rafmacalaba/datause-encoder-data huggingface dataset:
lead to disparities in chances to participate in and earn from non-farm activities.<sup>10</sup> As marketization deepens, the resulting differences in capital accumulation and in know-how are expected to further increase inequality. In addition, regional differences in the growth of rural non-farm sectors are also among important reasons.

In our opinion, some existing research on income distribution in rural China has a certain limits. First, most of these studies correspond to meso-economic analyses using provincial or county level data. Income is usually measured as an average at the meso level, such as regional GDP per capita or average income. However, farmers’ income distribution should be examined at the micro-level, as difference in income distribution may be dominated difference in regional characteristics when using meso data. Second, quite a few surveys show that households with higher income are usually the ones who work in the non-farm sector or run a business. However, we cannot conclude that households with higher income are more likely to participate in nonfarm activity and that development of non-farm sector will widen income gaps. Motivation and capability of participating in non-farm activities differ across households (Anderson and Leiserson, 1980). Poor and rich households may both be inclined to participate in non-farm activities because the former have a stronger motivation whereas the latter have greater capability. The relatively poor households usually choose to engage in non-farm activities characterized by a higher labor-capital ratio and a lower financial entry barrier. Therefore, compared with households with better farm production conditions, households with lower income caused may choose to participle in migration and operate non-farm activities, which tends to narrow the income gap and lead to a more equal income distribution. In addition, some empirical literature does not provide structural tests of the theoretical models, but only provides partial findings that can support or invalidate intuitions and in that sense support or invalidate the policy implications of the models (Lall et al., 2006).

In the literature, two methods are used to study the impacts of remittance on inequality. One considers remittances as an “ _exogenous transfer_ ” (Adams, 1994; Pyatt et al., 1980; Stark, 1991), and the other considers remittances as a “ _potential substitute_ ” for home earnings (Adams, 1989; Barham and Boucher, 1998). The first method provides a direct and simple measure of how remittances contribute to total income by decomposing total household income and studying the distribution of each income source and its contribution to total income inequality. As remittances

> 10 See Burgess (1998), Ravaillon and Chen (1999), Wagstaff (2005) and Zhou (1994).

8
30

# **Table 4. Real Exchange Rate Misalignment (rermis) and its Decomposition in Post-** **<u>conflict Countries</u>**

||Period|RERMIS Fun|damentals<br>C|Error<br>orrection<br>|Policy<br>Variables*<br>|Structural<br>Variables**|of which foreign<br>development<br>assistance net of<br>int'l reserves/GDP|
|---|---|---|---|---|---|---|---|
|**_Group Median_**|**_s:_**|||||||
||Pre-peace|-1.03|-0.74|0.61|-0.75|-0.56|0.00|
||Peace onset|10.56|1.79|11.21|1.27|0.90|11.00|
||Post-conflict 1|<br>10.25|0.68|10.52|-0.16|0.16|0.11|
||Post-conflict 2|<br>3.89|2.17|5.51|-0.15|1.18|0.02|
|**_Country Specif_**|**_ic:_**|||||||
|**Burundi**|Pre-peace|35.64|15.33|17.07|3.16|10.96|-0.27|
||Peace onset|11.10|6.35|2.76|5.07|3.43|0.52|
||Post-conflict 1|<br>4.22|-0.86|5.61|-3.47|2.22|1.22|
||Post-conflict2|<br>10.67|6.39|4.91|2.50|3.69|0.92|
|**El Salvador**|Pre-peace|-7.49|1.62|-9.11|3.83|-5.85|0.42|
||Peace onset|1.17|-3.61|4.77|-3.12|-1.44|0.01|
||Post-conflict 1|<br>24.68|0.68|24.89|-3.75|3.03|-0.09|
||Post-conflict2|<br>41.07|7.53|34.78|-0.20|6.19|0.09|
|**Ethiopia**|Pre-peace|5.16|4.68|0.26|10.09|-2.86|0.35|
||Peace onset|42.50|30.82|11.67|21.91|6.87|-0.06|
||Post-conflict 1|<br>-16.57|-18.40|0.86|-7.74|-14.07|0.37|
||Post-conflict2|<br>-1.60|5.91|-6.08|4.63|0.87|-0.22|
|**Mozambique**|Pre-peace|-11.14|-23.85|7.00|-10.30|-9.34|1.30|
||Peace onset|-19.36|-33.84|14.48|-15.96|-15.53|6.18|
||Post-conflict 1|<br>-4.62|-8.51|7.25|-9.51|-6.54|0.73|
||Post-conflict2|<br>18.11|3.43|11.17|0.96|2.45|-0.17|
|**Nicaragua**|Pre-peace|||||||
||Peace onset|-16.74|-26.48|16.81|-14.11|-25.55|2.36|
||Post-conflict 1|<br>11.81|0.72|43.92|-0.10|-9.88|-0.23|
||Post-conflict2|<br>6.25|3.34|2.45|3.17|32.99|-0.78|
|**Rwanda**|Pre-peace|-7.81|13.87|-18.46|13.59|-1.79|-1.13|
||<br>Peace onset|33.51|3.05|28.97|0.44|1.28|2.46|
||Post-conflict 1|<br>26.57|2.86|23.58|1.22|-1.13|0.28|
||Post-conflict2|<br>36.29|9.75|26.55|0.45|10.56|-0.65|
|**Uganda**|Pre-peace|||||||
||<br>Peace onset|21.11|5.96|13.65|3.80|2.09|0.38|
||Post-conflict 1|<br>11.98|-2.07|13.93|-1.39|43.76|0.24|
||Post-conflict 2|<br>6.65|12.07|-6.69|3.49|9.57|0.00|

Note: * Policy Variables are government consumption/GDP, taxes on non-traded goods, and trade openness. ** Structural Variables are terms of trade, productivity, net foreign income/GDP, and official development assistance net of int'l reserves/GDP. RER misalignment series and subsequent decompositions are based on the estimation results of the pooled mean group estimates of table 3. Countries: see Appendix A for country list. Time span is between 1970 and 2004.
<!-- Start of picture text --> ; —CErEErseanES<br>boo<br>Zs oe wr ee<br>Ei ge 60 p+ hgL— —<br>fae aes i i<br>2° 5 P+ te<br>ne =<br>5 ar — Dia,oa<br>°<br>Deciles of socioemotional skills >< 2 >2 * Deciles of cognitive skills<br>1<br><!-- End of picture text --> <!-- Start of picture text --> 6.5<br>$2 35<br>F : 3.0 rn<br>E ? +0<br>= Ba 35<br>3.0<br>1 2 S&S *£ 5 6 7 8 9 1<br>Decile of socioemotional skills<br><!-- End of picture text --> <!-- Start of picture text --> 6.5<br>35 35<br>: Foo<br>iG 40<br>= 3.5<br>3.0<br>1 2 8§ 4# 5 6 7 8 9 10<br>Decile of cognitive skills<br><!-- End of picture text -->
# **4. Results**

# A. _Mean child HAZ scores and mean levels of factors_

_Mean outcomes_ : Table 1 reports mean HAZ scores and stunting rates. In the pooled sample, the mean HAZ score was –1.69 SD below the international reference median, the moderate-to-severe stunting rate was 42%, and the severe stunting rate was 20%.<sup>27</sup> Slum children have significantly worse growth outcomes than nonslum children. Relative to nonslum children, slum children have a mean HAZ score that is lower by 0.58 SD, a moderate-to-stunting rate that is lower by 16 pp., and a severe stunting rate that is lower by 10 pp.<sup>28</sup> Figure 4 shows the cumulative distributions of HAZ scores separately for slum and nonslum children. The two distributions significantly differ, based on a Kolmogorov-Smirnov test.

Comparing our statistics for city corporations to published statistics from the 2014 DHS for rural and urban areas, we note three main findings. First, city corporations have poorer mean child growth outcomes than those for all urban areas. For example, the moderate-to-severe stunting rate in the pooled city-corporation sample is 42%, compared to 31% for all urban areas. Second, slum children in city corporations have poorer mean growth outcomes than rural children. For example, the moderate-to-severe stunting rate for slum children is 48%, compared to 38% for rural children. Third, nonslum children in city corporations have a lower moderate-tosevere stunting rate than rural children (31% versus 38%), but have a comparable severe stunting rate to children in all urban areas (31%). The estimates suggest that mean growth outcomes for urban areas conceal substantial variation in mean outcomes across and within urban entities, and that mean outcomes are especially poor for slum children not just as an urban subpopulation but as a national subpopulation.

_Mean levels of factors_ : Table 2 reports mean levels for the base set of child, maternal, household, and neighborhood-area factors that we include in our multivariate analysis of HAZ

> 27 A child is considered to be moderately-to-severely stunted if his or her HAZ score is more than two SD below the international reference population median. A child is considered to be severely stunted if his or her HAZ score is three SD below the international reference population median.

> 28 Our estimated stunting rates are a few percentage points off from those provided in the 2013 BUHS report (NIPORT – et al. 2015), which we suspect is due to the loss of 100 200 children from our sample identified to have invalid anthropometric measurements.

19
efficiency in primary and secondary education and the resulting high proportion of overage students. In order to bring the Haitians up the education ladder, it is key to improve the quality of government spending, provide for basic human needs, and invest in human capital. Investments in education, health, and nutritional status of the population contribute to a productive labor force, better living conditions, and higher per capita income.

# **Basic Infrastructure Services**

Basic infrastructure services contribute to greater well-being and productivity. Some services, such as potable water and sanitation, make a direct contribution to overall well-being and health status. Others, such as electricity and telephones, help households use their homes productively in order to generate income. Research shows that access to basic services is highly correlated with a lower probability of being poor. Inequities in access to such services abound in Haiti’s rural and urban areas, both between the poor and nonpoor and by geographical area. Key gaps for the rural poor include potable water, energy, and roads. Corruption and the impact on service delivery will not be addressed in this paper, although there is evidence that corruption and misuse of public funds have lowered the quality of all public services (World Bank 1998).

Access to public infrastructure services is generally poor in Haiti, especially in rural areas, and the rural- metropolitan gap is wide. In the public sector, only 20 percent of resources go to rural areas, where most people live (World Bank 1998). Haiti’s rural population has little access to safe water; only 7.9 percent have access to water supplied by a public or private company, compared to 28 percent in the metropolitan area (Table 7.4). Rural dwellers have less access to safe water than some of their peers in rural African countries such as Kenya (31 percent) and Uganda (46 percent)<sup>24</sup> , a fact having little to do with a highly dispersed nature of the rural population. In urban areas, only 17 percent of households have access to safe water. There are also large geographical differences in access to water supplied from a public or private company, for example 3 percent have access in the Southeast region while 16.5 and 14.8 percent have access in the West and Artibonite regions (Table 7.5).

|Table 7.4:HouseholdAccess toBasicInfrast<br>|ructurein Haiti,2001(percent)<br><br>||
|---|---|---|
|Metropolitan|Urban<br>Rural|Total Haiti|
|Electricity<br>91.4|22.5<br>9.8|23.9|
|Water<br>28.3|16.5<br>7.9|12.6|
|Trash collection<br>25.7|9.8<br>2.2|7.1|
|Paved road<br>18.8|10.6<br>5.0|8.1|
|Dirt road<br>41.0|50.4<br>32.8|37.7|
|Landline<br>13.9|3.3<br>0.8|3.2|
|Mobile phone<br>10.4|0.9<br>0.3|1.8|
|_Note:_Water: Supplied from private or public wat|er company. Trash: Collected by private o|r public company.|
|Paved road:paved andpartly paved road. Dirt roa|d: Dirt andgravel road.||

_Source: C_ alculations based on HLCS 2001.

> 24 Source: UNICEF database (2000).

40
Policy Research Working Paper 9808

# **Abstract**

Carbon pricing is increasingly used by governments to reduce emissions. The effect of carbon pricing on economic outcomes as well as mitigating factors has been studied extensively since the early 1990s. One mitigating factor that has received less attention is education quality. If technological change that reduces the reliance of production on emissions is skill-biased, then carbon pricing may increase the skill premium of earnings and subsequent wage inequality; however, a more elastic skill supply through better education quality may mitigate adverse economic outcomes, including wage inequality, and enhance the effect of carbon pricing on technological change and subsequently emissions. A general equilibrium, overlapping-generations model is proposed, with endogenous skill investment in which the average skill level of the workforce can affect the need for emissions in an aggregate production function. This study uses data on industrial emissions linked to the Organisation for Economic Co-operation and Development’s Programme for International Assessment of Adult Competencies dataset for European Union countries. The findings show that, within countries, cognitive skills are positively associated with employment in industries that rely less on emissions for production and in industries that, over time, have been able to reduce their reliance on emissions for production. In the estimated general equilibrium model, higher cognitive skills reduce an economy’s reliance on emissions for production. Having higher quality education—defined as the level of cognitive skills attained by workers per unit of cost—increases the elasticity of skill supply and, as a result, mitigates a carbon tax’s economic costs including output loss and wage inequity, and enhances its effect on emissions reduction. The implication is that investments in education quality are needed for better enabling green technological innovation and adaptation and reducing inequality that results from carbon pricing.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at hpatrinos@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
Diop, M., 2014, <u>speech at the 2014 NYU Africa Economic Forum:</u> "The African Gold Rush" <u>(April 26, 2014)</u> <u>Eichengreen</u> Barry and <u>Poonam Gupta, 2011, The Service Sector as India's Road to Economic</u> Growth Flaaen, Aaron & Ghani, Ejaz & Mishra, Saurabh, 2013. " **<u>How to avoid middle income traps ?</u>** " **<u>evidence from Malaysia</u>** <u>, Policy Research Working Paper Series 6427, The World Bank.</u> Feenstra, Robert C., and Gordon H. Hanson. 1999. “The Impact of Outsourcing and HighTechnology Capital on Wages: Estimates for the United States, 1979–1990.” _Quarterly Journal of Economics_ 114 (3): 907–40.

Glaeser, Edward and Abha Joshi-Ghani, 2013, Economic Premise, <u>Rethinking Cities: Toward Shared Prosperity</u> Glaeser, Edward L., Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2004. “Do Institutions Cause Growth?” _Journal of Economic Growth_ 9(3): 271–303.

Ghani, Ejaz (ed.), 2010. " **<u>The Service Revolution in South Asia</u>** <u>,"OUP Catalogue, Oxford</u> University Press, number 9780198065111.

Ghani, Ejaz & Kharas, Homi, 2010. " **<u>The Service Revolution</u>** <u>,"World Bank - Economic Premise,</u> The World Bank, issue 14, pages 1-5, May.

Ghani, Ejaz & Kerr, William R. & O’Connell, Stephen D, 2011. " **<u>Who Creates Jobs?</u>** <u>,"World Bank - Economic Premise, The World Bank, issue 70, pages 1-7, November.</u> Ghani, Ejaz, 2012. " **<u>Service with a Smile</u>** <u>,"World Bank</u> - Economic Premise, The World Bank, issue 96, pages 1-6, November.

Grossman, Gene M., and Esteban Rossi-Hansberg. 2008. “Trading Tasks: A Simple Theory of Offshoring.” _American Economic Review_ 98 (5): 1978–97.

Grünfeld, Leo A and Andres Moxnes (2003), “The Intangible Globalisation: Explaining the Patterns of International Trade in Services”, Discussion Paper No.657, Norwegian Institute of International Affairs.

Hausmann, Ricardo, and Dani Rodrik. 2005. “Self-Discovery in a Development Strategy for El Salvador.” _Economia: Journal of the Latin American and Caribbean Economic Association_ , 6(1): 43–102.

Hausmann, Ricardo, Jason Hwang, and Dani Rodrik (2007), “What You Export Matters”, _Journal of Economic Growth_ , 12(1):1-25.

23
- 12 -

**<u>Table 3 Ordinary Least Squares Results with County Fixed Effects</u>**

||<br>_OPD_|<br>_MAL_||<br>_DEL_||<br>_OPD_||<br>_MAL_||_DEL_||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.332|**<br>0.036|**|0.013|*|||||||
||(0.127)|(0.017)||(0.007)||||||||
|_GDRD_||||||0.022||-0.006||0.006|*|
|||||||(0.071)||(0.015)||(0.003)||
|_POPDEN_|-7.207|**<br>-0.723|**|-0.279||-1.438||-0.066||-0.069||
||(3.132)|(0.325)||(0.202)||(1.399)||(0.190)||(0.099)||
|_BED_|1.286|-0.059||0.235|***|1.598||-0.008||0.237|***|
||(1.350)|(0.136)||(0.061)||(1.569)||(0.160)||(0.070)||
|_CENTR_|-2.163|-0.181||-0.124||-1.153||-0.057||-0.093||
||(1.926)|(0.295)||(0.104)||(1.878)||(0.304)||(0.099)||
|Constant|3.158|1.353|***|0.061||9.321|***|2.079|***|0.271|**|
||(2.758)|(0.435)||(0.147)||(1.999)||(0.389)||(0.104)||
|Observations|69|69||69||69||69||69||
|R-squared|0.257|0.278||0.453||0.146||0.229||0.419||
|F-statistic|2.55|1.55||5.03||2.25||1.81||4.18||
|Number of<br>county||||||||||||
|dummies|8|8||8||8||8||8||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

The above results may be biased if the transport variables are endogenous. To examine this, the instrumental variable technique is used. The analysis is focused on the model with road density, which was found to be statistically significant. The results are broadly consistent with the above (table 4). Higher road density results in greater health care access in terms of total visiting patients and babies delivered.
# **Figure 19. NIRP announcements: changes in EMDE financial variables over one-day window**

# **A. Emerging market indexes**

# **B. 10-year government bond yields**

<!-- Start of picture text --> Basis points Percent Basis points<br>15<br>15 Average 0.8<br>Median 0.6 10 Average<br>10<br>0.4 5 Median<br>5 0.2 0<br>0 0.0 -5<br>-0.2 -10<br>-5<br>-0.4 -15<br>-10<br>-0.6<br>-15 -0.8<br>Exchange EMBI Equity<br>rate (RHS) spreads prices (RHS)<br>C. Nominal effective exchange rate D. Equity market indexes<br>Percent Percent<br>1.0 2.0<br>Average Average<br>1.5<br>0.5 Median Median<br>1.0<br>0.0 0.5<br>0.0<br>-0.5<br>-0.5<br>-1.0 -1.0<br>China Brazil India Russia Mexico Indonesia Turkey Poland South Africa Chile<br>China Brazil India Russia Mexico Indonesia Turkey Poland South Africa Chile China Brazil India Russia Mexico Indonesia Turkey Poland South Africa Chile<br><!-- End of picture text --> Sources: J.P. Morgan, Bloomberg, Haver Analytics, World Bank.

Notes: Basis points or percent change between closing values on the day before the NIRP announcement and closing values on the day of the announcement. NIRP announcements are those of the European Central Bank, the Bank of Japan, and that coinciding with the Swiss National Bank’s decision to abandon the Swiss franc’s floor against the euro. For emerging market indexes, exchange rate is the JP Morgan EM Foreign Exchange Index, EMBI spread is calculated as the average premium paid over a U.S. government bond with comparable 10 maturity, and equity prices are the MSCI Emerging and Frontier index. B. Bond yields are for 10-year government bonds.

C. A decrease indicates depreciation.

D. Equities are the main stock market index for each country, expressed in local currency.

_Monetary policy._ In some commodity-exporting EMDEs, sharp depreciations and balance sheet vulnerabilities have already increased financial stability risks. Several central banks have responded with pro-cyclical tightening of policy and foreign exchange interventions or controls. Narrowing monetary policy space, especially in commodity exporters, places a premium on credible monetary policy frameworks that maintain price stability over the medium term, and institutional reforms that limit the risk of pro-cyclical policies associated with capital outflows.

Moderate inflation and more stable currencies in commodity-importing EMDEs might allow counter-cyclical monetary policy, but rising interest rates in the United States might eventually 42
- Kaplan, 1. (1998), "The Put Option Approach to Banking Crises in Emerging Markets: Valuing Implicit Deposit Insurance in Thailand", Mimeo, University of Washington.

- La Porta, R., F. Lopez-de Silanes, and A. Shleifer (2000), "Governnent Ownership of Banks", Mimeo, Harvard University.

- Marcus, A. and I. Shaked (1984), "The Valuation of FDIC Deposit Insurance Using Option-Pricing Estimates", _Journal of Money, Credit, and Banking_ 16(4): 446-460.

- Merton, R. (1977), "An Analytical Derivation of the Cost of Deposit Insurance and Loan Guarantees", _Journal of Banking and Finance, 1:_ 3-11.

- Pennacchi, G. (1987), "A Reexamination of the Over- (or Under-) Pricing of Deposit Insurance", _Journal of Money, Credit, and Banking,_ 19(3): 340-360.

- Ronn, E. and A. Verma (1986), "Pricing Risk-Adjusted Deposit Insurance: An OptionBased Model", _Journal of Finance,_ 41(4): 871-895.

- White, H. (1980), "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity", _Econometrica_ 48: 817-838.

23
# **Appendix B.  Data Sources**

The following data sources provided variables used in the paper:

**Governance:** The KKM rule of law index and the KKM control of corruption index are available at: http://www.worldbank.org/wbi/governance/pubs/govmatters4.html **<u>.</u>**

They are weighted composite indicators based on pre-existing sources. The methodology is described in Kaufmann, Kraay, and Mastruzzi (2003) and on the website. The data are available at two-year intervals for the period 1996-2002, and after that period, that is from 2002 to 2006, at annual intervals..

**Democracy:** Democracy is proxied by the variable _Polity2_ , from the Polity IV database available at http://www.cidcm.umd.edu/inscr/polity/ (updated in May 2008).

**Income per capita:** Proxied by GDP per capita. The data are from the World Development Indicators data base (accessed in May 2008); values expressed in 2006 PPP dollars.

**Political system and alternation:** These data are constructed by the authors, based on Horowitz and Browne (2005), as explained in Section II and Horowitz, Hoff  and Milanovic (forthcoming). **Cumulative involvement in war** :  Our measure is defined as the proportion of time the country was involved in large-scale military conflict since 1989 or, for the former Soviet Republics, since their independence in August 1991.  The data are created by the authors.

**Share of fuel exports:** Our measure is the share in GDP of exports of oil, natural gas, and gold in the first year that World Bank data are available (1996 or 1997).  The source is the World Bank Development Data Platform (DDP) database.

**Literacy in the 1930s:** Percentage of adult population that is literate. Data from Darden and Grzymala-Busse (2006, p.113), Plestina (1992, p.181), and Rothschild (1974, pp. 37, 44, 92, 166-7, 285, 327, 332, 359, 369).

**Urbanization rates in 1989-90** :  Percentage of population living in urban areas. Data from World Bank (1996, p.175).

37