rafmacalaba/datause-encoder-data huggingface dataset:
26 Table 6. Population Density in Agricultural Areas in Several Countries

|(a)<br>Total<br>Pop<br>(1986)<br>(millions|(b)<br>(JNP/<br>capita<br>(1986)<br>) (USS)|(c)<br>Pop.<br>density<br>overall<br>(pop/<br>sq km)|(d)<br>Area<br>(000<br>sq Ian)|(e)<br>%<br>urban<br>(1986)|(1)<br>% of<br>land in<br> <br>agric|(g)<br>% of<br>labo, i<br>agric|(h)<br>Density<br>rn agric<br>areas|
|---|---|---|---|---|---|---|---|
|Zambia<br>7|3W|9|75Z|48|S3|73|1Z|
|Mali<br>8|180|6|1240|20|25|86|20|
|Mcxico<br>80|1860|41|1973|69|_50_|37|27|
|Thiland<br>53|810|103|514|18|36|71|183|
|Kenya<br>21|300|36|583|20|10|81|263|
|India<br>781|290|238|3288|25|_55_|70|272|
|Indonesia_166_|490|87|1919|25|16|57|277|
|BangladeshlO3|160|715|144|18|67|75|721|

NB: The assumption made was that 90% of t!le labor force workcing in agricllture lives on the part of the land designated as agricultural, e.g. (a x g x 0.9) - (d x _f)._ Source: Data from World Development Report, 198& are much higher in an area with only 12 persons per square kilometer thanin an area which has nearly 700. An area with high population density Is also more likely to have a well-developed road system and transportation infatructure; tadis, private trucking firms, and other transportation is more often found where a population exists to support these businesses.

The implications of population density for costs of service delivery are significant as well. If for example one wanted to distribute antimalarial drugs to people in agricultural areas, in Bangladesh about 9000 people would be2 found within a radius of two kilometers from a central point (3.14 x 2 km x 721 persons/sq km). In India, Kenya, and Indonesia, one would find about 3200 people; in Thailand, about 2400. But in thinly populated rural Mexico one would find about 300, and in Mali and in Zambia, less than 200. Therefore, to reach 10,000 people with antimalarial drup within a 2 km distance of their home, one would have to establish 45 times as many distribution points in Zambia as in Bangldesh. Delivery costs per person reached would of course be 45 times higher. More likely, one would hold down costs by expecting people to walk a greater distance, for example S or 10 km to the distribution point in less densely populated areas. Tbe success of the intervention would then be highly dependent on the value people place on the intervention, and the time and travel costs they incurred in travelling the extra distance. (Foster, 1987) For Africa, this means that costs of drug supply (and of other services) are significantly higher in rural areas, and many of the models tried
<!-- Start of picture text --> x<br>xSI = 0 0<br>kf wi pj +wi p r (18)<br>j=1 j i nf h<br>[∑ pj pr ]<br>xf<br>xMI = 0 + xnf<br>kf wi pj (19)<br>[∑j=1 j pji ] [p p r rh0 ]<br><!-- End of picture text --> In this application, the non-food deflator is a single commodity index (based on rent only): this is tantamount to assuming that the spatial price dynamics of all non-food items is the same as the one captured by housing prices. Reflecting the inherent non-tradable nature of housing services and typical tradeable nature of food items, this deflation approach is expected to maximize the relative spatial variability of the non-food price index compared to the food price index, and it is hence well suited to showcase empirical implications of proposition 2.<sup>11</sup> Regarding the temporal dimension of price adjustments, the _aggregate_ monthly CPI index, as estimated by the Statistical Center of Iran (SCI), is used throughout the analysis.<sup>12</sup>

# **6.2 Results**

Proposition 2 delivers a prediction as to the sign of the difference between a welfare aggregate deflated using a single index and one deflated using a multiple index approach. According to proposition 2, this difference would depend on the relative level of food and non-food price indexes as well as on observed consumption patterns (implicit differences in the level of welfare), i.e. on the food share level. What makes the analysis of data for the Islamic Republic of Iran particularly useful for an empirical illustration of Proposition 2 is the significant spatial variation observed in the Islamic 11 In practice, this assumption may seem too extreme, and welfare analysists may be reluctant to adopt it. A possible alternative, feasible under typical data constraints, would be to assume that the prices of non-food/nonrent items do not vary spatially, and that only housing expenditures are associated to the price dynamics of rent. Results obtained using this alternative approach are available in Appendix 2.

12 This is to maintain a clean connection with the theory presented in section 3. Results capture entirely and exclusively the impact of the SI and MI approaches for _spatial_ deflation.

27
**Table 1:** Determinants of the World Bank’s Influence at the Agenda-Setting Stage of the Policymaking Process

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|lnaid<br>~~d~~pl<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p|-0.06**|||||-0.01|-0.03|-0.06**|||
||(0.03)|||||(0.02)|(0.02)|(0.03)|||
|lnaid<br>~~i~~nv<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p||-0.01||||0.02|-0.11***|-0.07|-0.03|-0.02|
|||(0.04)||||(0.01)|(0.04)|(0.04)|(0.04)|(0.04)|
|n<br>~~e~~sw<br>~~m~~<br>~~1~~p|||0.21**|||0.08*|0.06|0.21**|0.21**|0.23***|
||||(0.08)|||(0.05)|(0.05)|(0.09)|(0.08)|(0.08)|
|dpl<br>~~p~~roj<br>~~n~~<br>~~m~~<br>~~1~~p||||0.32*|||||0.33*||
|||||(0.18)|||||(0.18)||
|pa<br>~~m~~<br>~~1~~p|||||0.01|||||0.03|
||||||(0.03)|||||(0.03)|
|lngdp<br>~~p~~c<br>~~1~~st<br>|-14.18**|-10.80*|-10.68*|-10.27*|-10.40*|0.65|-0.28<br>|-16.27***|-11.56*|-11.92**|
||(5.87)|(5.79)|(5.60)|(5.67)|(5.58)|(0.79)|(4.05)|(5.96)|(5.93)|(5.88)|
|lngdp<br>~~p~~c<br>~~1~~st<br>~~2~~|1.06***|0.83**|0.85**|0.82**|0.83**|-0.06|0.08|1.18***|0.89**|0.91**|
||(0.38)|(0.37)|(0.36)|(0.37)|(0.36)|(0.05)|(0.27)|(0.39)|(0.39)|(0.38)|
|lnpop<br>~~1~~st|0.21|-0.30|-0.27|-0.89|-0.62|-0.15***|-0.86|1.25|-0.01|0.31|
||(1.62)|(1.70)|(1.61)|(1.60)|(1.58)|(0.04)|(1.03)|(1.77)|(1.76)|(1.75)|
|aid<br>~~g~~ni<br>~~1~~st|-0.01|-0.01|-0.01|-0.01|-0.01|-0.00|-0.00|-0.01|-0.00|-0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|polity2<br>~~1~~st|-0.01|-0.00|-0.01|0.00|0.00|0.01|-0.02|-0.02|-0.02|-0.02|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.01)|(0.03)|(0.04)|(0.04)|(0.04)|
|left<br>~~1~~st|-0.58*|-0.52|-0.66**|-0.54*|-0.63**|-0.16|-0.14|-0.56|-0.41|-0.46|
||(0.33)|(0.33)|(0.32)|(0.31)|(0.32)|(0.10)|(0.22)|(0.35)|(0.34)|(0.34)|
|Respondent-level controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Policy Cluster Pair FEs|Yes|Yes|Yes|Yes|Yes|No|No|Yes|Yes|Yes|
|Country FEs|No|No|No|No|No|No|Yes|No|No|No|
|Policy Cluster FEs|No|No|No|No|No|Yes|No|No|No|No|
|Observations|686|692|719|719|719|672|787|672|692|692|
|BIC|3500|3480|3610|3621|3625|2636|3400|3322|3465|3495|
|AIC|2290|2327|2401|2408|2412|2366|2755|2208|2312|2324|

Notes: All regressions use inverse-probability weights to account for variation in unit non-response rate. * _p <_ 0 _._ 1;** _p <_ 0 _._ 05;*** _p <_ 0 _._ 01.

19
<!-- Start of picture text --> — Aluminum - 30<br>- -68<br>-- } LS)_— - 208<br>- Korea ~~ ,/* / United States -16a~ =<br>~ Japan - 2<br>- -102<br>~ China “is<br>; EUR-12 fs<br>i] 5———10 Bil 15 20 25 30 35 40"<br>Real GOP per capita (thousands of PPP-adjusted U.S. dollars)<br>- Copper - 25<br>- Korea - 90 Be<br>~ Japan 15 =<br>- J -10<br>; Ma YJ SB<br>— EUR-12'"” (inited States z<br>— 5 oa<br>oo<br>- China A Brazil -<br>0 5 10 15 20 25 30 35 a0<br>Real GOP per capita (thousands of PPP-adjusted U.S. dollars)<br>- Steel - 1000<br>- Korea - =<br>- - 800 &<br>- - sf<br>| r=<br>- | ¥ ‘Japan - £<br>-° EUR-121 brous Y -400 =5<br>United States - =<br>-hia / Bragil -7 200 o=<br>i]ee5 10 ee15 20 25 30 35 a a|<br>Real GOP per capita (thousands of PPP-adjusted U.S. dollars)<br><!-- End of picture text -->
Policy Research Working Paper 5641

# **Abstract**

Although it had a a lower income level than India in 1980, China’s 2006 per capita gross domestic product stands more than twice that of India’s. This paper investigates the role of the business environment in explaining China’s productivity advantage using recent firm-level survey data. The analysis finds that China has better infrastructure, more skilled workers, and more labor-hiring flexibility than India, but a worse access to finance and higher regulatory burden. Infrastructure appears to be a key constraint for India: it lags significantly behind China, yet it has important indirect effects for the effectiveness of labor flexibility. Labor flexibility is also likely a major constraint for India, as evident in the predominance of small firms, the importance of firm size in accounting for India’s disadvantage in productivity, and the complementarity of proxies of labor flexibility with infrastructure and access to finance. Interestingly, regulatory uncertainty has adverse effects in India but not in China. The empirical analysis suggests that it is important to consider countryspecific growth bottlenecks and the indirect effects of policy reforms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 author may be contacted at lxu1@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
21 upper bound of the contribution of NPS support can be derived by attributing the NRA from NPS equally to the ad valorem NRA for each covered product. When this is done, it makes little difference to the estimated indexes for developing countries, while for high-income countries it reduces the decline in the estimated WRI (but by a smaller degree than it reduces the decline in the NRA, because the NRA contributions so attributed to decoupled payments have zero variance across commodities and so reduce the variance in overall NRAs). It provides an approximate guide to the increased relative importance of decoupled payments and non-product-specific support to the overall WRI for high-income countries (Figure 6).<sup>8</sup>

[Insert Figure 6 about here]

# **Conclusions**

This paper contributes to the theoretical and empirical literature on trade and welfare reduction indexes. On the theory side, it develops a method of calculating trade and welfare reduction indexes for individual policy instruments from estimates of the rates of distortions of producer and consumer prices. The main contribution is to show that, provided one is willing to make simplifying assumptions about price elasticities, it is possible to use the same data as existing NRAs/CTEs (or PSEs/CSEs) indicators to estimate superior measures of the relative contribution of different policies to global reductions in trade and welfare. Empirically, the paper’s main contribution is to apply the methodology to generate a time series of indexes for agricultural products that are well-grounded in trade theory and answer precise questions about the trade- and welfare-reducing effects of different policy instruments. The paper estimates these contributions for a greater set of policy instruments than previous studies. Further, the indexes are generated for 75 developed and developing countries over the past half century. They are useful as inputs into trade negotiations and for monitoring national policy developments and making cross-country comparisons of their trade and welfare effects.

> 8 To better capture the welfare effects of these measures further research is needed. Serra, Zilberman, Goodwin and Featherstone (2006) find, for example, that decoupled payments affect input use and output mean and variance — which have potential welfare effects that may be non-trivial.
Kanaiaupuni, S. and K. Donato (1999), ‘Migradollars and mortality: the effects of migration on infant survival in Mexico’, _Demography_ , **36** (3), 339–53.

Kish, L. (1965), _Survey Sampling_ , New York: John Wiley.

Kugler, M. and H. Rapoport (2005), ‘Skilled emigration, business networks and foreign direct investment’, CESifo Working Paper Series 1455, ECSifo Group, Munich. Lipton, M. (1980), ‘Migration from rural areas of poor countries: the impact on rural productivity and income distribution’, _World Development_ , **8** , 1–24. Lokshin, M., M. Bontch-Osmolovski and E. Glinskaya (2010), ‘Work-related migration and poverty reduction in Nepal’, _Review of Development Economics_ , **14** (2), 323– 32.

Macours, K. and R. Vakis (2010), ‘Seasonal migration and early childhood development’, _World Development_ , **38** (6), 857–69.

Mansuri, G. (2006), ‘Migration, school attainment and child labor: evidence from rural Pakistan’, mimeo, Washington, DC: The World Bank.

Mattoo, A., I.C. Neagu and Ç. Özden (2008), ‘Brain waste? Educated immigrants in the US labor market’, _Journal of Development Economics_ , **87** (2), 255–69.

Mayr, K., and G. Peri (2008), ‘Return migration as channel of brain gain’, CReAM Discussion Paper Series No. 0804, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.

McKenzie, D., J. Gibson and S. Stillman (2006), ‘How important is selection?

Experimental versus non-experimental measures of the income gains from migration’, World Bank Policy Research Paper No. 3906, Washington, DC: The World Bank.

54
<!-- Start of picture text --> 6.5 8<br>6.0 os<br>7G<br>y<br>5.5 \ 2 “\<br>5.0 y 6 ~ \<br>\ in \\<br>45 5 \<br>4 " | f\ A s<br>4.0 QX ~ <—aie / je \ <= _—-—=Ss->=——<br>3.5 XS SeES .4 =— =<br>3.0 WSs~S" 3 esSe =——<br>~ ee<br>2.5 2<br>2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>— Norule = =—CEMAC rule — Norule = =—CEMAC rule<br>=== Smoothing mechanism —— CEMAC+ === Smoothing mechanism —— CEMAC+ rule<br><!-- End of picture text -->
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 11. Cape Verde’s pattern of capital investment in infrastructure differs from that of comparator countries**

Investment in infrastructure sectors as percentage of GDP, by source <!-- Start of picture text --> Cape Verde Middle-income countries<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>public sector ODA non-OECD financiers PPI<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2008).

_Note:_ Private investment includes self-financing by households. GDP = gross domestic product; ICT = information and communications technology; ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; PPI = private participation in infrastructure; WSS = water supply and sanitation.

# How much more can be done within the existing resource envelope?

Some $50 million of additional resources could be recovered each year by improving efficiency (table 11). By far the largest potential source of efficiency gains is the power sector, with potential efficiency gains valued at $25 million annually if underpricing and distribution losses are addressed. Low capital budget execution is also a significant issue, affecting both the transport and water sectors to the tune of $12 million annually.

**Table 11. Potential gains from greater operational efficiency**

<u>$ millions per year</u>

||ICT|Power<br>Transport|WSS|Total|
|---|---|---|---|---|
|Underpricing|—|15<br>n.a.|0|15|
|Overstaffing|n.a.|3<br>—|1|4|
|Distribution losses|—|12<br>—|2|15|
|Undercollection|—|4<br>n.a.|0|4|
|Low budget execution|0|1<br>5|6|12|
|Total|0|35<br>5|9|50|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ ICT = information and communications technology; WSS = water supply and sanitation. n.a. = Not applicable. — = Not available.

22
# **Table A2. The industry list of the SUST-RUS database.**

|**#**|**Code in SUST-**|**NACE**|**Description**|
|---|---|---|---|
||**RUS**|**classification**||
|1|A|Section A|Agriculture, hunting and forestry|
|2|B|Section B|Fishing|
|3|CA_col|CA.10|Mining of coal and lignite; extraction of peat|
|4|CA_gas|CA.11.10.2-3|Extraction of natural gas; service activities incidental to gas extraction, excluding surveying|
|5|CA_oil|CA.11.10.1|Extraction of crude petroleum; service activities incidental to oil extraction, excluding surveying|
|6|CB|Subsection CB|Mining and quarrying, except of energy producing materials|
|7|DA|Subsection DA|Manufacture of food products, beverages and tobacco|
|8|DB|Subsection DB|Manufacture of textiles and textile products|
|9|DC|Subsection DC|Manufacture of leather and leather products|
|10|DD|Subsection DD|Manufacture of wood and wood products|
|11|DE|Subsection DE|Manufacture of pulp, paper and paper products; publishing and printing|
|12|DF|Subsection DF|Manufacture of coke, refined petroleum products and nuclear fuel|
|13|DG|Subsection DG|Manufacture of chemicals, chemical products and man-made fibres|
|14|DH|Subsection DH|Manufacture of rubber and plastic products|
|15|DI|Subsection DI|Manufacture of other non-metallic mineral products|

91
Table 2: Balance of covariates: Observed <u>properties</u> uncorrelated with <u>program</u> timing

|independent variable:|TSC latrines per|capita|TSC s|tarted|
|---|---|---|---|---|
|mother literate|-0.46|(0.38)<br>|-0.01|(0.01)<br>|
|mother went to school|-0.30|(0.44)|0.00|(0.01)|
|father went to school|0.12|(0.43)|0.00|(0.01)|
|child receives extra tuition|0.26|(0.46)|0.00|(0.01)|
|school enrollment|-0.24|(0.32)|0.00|(0.00)|
|formal house|-0.45|(0.50)|-0.01|(0.01)|
|electrifcation|0.20|(0.50)|0.00<br>|(0.01)|
|village has road|0.18|(0.61)|-0.03<sup>_†_</sup>|(0.02)|
|village has ASHA|0.18|(0.64)|-0.01|(0.02)|
|village has school|-0.12|(0.61)|0.00|(0.01)|
|village has ration shop|-0.25|(0.69)|-0.02|(0.02)|

Observations are the same sample of six-year-olds used in our main analysis. Each estimate presents a coefficient and a clustered standard error in parentheses from a separate regression (of the form of equation 1) of the variables listed as the dependent variable, on either latrines per capita or an indicator for a positive number of latrines per capita (“TSC started”), with district and year fixed effects. Two sided _p_ -value: _† p <_ 0 _._ 1.

43
<!-- Start of picture text --> 1989<br>=; a = canta <== - a<br>by<br>dl is % ye XS<br>“ “id PM eae A“ y. :<br>i ae Sal ~y<br>. GF ais><br>Se = ».. ~ Z} \a f alF dy LA Ware\<br>wn dies. % ir Vy : ed / % F :<br>| = <es y i ) _- " bp. e<br>d LM bt y ¢ AN 4<br>+ = ‘ d N ° If ot ye<br>' a,<br>Vertically ” \ { : Vee ) > | aN<br>{1 Integrated Utility Y y \ f Mie) APN. fy<br>{0} Single Buyer Model . f f ) ai yaJ eo<br>Wholesale Competition a a y r wy. waa 7 met<br>—_ \aay oo y<br>‘1 3 WyqeT 41.a<br>‘Thiscolors,theSclepunescr World map denominations was Bankproducedsah Group, boonies and any by judgment theany Cartographyother information on the legal Unit of shownstatus the World onof thisany Bankterritory,map Group.do notor The imply,any boundaries,endorsement on the partof.or =3 = = : a<br><!-- End of picture text -->

<!-- Start of picture text --> July 2024<br>== = aaa - -_——— me,<br>ee OE i— — 5s<br>B ee . 3g Liew =<br>- r a - SN ee ee pot<br>is Soaad CSres ga € 8.Ee naefF Ww~— _-> wy r &<br>My ~ BI ars oe eS<br>- ‘a e ( U 4 7 w e i « =<br>{0 VerticallySingle Buyer IntegratedModel Utility Ma,lene - mS[ odd ‘a al a4 $<br>* rf Wholesale Competition \>" PS ” “a! “of<br>|<br>q<br>‘This map was produced by the Cartography Unit of the World Bank Group. The boundaries, 5 - - a<br>Pobielcolors,‘the World denominations Bank ele Group, and any judgmentany other information on the legal shown status on of this any territory, map do notor imply,any endorsement on the partorof ‘Wu os = : :<br><!-- End of picture text -->
Equation (8), which holds fixed the height differential d , while the dashed red line shows the relationship assumed in Galasso and Wagstaff (2016) which holds fixed mean height among adults who were and were not stunted in childhood and varies only the proportion stunted. Except at low rates of stunting, the first two methods give a very similar relationship between mean adult height and stunting rates in childhood. Moreover, this relationship is steeper than the relationship assumed in Galasso and Wagstaff (2016). This is because their approach does not take into account the increases in height among individuals who were not initially stunted as the stunting rate declines. The slope of the dashed red line is �d��6 , while the average slope of the other two lines over the range where they coincide is �10.2 . Consequently, a reduction in the rate of stunting q by ten percentage points raises attained adult height by 10.2 �0.1 or approximately one centimeter, or approximately 40 percent larger than in the calibrations of Galasso and Wagstaff (2016).

The main advantage of this calculation is that it provides a very simple way to calibrate the response of mean adult height to stunting in childhood, using only data on childhood stunting rates and the estimate of the adult height differential from cohort studies. An alternative approach to inferring shifts in the mean of the distribution of height associated with reductions in stunting is to estimate them directly. This can be done using the same cross‐country panel of DHS surveys described in the previous subsection. These surveys contain data on the incidence of stunting, as well as average attained height of children of different ages. A country‐fixed‐effects regression of average height of two‐year‐olds on the fraction of children who are stunted yields a slope coefficient of ‐0.12 and a standard error of 0.012. This implies that a reduction in the stunting rate of 10 percentage points is associated with an increase in average height among two‐year‐olds of 1.2 centimeters. Under the assumption that height deficits in two‐year‐olds persist into adulthood, this implies a reduction in average adult height of about the same amount. This estimate is slightly larger than but quite close to the one obtained by the calibration approach discussed above. To be conservative, the HCI uses the smaller of the two estimates by setting β������,�������� ��10.2 , with an overall “return” to reduced stunting of γ�������� � γ������ �β������,�������� ��0.034 ��10.2 �0.35 .

46