rafmacalaba/datause-encoder-data huggingface dataset:
trends model is that it extrapolates past trends to predict district mean welfare in 2014-15 rather than using actual data on district characteristics in 2014-15. While this is more likely to give a rate of poverty reduction in line with past trends, it will ignore the signal from district level indicators in characteristics.

The second alternative model tested includes dummy variables for the three types of expenditure instead of the actual expenditure amounts. The dummy variables are equal to one if the household incurred any expenditure for the particular item. We refer to this model as “Expenditures at the Extensive Margin”. This has the advantage of potentially being more robust to outliers in consumption, as well as changes in the way that the question was asked. However, it potentially ignores valuable information on the level of expenditures.

Lastly, we use only the 2011-12 data to predict into 2014-15. Because only one round of data is used to estimate the model, no variables are interacted with a time trend. We refer to this as the “Constant Coefficient” model, which has been utilized in past studies of survey to survey imputation.

We report predicted poverty rates of the four models in Table 5. We choose the primary model as our preferred specification because it utilizes all the available data, including levels of expenditure, imposes the least restrictive modeling assumptions on the data and has the most accurate predictions. We validate each model’s predictive quality by conducting two tests. First, for each specification we use data from 200405 and 2009-10 to predict poverty in 2011-12. We refer to this as forward projection. In the second analysis, we reverse project poverty in 2004-05 using the 2009-10 and 2011-12 data (see e.g., Deaton and Dreze (2002)). In the forward and reverse projections, we compare the actual versus predicted headcount of poverty, in 201112 and 2004-5 respectively.<sup>33</sup> Our final choice of model is based on which yields the most accurate estimates of poverty, on average, in both tests.

# **C.1 Forward projection into 2011-12**

Table 6 compares actual poverty in 2011-12 with the predictions of the four models. For urban areas, the projection of model 1 (14.6%) is closest to the actual poverty number (13.4%). Model 3, the extensive margin model, gives predictions that are closet to the actual poverty number for rural areas (24.8%), followed by model 1 (27.4%). Therefore, models 1 or 3 could be possibilities for the final choice based on the forward projection test.

> 33Using the $1.90 per day threshold.

15
# Air Pollution and Poverty: PM2.5 Exposure in 211 Countries and Territories

**Jun Rentschler**<sup>**1**</sup> **, Nadia Leonova**<sup>**1**</sup>

**JEL codes:** Q53, Q56, I14, O44 **Keywords:** air pollution, PM2.5, poverty, exposure, health

**Acknowledgments:** This working paper contributes to the World Bank’s 2022 flagship report on environmentally harmful subsidies. It has benefited from helpful comments, feedback, and inputs by Mattia Amadio, Esteban Balseca, Samira Barzin, Lander Bosch, Richard Damania, Ira Dorband, Xinming Du, Bramka Jafino, Kichan Kim, Christoph Klaiber, Helena Naber, Jason Russ, Melda Salhab, Ernesto Sanchez-Triana, Margaret Triyana, and Esha Zaveri.
# **Table 8: Receiving SMS vs. Reporting Safer Greetings**

|||Safer Gre<br>|etings=1; D<br>|o not=0<br>||||Safer Gre<br>|etings=1;<br>|Do not=0<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.265***|0.256***|0.242***|0.043***|0.043***|week|0.199***|0.217***|0.158***|0.068***|0.071***|
||(0.007)|(0.007)|(0.007)|(0.006)|(0.006)||(0.016)|(0.017)|(0.019)|(0.020)|(0.020)|
|Urban||-0.034***|-0.034***|||Urban||-0.029***|-0.030***|||
|||(0.010)|(0.010)|||||(0.011)|(0.010)|||
|Ln(income pc)||0.030***|0.030***||0.004**|Ln(income pc)||0.033***|0.033***||0.004**|
|||(0.002)|(0.002)||(0.002)|||(0.002)|(0.002)||(0.002)|
|Constant|0.707***|0.625***|0.258***|0.760***|0.747***|Constant|0.759***|0.665***|0.281***|0.768***|0.755***|
||(0.005)|(0.009)|(0.034)|(0.001)|(0.006)||(0.005)|(0.009)|(0.034)|(0.000)|(0.005)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.064|0.090|0.186|0.003|0.003|R-squared|0.003|0.034|0.136|0.001|0.001|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 14
Figure 1: Government revenue from hydrocarbon and non-hydrocarbon sources (as a percentage of GDP), 1992-2012 <!-- Start of picture text --> 45"<br>40"<br>35"<br>30"<br>25"<br>20"<br>15"<br>10"<br>5"<br>0"<br>Total"revenues" Non"Hydrocarbon" Hydrocarbon"<br><!-- End of picture text --> Source: International Monetary Fund, authors’ calculations.

_β_ 1 is the coefficient of interest, _Xit′_<sup>isthevectorofcontrolvariables,</sup><sup>_β_2thevectorof</sup> associated parameters, and _αi_ and _γt_ are country and year fixed effects, respectively.

Since the main objective of the paper is to inform policy makers on the likelihood of a crowding out of domestic tax revenue by oil revenue in Uganda, we will later modify this specification and consider a nonlinear probability model that allows the use of information from the Ugandan economy for the marginal analysis. Clearly, the idea consists of estimating a probability model on countries exploiting natural resources and then using the estimated model to make inference about Uganda through a marginal effects analysis based on Uganda-specific data. This model is specified as follows: If _yit_ = 1, the country increases its non-hydrocarbon tax revenue relative to the previous year, whereas if _yit_ = 0, the country’s non-hydrocarbon tax revenues decrease relative to the previous year. Therefore, we consider the following binary-choice model: where _ηit ∼_ i.i.d has a normal or a logistic distribution. Then, we estimate the following 7
review suggests that this shortcoming is best addressed through global general equilibrium (GE) analyses.

Computable GE simulations by the World Bank (2006), and Walmsley et al. (2009), predict that greater liberalization of labor movement from the South to the North would lead to global welfare increases. In these studies, the migrant-sending, less developed countries receive large shares of the welfare gains. Walmsley’s and Ahmed’s (2008) CGE analysis examined multiple counterfactual scenarios where both traditional migration destinations, as well as developed East Asia countries liberalize their migration policies. The study found that regional liberalization seemed to have more positive effects (or less negative effects in the short run) on East and Southeast Asian labor-sending economies. Intraregional policies can thus offer an important mechanism by which Asian economies can reap some of the gains from liberalizing migration; particularly given the reluctance of Europe and traditional migration destinations to open their borders.

# **2.3  Migration and Migration Policy in Asia**

Due to the potential challenges and opportunities presented by international labor movements, historical migration and migration policy have come under great scrutiny by policy makers. Manning and Sidorenko (2007) point out that intra-regional liberalization of skilled worker migration would address the growing phenomena of skill shortages and surpluses in the same occupations across Asian countries, such as seen among ASEAN member states. The study cites Singapore as an example, where there has been growing excess demand for healthcare professionals, managers, accountants, and engineers. At the same time, there is evidence that neighboring Indonesia and the Philippines have surpluses in several of these professions. A policy framework that encourages intra-regional migration could have tremendous potential welfare gains for all countries concerned.

7
21

# References

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Appelbaum, Eli (1982) 'The Estimation of the Degree of Oligopoly Power," J. of Econometrics 19, pp.287-299.

Blum. U.C.H., G. Foos and M.J.I. Gaudry (1988) 'Aggregate Time Series Gasoline Demand Models: Review of the Literature and New Evidence for West Germany," Transportation Research A, 22, 75--88.

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Friedlaender, A.F. and R.H. Spady (1980) 'A Derived Demand Function for Freight Transportation,* Review of Economics and Statistics, 62, 432--41.

Hensher, D.A. and Louviere, J.J. (1983) 'Identifying Individual Preferences for International Air Fares: An Application of Functional Measurement Theory,* Journal of Transport Economics and Policy, 17, 225--45.

HcFadden, D. (1974) 'The Measurement of Urban Travel Demand," Journal of Public Economics, 3, 303--28.

Oum, Tae H. (1979), 'A Warning on the Use of Linear Logit Models in Transport Mode Choice Studies,' Bell Journal of Economics (Spring, 1979), pp.374-388.

Oum, Tae H. (1979b), 'Derived Demand for Freight Transportation and Intermodal Competition in Canada,' Journal of Transport Economics and Policy, (May, 1979), pp.149-168.

Oum, Tae H. (1979c), 'A Cross-sectional Study of Freight Transport Demand and Rail-Truck Competition in Canada," Bell Journal of Economics, (Autumn, 1979), pp.463-482.

Oum, T.H. (1989) 'Alternative Demand Models and Their Elasticity Estimates," Journal of Transport Economics and Policy, 23, 163--87.

Quandt, R.E. (1968), 'Estimation of Modal Splits,' Transportation Research, vol.2, 41-50.

Quandt, R.E. and W.J.Baumol (1966) 'The Demand for Abstract Transportation Modes: Theory and Measurement," Journal of Regional Science (December), 13-26.

Slade, Margaret E. (1984) 'Conjectures, Firm Characteristics and Market Structure: An Analysis of Vancouver's Gasoline Price Wars,' Discussion Paper No. 84-25, Department of Economics, the University of British Columbia, Vancouver.
study. In the countries of Eastern Europe and Central Asia, only Turkey had some gender gaps in these laws; it no longer does. In Latin America, there are now only two exceptions to the laws on legal capacity and property rights. And Sub-Saharan Africa, the region with the highest rates of gender gaps in the initial period, has removed more than half of them by 2010. Progress towards women’s equal rights is possible (see companion paper for a more detailed discussion of legal reforms and the factors that help trigger reforms).

At the same time, there are still countries with gender gaps in these rights. And some countries have increased the number of constraints since the 1960s. On net, both in South Asia and the Middle East & North Africa, the net number of constraints has remained fairly stable, with both some reforms and some new constraints being introduced.

Given the variation in the legal rights both across countries and over time, how are they correlated with outcomes? We focus on employment, health and education as these are traditionally areas where women and girls have not enjoyed the same benefits and access as men and boys. They are also key indicators of well-being.

The paper begins by looking at comparisons across countries. This has the advantage of allowing a wider range of outcome variables to be examined. Unfortunately, gender disaggregated data are not available for many of the indicators except in recent years. However, there are indicators for employment that go back 30 years, and there are some health and education indicators that cover the entire 50 years.

For those indicators for which we have sufficient time variation that straddle reform episodes, we look at the impacts of the law within countries over time. Finally, we examine whether the effects of legal reforms vary either by level of development or by the strength of the rule of law. Is it that having formal recognition of rights is only important once countries reach a 3
_19_ _I don't see us vaultlng to higher growth from a positive regime change but rather from a negative downturn, and that inducing regime transformation._ _MR. RAO. I am having some trouble understanding what exactly the system shock thesis Is. I might put forvard two alternative hypotheses or descriptlons of it, and I wonder which one we are really talking about._

**_Systems_** _If one accepts the statement that in the 1950s and 1960s_ **_shocks........._** _growth was somehow stimulated by some system shock -- and I see the definition of the system shock here as being more skillful macroeconomic management and Bretton Woods and all of that -- and now we see In recent years some sort of stagnatlon settIng in. Are we observing the natural running down of the effect of any shock so that the present stagnation is simply the netural plateauing after the initial response to the stimulus provided by a shock? Alternatively, are we seeing the unwinding of the improvements that brought about the 1950s and the 1960s growth?_

_In this second interpretation, we are seeing not a shock and a plateauing, but rather an improved -egime that had good results backsliding to a less appropriate or salubrious regime for international growth, and if that Is the case -- then I think Professor Bradford's thesis is that a sort of negative shock might be what we are looking for. In other words, you sink so low, you hit rock bottom and then everyone is jolted in realizing how bad things can get and then say, well, you have got to do something about this, which is the sort of political shock that I think Professor Bradford was referring to._ _So I would like to know which of these hypotheses one is talking about. My next question is: looking at [the assumption in Professor Boltho's paper), the data of GDP weights for 1965, 1985 and 2000, one sees developing country weights going from 16 percent in 1965 to 22 percent in 1985, but being projected to grow to 33 percent in 2000._ _Now that, If it happens, would itself be a shock -- notr just a shock in realizing that this is what is happening, but also a shock to the international system because surely, if developing countries go from 22 to 33 percent of world GDP (excluding Eastern Europe and USSR), they must be doing something to the international system which would constitute somewhat of a shock to the system._ _PROF. BOLTHO: I can answer these questions and I can answer some points that have been raised in the discussion, particularly by Goran Ohlin and Leslie Lipschitz._ WP/PROC/SF: **Jsq**
35 Appendix Table AZ-2: Partia Correlaton or Labor Force Growth with Growth Rate of Output per Worker for various Samples of Counties, 1960-1969.

|Control variables:|None|.<br>0|Y0*BMP.LLY.XS|N|
|---|---|---|---|---|
|All<br>_____________________|-.26<br>(.02)|(.03)|-.07<br>_(.SS)_|79|
|OECD<br>._________|__ (.02)|D<br>0.04<br>(.84)|-.33<br>(.19)|22|
|Developing<br>._________________|0.12<br>___ (.38)|0.11<br>(.43)|0.12<br>(.41)|_56_|
|Africa|-.06<br>(.90)|-.03<br>(-92)|-.20<br>_(A9)_|_19_|
|Lain America|0.31<br>(.2 1)|031<br>(.2)|0.45<br>(.10)|18|
|AsWiPacific|0.62<br>(.03)|0.70<br>(.02)|0.82<br>(.01)|12|
|Low Income|0.05<br>(.83)|0.02<br>(.92)|-.19<br>(.43)|24|
|Middle Income|-28<br>(.13)|-.31<br>(.09)|-. 7<br>(.39)|32<br>.|
|LandPoor<br>wto SGP|-.20<br>(.21).|_9_<br>.<br>_(26)_|-.08<br>(.67)|38|
|LandRich<br>.|-.30<br>(.05)|-.32<br>(.05)|0.09<br>(.62)<br>-|40|

Notes: 1) Number in parenEhes **is** the p-level of the test the correlation is zero. 2) Control variables: YO is init incomre BMP is die black market premiuna. LLY is die ratio of liquid liabUties to GDP. XS is the exort wo GDP ratio.
- 17 -

|Agro-industry|0.4032|(0.0912)|***|0.2519|(0.1158)|**|
|---|---|---|---|---|---|---|
|Chemicals|0.2640|(0.0929)|***|0.1439|(0.1057)||
|Light manufacturing|0.2090|(0.0938)|**|0.1296|(0.1020)||
|Service|-0.7168|(0.0881)|***|-0.2735|(0.1033)|***|
|Generator|0.1139|(0.0709)||0.1785|(0.0820)|**|
|ln_YEAR_|0.0621|(0.0294)|**|0.0687|(0.0330)|**|
|Female|0.1235|(0.0563)|**|0.1363|(0.0537)|*|
|Export share|0.0031|(0.0014)|**|0.0030|(0.0019)||
|Importing|0.2094|(0.0702)|***|0.1453|(0.0835)|*|
|Secondary education|0.2179|(0.0710)|***|0.1309|(0.0596)|**|
|Tertiary education|0.4072|(0.0779)|***|0.2353|(0.0725)|***|
|ln_LOCAL_|-0.0823|(0.0442)|*|-0.0465|(0.0583)||
|ln_MAI_|0.0408|(0.1766)||0.1810|(0.2369)||
|ln_PORT_|0.0565|(0.0304)|*|0.0546|(0.0297)|*|
|ln_N_|-0.0622|(0.0974)||-0.1381|(0.1148)||
|_t_|-0.0692|(0.2932)||-1.2878|(0.3938)|***|
|Constant|4.2699|(1.1817)|***|2.2652|(1.3859)||
|Obs.|903|||903|||
|R squared|0.9654||||||
|Wald chi 2|49023.7|||44707.2|||

**<u>Table 4. Implied own price elasticities</u>**

||SUR<br>Elasticity|Std. Err.||SFA<br>Elasticity|Std. Err.||
|---|---|---|---|---|---|---|
|Labor|-0.662|(0.015)|***|-0.629|(0.057)|***|
|Electricity|-0.282|(0.229)||-0.252|(0.331)||
|Materials and goods|-0.527|(0.012)|***|-1.028|(0.184)|***|

**<u>Table 5. Output elasticities with respect to input prices</u>**

||SUR<br>Elasticity|Std. Err.||SFA<br>Elasticity|Std. Err.||
|---|---|---|---|---|---|---|
|Labor|-0.433|(0.046)|***|-0.604|(0.162)|***|
|Electricity|-0.079|(0.030)|***|-0.165|(0.116)||
|Materials and goods|-0.353|(0.036)|***|0.097|(0.099)||
9

# **_Demography_**

-  Gender, age

# **_Geography_**

Male dominated, but increasingly also Predominantly male; feminization in certain female (including for economic regions reason); rural-urban: young; urbanrural: all ages Secondary town/city S-S (38%); S-N (33%); N-N (23%), N-S (6%); 3 main corridors (South-Asia-Gulf countries; Latin America-North America; within West Africa and Southern Africa)
absolute performance standard. But they also have a major downside: a narrow focus on the World Bank’s PSM projects and their performance from a corporate perspective. The contribution of this review to the literature on PSM reform is thus within a niche—it tests whether theoretical claims emerging from the broader literature on PSM reform can explain performance patterns in PSM projects—but its findings need not have external validity for government PSM reforms.

Second, this paper applies basic concepts from the political economy literature to predict PSM reform patters. It looks at how “inclusive” or “extractive” political (and economic) institutions (Acemoglu and Robinson 2012) or, similarly, “open-access” and “limited-access orders” (North, Wallis, and Weingast 2009) affect PSM projects. More specifically, it looks at the role of aid and natural resource rents in undermining the accountability relationship between the state and its citizens as taxpayers (see, for example, Bates 1992 and Knack 2002). The paper also considers political market imperfections (see Cruz and Keefer 2010), in particular the absence or existence of programmatic political parties as a potential determinant of PSM project performance.

Third, the paper adds to previous studies that employ IEG outcome measures by (i) exploring the performance determinants of public sector projects as a specific subset of the Bank’s larger project portfolio; and (ii) by comparing performance determinants for PSM projects to performance determinants for the broader universe of Bank investment lending projects.

It closely relates to recent work by Denizer, Kaufmann, and Kraay (2011), who employ a large sample of World Bank − supported projects across sectors to identify “macro- [that is, country context] and micro[that is, project management] correlates of World Bank project performance.” Earlier work in this direction includes Dollar and Levin’s (2005) review of “institutional quality and project outcomes in − developing countries,” which explores World Bank supported projects across sectors and countries. Two studies stand out for their similarity to this review. Cruz and Keefer (2010) employ performance data on World Bank − supported public sector projects to test the theoretical prediction that the existence of programmatic political parties positively influences politicians’ incentives for developing a wellperforming public administration. An early paper by Isham, Kaufmann, and Pritchett (1995) closely relates to this paper in that it explores the impact of civil liberties and democracy (as measured by − Freedom House indicators) on the performance of World Bank supported projects (but takes no specific interest in the performance of public sector projects).

In sum, this paper contributes to the mostly qualitative literature on PSM reform by testing whether theory can help explain the performance ratings of World Bank PSM projects. Within this niche, it contributes to filling a gap of cross-country empirical research on the determinants of PSM reform 7
15

# 4. **DATA**

# 4.1. **Data Sources**

As shown in Appendix 4, the World Bank’s World Development Indicators (World Bank, 2005) is the source for most variables in our analysis. The main dependent variable, GDP per capita growth (GDPPCG), is the annual real change in gross domestic product (in local currency) minus annual population growth, both indicators extracted from World Bank (2005). Other dependent variables are also obtained from this source are gross capital formation as a share of GDP (IGDP), the share of exports of goods and services in total GDP (XGDP), the share of manufacturing exports in total merchandise exports (MANX), and industrial valued added as a share of GDP (IND). UNCTAD’s Handbook of Statistics (UNCTAD, 2006) is our source for our measure of exports concentration (which is based on the Herfindahl-Hirschman Index). Among our independent variables, World Bank (2005) is the source of total population and our estimate of GNI per capita relative to the one of the United States. It is also the source of our measure of terms of trade, current account balance, fiscal balance, and inflation. Our list of natural disasters comes from the OFDA/CRED Emergency database (EM-DAT, 2006) and we use estimates of real exchange rate misalignments (RXRDEV) from Elbadawi and Soto (forthcoming). In addition to regression variables, we use other statistics to compare the general economic status and evolution of countries in and out of the sample, and before and after trade liberalization. These statistics are all listed and described in Appendix 4.

# 4.2. **Data Description**

Developing countries included in our sample did not differ considerably or systematically from those left out during the period under consideration (1970-2004). According to the average values presented in Appendix 6, countries in the sample had higher output growth, but lower income per capita and lower level of industrialization. The general macroeconomic outlook is slightly better for in-sample countries having lower inflation, debt to GDP share, current account deficits, and fiscal deficits. On the