rafmacalaba/datause-encoder-data huggingface dataset:
that South Asia has the potential to accelerate growth and reduce poverty by exploiting four underutilized spatial features of the region: geography, transportation, factor mobility, and scale economies. Regional cooperation can facilitate this process.

**Figure 9.  Percentage of National GDP Damaged by Select Natural Disasters**

<!-- Start of picture text --> 70 140,000<br>63.68<br>60 120,000<br>50 100,000<br>40 80,000<br>30 60,000<br>20 40,000<br>12.07<br>7.37<br>10 20,000<br>3.29 4.98 5.16<br>1.92<br>0.06 0.1 0.17 0.37<br>0 0<br>% GDP previous year Damages in 2005 US$<br>United States 2004, Jeanne (hurricane) United States 2005, Katrina (hurricane) Japan 2004, Tokage (typhoon) China P Rep 2003, (flood) Japan 1995, Kobe (earthquake) China P Rep 1998, (flood) Taiwan (China) 1999, Chi-Chi (earthquake) Pakistan 2005, Kashmir (earthquake) Sri Lanka 2004, (tsunami) Bangladesh 2004, (flood) Maldives 2004, (tsunami)<br><!-- End of picture text --> _Source:_ <u>http://www.unisdr.org/disaster-statistics/top50.htm</u>

- First, as mentioned earlier, South Asia is densely populated, with a significant proportion of the population living close to the borders between countries. After Europe, South Asia has the largest concentration of people living close to the border. It has the maximum “city pairs” within 50 km with a population of more than 25000 people<sup>9</sup> . Almost all the South Asian countries share a common border with the largest regional partner (India). Regional integration initiatives will unlock the growth benefit of geography and support income convergence across regions and countries. Regional trade is more sensitive to transport costs, scale economies and factor mobility than global trade.

- Second, South Asia suffers from high trade and transportation costs compared to other regions because of border restrictions and poor transport. The cost of trading across borders is nearly double for India and Bangladesh compared to China. It is more than three times higher for Afghanistan, Bhutan and Nepal. The quality of transport infrastructure, especially the highway networks, in South Asia is poor. Truck operating speeds are low, delays at state/provincial check posts are frequent and can be long, and delivery times are consequently subject to significant

> 9 We are grateful to Souleymane Coulibaly for the data on city pairs.

18
|Capacity agglomeration<br>|0.023|0.018|-0.004|0.030|
|---|---|---|---|---|
|Informal competition<br> <br>|-0.013*|0.007|0.001|0.013|
|PM 2.5<br>|-0.0002<sup>×</sup>|0.0001|0.003**|0.001|
|<br>PM 2.5<sup>2</sup>|||-0.00002**|0.00001|
|<br> <br>**Other controls**|**Yes**||**Yes**||
|<br>**Industry dummies**|**Yes**||**Yes**||
|Observations|35,736||26,910||
|Adj. R squared|0.111||0.289||

> × , *, ** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the city level. Other controls include year dummies, missing dummy for BE variables, and base controls.

The relationship between PM 2.5 and labor productivity, on the contrary, appears to be nonlinear. The effect is positive (0.003) at low levels of pollution, although decreasing as pollution levels rise (-0.00002). The intuition behind this result is that lower levels of this pollutant may identify an area in the earlier stages of development. There, the negative effects of pollution on productivity are offset by the positive effects of growth. As the level of pollution rises, the negative effects are more notable and start to show a negative correlation with labor productivity.<sup>13</sup> The next step is to analyze whether this correlation between air pollution and firm performance is different (or stronger) for African firms. To do this, we limit our sample to only African firms, and run our analysis. A cubic specification helps explain the relationship between pollution and both dependent variables better. We show these results in Table 9.

**Table 9. Pollution Effects on African Firms**

||**L-gro**<br>**coef**|**wth**<br>**se**|**LP-gro**<br>**coef**|**wth**<br>**se**|
|---|---|---|---|---|
|Security Cost|-0.163|0.130|-0.166|0.313|
|Corruption obstacle|-0.043*|0.018|-0.178*|0.068|
|Overdraft facility|-0.015|0.018|0.104<sup>×</sup>|0.053|
|Trade credit|-0.026|0.028|0.160<sup>×</sup>|0.092|
|Skilled labor obstacle|0.042<sup>×</sup>|0.022|0.038|0.083|
|Labor regulation obstacle|0.036|0.024|-0.176**|0.061|
|Tax rate obstacle|0.001|0.018|0.112|0.078|

13 The inverted U-shape relationship between pollution and growth is widely known as the Environmental Kuznets Curve (Grossman and Krugman, 1991). This states that countries in their earlier face of development will increase pollution levels until they reach a maximum where they learn to manage pollution and still be able to keep developing. Developing nations are then on the positive segment of the Environmental Kuznets Curve, while more developed nations are placed at the end, on the negative segment.

19
# **APPENDIX: NON-EXPERIMENTAL REGRESSIONS**

Dependent Variable: Weekly work income for Columns 1-3 and 6-8.

Current Weekly work income - past weekly work income for columns 5 and 6.

||(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>DD|(5)<br>DD|(6)<br>IV|(7)<br>IV|(8)<br>FIML|
|---|---|---|---|---|---|---|---|---|
|Dummy for Migration|383.490|385.880|360.009|375.226|328.498|498.797|304.999|298.034|
||(8.27)**|(8.41)**|(8.73)**|(8.09)**|(7.68)**|(2.38)*|(3.26)**|(4.30)**|
|Male Dummy|176.214||75.998|162.739|80.517|200.683|97.477|97.608|
||(2.62)*||(3.29)**|(2.29)*|(3.70)**|(2.36)*|(4.70)**|(4.01)**|
|Married Dummy|-115.798||-31.548|-125.159|-45.883|-109.672|-39.565|-39.703|
||(1.87)||(1.15)|(1.94)|(1.77)|(1.84)|(1.60)|(1.52)|
|Age|4.706||2.797|2.314|0.083|4.807|0.872|0.824|
||(1.38)||(1.57)|(0.67)|(0.04)|(1.44)|(0.49)|(0.45)|
|Years of Education|-2.056||-2.589|-17.712|-2.940|-10.519|2.531|2.717|
||(0.18)||(1.03)|(1.44)|(1.30)|(0.60)|(0.82)|(0.63)|
|Born on Tongatapu|74.661||38.288|43.119|32.406|63.531|49.717|50.244|
||(1.75)||(1.76)|(1.00)|(1.40)|(1.44)|(2.10)*|(1.92)|
|Height|7.094||3.589|7.404|5.964|6.397|5.960|5.981|
||(2.38)*||(2.56)*|(2.42)*|(3.89)**|(2.07)*|(4.29)**|(4.26)**|
|Past Income||||||0.031|0.299|0.313|
|||||||(0.10)|(1.02)|(1.24)|
|Constant|-1,393.759|41.906|-664.329|-1,211.553|-1,022.750|-1,253.125|-1,478.666|-1,094.673|
||(2.88)**|(9.94)**|(3.09)**|(2.36)*|(3.95)**|(2.47)*|(3.41)**|(4.22)**|
|Sample<br>Instrument|oldest 25-35|all 18-45|all 18-45|oldest 25-35|all 18-45|oldest 25-35<br>network|all 18-45<br>log distance|all 18-45<br> log distance|
|First stage F-statistic||||||14.24|21.93||
|Number of Observations|118|230|230|116|226|116|226|226|
|R-squared|0.48|0.45|0.51|0.45|0.45||||

Note: Oldest member 18-45 in the household is used when the household doesn't contain a 25-35 year old. Robust t statistics in parentheses Average first quarter 2005 exchange rate of 1 Pa'anga = 0.729 NZ Dollars used for comparing mean incomes

- and ** indicate significance at the 5% and 1% levels respectively.

47
rate, and the importance of the plant in the local labour market). Regulators thus appear to show a reluctance to enforce standards in a way that may have an impact on employment.' .

Empirical studies on monitoring and enforcement issues have thus been of only recent interest and remain limited in numbers.<sup>7</sup> Moreover, they have been performed only in the context of developed countries: We are not aware of any empirical study on the impact and determinants of monitoring and enforcement activities of an environmental agency of a developing country at the plant level.<sup>8</sup> The current paper is a first step toward filling this important gap. Building upon MV (1990) and LR (1996), in this paper we analyse the role of inspections, pollution charges, and citizens' complaints as determinants of the environmental performance of industrial polluters in Zhenjiang, China.<sup>9</sup> Our analysis of plant-level data shows that inspections do have a statistically significant impact on the firms' environmental performance. In the current paper, our results suggest that pollution charges do not have a statistically significant impact on firms' performance. However, this result may simply be explained by the lack of variation in pollution charges within the city of Zhenjiang which therefore prevents us from effectively capturing the impact of pollution charges. Our results also demonstrate that complaints do impact inspections significantly. Given the impact of inspections on pollution, citizens' complaints therefore do have a significant impact on pollution control. These results may provide a key to China's future quality of environment. These results may provide a key to China's future quality of environment.

- 6 Dion et al. (1998) thus argues that both the public interest theory of regulation and the economic theory of regulation contribute to explaining the decision of regulators to monitor the environmental performance of regulated plants.

- 7 In their paper, Deily and Gray (1991) writes that this is _"thefirst_ empirical study of the EPA's enforcement activity at the plant level" (p. 260).

- 8 Pargal and Wheeler (1996) have examined the impact of non-regulatory activities on environmental quality in Indonesia.

- 9 Dasguta and Wheeler (1997) have analysed the determinants of citizens' complaints in China and found that they were an increasing function of the levels of income and education.

6
|**1970-**<br>|**1980**<br>|**1960-1**<br>|**970**<br>|
|---|---|---|---|
|United States|27.5%|United States|27.3%|
|Japan|18.3%|Japan|22.9%|
|Germany|6.0%|Germany|7.2%|
|France|5.1%|France|5.5%|
|**_Brazil_**|4.7%|Italy|4.8%|
|Italy|4.5%|United||
|**_Mexico_**|3.2%|Kingdom|3.9%|
|United||Spain|2.6%|
|Kingdom|3.0%|Canada|2.4%|
|Canada|2.6%|**_Mexico_**|1.9%|
|Saudi Arabia|2.1%|**_Brazil_**|1.9%|
|Spain|1.9%|Netherlands|1.5%|
|**_China_**|1.6%|Australia|1.4%|
|Korea, Rep.|1.3%|Switzerland|1.3%|
|Netherlands|1.2%|**_Argentina_**|1.1%|
|Australia|1.1%|Sweden|1.1%|

*Not included: Russian Federation nor FSU nor ECA former Communist states, for 1980- 1990 period. Source:  World Development Indicators, IMF database (for selected countries for 1960 data). Calculations based on change in country‘s GDP in constant US dollars as a share of the change in world GDP in constant US dollars, by decade.

The BRICs and other emerging economies also have increased their share of trade, capital flows and innovation. A growing trend within these dimensions is ―south-south‖ globalization as economic interactions between emerging markets have increased at an even faster pace than they have between high-income countries and developing countries. As growth poles emerge in the developing world, one way that the more rapid growth could be sustained is through increased ―south-south‖ globalization. Part of this phenomenon over the last decade may have been due to one-off effects, and the fact that the process started from a very low base. On the other hand, the growing integration of supply chains across emerging markets and the potential for intra-regional ―south-south‖ trade could be a driving force behind the ―great convergence‖ into the future.

To understand the patterns of south-south globalization, one can start by examining the evolution of the large fast-growing BRICs. In the trade dimension, after decades of stagnation in the developing country share of world exports, this share began to increase sharply at the turn of the century, and it now stands at about 30 percent. We see, however, that much of this recent evolution was driven by the increasing share from BRIC countries (Figure 9a). The BRIC share more than doubled from 2000 to 2009. A large part of the story has to do with increasing trade between developing countries – a trend that actually dates back a couple of decades (Figure 9b)—as south-south bilateral and regional trade agreements proliferated and as supply chains integrated across the developing world. The share of developing country imports that originate in other developing countries is now nearly double the share of the 1960s. The old global division of labor characterized by developing countries (the ―south‖) exporting primary products to the high income countries (the ―north‖) to produce manufacturing goods has become much 15
countries is associated with slower financial development and growth and lower quality financial intermediation (La Porta, Lopez-de-Silanes, and Shleifer, 2002 and Cole, 2009b).

# **5. Firm Financing in Developing Countries**

In this section, we focus on access to finance issues at the firm-level taking into account the institutional constraints that firms in developing countries operate under, as discussed in the previous section.

# **_5.1 Financing Constraints_**

The studies in section 4 explored the broad intuitional causes of financial constraints faced by firms in developing countries. The more recent wave of studies has focused on describing the specific mechanisms by which institutions shape financial constraints of firms. Financial constraints affect firms in both developed and developing countries and are an active area of research in corporate finance. One strand of this literature has tried to infer financing constraints indirectly from investment-cash flow sensitivities (e.g. Fazzari, Hubbard, and Petersen, 1988; 2000)<sup>18</sup> or the propensity of firms to save cash out of incremental cash flow (e.g. Almeida, Campello, and Weisbach, 2004). Most of these studies are based on large firms in developed economies which have limited implications for financing constraints of firms in developing countries, many of which face a broader set of constraints and for which there is less available financial data. Hence, below we focus only on studies that use more direct measures of firm financing constraints.

Beck, Demirguc- Kunt, and Maksimovic (2005) provide the first direct evidence of firms‟ perception of financing constraints and how they impact firm growth. They use a size-stratified survey of over 4000 firms in 54 countries, where firms reported on a scale of 1 (no obstacle) to 4 (major obstacle) the extent to which financing, legal, and corruption problems presented obstacles to the operation and growth of their businesses They use country random effects regressions to assess the impact of the three obstacles on firm growth controlling for various firm and country-specific variables. Table 4 below, adapted from their paper, shows that all three

> 18 Several papers have challenged the usefulness of investment-cash flow sensitivities to measure financing constraints on both theoretical and empirical grounds. See Kaplan and Zingales (1997, 2000), Almeida and Campello (2002), Cleary (1999), Erickson and Whited (2000), Alti (2003), Gomes (2001), Moyen (2004).

36
- **Glazer, Amihai and Priya Ranjan** , “Trade protection to reduce redistribution,” _European Journal of Political Economy_ , 2007, _23_ (3), 790–805.

- **Gresser, Edward** , “Toughest on the Poor: America’s Flawed Tariff System,” _Foreign Affairs_ , November/December 2002, _81_ (6), 9–14.

- **Haberler, Gottfried** , “Some Problems in the Pure Theory of International Trade,” _Economic Journal_ , 1950, _60_ , 223–240.

- **Hagen, Everett** , “An Economic Justification of Protectionism,” _Quarterly Journal of Economics_ , 1958, _72_ , 496–514.

- **Houthakker, H. S.** , “An International Comparison of Household Expenditure Patterns, Commemorating the Centenary of Engel’s Law,” _Econometrica_ , 1957, _25_ (4), 532–551.

- **Krueger, Anne O.** , “The Political Economy of the Rent-Seeking Society,” _The American Economic Review_ , 1974, _64_ (3), 291–303.

- **Murgai, Rinku, Martin Ravallion, and Dominique van de Walle** , “Is Workfare Cost-Effective against Poverty in a Poor Labor-Surplus Economy?,” Policy Research Working Paper 6673, The World Bank, Washington D.C. October 2013.

- **Porto, Guido G** , “Using survey data to assess the distributional effects of trade policy,” _Journal of International Economics_ , 2006, _70_ (1), 140–160.

- **Sharma, Ramesh** , “Food Export Restrictions: Review of the 2007-2011 Experience and Considerations for Disciplining Restrictive Measures,” FAO Commodity and Trade Policy Research Working Paper 32, Food and Agriculture Organization May 2011.

# **A Appendix: Proofs of Propositions**

## **A.1 Proof of Proposition 1**

We have left to find the conditions such that in every country, the poor do not consume any Garments, while the rich consume both. First, note that aggregate income in country _c_ = 1 is given by _p_ ̇( _X_<sup> ̇</sup> 1)(Φ1 _− X_<sup> ̇</sup> 1) + _p_ ̈( _X_<sup> ̇</sup> 1) _X_<sup> ̇</sup> 1 + _q_ ̇( _X_<sup> ̇</sup> 1)Γ1 . � � 12
# Figure 6: Predicted IMR, 1987 for 1000 women enrolled in 1965

<!-- Start of picture text --> 140<br>IMR<br>120 -__<br>100 -.- _ _<br>80 - ---. _<br>60<br>40-<br>20-<br>0<br>0 10 20 30 40<br>Secondary enrollment rate (%), 1965<br>Note: results based on regressions controlling for spouse effect (all LDCs, n-45)<br>Source: Subbarao & Raney, 'Social Gains from Female Education.' forthcoming. PHRWD<br><!-- End of picture text -->
The procedure used here for calculating the alternative set of PPPs to bench mark against those implied by the ICP PPPs is due to Coondoo, Majumder and Chattopdhyay (2011). The present study extends the Coondoo et al. (2011) study by using their suggested procedure proposed in the single country context of India to provide evidence on sub-national PPPs to the current cross country context to estimate an alternate set of PPPs that provide a useful comparison with the recently released ICP PPPs from the 2011 round.

The Coondoo, Majumder and Chattopadhyay (2011) procedure is based on the estimation of Engel curves on a single cross section data. The attractiveness of their procedure has been described by the authors as follows: “The novelty of the procedure is that it overcomes the problem of data inadequacy, a problem that is shared by most of the developing countries. The procedure does not require item-specific price/unit value data and price index numbers can be calculated from consumer expenditure data grouped by per capita income/total consumer expenditure class in a situation where unit level data are not available.” (p. 138) In the current context, this offers a significant advantage for benchmarking the ICP PPPs, since much of the controversy over the accuracy of the ICP PPPs has centered around questions over the accuracy of the price information used in the ICP 2005 and ICP 2011 rounds. The counterfactual PPPs presented here allow a clean comparison since the procedure does not need to use the controversial price information collected in the ICP 2011 round. Moreover, as this study illustrates, the published World Bank data does contain the disaggregated expenditure information (by items and by expenditure classes) at country level required to implement the procedure to calculate the alternative set of PPPs.

The use of estimated Engel curves to construct price indices has a rich history. The idea can be traced back to Costa (2001) and Hamilton (2001) who used it to investigate and correct the ‘biases’ in official consumer price indices. Recent applications include Almas (2012) to estimate the consequence of ‘PPP bias’ on real income comparisons between countries, Almas, Kjelsrud and Somanathan (2013) to examine the bias in calculating poverty rates in India, and Beatty and Larsen (2005), Gibson, Stillman and Le (2008), Barrett and Brozozwoski (2010) to estimate the bias in CPI in Canada, the Russian Federation and Australia, respectively. The procedure rests on the underlying assumption of Costa (2001) and Hamilton (2001) that (a) 23
# **Appendix 3: Probit for perversely-ordered vignettes**

|**Coeff.**|**s.e.**|
|---|---|
|Log expenditure per capita<br>0.045|0.092|
|_Household Head Demographics_||
|Female<br>0.190|0.141|
|Age<br>-0.028|0.025|
|Age squared<br>0.000|0.000|
|_Ethnicity (Reference: Tajik)_||
|Uzbek<br>-0.227|0.155|
|Russian<br>0.184|0.439|
|Other<br>0.629**|0.289|
|_Education (Reference: No Education)_||
|Primary<br>-0.368|0.244|
|Basic<br>-0.536**|0.266|
|General Secondary<br>-0.561**|0.235|
|Special Secondary<br>-0.771**|0.309|
|Technical Secondary<br>-0.356|0.265|
|<br>Higher Education<br>-0.800***|0.291|
|_Household Characteristics_||
|Household size<br>-0.017|0.052|
|Number of children (<15)<br>-0.028|0.062|
|Number of elderly (65+)<br>-0.278**|0.133|
|Number of migrants<br>-0.071|0.086|
|_Head’s Employment (Reference for Occupation: Unemployed)_||
|Number of employed<br>0.086|0.060|
|Agriculture / Fishing / Forestry<br>0.065|0.187|
|Manufacture / Mining<br>-0.509|0.363|
|<br>Services<br>0.314|0.306|
|Construction<br>0.467**|0.219|
|Public Administration / Education / Health<br>0.078|0.200|
|Sales and Services<br>0.265|0.190|
|Other<br>0.055|0.323|
|_Ownership of Land / Livestock_||
|Small Holding<br>0.174|0.191|
|Medium Holding<br>0.153|0.243|
|<br>Large Holding<br>0.449**|0.214|
|Any livestock, poultry, etc.<br>0.214|0.166|
|_Geography_||
|Sogd Urban<br>0.219|0.225|
|Sogd Rural<br>-0.389|0.262|
|Khatlon Urban<br>-0.393|0.317|
|Khatlon Rural<br>-0.348|0.271|
|RRP Urban<br>0.507**|0.248|
|RRP Rural<br>-0.396|0.256|
|Gbao Urban<br>-0.030|0.450|
|Gbao Rural<br>0.348|0.252|
|_Relative Subjective Welfare_||
|<br>Neighbor's Step<br>-0.247***|0.074|
|Number of observations<br>4,852||
|Pseudo R2<br>0.133||

30
health outcomes—nearly 100 percent chances of prenatal care, delivery care, and immunizations and a low chance of infant mortality. In contrast, as of 2012, the least advantaged child has only a 54 percent chance of prenatal care, a 40 percent chance of skilled delivery, a 65 percent chance of full immunization, and a 2.2 percent chance of dying in the first year of life. With the exception of immunizations, there have been improvements over time in the least advantaged children’s prospects, but gaps remain large.

**Figure 11. Most Advantaged and Least Advantaged Simulations for Health and Survival by Year (Percentages)**

<!-- Start of picture text --> 2003/4 2011 2012<br>99 99 99 100 99 100 98 96<br>100<br>90 83<br>80<br>70 65<br>60 54<br>50<br>40<br>37 36<br>40 34<br>30 24<br>20<br>10 4.5 2.2 1.1 0.3<br>0<br>Prenatal Delivery Immunizations Infant mortality<br>Percentage<br>Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

# 4.3.2 Nutrition

Inequality in nutrition in terms of being stunted, underweight, and wasted is low and has not increased over time (Figure 12). Differences in some years are likely due to chance (not statistically significant). Although only one year of data is available (so we are unable to assess trends), the level of inequality in access to adequately iodized salt is very high. For children to have equal chances, 30 percent of the opportunities to access adequately iodized salt would have to be redistributed. Looking at the factors contributing to inequality, wealth matters a great deal, specifically for stunting and access to iodized salt, and urban/rural differences also contribute substantially to inequality, along with regional differences. As is the case across outcomes, gender differences contribute relatively little to inequality.

17