rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Contribution of Variables to Average Level of Log Welfare Ratio**

||**Urban**|**Rural**|**Urban-**<br>**Rural**<br>**Difference**|
|---|---|---|---|
|Mean log welfare ratio|0.689|-0.024|0.713|
|**Decomposition**||||
|Constant|-0.336|-0.279|-0.057|
|Geographic variables|-0.158|-0.141|-0.017|
|Demographic variables|0.418|0.185|0.233|
|Education variables|0.817|0.328|0.489|
|House ownership variable|-0.054|-0.007|-0.046|
|Occupationvariable|0.001|-0.110|0.111|

_Source:_ Own calculations based on ECV Survey.

The comparison between urban areas or rural areas of two provinces is based simply on the comparison of the coefficients of province dummy variables in Table 2. For the urban areas, only in Azuay is the coefficient estimate positive, while in the rural areas all of the significant province coefficient estimates are negative. Households living in Pichincha appear to be better off or equal to their urban and rural counterparts from other provinces, after controlling for the non-geographic characteristics.

The second component of the right side in equation (3) reflects differentials in urban and rural consumptions due to both differences in returns and in characteristics. It does not quantify structural differences, which is accomplished by comparing the expected gain in consumption from living in urban areas of a given province over rural areas, given the national means of all non-geographic characteristics,

**X** * .  For province _j_ this is given by:

> where **G** _j_ is a vector with zeros in all its entries except for entry _j_ if the household belongs to province _j_ . The first term in equation (4) is the same as in equation (3), and the second term gives the effect of urban-rural differences in the returns to household characteristics.  The sum of these two terms is 0.24, which can be obtained from coefficients in Table 2 and means at national level in Table A1, and accounts for the difference between the conditional log consumption of households living in urban and rural areas of the Pichincha province when conditioning on national means.  The third term is close to zero for seven of the provinces, meaning that in these provinces the differences in expected log consumptions between urban and rural areas are similar to Pichincha province (Table 5).  For most of the remaining provinces, the differences are moderate to large; this suggests that residence in a given province is related to differences in expected consumption, once we control for household characteristics.  The province of Morona Santiago has the largest difference in consumption between urban and rural area.

9
# **Introduction**

Estimates regarding the cost of corruption in infrastructure suggest that 5 to 20 percent of construction costs are being lost to bribe payments, and as much as 20 to 30 percent of electricity is being stolen by consumers in collusion with staff (Gulati and Rao, 2006). Assuming that 5 percent of investment and maintenance costs in infrastructure are lost to corruption, the financial burden alone may add up to about $18 billion a year in developing countries.<sup>2</sup> This paper examines what we can say about the extent and impact of corruption in infrastructure in developing countries using existing evidence. It discusses both petty corruption (here taken to include ‘speed payments’ and other small bribes to obtain everyday services) and grand corruption (including payments to secure government contracts or major licenses, change regulations or influence the shape of laws). It looks at different approaches to estimating the extent of corruption (expert perceptions, surveys, indirect techniques) and reports on the results of such studies. It suggests that there is considerable evidence that most existing perceptions measures appear to be very weak proxies for the actual extent of corruption in the infrastructure sector, largely (but inaccurately) measuring petty rather than grand corruption. Existing survey evidence is more reliable, but limited in extent and still subject to some uncertainty. The paper discusses evidence for the relative costs of corruption impacts and suggests that a focus on bribe payments as the indicator of the costs of corruption in infrastructure may be misplaced. It draws some conclusions regarding priorities for infrastructure anticorruption research and activities in projects, in particular regarding disaggregated and actionable indicators of weak governance and corruption.

# **Making Estimates of the Extent of Corruption in Infrastructure**

One direct way to examine the extent of corruption in a country or sector is to look at cases where it has been revealed as part of a criminal investigation. Of course, such a technique is open to a number of serious biases –for example, there will be more cases where the justice system is efficient, itself less corrupt, and is focusing on the prosecution of corruption. There will also be more cases when corrupt activities themselves are less sophisticated and easier to detect. As a result, other approaches are usually preferred if attempting to make cross-country or cross-sectoral evaluations of corruption, most commonly involving perceptions, surveys and indirect measures.

The extent of corruption is most frequently illustrated through ‘corruption perception’ indices. Assessments (including elements of Transparency International’s Corruption Perceptions Index and the Economist Intelligence Unit rankings) ask ‘experts’ including senior corporate officials to rank their perceptions of the level of corruption in various countries.

> 2 Based on investment and maintenance estimates from Fay and Yepes, 2003.

4
investigate the medium to long-run effects of political instability, as well as to pinpoint the precise mechanisms by which heterogeneity in sectoral effects arises.

# **References**

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- Abel, A.B., Dixit, A.K., Eberly, J.C., and Pindyck, R.S. (1995) “Options, the value of capital, and investment” _The Quarterly Journal of Economics_ , 111, 753-777.

- Alfaro, L., Kalemli-Ozcan, S., and Volosovych, V. (2008) “Why doesn’t capital flow from rich to poor countries? An empirical investigation” _Review of Economics and Statistics_ , 90, 347-368.

- Asiedu, E. and Lien, D. (2011) “Democracy, foreign direct investment and natural resources” _Journal of International Economics_ , 84(1), 99-111.

- Behar, A. and Freund, C. (2011) “The trade performance of the Middle East and North Africa” MENA World Bank Working Paper 53, 2011.

- Blonigen, B.A. and Piger, J. (2013) “Determinants of foreign direct investment” _Canadian Journal of Economics_ , forthcoming.

- Boston Globe (2013) “UK Envoys says BP committed to Algeria after attack”, available at: <u>http://www.boston.com/news/world/middle-east/2013/06/02/envoy-says-committedalgeria-after-attack/eRwSODgme46YgB2RA9tkVN/story.html</u>

- BP (2013) _In Amenas Situation Update – January 22, 2013_ Available at : <u>http://www.bp.com/en/global/corporate/press/press-releases/in-amenas-situationupdate1.html</u>

- Bruno, G.S.F. (2005) “Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models” _Economics Letters_ , 87, 361-366.

- Bun, M.J.G. and Kiviet, J.F., 2003. “On the diminishing returns of higher order terms in asymptotic expansions of bias” _Economics Letters_ , 79, 145-152.

21
# **Paris Climate Agreement and the Global Economy:**

# **Winners and Losers**

# Muthukumara Mani<sup>1</sup>

Zekarias Hussein Badri Narayanan Gopalakrishnan Deepika Wadhwa

**.**

Keywords: international climate negotiations, global stock taking, Carbon taxation, Paris Agreement, INDC, computable general equilibrium (CGE) modelling JEL codes: C60, F47, F64, Q40, Q50, Q34, Q54, Q58

> 1 Corresponding author. Muthukumara Mani is with the office of the Chief Economist for South Asia. Zekarias Hussein is with the US Environmental Protection Agency. Badri Narayanan Gopalakrishnan is with the University of Washington, Seattle. Deepika Wadhwa is with Infinite Sum Modeling Inc. The authors would like to thank Martin Rama, Anil Markandya and Govinda Timilsina for their very thoughtful comments and suggestions on an earlier version.
Ravallion, M., and M. Huppi. 1991. “Measuring Changes in Poverty: A Methodological Case Study of Indonesia during an Adjustment Period.” _The World Bank Economic Review_ 5(1):57–82. Available at: http://wber.oxfordjournals.org/cgi/doi/10.1093/wber/5.1.57.

- Seddon, D. 2005. “Nepal’s Dependence on Exporting Labor.” _Migration Information Source_ :On line. Available at: http://www.migrationpolicy.org/article/nepals-dependence-exporting-labor/.

- Shakya, K., and B. Gubhaju. 2016. “Factors Contributing to Fertility Decline in Nepal.” _Journal of Population and Social Studies_ 24(1):13–29.

- Shapley, L. 1953. “A value for n-person games.” In H. W. Kuhn and A. W. Tucker, eds. _Contributions to the Theory of Games (AM-28)_ . Princeton University Press.

- Shorrocks, A.F. 1999. “Decomposition Procedures for Distributional Analysis: A Unified Framework Based on Shapley Value.”

- World Bank. 2011. “Large-Scale Migration and Remittance in Nepal: Issues, Challenges and Opportunties.” Available at: http://ceslam.org/mediastorage/files/Large scale migration and remittance in nepal issues and challenges - hissan.pdf.

- World Bank. 2016a. “Moving up the ladder: poverty reduction and social mobility in Nepal.” Available at: http://documents.worldbank.org/curated/en/171641467117954924/Moving-up-the-ladderpoverty-reduction-and-social-mobility-in-Nepal.

- World Bank. 2013. “Nepal Bigger Strides in Post-Conflict Poverty Reduction Poverty Diagnostics, 201011.”

- World Bank. 2006. “Nepal Resilience Amidst Conflict An Assessment of Poverty in Nepal, 1995-96 and 2003-04.” Available at: http://documents.worldbank.org/curated/en/2006/06/6919674/nepalresilience-amidst-conflict-assessment-poverty-nepal-1995-96-2003-04.

- World Bank. 1991. “Nepal: Poverty and Incomes.” Available at: http://wwwwds.worldbank.org/servlet/WDSContentServer/WDSP/IB/1999/09/17/000178830_9810190148263 7/Rendered/PDF/multi_page.pdf.

- World Bank. 1999. “Nepal: Poverty at the Turn of the Twenty-First Century Main Report and Background Studies.”

- World Bank. 2014. _Poverty and Inequality Measures in Practice: A Basic Reference Guide with Stata Examples_ . Washington, D.C.

- World Bank. 2016b. “The World Development Indicators.” Available at: http://data.worldbank.org/datacatalog/world-development-indicators.

World Bank. 1989. _World Development Report 1989 : Financial Systems and Development_ .

35
Zenebe Gebre, 2014). The translation for the other languages was more problematic and undertaken in a centralized conference in Addis Ababa; to this point none of these languages had widely been translated into the Roman script (Boothe and Walker, 1997; Heugh et al., 2007; Zenebe Gebre, 2014). This process led to the delay of the initial implementation of the other three first-wave languages to the 1992 school year, and the eventual repeating of the process and 1993 introduction of the following four languages.

The analysis of MTI uses dates and languages that are independently corroborated by Boothe and Walker (1997) and Zenebe Gebre (2014). Boothe and Walker (1997) is the most contemporaneous source of information of which I am aware, and Zenebe Gebre (2014) directly contacted each region’s education department more than a decade and a half later to gather information on the implementation of MTI throughout the country. Both sources corroborate the introduction of seven of the eight languages outlined here, only Somaligna is missing in Zenebe Gebre (2014), and is removed from the main analysis of the paper to ensure as much accuracy as possible.<sup>23</sup>

# **C.2 MTI Intensity Measure, by Region**

## **C.2.1 Oromia**

Table C.1: Oromia – MTI Implementation

|||||**Fracti**<br>**MT Speakers**|**on of:**<br>**Region Speaking**|
|---|---|---|---|---|---|
|**Language**|**Year**|**Region**|**Grades**|**Living in Region**|**Language as MT**|
|Oromigna|1992|Oromia|1-8|0.93|0.84|

_Source_ : Author’s analysis based on information from Boothe and Walker (1997) and Zenebe Gebre (2014). _Note_ : MTI = mother tongue instruction; MT = mother tongue.

Assuming that students enter the classroom on time, at age 7, the following equation describes the maximum magnitude of the MTI effect for each cohort in the Oromia region.

> 23Alternative definitions of the MTI implementation are explored in Appendix Section D. Results using the specification that includes Somaligna as defined by Boothe and Walker (1997) can be found in Panel P of Appendix Tables D.4 and D.5, and another includes an additional early wave of language introductions through 1994, as defined by Zenebe Gebre (2014), these estimates can be found in Panel Q.

A.12
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# **3. Empirical Strategy**

## 3.1 Measuring Income and Income of the Poor

We measure mean income as real per capita GDP at purchasing power panty in 1985 intemational dollars, based on an extended version of the Summers-Heston Penn World Tables Version 5.6.5 In general, this need not be equal to the mean level of household income, due to a variety of reasons ranging from simple measurement error to retained corporate eamings. We nevertheless rely on per capita GDP for two pragmatic reasons. First, for many of the country-year observations for which we have information on income distribution, we do not have corresponding information on mean income from the same source. Second, using per capita GDP helps us to compare our results with the large literature on income distribution and growth that typically follows the same practice. In the absence of evidence of a systematic correlation between the discrepancies between per capita GDP and household income on the one hand, and per capita GDP on the other, we treat these differences as classical measurement error, as discussed further below.6 5 We begin with the Summers and Heston Penn World Tables Version 5.6, which reports data on real per capita GDP adjusted for differences in purchasing power parity through 1992 for most of the 156 countries included in that dataset. We use the growth rates of constant price local currency per capita GDP from the World Bank to extend these forward through 1997. For a further set of 29 mostly transitfon economies not included in the Penn World Tables we have data on constant price GDP in local currency units. For these countries we obtain an estimate of PPP exchange rate from the fitted values of a regression of PPP exchange rates on the logarithm of GDP per capita at PPP. We use these to obtain a benchmark PPP GDP figure for 1990, and then use growth rates of constant price local currency GDP to extend forward and backward from this benchmark. While these extrapolations are necessarily crude, they do not matter much for our results. As discussed below, the statistical identification in the paper is based primarily on withincountry changes in incomes and incomes of the poor, which are unaffected by adjustments to the levels of the data.

6 Ravallion (2000) provides an extensive discussion of sources of discrepancies between national accounts and household survey measures of living standards and finds that, with the exception of the transition economies of Eastem Europe and the Former Soviet Union, growth rates of national accounts measures track growth rates of household survey measures fairly closely on average.

11
20 years. Trade in primary goods includes exports and imports of agricultural raw materials, food, fuel, mineral ores and metals. Trade in manufacturing goods comprises exports and imports in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). Trade in primary and manufacturing goods is expressed as a percentage of GDP and the data are collected from the World Bank’s World Development Indicators.

The (median) value of trade in primary goods increased in Sub-Saharan Africa from 19.6 percent of GDP in 1990-94 to 22.3 in 2010-14 while trade in manufacturing goods grew from 16.8 percent of GDP in 1990-94 to 21.9 percent of GDP in 2010-14. This implies that the intensity of trade in manufacturing goods increased at a faster pace in Sub-Saharan Africa (by about 5 percentage points of GDP). In the case of resource rich countries, there is a sharp decline in the trade of primary goods (from 65.8 percent of GDP in 1990-94 to 38.7 percent of GDP in 2010-14) while trade in manufacturing goods increased (from 15.1 percent of GDP in 1990-94 to 23.9 percent of GDP in 2010-14). Finally, trade in both primary and manufacturing goods (as percentage of GDP) increased among non-resource rich countries over the past 20 years. As expected, trade in primary goods is smaller in non-resource rich countries (relative to resource rich countries) and trade in manufacturing goods for this group is higher than that of primary goods.

# _Regional trade: Patterns of inter-regional and intra-regional trade_

To close the characterization of the integration with global markets of goods, it matters how much, what and to whom you have trade linkages with. Table 1 shows the (median) amount of trade conducted by Sub-Saharan Africa with other world regions (inter-regional trade) and the amount of trade undertaken within the region (intra-regional trade). The data are gathered from the World Bank’s World Development Indicators and COMTRADE.

The (median) value of inter-regional across Sub-Saharan African countries increased over the past two decades from 31.9 percent of GDP in 1990-94 to 34.8 percent in 2010-14, while intra-regional trade grew from 20.04 percent of GDP in 1990-94 to 23.75 percent of GDP in 2010-14. Inter-regional trade grew at a faster pace among resource rich countries from 1990-94 to 2010-14. Inter-regional trade increased by about 8 percentage points of GDP (from 40.4 percent of GDP in 1990-94 to 48.6 percent of GDP in 2010-14) while intra-regional trade grew by nearly 3 percentage points of GDP (from 19.4 percent of GDP in 1990-94 to 21.98 percent of GDP in 2010-14). Finally, inter-regional trade for non-resource rich countries in the region grew by approximately 5 percentage points of GDP (from 26.7 percent of GDP 10
They will select data that are believed to be valuable after accounting for information processing costs (Handel and Schwartzstein, 2018).

We report effect estimates of two components of an information campaign in relation to a profitable and climate-friendly technology investment in rural Senegal. One component provided information about the benefits and quality of solar lamps in a general way, while another singled out for each of the main applications the most suitable type among a large variety of solar lamps available at local markets and the associated benefit. The significant advantage of the second component is a reduction in data processing costs through simplification of choices.

We find that the provision of information on optimal lamp types was required to increase adoption of solar lamps on the extensive margin. However, providing information about general benefits and quality was sufficient to increase the number of owned solar lamps among existing customers (intensive margin).

In our mechanism analysis, we further show that the generic information component does not seem to affect any intermediate variables in the extensive margin sample, whereas the addition of information about optimal product types significantly increases the perceived quality of solar lamps. The only exception are the results for the locus of control measure that arguably captures the extent to which households felt overwhelmed with translating information of the campaign into optimal investment. Here, we find significant effects of both campaign components. However, while the provision of generic information was negatively correlated with the measure of locus of control, information that simplified the choice set removed this negative effect. Among early adopters of solar lamps, results are different. We show that the generic information intervention is positively correlated with quality expectations. Hence, existing consumers update priors as a result of the generic information.

A simple learning model of selective attention along the lines of Hanna et al. (2014) is presented to rationalize the experimental data. Households face an abundance of potential features that might affect home production but cannot possibly attend to everything because paying attention is effortful. We argue that, in our experiment, households did not pay attention to the generic information because they anticipated that, while they would more quickly identify the lamp type that yields best results, the costs of doing so would still be large. Only households that invested in the technology anyway focused on this information because for them the shadow costs of doing so was considerably lower (i.e., they could build on prior market knowledge). Information on the optimal product-types, on the contrary, was effective because it made iterative learning for the most part obsolete.

26
coastlines, and the inclusion or exclusion of small islands and peninsulas, was not consistent. These factors precluded the direct use of these data in a single spatial model. To overcome these incompatibilities, and generate a fully standardized suite of input grids on an identically defined geographic template, a processing chain with the following stages was developed. First, each input data source was re-projected, where necessary, using a standardized equirectangular Plate Carrée projection under the World Geodetic System 1984 coordinate system. Second, where input grids were defined at differing spatial resolutions, they were re-sampled to 1×1 km. Third, grids were either extended or clipped to match a standardized extent. Fourth, a bespoke algorithm was developed that compared each rectified and re-sampled grid to a “master” land-sea template for Nigeria and used a simple interpolation and/or clipping procedure to align new grids to this master template, thus ensuring that all the coastline was perfectly consistent on a pixel-by-pixel basis.

# **3. METHODOLOGY**

# **Model-based geo-statistics**

The predictive approach used in this study to generate fine-scale maps of each water and sanitation indicator across Nigeria was based on a body of statistical theory known as model-based geostatistics (MBG). In an MBG framework, the observed variation in cluster-level indicator values is explained by one of the following four components:

- (1) _A sampling error_ , which can often be large given the small sample sizes of individual clusters, is represented using a standard sampling model (e.g., a binomial model where cluster-level data consist of a selection of “poor” households from the total number sampled).

- (2) Some non-sampling variation can often be explained using fixed effects – whereby a multivariate regression relationship is defined by linking the dependent poverty variable with a suite of geo-spatial covariates.

- (3) An additional non-sampling error not explained by the fixed effects is usually spatially auto-correlated, and this is represented using a random effect component. A spatial multivariate normal distribution known as a Gaussian Process is employed, parameterized by a spatial covariance function.

7