rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4305

# **Abstract**

This study examines the impact of climate change on cropland in Africa. It is based on a survey of more than 9,000 farmers in 11 countries: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia, and Zimbabwe. The study uses a Ricardian cross-sectional approach in which net revenue is regressed on climate, water flow, soil, and economic variables. The results show that net revenues fall as precipitation falls or as temperatures warm across all the surveyed farms.

In addition to examining all farms together, the study examined dryland and irrigated farms separately. Dryland farms are especially climate sensitive. Irrigated farms have a positive immediate response to warming because they are located in relatively cool parts of Africa. The study also examined some simple climate scenarios to see how Africa would respond to climate change. These uniform scenarios assume that only one aspect of climate changes and the change is uniform across all of Africa. In addition, the study examined three climate change scenarios from Atmospheric Oceanic General Circulation Models. These scenarios predicted changes in climate in each country over time.

Not all countries are equally vulnerable to climate change. First, the climate scenarios predict different temperature and precipitation changes in each country. Second, it is also important whether a country is already hot and dry. Third, the extent to which farms are irrigated is also important.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to mainstream climate change research. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at robert.mendelsohn@yale.edu.

_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
17 rough calculation shows the income elasticity to be between 1 and 2. Looking closely, this result is generated primarily by gas -- the rich consumed about twice as much as a proportion of their expenditures as the poor (2.7 percent of their budget as compared to 1.3 percent) -- and heat consumption -- where the fifth quintile consumed about 2.3 times more than the poorest quintile<sup>13</sup> (Table 3). This implies that price controls in the energy sector, especially those on gas and district heat are regressive and hence involve a lot of "leakage" of the price subsidy to the non-poor in social welfare terms.

**Table 4: Poland- Estimated Income Elasticity of Energy Demand**

|expenditure group|percentage change in|percentage change in|income elasticity|
|---|---|---|---|
||energy expenditures|income||
|I to 2|0.73|0.46|1.60|
|2 to 3|0.44|0.27|1.65|
|3 to 4|0.35|0.27|1.26|
|4 to 5|0.51|0.70|0.73|

Source: Authors' calculation .rom Poland Household Budget Survey, 1993.

_Consumption by socio-economic group._ The second main finding comes from examining energy budget shares for different social welfare groups (Table 5). There are notable differences in energy expenditures among different socioeconomic groups. Pensioners spent a significantly larger share of expenditures on energy (11.7%) than any other group. This is consistent with the stylized fact that in transition economies, pensioners tend to be "house rich and income poor", especially in urban areas. Heating and lighting their relatively large apartments and houses thus places a greater burden on them (8.8% of total expenditures) than on other groups (6.9% for all groups taken together). Also evident is the different types of energy each segment of the population uses. Farmers and mixed families use coal and wood for heating more than other groups. This is a 13/ "Heat consumption" is comprised of both district heat (Table 3 col. 1) and residential coal use (Table 3 col. 4). As shown in table 3, the lowest quintile spends 1.9 percent of its budget on heat, while the richest quintile spends 4.7 percent of its budget.
WPS3694

# **Governance in the Gullies: Democratic Responsiveness and Leadership in Delhi’s Slums***

Saumitra Jha, Department of Economics, Stanford University Vijayendra Rao, Development Research Group, The World Bank Michael Woolcock, Development Research Group, The World Bank

## _Abstract_

We use detailed ethnographic evidence to design and interpret a broad representative survey of 800 households in Delhi’s slums, examining the processes by which residents gain access to formal government and develop their own, informal, modes of leadership. While ethnically homogeneous slums transplant rural institutions to the city, newer and ethnically diverse slums depend on informal leaders who gain their authority through political connections, education and network entrepreneurship. Education and political affiliation are more important than seniority in determining a leader’s influence. Informal leaders are accessible to all slum dwellers, but formal government figures are most accessed by the wealthy and the well-connected.

## World Bank Policy Research Working Paper 3694, September 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful to the World Bank’s Research Department for funding the survey upon which this study is based. Valuable comments were received from two anonymous referees, Susan Athey, Anthony Bebbington, Liran Einav, Deon Filmer, Ravi Kanbur, Jessica Leino, Michael Lokshin, Pedro Miranda, Paul Shaffer and seminar participants at the American Planning and Geographical Associations, Oxford, Stanford and the World Bank. The data was collected cooperatively between the authors and a team including Lester Coutinho and Arup Mitra. Please address correspondence to Vijayendra Rao, Mailstop MC3-306, 1818 H Street NW, Washington, DC 20433. Telephone: (202) 458 8034. Fax: (202) 522 1153. Email: vrao@worldbank.org
10 An effective innovation system provides an environment that nurtures research and development (R&D), which results in new goods, new processes and new knowledge, and hence is a major source of technical progress. The OECD (OECD, 1993) defined R&D to “comprise of creative work undertaken on a systemic basis in order to increase the stock of knowledge and the use of this stock of knowledge to devise new applications”.

There have been a number of studies that show that innovation or the generation of technical knowledge has substantial positive effects on economic growth or productivity growth. For example, Lederman and Maloney (2003), using regressions with data panels of five-year averages between 1975 to 2000 over 53 countries, finds that a one-percentage point increase in the ratio of total R&D expenditure to GDP increases the growth rate of GDP by 0.78 percentage points. Guellec and van Pottelsberghe (2001) investigated the long-term effects of various types of R&D on multifactor productivity growth using panel data for the OECD over the period 1980-98. They find that business, public<sup>11</sup> and foreign<sup>12</sup> R&D all have statistically significant positive effects on productivity growth. Adams (1990), using the number count of academic scientific papers of various scientific fields<sup>13</sup> to proxy for the stock of knowledge, finds that technical knowledge contributed significantly to the total factor productivity growth of U.S. manufacturing industries for the period 1953-1980.

Currently, the majority of technical knowledge is produced in the developed countries: more than 70 percent of patenting and production of scientific and technical papers are accredited to researchers in industrialized countries. The disparity in the production of technical knowledge per capita between developed and developing countries is even greater than the disparity in income. However, note that domestic technological innovation is not the sole source of generation of technical knowledge. There are many ways for developing countries to avoid reinventing the wheel and tap into, adopt and adapt technical knowledge that was created in other developed countries. Therefore, a key element of a developing country’s innovation strategy is to find the best ways to tap into the growing global knowledge base and to decide where and how to deploy its domestic R&D capability.

One factor that affects the magnitude to which an economy is able to tap into the global knowledge base is the economy’s openness to international trade and foreign direct investment. Numerous studies have indicated that imports and foreign direct

> 11 Guellec and van Pottelsberghe (2001) define public R&D as R&D performed by government and higher education sectors.

> 12 Guellec and van Pottelsberghe (2001) define foreign R&D as business R&D performed in other 15 OECD countries.

13 Adams used worldwide annual counts of publications in nine sciences: agriculture, biology, chemistry, computer science, engineering, geology, mathematics and statistics, medicine, and physics.
- **PanTex** , which is a built-up presence index derived from the grey-level co-occurrence matrix (Pesaresi et al, 2008)

- **Structural Feature Sets** (SFS), which are statistical measures to extract the structural features of direction lines (Huang et al, 2007)

These spatial features are calculated by comparing pixels with their neighbors and then reporting this value back to the individual pixel (in this case 10m). The number of neighboring pixels considered in the comparison is the scale, which varies by the feature being calculated. For most features, we use scales of 3, 5, 7, which correspond to squares of 3 pixels by 3 pixels, 5 pixels by 5 pixels, and 7 pixels by 7 pixels.

The remote sensing data used for Tanzania, while largely derived from publicly available imagery, was supplemented by proprietary data on building footprints produced by Ecopia and Maxar.<sup>10</sup> The publicly available layers considered for Tanzania included:

- **Night-time lights** , taken from the same VIIRS imagery used for Sri Lanka

- **Population estimates** taken from WorldPop, which is in turn derived from the 2011 census and distributed using measures of built-up area.

- **Precipitation estimates** taken from Weilcott and Matsuura (2018)

- **Elevation and Slope** taken from Jarvis (2008) and downloaded from AidData’s geoquery database

- **Built-up area** from the same GHSL data used for Sri Lanka, for the same periods.

- **Climactic region,** obtained from Kottek et al (2006). This indicator divides land into five main climate groups (tropical, dry, temperate, continental, and polar), with each group being divided based on seasonal precipitation and temperature patterns, based on a raster version of the map available at 0.5° resolution.

- **Crop yield estimates** for Maize, Sorghum, and Rice, from the HarvestChoice Dataverse (Wood-Sichra et al. 2016) capture the estimated yield of various crops (ton/km) at 0.5° resolution.

The full set of satellite indicators used for each country are listed below in Table 2.

_Table 2: Indicators and sources for auxiliary satellite data_

|Satellite indicator|Sri Lanka|Tanzania|
|---|---|---|
|**Urbanization**|||
|Night-time-lights|VIIRS|VIIRS*|
|Building footprints||Ecopia and Maxar, via Gates<br>Foundation*|
|Built-uparea||Global Urban Footprint*|
|Built-uparea|Global Human Settlement Layer*|Global Human Settlement Layer|
|Population||WorldPop|
|Agglomeration index||Belghith et al (2020) (See<br>Appendix A)|
|Spatial features|Sentinel 2*||

10 The team also calculated spatial features using the Sp.Feas package for Tanzania based on Sentinel 2 imagery, but these were found to be highly colinear with the building footprint data and were therefore discarded.

8
no effect of the program after one year of the program when the evaluation did not have any implementation problem.

Table 5: Regression Analysis of the impact of PEC on School Performance

||School Average|Spanish|Math|3<sup>rd</sup>grade|4<sup>th</sup>grade|5<sup>th</sup>grade|6<sup>th</sup>grade|
|---|---|---|---|---|---|---|---|
|Years PEC before|-8.6|-8.6|-6.7|-10.5|-4.1|-7.5|-12.6|
||(3.99)**|(4.08)**|(4.43)|(4.49)**|(5.54)|(4.13)*|(5.75)**|
|Treatment|-1.1|0.2|-4.4|-4.2|-2.4|-3.0|6.5|
||(7.17)|(7.31)|(7.54)|(7.41)|(9.08)|(7.75)|(10.02)|
|**Treatment*2006**|**-2.0**|**-1.9**|**0.7**|**3.5**|**2.2**|**-1.3**|**-12.9**|
||**(4.54)**|**(4.37)**|**(4.44)**|**(7.72)**|**(7.95)**|**(7.06)**|**(8.32)**|
|Constant|458.4|457.2|456.9|476.2|452.5|455.5|450.7|
||(14.75)***|(14.97)***|(15.45)***|(16.12)***|(19.16)***|(16.75)***|(17.74)***|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.0606|0.0522|0.0548|0.0618|0.0632|0.0615|0.0674|
|Observations|26,841|27,078|27,30|6,493|6,871|6,709|6,768|
|Clusters|98|98|98|98|98|98|98|

School-Level clustered standard errors in parentheses. *p<0.10, **p<0.05, ***p<0.01 We use specifications as in Table 5 using all the years of the intervention and of the program; the results show that there is no impact of the program for the schools in the experiment. The results of these specifications suggest that a formal SBM intervention with cash grants did not have an impact on student learning. Since schools in the experimental evaluation sample were equal in expectation before the treatment, also suggests that both groups already had in place formal school autonomy practices and some also had experienced cash grants; then, the intervention just supported these practices but did not change them into better school performance. Finally, schools with more years in PEC had lower and significant school performance (close to a 0.09 σ) at the baseline.

# **DD Approach – To Test for Heterogeneous Effects Such as Intensity of Treatment**

To analyze the change in school average performance that can be attributed to the intervention, two sets of estimations were used. First, it estimated the effect of PEC program on school average test scores from 2005 and 2012; PEC schools are identified from administrative records. Second, it estimated the effect of the intensity of treatment on school performance; PEC schools identified as treatment in the evaluation sample. The second empirical approach assumes that PEC had different intensity among participating schools. This approach considers not only initial years in the program before the treatment but also four years after the program. In this case, the inclusion of dummies defines schools with different years of PEC. Table 6 shows the results of the DD estimation of the impact of PEC on school performance. The first column shows the impact of the PEC on school average; this effect was only significant for 2010 by 0.15 σ, a year after the end of the impact evaluation. The second column shows the results when the program variable is a dummy for the treatment schools; again, there was no impact.

19
Casini, P and Vandewalle, L. 2011. Public Good Provision in Indian Rural Areas: The Returns to Collective Action by Self -Help Groups. _University of Namuar Department of Economic Working Paper_ , WP1119.

Clingingsmith, D, Khwaja, AI and Kremer, M. 2009. Estimating the Impact of the Hajj: Religion and Tolerance in Islam's Global Gathering. _Quarterly Journal of Economics_ , 124(3): 1133-1170.

Deininger, K and Liu, Y, 2013a. Economic and Social Impacts of an Innovative SelfHelp Group Model in India. _World Development_ , 43: 149-163.

Deininger, K and Liu, Y. 2013b. Evaluating Program Impacts on Mature Self-help Groups in India. _World Bank Economic Review_ , 27(2): 272-296.

Desai, Raj and Joshi, S. 2013. Collective Action and Community Development Evidence from Self-Help Groups in Rural India. _World Bank Policy Research Working Paper_ , 6547.

Fafchamps, M and La Ferrara, E. 2012. Self-Help Groups and Mutual Assistance: Evidence from Kenyan Slums. _Economic Development and Cultural Change_ , 60(4): 70734.

Feigenbaum, B, Field, E, Pande, R, Rigol, N and Sarkar, S. 2013. Do Group Dynamics Influence Social Capital and Female Empowerment? Experimental Evidence from Microfinance. Available at <u>http://economics.mit.edu/files/9208.</u> Feigenbaum, B, Field, E and Pande, R. 2012. The Economic Returns to Social Interaction. _Review of Economic Studies_ , 80(4): 1459-1483.

Henrich, J, Boyd, R, Bowles, S, Camerer, C, Fehr, E and Gintis, H. 2004. _Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies_ . New York: Oxford University Press.

Hoffman, E, McCabe K, and Smith, V. 1998. Behavioral Foundations of Reciprocity: Experimental Economics and Evolutionary Psychology. _Economic Inquiry_ , 36: 335–52.

Johnson, ND. and Mislin, AA. 2011. Trust Games: A Meta-Analysis. _Journal of Economic Psychology_ , 32: 865-89.

Karlan, D. 2005. Using Experimental Economics to Measure Social Capital and Predict Financial Decisions. _American Economic Review_ , 95(5): 1688-1699.

Kast, F, Meier, S and Pomeranz, D. 2012. Under-Savers Anonymous: Evidence on SelfHelp Groups and Peer Pressure as a Savings Commitment Device. _NBER Working Paper_ , 18417.

29
# **Figure 10(a): Thailand: Return to skill by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 2<br>1.8<br>1.6<br>1.4<br>1.2<br>Agriculture/Mining<br>1<br>Manufacturing<br>0.8<br>0.6 Trade/Services<br>0.4<br>0.2<br>0<br>1990 1994 2000 2004<br>Return<br><!-- End of picture text -->

**Year**

**Figure 10(b): Thailand: Proportion of skilled workers by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25 Agriculture/Mining<br>20 Manufacturing<br>15<br>Trade/Services<br>10<br>5<br>0<br>1990 1994 2000 2004<br>% skilled<br><!-- End of picture text -->

**Year**

49
hypothetical taxes and other fiscal instruments. While the amount of computation and data requirements (survey, census, and social accounting matrix) may be significant, there are many countries that already possess such resources. Geographic targeting is already widely used in developing countries, but it is often formulated independently of their macroeconomic policies. Our method of combining the integrated microsimulation-CGE model with small-area estimation provides an opportunity for linking the two to achieve a more complete set of microeconomic and macroeconomic objectives.

# **REFERENCES**

- Anderson, K. (1999). _Vietnam’s Transforming Economy and WTO Accession_ . Singapore: Institute of Southeast Asian Studies.

- Bhagwati, J., and T. N. Srinivasan (2002). ‘Trade and Poverty in Poor Countries’. _American Economic Review_ , 92(2): 180–183.

- Bourguignon, F., A. Robilliard, and S. Robinson (2005.) ‘Representative Versus Real Households in the Macroeconomic Modeling of Inequality’, in T. J. Kehoe, T. N. Srinivasan, and J. Whalley (eds), _Frontiers in Applied General Equilibrium Modelling_ . Cambridge: Cambridge University Press.

- Dollar, D., and A. Kraay (2002). ‘Growth is Good for the Poor’. _Journal of Economic Growth,_ 7(3): 195–225.

- —— (2004). ‘Trade, growth, and poverty’. _Economic Journal_ , 114: F22–F49.

- Elbers, C., J. O. Lanjouw, and P. Lanjouw (2002). ‘Welfare in Villages and Towns: Micro-level Estimation of Poverty and Inequality.’ Working Paper 2911. Washington, DC: World Bank.

- —— (2003). ‘Micro-level Estimation of Poverty and Inequality’. _Econometrica_ , 71(1): 355–364.

Foster, J., J. Greer, and E. Thorbecke (1984). ‘A Class of Decomposable Poverty Measures’. _Econometrica_ , 52(3): 761–765.

30
31 growth does not identify the direction of causality.<sup>2 0</sup> Recently, researchers have taken steps to address some of these weaknesses. For example, King and Levine (1993a,b,c) study 80 countries over the period 1960-1989, systematically control for other factors affecting long-run growth, examine the capital accumulation and productivity growth channels, construct additional measures of the level of financial development, and analyze whether the level of financial development predicts long-run economic growth, capital accumulation, and productivity growth.<sup>2</sup> ' They use four measures of "the level of financial development" to more precisely measure the functioning of the financial system than Goldsmith's size measure. Table I summarizes the values of these measures relative to real per capita GDP (RGDP) in 1985. The first measure, DEPTH, measures the size of financial intermediaries and equals liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP. As shown, citizens of the richest countries -- the top 25 percent on the basis of income per capita -- held about twothirds of a year's income in liquid assets in formal financial intermediaries, while citizens of the poorest countries -- the bottom 25 percent -- held only a quarter of a year's income in liquid assets. There is a strong correlation between real per capita GDP and DEPTH. The second measure of financial development, BANK, measures the degree to which the central bank versus

**20** Goldsmith (1969) recognized these weaknesses, e.g., "(T)here is no possibility, however, of establishing with confidence the direction of the causal mechanisms, i.e., of deciding whether financial factors were responsible for the acceleration of economic development or whether financial development reflected economic growth whose mainsprings must be sought elsewhere." (p.48)

**21** Also, see Alan Gelb (1989), Gertler and Rose (1995), Nouriel Roubini and Xavier Sala-iMartin (1991, 1992), William Easterly (1993), and the overview by Marco Pagano (1993).