rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6042

# **Abstract**

The Nepal Poverty Alleviation Fund is a World Bank supported community-driven development program. Its objective is to improve rural welfare, particularly for groups that have traditionally been excluded for reasons of gender, ethnicity, caste, and location. Since its launch in 2004, the Fund has covered the 40 poorest districts of the country, supported some 15,000 community organizations, and benefited more than 2.5 million people. This paper attempts to estimate the impact of this large-scale program using a randomized phasein approach, in which certain localities are randomly selected for earlier intervention than others. Using two rounds of survey data and a difference-in-difference combined with instrumental variable estimation method, it finds statistically significant causal impact of the program on key welfare outcomes. The treatment-onthe-treated estimate on real per capita consumption is 19 percent growth. Other impacts include a 19 percentage points decline on incidence of food insecurity (defined as food sufficiency for six months or less) and a 15 percentage points increase in the school enrollment rate among 6–15 year-olds. Impacts (positive or negative) are yet to be detected on indicators associated with child malnutrition, social capital, and empowerment. The policy implications of these results should be of interest to the government and to development partners in determining what may be effective instruments to deliver services to marginalized communities in what remains a fragile and difficult political environment.

This paper is a product of the Sustainable Development Unit and the Human Development Unit, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at dparajuli@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and 2005. For a cross-country and time-series analysis like this research, it is difficult to control all the factors; therefore, the Gross World Production (World GDP) is employed to normalize the disaster impact data by controlling the size of economy in question. Figure 4-2 illustrates the trends of disaster impacts as the share of world GDP. The trends appear very similar to the ones in absolute value (figure 4-1). However, the statistical analysis<sup>23</sup> indicates that only the trends of damages and losses are statistically significant, but being rather weak at 10% level, with a linear trend line between time and GDP share of total impacts, while the trend of total impacts is not statistically significant. Not significant trend in total impacts is in fact consistent with other studies using normalized disaster impact, including abovementioned Pielke _et al._ (2008). However, the inconsistency between statistically significant trends of damages and losses and insignificant total impacts needs to be further investigated, perhaps including smaller intensity of disasters.

<!-- Start of picture text --> 0.40%<br>0.35%<br>0.30%<br>0.25%<br>0.20%<br>0.15%<br>0.10%<br>0.05%<br>0.00%<br>Damages Estimated Losses Estimated Total Impacts<br>24<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Figure 4-2. Historical Trends of Normalized Disaster Impacts The historical trends of normalized economic impacts appear quite different across the types of disaster (see Figure 4-3). Climatological disasters were increasing the economic impacts until the early 1980s; and then there was a lull for the remaining

> 23 Please see the details of statistical analysis with normalized data in Appendix 2.

> 24 Lines in the figure are the linear regression line for each item, and both lines are statistically significant with 10% level (please see the details in Appendix 2).

18
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_

**Figure 1. Top 13 Importers of Intermediate ICT Goods, 1988-2014, billions of current US$**

<!-- Start of picture text --> 300.00<br> 250.00 China<br>Hong Kong, China<br>United States<br> 200.00<br>Singapore<br>Korea, Rep.<br>Other Asia, nes<br> 150.00<br>Japan<br>Germany<br> 100.00 Malaysia<br>Mexico<br>Netherlands<br> 50.00 Vietnam<br>Thailand<br> -<br><!-- End of picture text --> Authors’ note: While Singapore does import intermediate ICT goods, a significant portion is re-exported. Hong Kong SAR, China, and the Netherlands also have extremely high re-exporters.

Source: World Bank MC-GVC Database using a consistent 175 country panel that accounts for 95-98% of world trade, see: http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator Complete data on imports and exports of intermediate and final goods for the world’s top 15 traders in 2014 clearly shows China position as the world’s leading production hub for ICT hardware more fully (see Table 2). China is the largest importer and exporter of intermediate electronic goods, and the largest exporter of final ICT goods with an astonishing 43% of world exports. Most of this activity is concentrated in the vicinity of the Yangtze and Pearl River Deltas (near Shanghai and Shenzhen respectively). China’s dominant share (34%) of intermediate ICT goods exports reflects the growth of local component manufacturing in the country to supply local assembly factories, mainly by foreign-invested enterprises headquartered in Chinese Taipei, China. China is also a large market for finished ICT goods, and ranks second in imports after the United States, when figures are combined with those of Hong Kong SAR, China, which re-exports much of what it imports to China. The higher share (nearly 95%) of intermediate goods than final goods imports (about 83%) in the top 15 traders reveals that production of both intermediates and final goods are significantly more concentrated than markets for final goods.

# **Key Private Sector Actors in ICT GVCs**<sup>**7**</sup>

The ICT hardware sector includes three principal actors: lead firms, contract manufacturers, and component suppliers, with ‘platform leaders’ such as Intel, Microsoft, and Qualcomm being the most important of the latter category. The value captured by the most powerful firms in GVCs—lead firms with global brands—can be extremely high. Of course, dozens of other entities play important roles in the broader industry, including software vendors

> 7 This section drawn on Sturgeon and Kawakami, 2011.

6
Table 6: Aid & Bilateral OIL- 1<sup>st</sup> stage

|)<br>|<br>1<br>=<br>(<br>_Z_<br>_aid_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|0.158***<br>(0.008)|0.154***<br>(0.009)|0.155***<br>(0.009)|0.153***<br>(0.009)|0.149***<br>(0.009)|
|_Ln(Affected)_|0.064***|0.056***|0.058***|0.059***|0.055***|
||(0.003)|(0.004)|(0.004)|(0.004)|(0.004)|
|_Ln(GDP p.c.)_|-0.094***|-0.141***|-0.130***|-0.084***|-0.094***|
||(0.017)|(0.023)|(0.025)|(0.024)|(0.024)|
|_Ln(Population)_|-0.304***|-0.283***|-0.284***|-0.287***|-0.289***|
||(0.013)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Trade (% of GDP)_|-0.004***|-0.003***|-0.003***|-0.003***|-0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.070***|-0.072***|-0.073***|-0.072***|-0.072***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|_Affinity index_|-0.627***|-0.601***|-0.601***|-0.599***|-0.588***|
||(0.093)|(0.102)|(0.102)|(0.102)|(0.102)|
|_Common religion_|0.115***|0.081**|0.081**|0.079*|0.074*|
||(0.036)|(0.041)|(0.041)|(0.041)|(0.041)|
|_Common language_|0.278***|0.252***|0.249***|0.264***|0.264***|
||(0.049)|(0.059)|(0.060)|(0.059)|(0.060)|
|_Former colony_|-0.009|-0.023|-0.023|-0.020|-0.012|
||(0.051)|(0.053)|(0.053)|(0.053)|(0.054)|
|_Ln(GDP p.c.) donor_|0.607***|0.602***|0.602***|0.601***|0.602***|
||(0.016)|(0.017)|(0.017)|(0.017)|(0.017)|
|_Pop. (in mio.) donor_|0.001***|0.001***|0.001***|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Fuel exports (% of_|0.002***|0.004***|0.006***|0.003***|0.012***|
|_merchandise exports)_|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|
|_Regulatory quality_||0.072|0.044|||
|||(0.049)|(0.051)|||
|_Fuel exports_×|||0.002|||
|_Regulatory quality_|||(0.002)|||
|_Corruption control_||||-0.134***|-0.213***|
|||||(0.041)|(0.045)|
|_Fuel exports_×|||||0.012***|
|_Corruption control_|||||(0.002)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|Constant|-1.694***|-1.589***|-1.688***|-2.042***|-1.910***|
||(0.350)|(0.390)|(0.398)|(0.400)|(0.407)|
|N|26811|22246|22246|22246|22246|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is aid, a dummy that switches to 1 if the donor has made a contribution. ***, **, * indicate significance at the 1, 5 and 10%level, respectively.

20
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32
stock of migrants from country _j_ in country _k_ in year _t_ (either 1990 or 2000), of gender _g_ and skill (education) _s_ . The explanatory variables comprise an historical bilateral time-varying diaspora variable, _d_<sup>_jk_</sup> _g,t_<sup>,whichisakeydeterminantoffuturemigrationlevels(Beineetal</sup> 2011). In addition, we have various time-invariant bilateral variables, denoted by _b_<sup>_jk_</sup> , such as geographic distance, common language, contiguous borders and shared colonial heritage that account for cultural, political and geographic linkages. In this ideal set-up, origin country characteristics (such as economic, political and social push factors) are captured by a set of origin fixed effects introduced through the vector _γg,s,t_<sup>_j_.Similarlycomparable(pull)</sup> factors at destination would be accounted for by the inclusion of vector _λ_<sup>_k_</sup> _g,s,t_<sup>ofdestination</sup> fixed effects. Since our goal is to impute migration data for those cells for which destination data are missing, it is not possible to include vector _λ_<sup>_k_</sup> _g,s,t_<sup>inourregressionshowever.This</sup> gives rise to our _prediction problem_ , which our three-step estimation seeks to address.<sup>11</sup> Our preliminary step is to run a first-stage gravity regression with education-aggregated migration data disaggregated by gender (but not skill level), obtained from OPSW on the left hand side, to recover estimates of the gender specific pull variable _λ_<sup>_k_</sup> _g,t_<sup>(i.e.destination</sup> fixed effects aggregated over the education dimension) for all countries in our sample. This takes the form:

> 11A second issue is that the sample of countries for which data are missing is likely a non-random sample. Addressing this second issue is beyond the scope of the current work.

17
**Appendix 1: Shares by trade regimes and ownership among China’s exports in 2005**

**Panel I: For indirect exports only**

||Normal trade|Processingtrade|Total|
|---|---|---|---|
|SOEs|0.5614|0.0485|0.6099|
|Collpriv|0.3676|0.0199|0.3875|
|Foreign|0.0024|0.0002|0.0026|
|Total|0.9314|0.0686|1.0000|

# **Panel II: For direct exports only**

|Normal trade|Processingtrade|Total|
|---|---|---|
|SOEs<br>0.0783|0.0205|0.0988|
|Collpriv<br>0.1366|0.0199|0.1564|
|Foreign<br>0.1654|0.5794|0.7448|
|Total<br>0.3803|0.6197|1.0000|
|**Panel III: For total exports**<br>Normal trade|Processingtrade|Total|
|SOEs<br>0.1724|0.0259|0.1983|
|Collpriv<br>0.1816|0.0199|0.2014|
|Foreign<br>0.1337|0.4666|0.6002|
|Total<br>0.4876|0.5124|1.0000|

Notes: Processing trade – processing with imported materials. SOE – state-owned enterprises; Collpriv – collective and private firms. Foreign – foreign wholly owned companies, equity joint venture, and contractual joint ventures. All other trade regimes and ownership types are left out from the above calculation.

42
random conditional on their individual- and household characteristics as well as PSU fixed effects, the coefficient of _D_ in Equation (1) is the effect of downward mobility on support for redistribution.

We use the first wave of the Life in Transition Survey (LITS I), a joint initiative of the European Bank for Reconstruction and Development and the World Bank. LITS I, which is done in 2006, covers 29 countries in Eastern Europe and Central Asia, and surveys a nationally representative sample of 1,000 individuals within each country.<sup>7</sup> The data set includes household level data such as households’ roster, assets, and expenses. It also has information on the attitude and values, current activities, labor supply, and life history of 29,000 individuals who are chosen at random among adults within each household. We exclude Mongolia and Turkey to focus on the 27 former socialist countries in Eastern Europe and Central Asia; we also exclude mobility experiences in 2006 to make sure we examine the effects of past mobility experiences only.<sup>8</sup> We have, therefore, the labor-market and life histories of about 27,000 individuals for seventeen years from 1989 to 2005.

We use individuals’ life history to construct measures of _downward mobility_ . In the survey, individuals are asked whether they experienced some types of hardships, or whether they responded to these hardships by taking extreme measures, in each year from 1989 to 2005. The questions are, among others, whether they received unemployment benefits, had to accept wage cuts, had to sell some of their household assets, and had to cut down on basic food consumption.

We construct three measures of the intensity of _downward mobility_ as follows. First, we create three dummies: one, a dummy of whether an individual experienced any types of shocks; two, a dummy of labor-market-related shocks only; and, three, a dummy of shocks that individuals responded to with extreme measures such as cutting down on food

> 7 See LITS (2006) for more details of this survey.

> 8 We exclude shocks in 2006 to avoid complications from possible endogeneity problems (our outcome variables are for the year 2006).

7
# **Appendix B: Regression tables – Tax rate procyclicality**

|Table B.1: Fiscal (pro)cyclicality of individual inco|me tax rates, whole sam|ple (2000-2016): Im|pacts of devel|opment level, tax b|ase, credit con|straints, corrupti|on, rule of law and|order|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Dependent variable: Individual income tax|||||||||||
|Cyclicality of real GDP|-0.338***<br>(0.041)|-0.709<br>(0.535)|0.166<br>(0.313)|0.403***<br>(0.044)|-0.695<br>(0.598)|-0.801**<br>(0.353)|-1.641<br>(2.008)|-0.385<br>(0.292)|2.220<br>(2.557)|0.273<br>(0.577)|
|**Development level**|||||||||||
|Crgdp*dvpt.level (polytomic)||0.122<br>(0.170)|||||||||
|**Tax base effect**|||||||||||
|Crgdp*tax|||-0.068*<br>(0.037)||||||||
|Crgdp*tax.base.variability||||-1.268***<br>0066|||||||
|||||(.)|||||||
|**Credit constraints**|||||||||||
|Crgdp*credit.ratio|||||0.009<br>(0.012)||||||
|Crgdp*kaopen||||||1.611*<br>|||||
|||||||(0.898)|||||
|**Perceived corruption**|||||||||||
|Crgdp*pol.corruption|||||||2.496<br>(3.279)||||
|Crgdp*pub.service.corruption||||||||0.329<br>|||
|||||||||(0.647)|||
|**Rule of law and order**|||||||||||
|Crgdp*law.order|||||||||-0.598<br>(0.642)||
|Crgdp*rule.of.law||||||||||-1.152<br>(1.643)|
|Constant|2.011**|66.846***|-1.163|2.659|6.789**|7.041**|41.887***|42.290***|2.591|32.860***|
||(0.890)|(0.970)|(3.887)|(1.955)|(2.998)|(3.399)|(3.616)|(3.446)|(4.488)|(4.651)|
|Adjusted R-squared|0.874|0.874|0.947|0.948|0.881|0.881|0.835|0.837|0.880|0.832|
|<br>Rmse|4.325|4.328|3.175|3.154|4.237|4.246|4.282|4.246|4.292|4.320|
|Joint significance (p-value)|-|0.000|0.000|0.000|0.000|0.000|0.046|0.001|0.002|0.027|
|<br>CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|<br>Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|826|826|289|289|761|725|689|689|746|689|
|# countries|50|50|19|19|50|50|47|47|48|47|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

30
|**Table 3: Characte**|**ristics associat**<br>M<br>|**ed with unem**<br>ale<br>|**ployment**<br>Fe<br>|male<br>|
|---|---|---|---|---|
||Youth|Adults|Youth|Adults|
|Age|1.19|0.77|1.39|0.75|
||[0.97]|[12.08]***|[0.87]|[8.35]***|
|Age Squared|0.99|1|0.99|1|
||[1.56]|[14.32]***|[1.10]|[10.56]***|
|Married|0.37|0.2|1.48|0.67|
||[8.09]***|[19.88]***|[2.14]**|[3.46]***|
|Had Training|1.17|0.84|1.42|1|
||[0.75]|[0.79]|[1.02]|[0.00]|
|_Education (no formal education as_|_comparison)_||||
|Below Primary Education|1.24|0.89|1.19|0.8|
||[1.54]|[0.72]|[0.52]|[0.62]|
|Primary Education|1.27|1.01|1.33|0.79|
||[2.56]**|[0.06]|[1.38]|[1.15]|
|Middle|1.07|1.05|1.19|1.52|
||[0.66]|[0.46]|[0.65]|[1.61]|
|Matric|1.74|1.06|1.42|1.04|
||[5.74]***|[0.65]|[1.67]*|[0.22]|
|Inter|2.2|1.23|1.15|0.58|
||[5.56]***|[1.70]*|[0.55]|[2.05]**|
|Bachelor or Above|3.37|1.06|1.03|0.66|
||[7.12]***|[0.50]|[0.09]|[2.23]**|
|Migration within past 10 years|1.28|2.14|1.45|0.64|
||[1.72]*|[7.64]***|[1.38]|[1.71]*|
|Household size|0.89|0.97|0.93|0.97|
||[11.12]***|[3.87]***|[3.94]***|[2.47]**|
|# employed/# not employed|0.02|0.06|0.03|0.07|
||[28.06]***|[20.58]***|[15.22]***|[17.55]***|
|Urban|1.44|1.24|1.28|1.96|
||[5.76]***|[3.28]***|[1.69]*|[6.21]***|
|_Provinces (Punjab as comparison_|_group)_||||
|Sindh|0.54|0.69|1.15|1.28|
||[7.60]***|[4.52]***|[0.69]|[1.74]*|
|NWFP|0.81|1.52|3.09|3.37|
||[2.52]**|[5.56]***|[6.95]***|[10.69]***|
|Balochistan|0.54|0.4|0.85|1.39|
||[5.17]***|[6.28]***|[0.51]|[1.55]|
|Number of Observations|15584|34716|3213|7073|

Note: 1. Absolute value of z statistics in brackets 2. * significant at 10%; ** significant at 5%; *** significant at 1% 24
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