positive eBird observations inside a given protected-area year and the number of observations is 3,625 (SI A.4). The ATT corresponding to this figure is displayed in Table S3.

23
<!-- Start of picture text --> 4<br>3<br>=oO<br>oO§ .1<br>0<br>“1<br>C) Topcbr Manager ext with[5 BA+car int [7] TopSSBF EXT Manager Studied[EG AbroadSSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> Top Manager with BA+ Top Manager Studied Abroad<br>80<br>60<br>a2<br>s3©© 40<br>2<br>a<br>20<br>° Formal Informal Small Medium Large —SaAFormal Informal Small Medium Large<br><!-- End of picture text --> <!-- Start of picture text --> 015<br>.01<br>¢<br>@3G<br>= .005<br>8<br>o<br>0<br>-.005<br>% of workers with % of workers with % of workers with<br>(Jsecondarycer*extschool(GG carvocationalint [2]traininssBrexT. college(GGadegreeSSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> % of Workers with % of Workers with % of Workers with<br>Secondary School Vocational Training College Degree<br>20<br>o 15<br>2><br>5<br>g<br>a® 10<br>mol&2<br>molé 5<br>0<br>esFEEL> ¢ SE eSEF> ESESEs w &EEE>OS - wSESE<br><!-- End of picture text -->
Table 12: Heterogeneous effects of savings default on net deposits, day of transfer (+0 days)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Abo<br>Assets|ve median:<br>Patience|
|Savings Default|5871.069***<br>(949.451)|5264.667***<br>(1508.287)|4774.141***<br>(993.112)|6065.325***<br>(1113.276)|5600.016***<br>(1164.604)|
|Observations|312|153|234|227|191|
|Mean of dependent variable in control group|1735.74|3076.49|1807.74|1744.83|1758.24|
|R-squared|0.34|0.44|0.42|0.42|0.20|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Bel<br>Assets|ow median:<br>Patience|
|Savings Default|3973.744***|5513.853***|6206.099***|4596.604***|5544.467***|
||(1108.769)|(814.781)|(1068.182)|(939.405)|(938.608)|
|Observations|162|321|240|247|283|
|Mean of dependent variable in control group|1441.86|917.20|1453.78|1508.62|1544.69|
|R-squared|0.17|0.18|0.15|0.18|0.37|
|P-value: equal efect of savings default|0.180|0.881|0.316|0.302|0.969|

The top panel includes respondents from female-headed households, those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values are from tests of equality of Savings Default coefficients in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 38
reported wage and employment data, and the importance of coffee to Ethiopia's economy.7 Coffee constitutes Ethiopia's single most important crop, contributing over half of the agricultural GDP. Coffee is also responsible for nearly 60% of the country's foreign exchange earnings. Teppi is the second largest coffee plantation in Ethiopia, administering 6245 hectares of fully-developed coffee-producing land. In Ethiopia, Teppi is second in size and output to the adjoining coffee plantation of Bebeka.

OSD is typically prevalent in the most fertile land areas of northwest, southwest, and western parts of Ethiopia. A 1984 study estimated the number of cases of OSD in Ethiopia to be 1.4 million, with a population at risk of 7.3 million (or 17.4% of the population). Point prevalence, determined by the skin-snip method, ranges from a high of 84% in the western endemic zone through 60% in the southwest, and a low of 19.5% in the northwest. Therefore, based on the WHO classification of endemicity, there are hyperendemic and mesoendemic regions in Ethiopia. The mean microfilarial load per infected person ranges from 3.6 to 34 microfilariae per mg of skin snip, depending upon the community. Most recently, Aga _et_ **_al._** (1995) reported a microfilarial load of up to 382 microfilaria per mg skin snip in a rural farming village in southwest Ethiopia.

# _Economic Methodology_

We estimate the daily wage equation for plantation workers<sup>8</sup> , which in the semi-log formulation<sup>9</sup> , is given by:

|||**_in_**_W= a, X +a2_ **_0_**_+a3_**_I_**_+_**_e_**|
|---|---|---|
|where,|**_W:_**<br>**_0:_**|Daily wages earned.<br>Severe OSD = 1.|
||**_l:_**<br>_X._|Intermediate OSD = 1.<br>Vector of control variables including age, age squared, gender of<br>individual, and type of employment (permanent, temporary, or<br>contract).|

- 7 Ethiopia is classified as a low-income economy by the World Bank. In 1994, it had a per capita Gross National Product (GNP) of US$130 with an average annual growth rate of -0.7% over the past decade.

- 8 For theoretical details on health-status augmented wage equation formulations see Grossman and Benham (1974).

- 9 In this formulation, the coefficients on the independent variables represent the _percent_ change in the daily wage caused by a unit change in any given independent variable (controlling for other independent variables).

3
# **Reasons and Motivations for Applying for RPL Assessment**

The baseline survey also asked all the applicants about the main reasons behind their applying for RPL assessment and how much they would agree to different opinion statements about RPL assessment and certification. Most of them (66 percent) indicated that they need the certificate to find better jobs (figure 2.3). Another 14 percent said that they need the certificate for seeking promotion with their current employer. Most of the applicants demonstrated very high expectations of the RPL certificate to help them get better jobs, improve future earning, and boost reputation among peers and clients (figure 2.4). It is clear from these responses that the applicants hold a high expectation about the potential power of the RPL certificate to signal their skills levels and improve their job prospects and quality. Supposing the RPL certificates live up to these high expectations, they can then bring tremendous benefits to employed and unemployed workers alike, especially in a country like Bangladesh, where there are a high job market churn and a growing private industry sector. Employees are continually looking for and moving into better employment opportunities.

**Figure 2.3 Primary Reasons for Applying for RPL Assessment**

<!-- Start of picture text --> Need certificate to find better job 66%<br>For promotion with current employer 14%<br>Need certificate to find employment 13%<br>To develop my skills 5%<br>Others 2%<br>The employer asked me to apply 0.3%<br><!-- End of picture text --> _Source:_ Original figure for this publication.

**Figure 2.4 Opinions on RPL Assessment and Certification**

<!-- Start of picture text --> RPL certification will improve my future earnings 9.0<br>RPL certificate will be valuable for getting better<br>9.0<br>jobs<br>The RPL certificate will boost my reputation at<br>8.8<br>work and with my clients<br>I believe my employer or my clients will<br>8.8<br>appreciate my RPL certificate<br>RPL assessment will be useful for identifying my<br>8.8<br>skills gap<br><!-- End of picture text --> 10
interventions. While the corrections may not imply changes of target groups as such (at least not at a relatively aggregated level), they do affect significantly the quantification of resources needed for interventions. Even at the qualitative level, the changes in interregional and inter-group differentials following the corrections may make the difference between triggering interventions or not.

# V. **Poverty Estimates**

The fact that mean per capita expenditures are generally underestimated as a result of the sampling bias, suggests that poverty is likely to have been _overestimated_ in the early years of the survey. In making an assessment of how the H-S weights affect measures of poverty, we compare poverty measures computed with and without the weights.

Estimates of the **Pa** class of poverty indicesl/ have been computed for all four years of the CILSS by Grootaert (1992). These estimates are based on the aggregates reported in Demery and Grootaert (1992), a regional price index estimated by Grootaert and Kanbur (1992) and on the application of the H-S weights. The poverty line (128,600 CFAF) is selected to classify 30 percent of the population as poor in 1985. An alternative line (75,000

> 5/ The **Pa** index is given by

where n is the population size, q is the number of people below the poverty line (Yp) and Y, (i = 1,...,q) are the incomes of the poor. a is a policy parameter chosen by users to reflect their aversion to poverty. For _a_ **_=_ 0,** the index is simply the headcount index (H = q/n). With a **=** 1, the index becomes HI, where I is the income gap ratio, 21
**Table 10:** GATS **Commitments on Mode 1 and 2 in Selected Service Sectors**

||~~|~~Nme<br>**of**|X|**Su**|**X:**(%|C0 /|w$E'>|||
|---|---|---|---|---|---|---|---|
|_~~~~~~~~~~F|I||**Part**|**NoE**|**J< lI**|**PB rt:**|**_o**|
|PROFESSIONAL SERVICES|74|19|17|64|14|10|76|
|COMPUTER AND RELATED<br>SERVICES||||||||
|a. Consultancy service related to the<br>installation of computer hardware|51|57|20|24|51|22|27|
|b. Software implementation|56|54|27|20|48|29|23|
|c. Data processing|54|54|26|20|46|31|22|

1 Full: full commitment; Part: partial commitment; No: no commitment. _Note:_ Percentage may not add up to 100 due to rounding.

40
significant correlation between _Predicted Inflowsmt_ and _IDPmt_ . The tables also show that the F- statistic for excluded instruments is always higher than 10, alleviating concerns of biases induced by a weak instrument.

The second condition that must be met to guarantee that our estimates are valid is the exclusion restriction. It implies that the interaction of the aggregate time component of our instrument (forced migration outflows) and the geographic municipal component (earlier settlements of migrants), should only be correlated with firm outcomes through IDP inflows. Given that our estimates include fixed effects by year and firm (or municipality) aggregate time components or time-invariant firm characteristics are not a threat for our identification strategy. Our estimates will only be threatened by time-variable covariates (not controlled for in _Xmt_ ) that may be correlated with the instrument and directly affect firm outcomes.

One relevant threat to our identification strategy is that since forced migrants are fleeing violence and conflict, they may be moving to areas with presumably lower levels of conflict. It is also possible that, upon arrival in their new locations, displaced individuals may be increasing local violence levels or eroding the rule of law either by becoming perpetrators or victims of violence. In that sense, higher violence and conflict could also affect firm performance. In fact, Camacho and Rodriguez (2013) and Rozo (2018) show that more violence increases firm exit, using the same data we employ in our analysis. To account for this possibility we control in all our estimates by homicide rates (as a proxy for violent crime) and conflict variables (including clashes between armed groups and armed actions).<sup>14</sup> Our results are robust to the inclusion of these controls. Another possible threat to our identification is that municipalities that had larger earlier settlements of migrants also had different prevalent characteristics relative to the other municipalities before the conflict intensified, and these differences may be inducing divergent time patterns which are not explained by IDP inflows. It is possible, for instance, that municipalities with larger pre-

> 14Data on homicide rates come from the Observatory of Human Rights from the Colombian Vice Presidency. Data on the Colombian civil conflict was obtained from the _Centro de Memoria Hist ́orica_ (CMH). As of today, this is the best source of data on civil conflict as it combines information from NGOs and governmental records with testimonies from the victims of the civil conflict. We used data from CMH to construct two measures for the intensity of the Colombian civil conflict: total number of clashes between armed groups and total number of armed actions or attacks which took place between armed groups (i.e., illegal armed groups or the Colombian military).

15
We use data from Tajikistan. For the purpose of this paper, we specially designed and included a set of vignettes in the 2007 national household survey for Tajikistan. Respondents were asked to place themselves on a subjective welfare ladder with six rungs. Later in the questionnaire they were asked to place four vignette households on this ladder, and finally to (again) place their own household on the same ladder after ranking the vignettes. Unlike past research using vignettes, we have assured that the subjective welfare questions were asked both before and after the vignettes. The second (post-vignette) subjective reporting enables the rescaling of subjective responses, since we need respondents to place their household in reference to the vignettes themselves.

We begin with a description of our data in section 2. Section 3 presents our results on FORB while section 4 concludes.

# **2. Data**

Tajikistan is one of the poorest and most isolated of the countries in the former Soviet Union, with a per capita income of 430 USD in 2007. Its mountainous location and deteriorating physical infrastructure make transportation difficult and leave certain parts of the country completely isolated during winter months. About one-third of households are located in the capital Dushanbe or other urban areas. Overall, 47 percent of the population lived below the country’s poverty line,<sup>12</sup> and 21 percent in 2004 lived below the World Bank’s international poverty line of $1.25 a day at 2005 purchasing power parity (estimated using PovcalNet at iresearch.worldbank.org/PovcalNet/jsp/index.jsp).

# _Survey data and instrument design_

The 2007 Tajikistan Living Standards Measurement Survey (TLSMS) surveyed a random sample of 4,860 households in September and November 2007. The sample is designed to be representative at the national level, urban and rural levels, and at the _oblast_ (administrative region) level. Data were collected in two visits, with the subjective welfare modules being asked in the first visit. Summary statistics for the variables used can be found in Appendix 1.

> 12 See World Bank (2008). The poverty line is based on the cost of 2,250 calories per day and includes an additional non-food component calculated from the share of total consumption going to nonfood purchases with the reference population being households with food spending just above the food poverty line. All values are regionally deflated.

6
**Table 5: Impact of the crisis on** **_income_ poverty and inequality**

|**_Poverty/inequality measure_**|**Actual**|**Counterfactual**<br>**(all shocks zero)**|**Impact**<br>(%)|**(without L-**<br>**shock)**|**Counterfactu**<br>**(without E-**<br>**shock)**|**al**<br>**(without LE-**<br>**shock)**|
|---|---|---|---|---|---|---|
|Mean consumption|26547|28437|-6.6|27010|27254|27268|
|(per capita per year)|_527_|_556_||_531_|_548_|_531_|
|||||124.51|[37.4]|[38.1]|
|Headcount index(%)|31.7|28.3|12.0|31.2|29.9|30.6|
||_0.45_|_0.43_||_0.45_|_0.44_|_044_|
|||||[14.6]|[53.5]|[31.9]|
|Poverty gap index(%)|11.1|9.5|16.0|10.9|10.2|10.6|
||_0.22_|_0.20_||_0.22_|_0.21_|_0.21_|
|||||[10.4]|[57.0]|[32.6]|
|Squared poverty gap index (%)|5.48|4.67|17.5|5.41|5.00|5.22|
||_0.142_|_0.132_||_0.142_|_0.136_|_0.140_|
|||||[8.5]|[59.6]|[31.9]|
|Theil's T-index|0.636|0.627|1.5|0.635|0.634|0.631|
||_0.069_|_0.068_||_0.068_|_0.071_|_0.068_|
|Variance of logs|0.785|0.779|0.8|0.795|0.770|0.789|
||_0.015_|_0.014_||_0.015_|_0.014_|_0.015_|
|Generalized entropy measure (e=2)|2.87|2.76|4.2|2.81|2.92|2.77|
||_0.605_|_0.585_||_0.585_|_0.636_|_0.574_|
|Generalized entropy measure (e=3)|68.8|63.8|7.8|65.5|71.8|63.9|
||_22.9_|_21.5_||_21.8_|_24.4_|_21.2_|

_Note:_ Figures in _italics_ are the standard errors corrected for sample design effect. The figures in square brackets give the relative contribution of the three shocks to the total impact (not calculated for the inequality measures for which these contributions are not additive. _Source:_ Calculated from 1998 APIS data.

32
# **Figure 4**

<!-- Start of picture text --> M id d le E a s t M ig r a t i o n t o O E C D b y d e s t io n a t io n , 2 0 0 0<br>1 0 0 %<br>9 0 %<br>8 0 %<br>7 0 %<br>6 0 %<br>5 0 %<br>4 0 %<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>U S A / C A N / A U S F R A U K G e r m a n y / N e t h e r la n d s S p a in<br>Algeria Bahrain Djibouti Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya M orocco O man Q atar S. Arabia Syria Tunisia Turkey UAE Yemen<br><!-- End of picture text -->

**Figure 5**

<!-- Start of picture text --> M i d d l e E a s t M i g r a t i o n t o O E C D C o u n t r i e s<br>1 0 , 0 0 0 , 0 0 0<br>1 , 0 0 0 , 0 0 0<br>1 0 0 , 0 0 0<br>1 0 , 0 0 0<br>1 , 0 0 0<br>1 0 0<br>1 9 9 0 2 0 0 0<br>A lg e ria B a h ra in D jib o u ti E g y p t Ira n Ira q Is ra e l J o rd a n K u w a it L e b a n o n L ib y a Mo ro c c o Oa nm Qa ta r S . A ra b ia S y ria T u n is ia T u rk e y U A E Y e me n<br><!-- End of picture text --> Source: Docquier and Marfouk (2006) There is substantial variation in the destination of migrants (Figure 5), partially reflecting geography and partially historical linkages. For example, the majority of migrants from Algeria, Morocco and Tunisia go to France while Turkish migrants have gone to Germany, the Netherlands and Belgium. Migrants from ex-British colonies such as Egypt, Iraq and Jordan often migrate to the UK as well as the other English speaking “new world” countries such as the US, Canada and Australia. There is also substantial migration to Spain from Morocco and to the UK from the oil-rich countries Gulf 8