rafmacalaba/datause-encoder-data huggingface dataset:
economies, compared to medium-sized cities that might suffer from market access problems, lack of intermediate goods and infrastructure, and other impediments to grow fast. In the developed world this problem may be less severe, thus providing growth opportunities to medium-sized locations that are not present in India. Comparing the U.S. and India in the service sector, we indeed find that agglomeration economies peak for intermediate-sized locations in the U.S., whereas the large megacities are the winners in India. This finding is not common to all emerging economies. Although for want of high quality sectoral employment data at the local level we refrain from an in-depth study of China, our preliminary exploration suggests that China looks more similar to the U.S. in that decreasing returns dominate in high-density cities. The finding that “India is different”, because of both the failure of medium-density locations to grow faster and the importance of its service sector, justifies studying the spatial development of that country in further detail.

# **2. Data**

To study employment dynamics across space in India, a first issue is to decide on the level of spatial disaggregation at which we have reliable data. India is divided into 35 states (or union territories) and 640 districts. While certainly the quality of the data are more reliable at the state than at the district level, work on the U.S. by Desmet and Rossi-Hansberg (2009) shows that having a high degree of spatial disaggregation is important. Indeed, agglomeration economies and congestion effects may get lost at higher levels of aggregation, so that focusing on districts is better. In addition, having a broad distribution of places (going from small to intermediate to large) is also important, since previous work for the U.S. has shown that the scale-dependence of growth may be non-linear.

India does not collect comprehensive sectoral employment data at the district level. We therefore rely on micro-data from surveys. India runs two firm-level surveys, the Annual Survey of Industries (ASI) and the one conducted by the National Sample Survey Organisation (NSSO). The ASI survey has information on the so-called organized manufacturing sector (essentially comprising of firms with more than 10 workers), whereas the NSSO covers the unorganized manufacturing sector and the services sector. Both surveys, the ASI and the NSSO, overlap for the fiscal years 1989-90, 1994-95, 2000-01 and 2005-06. However, the service sector has only been surveyed more recently, in fiscal years 2001-02 and 2006-07. Given that part of our focus will be on the difference between manufacturing and services, we will use 2000-2005 for manufacturing and 2001-2006 for services.

3
**Table 3:** Effects of Turnovers on Morale and Effort

||||**Bureaucrat survey**||
|---|---|---|---|---|
||Enthusiasm|Motivation|Interacts dailyw/ citizens|Frequencyindex|
||(1)|(2)|(3)|(4)|
|New village head|0.487<sup>_∗∗∗_</sup>|22.868|0.197<sup>_∗∗_</sup>|0.405<sup>_∗∗_</sup>|
||(0.143)|(15.03)|(0.088)|(0.156)|
||[0.165]|[18.495]|[0.102]|[0.183]|
|Observations|1064|1062|1064|1064|
|Control mean|-0.057|100.6|0.57|0.32|
|Robust p-value|0.001|0.153|0.029|0.012|
|<br>Bandwidth size (%)|20.8|21.5|18.5|16.3|
|Effective obs.|522|533|487|441|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, a standardized z-score of self-reported enthusiasm; in column 2, a continuous measure of motivation anchored at 100 at baseline and winsorized at the top 1%; in column 3, a dummy variable equal to 1 if the bureaucrat reports interacting with citizens on a daily basis; in column 4, a standardized z-score of the frequency of bureaucrat-citizen interactions measured on a 1-5 scale. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors clustered by village in brackets. Significance levels are based on the robust bias-corrected standard errors clustered by village.

35
# Ahmedabad - Growth of slum and chalis population and areas between 2001 and 2011

||2001|2011|Absolute<br>increase|% increase ||Yearly<br> increase|
|---|---|---|---|---|---|
|Ahmedabad Population (AMCarea) [1]|4,520,255||5,570,585|1,050,330|23%|2.1%|
|Built-up area (Km2)<br>[2]<br>|185<br>|212<br>|27<br>|14%<br>|1.4%<br>|
|Built-updensity (p/ha)|244|262|19|8%|0.7%||
|Slumandchalisarea(Km2)[3]|14.9|G3}|0.4|2%|0.2%||
|<br>Density in slums (p/ha) [4]<br>|1070<br>|1230<br>|160<br>|15%<br>|1.4%<br>|
|population inslumsand chalis|1,596,800|1,880,300|283,500|18%|1.6%||
|% ofslumand chalis population overtotalAMC population|35.3%|33.8%|-1.6%|-A%|-0.5%|
|Population living in formal areas (outside slums and chalis)|2,923,455|3,690,285|766,830|26%|2.4%|
|Areas offormal development<br>|170<br>|197<br>|26<br>|16%<br>|1.5%<br>|
|Densitiesinformalareas|171|187|15.9|9%6|0.9%||

[1] 2001 and 2011 census data corresponding to current AMC boundaries

[2] built up area as measured on Google Earth imagery dated Oct 2000 and jan 2001 for the year 2001 and May and Nov 2010 for 2011

[3] Slum areas measured on Google Erath imagery dated as above. Were included slums that have no apparent planned street structures and settlements with streets narrower than about 3 meters

[4] Average densities in slums extrapolated from a detailed topographical and social survey in 15 slums done by SEWA-MHT in 2001 and 2011
P are the income and price level in Spain used to capture demand conditions. Winters and Chang

[2000] are interested in the value of the coefficients a; through a, as well as certain restrictions implied by theory. Their results imply that a 1% decline in the tariffs faced by i (EU countries) causes a 0.56% decline in the relative prices of US exporters to Spain. They estimate a similar equation for Mercosur in Winters and Chang [2002] in which the dependent variable is the ratio of US (excluded country) exports’ prices in Brazil and the rest of the world.

As opposed to these studies, our focus is on the prices received by the exporters, net of tariffs and other transactions costs that are included in mi, and mbow Let Di (pROW) denote the pretariff prices received by exporters of k from 1 (ROW). th represent the preferential tariff imposed on i and tow is the MFN tariff rate imposed by the US on the ROW. Our estimating equation is similar to above equation (2) but the dependent variable is the ratio of pre-tariff prices: In (Die PEC) = BotBr (tO — tis) +B Xi. +83Mgo" +5 > vats bpPet+>_ OVit+ey (3) i k t In (pi,/pECTM) is the approximate difference (in percentage) of net prices of received by exporters from CBI and the rest of the world. tow — ti, is the average preference margin enjoyed by the exports of the beneficiary country 2. Since the tariff imposed by the U.S. on the rest of the world, thOw did not vary considerably in our sample over time, we use the tariff difference in estimation rather than two separate tariffs. We also include the total export volume of country 7 in category k, denoted by Xi, and total imports of the US in that category, denoted by MPO” We add country, product and year dummy variables, denoted 0,® and W, respectively® to capture variables that are missing from the estimation equation. Among these are exchange rates and wages that are included in the previous equations (1) and (2) as well as other variables such as differences in quality and transport cost’ that are unrelated to the effects of preferential market access programs. As we mentioned earlier, using these dummy variables enables us to isolate all of these effects that influence prices and focus on the impact of preferential programs.

The implementation of a preference program is equivalent to a decline in the tariff rate faced <u>~ 8We also test other specifications with country-product</u> fixed effects etc. We identify these in more detail in the results section. “We can also include quotas imposed on other exporting countries, shocks to demand in the US and supply shocks in the apparel sector.

9
- <mark>Dreze, J., & Srinivasan, P. V. (1997). Widowhood and poverty in rural India: Some inferences from household survey data.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>54</mark>_ <mark>(2), 217-234.</mark>

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- ESCWA, 2021 “Targeted social protection in Arab countries before and during the Covid-19 crisis”. Economic and Social Commission for Western Asia.

- Ferreira, Francisco H. G., Julian Messina, Jamele Rigolini, Luis-Felipe López-Calva, and Renos Vakis. (2012). _Economic Mobility and the Rise of the Latin American Middle Class._ Washington DC: World Bank.

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- Gammage, Sarah. (1998). “The Gender Dimension of Household Poverty: Is Headship Still a Useful Concept?” International Center for Research on Women (Washington DC)

- <mark>Ghomi, M. (2022). Who is afraid of sanctions? The macroeconomic and distributional effects of the sanctions against Iran.</mark> _<mark>Economics & Politics</mark>_ <mark>,</mark> _<mark>34</mark>_ <mark>(3), 395-428.</mark>

- Grant, Monica J., and Jere R. Behrman. (2010). “Gender Gaps in Educational Attainment in Less Developed Countries.” _Population and Development Review_ , Vol. 36(1), pp. 71-89.

- Horrell, Sara, and Pramila Krishnan. (2007). “Poverty and productivity in female-headed households in Zimbabwe.” _Journal of Development Studies_ , Vol. 43(8), pp. 1351-1380.

- IMF. (2011). Islamic Republic of Mauritania: Poverty Reduction Strategy Paper, IMF Country Report No. 11/252, August, www.imf.org/external/pubs/ft/scr/2011/cr11252.pdf.

- Klasen, Stephan, Tobias Lechtenfeld, and Felix Povel. (2015). “A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam.” _World Development_ , Vol. 71, pp. 36-53.

- <mark>Lanjouw, P., & Ravallion, M. (1995). Poverty and household size. E</mark> _<mark>conomic Journal</mark>_ <mark>,</mark> _<mark>105</mark>_ <mark>(433), 1415-1434.</mark>

- Liu, Chia, Albert Esteve, and Rocío Treviño. (2017). “Female-headed households and living conditions in Latin America.” _World Development_ , Vol. 90, pp. 311-328.

30
|**Table 4: Se**<br>Round 11(1<br>**Logit: Mean**<br>|**xual behavio**<br>1/99-9/2000)<br>**of Marginal**<br>|**r and AIDS re**<br>,MRC,Genera<br>**Effects, obse**<br>|**lated prac**<br>l Populatio<br>**rvation by**<br>|**tices.**<br>n Cohort.<br> <br>**observation**<br>|Individua<br> <br>|ls aged 18<br>|and above<br>|: Byagegroup.<br>|
|---|---|---|---|---|---|---|---|---|
|**Dependent**|**variable:**|**Ever used**<br>**a condom?**|**Used**<br>**condom**|**Number**<br>**partners**|**Ever**<br>**visited**|**Ever got**<br>**result**|**Age at**<br>**first sex**|**Used condom**<br>**last time**|
||||**last**<br>**time**|**last year**<br>**if active(*)**|**VCT**|**test**|**(*)**|**if multiple partners**<br>**partners**|
|**All**|Years of|**0.021**|**0.012**|**0.002**|**0.008**|**0.006**|**0.17**|**0.024**|
||education|[10.10]|[5.30]|[0.36]|[3.58]|[2.86]|[9.19]|[3.77]|
||N =|3253|2719|2816|3811|3811|3040|336|
||Mean|0.256|0.087|1.33|0.113|0.089|16.83|0.214|
||R-square|0.22|0.25|0.12|0.06|0.07|0.18|0.24|
|**Age 18-29**|Years of|**0.026**|**0.016**|**0.004**|**0.01**|**0.007**|**0.11**|**0.025**|
||education|[6.75]|[4.99]|[0.28]|[3.39]|[2.66]|[4.89]|[3.01]|
||N =|1340|1293|1197|1505|1505|1218|237|
||Mean|0.424|0.176|1.419|0.113|0.086|16.43|0.324|
||R-square|0.153|0.24|0.13|0.09|0.1|0.11|0.24|
|**Age 30**|Years of|**0.012**|**0.008**|**0.009**|**0.012**|**0.007**|**0.18**|**|
|**and above**|education|[4.5]|[1.84]|[1.58]|[2.40]|[1.71]|[6.87]|**|
||N =|1913|1288|1619|2272|2207|1822|**|
||Mean|0.16|0.037|1.265|0.112|0.091|17.09|0.107|
|Note:<br>Age and|R-square<br>village dummie|0.21<br>s, controls for gen|0.15<br>der, marital s|0.14<br>tatus, religion a|0.06<br>nd wealth|0.07<br>included.|0.22|**|

<u>Note: Age and village dummies, controls for gender, marital status, religion and wealth included.</u> Robust z-stat in parentheses. (*) = linear regression, ** sample too small for meaninful regression. Age dummies reduce slightly the sample size, as at old ages, the outcome is predicted perfectly. "Did you ever used a condom" was asked to all individuals who ever had sex, while condom use at the last intercourse was asked to all individuals sexually active in the last year.

42
- 35 -

**Figure 8. Number of Public Facilities under ESCO projects**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Note: Some of the projects involve more than one facility.<br>Source : Author’s survey data.<br>Admin. office Hospital Community center School Pool Gym Library Hotel Museum Welfare facility Athletic field Research center University Skate link Other<br><!-- End of picture text --> The Japanese ESCO projects are mainly motivated for two objectives: (i) to reduce energy spending, and (ii) to contribute to reducing emissions (Figure 9). Some projects were partially motivated to support industrial policies, such as local job creation. These policy objectives are, by and large, reflected by the weighted evaluation criteria.<sup>28</sup> More weights are put on energy savings and technical and reliability aspects. The most popular criterion is about concreteness and reliability of technologies proposed, followed by the reliability of O&M, monitoring and verification, and the 15-year total revenue criterion.<sup>29</sup> ESCOs propose different technologies, depending on the evaluation criteria. Major technologies used are energy-efficient lighting and air conditioning systems. The total investment amounts to about JPY250 million on average (Table 9). Investment is financed directly by a facility owner (i.e., municipality) if the contract follows the guaranteed savings

> 28 See Annex Table for the list of criteria used in the Japanese ESCO market.

> 29 Despite the large number of criteria used, the weights are remarkably similar to each other. On average each criterion has a weight of 6.5 to 8 out of 100. This means that procurers’ preferences may not be represented by the attached weights but by the selection of criteria. For each project, the evaluation formula involves about 1215 criteria.
where Ѳ _i_ ,1 , Ѳ _i_ ,2 , and Ѳ _i_ ,3 represent the long-run dynamics of the demand function, such that: and λ*, δ*, φ*, and γ* capture the short-run relationship, where: Specifically, Ѳ _i_ ,1 and Ѳ _i_ ,2 are the long-term elasticities of demand with respect to a rise in per capita income, whereas Ѳ _i_ ,3 is the long-run elasticity of demand with respect to real price.

ρi = −(1 −∑pk=1 λij,k) denotes the speed of adjustment towards the long-term equilibrium relationship.

From equation (2), income elasticities for each commodity and country are calculated by taking the partial derivative of the long-run estimation with respect to income, as follows: The model is applied to three energy commodities (crude oil, coal, and natural gas) and three metal commodities (aluminum, copper, and zinc), which together make up 85 percent of energy and base metals consumption respectively. Annual data from 1965-2016 for 33 countries were used in the analysis. The model is also applied to four food commodities (rice, wheat, maize, and soybean) which, together, account for 70 percent of arable land. Due to data limitations, a different dataset was chosen for food commodities, with predominantly EMDE representation and fewer advanced economies (Table 3).

Data on per capita income (expressed in real 2005 terms) were obtained from the World Bank’s World Development Indicators; commodity consumption was taken from the BP Statistical Review (energy), United States Department of Agriculture (food), and World Bureau of Metal Statistics (metals); world commodity prices were taken from the World Bank’s _Pink Sheet_ , and were converted into real terms by using country-specific GDP deflators. Exchange rates were taken from the St. Louis Federal Reserve Bank’s FRED database.

8
and health (contraceptive acceptance, antenatal care, deliveries by skilled attendants, 2008/092011/12) from woreda-level sectoral administrative data; and information on ethnicity, the frequency of droughts, and other control variables. In addition, regional data on capital expenditures and zonal data on crop yields have been linked to woreda data.

An important caveat is that our horizontal equity analysis is based on woreda-level administrative data. The capacity of regional governments to collect this data varies. Some regions that are more adept at collecting it have more reliable data than others. But discrepancies – which do exist – between administrative data and more reliable household survey data affect means and not trends over time. The Ethiopia Demographic Health Survey (DHS), and other internationally accepted surveys, show trends over time similar to those shown by administrative data, albeit with lower means. Hence the direction of our findings, which uses both DHS survey data and administrative data, are reliable.<sup>10</sup> Another limitation of administrative data is its scope. There is no information at the local level for the years before decentralization to woredas, limiting the time scale we can analyze.

By contrast, our vertical equity analysis focuses on households and individuals, and so uses available DHS data, from 2000, 2005, 2011 and 2014. The 2000-2011 surveys are from the DHS program, and the 2014 survey is a mini-DHS implemented by the Central Statistical Agency. These are far more reliable, representative data, generated according to internationallyrecognized household survey standards.

One reason an analysis of this sort has not been undertaken for Ethiopia until now is the absence of woreda-level data on local economic, demographic, fiscal and other characteristics. Indeed, it is difficult to overstate the difficulty of doing subnational empirical work on Ethiopia. When we began this project, relatively little subnational data was collected, the data was often of poor quality, and few attempts were made to systematize the results into any obviously

> 10 Questions have been raised about the quality of administrative data in Ethiopia, and discrepancies do exist between data published by the independent Central Statistical Agency (CSA) and line ministries. Some discrepancies can be explained by the way line ministries collect data through establishment reporting; also, in the case of the Ministry of Health, the use of cluster surveys rather than household surveys using more representative census-based samples.  Various initiatives have addressed such discrepancies.  Recently, an inter-ministerial committee charged the CSA to carry out Data Quality Assessments (DQAs), funded by external donors, to assess data quality and make recommendations for improvement. Administrative data has seemed to improve since.

14
Given the above, measures of project performance, project characteristics, and country characteristics are defined as follows:

# **_Measures of project performance:_**

Two measures are used.

- Pj = Success of Project Outcome (PO), is rated 1 if the project is rated moderately satisfactory or higher, and 0 otherwise, available from 1985 and onwards.

- Sj= Success of Project Outcome (PO), is rated on a scale from 1 (highly unsatisfactory) to 6 (highly satisfactory), available from 1995 and onwards.

# **_Measures of project characteristics_**

- Xj = Project characteristics (sector, size of commitment, preparation and supervision costs each as a share of total commitment; project duration as defined by years from appraisal to completion), whether the project was ever flagged as having a problem in the first half of its life, whether a project was flagged as having a potential problem in the first half of its life, whether the project was restructured in the first half of its life, a dummy variable that is assigned one if there was an in‐depth evaluation by IEG, etc.).

- Tj = Is a time trend variable based on either the year the project was approved (for regressions that include the dummy variable for whether an ERR is reported in the PAD or in both the PAD and ICR); or the year in which the project exited/completed disbursements (for regressions that include the dummy variable for whether an ERR is reported in the ICR only).

- OBj=Is a proxy for optimism bias represented by a dummy variable which is 1 if the ERR at appraisal is greater than the ERR estimated at completion and 0 otherwise.  This is entered as an independent variable for regressions which include the discrepancy between ERR at appraisal and completion.

# **_Country or macro level characteristics_**

- Cj = Country or macro level variables (GDP per capita growth and CPIA,<sup>1</sup> each averaged over the life of the project, dummy for the region in which the project is located, dummy for the period before 1998 when a change occurred in the CPIA rating scale, dummy for sector in which the project is located, interaction terms between the sector dummy and the approval year dummy, and the project evaluation year dummy, respectively.

# **_Team leader characteristics_**

- Lj = captures task team leader (TTL) effects, defined as the average ratings for project outcomes over all previous projects that the current TTL managed.

Given the above variable definitions, three key questions are examined:

- (i) Is there a continued decline in the frequency of project economic analysis (EA)?

> 1 We also tried to use World Wide Government Indicators such as government effectiveness, corruption, regulatory quality, which are, however, highly correlated with each other; these did not seem to explain the probability of the project success/failure. Therefore, they are not included in the reported regressions.  GDP per capita is averaged over the years covering project appraisal up to the project exit year.  CPI is used to explain the discrepancy between ERR at appraisal and project completion, which is also measured as the average for the project period up to the project exit.

7