rafmacalaba/datause-encoder-data huggingface dataset:
services employment (described next) even without a registered manufacturing base (and vice versa). In some analyses below, we focus in greater detail on the growth of specific sectors, with additional restrictions in sample size.

Even with these expansions, the current sample is smaller than the total number of districts in India of 630. Nevertheless, the sample accounts for almost all of the employment in the manufacturing sector throughout the period of study. The reductions from the 630 baseline occur due to requirements that manufacturing or services employment be observed in every period for the district (i.e., we have balanced panels of districts from 2000 to 2010). Even with these requirements, some districts have a small number of observations, and this could be worrisome given that our data do not constitute a complete census of Indian businesses and have state-level survey stratification. We consider several checks below (e.g., excluding smaller districts) to verify that the results discussed are robust to these considerations.

# _Indian Services Data_

We also consider the services sector in this study. In the services sector, there is no legal distinction between organized and unorganized establishments as in manufacturing. Service establishments, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. The NSS surveys for the services sector are thus comprehensive. Establishments are again surveyed with state and four-digit NIC stratification. The surveys provide sample weights that we use to construct population-level estimates of services employment by district in 2000, 2005, and 2010. We consider 16 two-digit industries within the services sector for most of our empirical work, similar to manufacturing. Additional data development steps are comparable to those described for the manufacturing sector. It is worth noting that the Indian services sector has a relatively broad definition, such that the NIC classification structure contains industries that in other settings would be considered part of retail trade (e.g., restaurants) or transportation.

# _Specialization and Diversity Indices_

We follow Duranton and Puga (2000) and Ghani, Kerr and Tewari (2013) in the indices that we use to measure specialization and diversity for Indian districts. Before providing the 7
This paper provides the first rigorous assessment of the longer-term impacts of a poor-area program. The program is the World Bank’s Southwest China Poverty Reduction Project — the Southwest Program (SWP) for short. This comprised a package of multi-sectoral interventions targeted to poor villages using community-based participant and activity selection. The aim was to achieve a large and sustainable reduction in poverty. The paper reports results from an intensive survey data collection effort over 10 years, initiated by two of the authors and done in close collaboration with the Rural Survey Organization of China’s National Bureau of Statistics.

Assessing development aid effectiveness at the project level raises a number of challenges. A long-term commitment to collecting high-quality survey data is crucial, but it is not sufficient. Impact can only be meaningfully assessed relative to a counterfactual; our counterfactual is the absence of the SWP, which means that we assess the incremental impacts, on top of pre-existing governmental spending. As in any observational study, there are concerns about selection bias, i.e., differences in counterfactual outcomes between SWP participants and non-participants. Our data collection effort allows us to “difference out” the time-invariant component of the selection bias (arising from non-random placement). However, it is not obvious on _a priori_ grounds that the bias would be constant over time, given that the initial village characteristics that attract the program (such as poor infrastructure) may also influence the growth rate under the counterfactual. We use both propensity-score weighted regression and kernel-matching methods to balance the observable covariates between sampled SWP and non-SWP villages.

A further problem is that aid-financed poor-area development projects are likely to violate the common assumption in impact evaluations (both experimental and nonexperimental) of no interference with the comparison units.<sup>4</sup> A plausible source of interference in this setting is through local public-spending spillover effects to non-SWP villages. The local government cuts its own development spending in the villages targeted for external aid, and the spending is diverted in part at least to the non-

> 4 This assumption is often implicit in impact evaluations but it was made explicit by Rubin (1980), who dubbed it the stable unit treatment value assumption (SUTVA). SUTVA is known to be implausible in certain bio-medical evaluations.

3
# **Box 7.  Impact of increased climate variability on livestock assets of pastoralists**

Pastoralists live in regions where the impacts of climate change are likely to be large (Thornton et al., 2006), including the Sahelian rangelands, southern Africa, and parts of East Africa. These are some of the most vulnerable livestock keepers on the planet. They rely on livestock as their primary form of living. Livestock provide a number of benefits to pastoral families in the form of milk, meat, hides, manure and others. At the same time they represent a considerable asset that can be traded or sold in hard times or for purposes such as paying school fees or providing a dowry (Nkedianye et al., 2009). The impact of drought on herd performance and asset values has been widely documented. In large areas of Africa, highly variable climate with frequent droughts can decimate herds and displace pastoralists. Drought frequencies of one in four or five years are not uncommon (Orindi et al., 2007). Emergency services and humanitarian relief efforts are often needed to support pastoralist families during considerable parts of the year in these regions.

We ran a herd dynamics model to investigate the potential impacts of increased climate variability, represented here as increased drought frequencies, on herd dynamics and livestock numbers. We used baseline information on mortality, reproduction and herd structures from pastoralist herds in Kajiado, Kenya. The model was run over 20 years assuming a herd baseline size of 200 animals, of which 60 where adult females. We ran two scenarios: a baseline scenario simulating realistic climate variability of one drought every five years and an alternative scenario of increased frequency of droughts -- one year in three. Such increases in climate variability may be anticipated as a result of global warming (IPCC, 2007), although details are far from clear. In years of drought, animal mortality rates increase and reproductive performance of adult females declines, potentially resulting in lower numbers of offspring and a declining herd size.

Results indicate that a drought once every five years (i.e., representative of current conditions) keeps herd sizes stable (Figure B7-1), and this has in fact been observed in Kajiado for a long time (Rutten, 1992). At the same time, the district has seen substantial increases in human population, meaning that the proportion of the population that can thrive in a pastoral setting has plummeted, because animal numbers per adult equivalent are simply not sufficiently high to support pastoralism. This might reflect that the ecosystem simply cannot support more animals (except at the possible expense of wildlife, with other income-related effects).

58
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|Figure 3.4.Trade flows in EAPP in 2015 (TWh)|42|
|---|---|
|Figure 3.5.Net imports as a share of domestic demand (percentage)|43|
|Figure 3.6. Power generation mix|44|
|Figure 4.1.East Africa‘s ICT backbone infrastructure|47|
|Figure 4.2.Proposed fiber optic connectivity in East Africa|53|
|Figure 5.1.Spending for regional infrastructure as a share of GDP|59|
|Figure 5.2.Spending for regional infrastructure as a percentage of national infrastructure spending|59|

# List of tables

|Table 1.2.Progress and challenges for regional integration in East Africa|6|
|---|---|
|<br>Table 2.1.Relative performance of transport corridors in Africa|10|
|Table 2.2.Road condition along major transit corridors in East Africa|14|
|Table 2.3.Condition of East Africa‘s regional road network by member country, based on 2008 data|18|
|Table 2.4.Performance of East African railways, 2005|21|
|Table 2.5. Growth in containerized and general cargo traffic between 1995 and 2005|22|
|Table 2.6. Comparative port performance across African regions|24|
|Table 2.7. Comparative performance across East African ports|24|
|Table 2.8.Benchmarking air transport in EAC and other regional economic communities|28|
|Table 2.9.International and domestic connectivity for 2007 (number of city pairs served)|29|
|Table 2.10.All flights from one week in November 2007 for EAC and East Africa|30|
|<br>Table 2.11. Proportion of direct flights and speed of air service in East Africa|31|
|Table 2.12. Grading of the level of the implementation of the Yamoussoukro Decision|31|
|Table 2.13. Changes in air transport market in East Africa, 2001–07|32|
|Table 2.14. Air cargo freight rates|34|
|Table 3.1. Benchmarking power infrastructure and capacity, access, and utility performance|37|
|Table 3.2.Demand and suppressed demand in the East Africa Power Pool–Nile Basin countries|38|
|Table 3.3. Annualized costs of system expansion in EAPP, 2015|39|
|<br>Table 3.4.Additional infrastructure requirements for trade expansion (MW)|39|
|<br>Table 3.5. Differences in electricity production and CO2 emissions in trade expansion and stagnation|44|
|<br>Table 3.6.Long-run marginal costs of power in EAPP|45|
|Table 3.7. Rate of return to power trade at country level|46|
|Table 4.1.Benchmarking ICT infrastructure across Africa‘s regional communities|48|
|Table 4.2.Benchmarking ICT in EAC member states|49|
|Table 4.3.GSM roaming in East Africa|49|
|Table 4.4.Foreign investors in the East Africa‘s telecom sector|51|
|Table 4.5.Prices of Internet and phone calls in Sub-Saharan Africa, with and without access to submarine cables|53|
|<br>Table 4.6.ICT pricing trends for EAC countries, 2005–10 (US$)|54|
|<br>Table 4.7.Gaps in intraregional connectivity and total investment required to attain minimum levels of regional connectivity|55|
|Table 4.8.Intercontinental and intraregional spending needs for East Africa for 10 years|55|
|Table 4.9. Summary of cost-benefit analysis of regional integration|56|
|Table 5.1.Annual regional spending requirements by sector over 10 years|58|

iv
# intervention are not different by gender).

Our evidence, however, suggests that, at least in the context of property taxes in Argentina, men and women seem to be equally compliant. One potential explanation is that the enforcement of property taxes in our context is weak. If the main reasons women are more compliant are beliefs about enforcement and penalties, and in our setting these are overwhelmingly low across genders, any compliance disparities might be muted. If, on the other hand, disparities are driven by higher tax morale among women, we would have expected larger gender differentials. On this last point, we note that the recent experimental study by Guerra and Harrington, 2018 finds that individual self-reported tax morale cannot predict actual evasion choices. Therefore, our results should encourage more investigation into whether gender differentials in tax compliance are widely observed in the field, as opposed to in lab experiments or self-reported surveys.

15
Gan, Jie, 2007, The Real Effects of Asset Market Bublles: Loan- and Firm-Level Evidence of a Lending Channel, _Review of Financial Studies_ 20, 1941-73.

- Gertler, Mark, and Simon Gilchrist, 1994, Monetary Policy, Business Cycles and the Behavior of Small Manufacturing Firms, _Quarter Journal of Economics_ 109, 309-340.

- Gross, David, B. and Nicholas,S., Souleles, 2002, An Empirical Analysis of Personal Bankruptcy and Delinquency, _Review of Financial Studies_ 15, 319-347.

- Holder, Roland and Paul, Raschky, 2014, Regional Favortitism, _Quaterly journal of economics_ 129:9951033.

- Houston, Joel, Liangliang Jiang, Chen Lin and Yue Ma, 2014, Political Connections and the Cost of Bank Loans, _Journal of Accounting and Economics_ 52(1): 193-243.

- Kasara, Kimuli, 2007, Tax Me if You Can: Ethnic Geography, Democracy, and the Taxation of Agriculture in Africa, _American Political Science Review_ 101, 159-172.

- Keys, Benjamin J., Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, 2010, Did Securitization Lead to Lax Screening? Evidence from Subprime Loans, _Quaterly Journal of Economics_ 125(1), 307-362.

- Khwaja, Asim Ijaz and Mian, Atif, 2005, Do Lenders Favor Politically Connected Firms? Rent Provision In an Emerging Financial Market, _Quaterly Journal of Economics_ 1371-1411.

- ---, 2008, Tracing the Impact of Bank Liquidity Shock. _American Economic Review_ 98,1413-1442.

Kikeri, Sunita, John Nellis, and Mary Shirley, 1994, Privatization: the Lessons from the Market Economies. _The World Bank Research Observer_ 9,241-271.

- Kostovetsky, Leonard, 2015, Political capital and moral hazard, _Journal of Financial Economics_ 116, 144159.

- Kraay, Aart, and Peter Murrell, 2015, Misunderestimating Corruption, _Review of Economics and Statistics_ forthcoming.

- La Porta, Rafael, and Florencio López-de-Silanes, 2003, The Benefits of Privatization: Evidence from Mexico, _Quarterly Journal of Economics_ 114(4), 1193-1242.

- La Porta, Rafael, Florencio López-de-Silanes, and Guillermo Zamarripa, 2003, Related Lending, _Quarterly Journal of Economics_ 118, 231-268.

Martin, Kelly, John, Cullen, Jean Johnson, and Praveen Parboteeah, 2007, Deciding to Bribe: a Cross-Level Analysis of Firm and Home Country Influences on Bribery Activity, _Academy of Management Journal,_ 50(6),1401-1422 _._

- McMillan, John, and Pablo Zoido, 2004, How to Subvert Democracy: Montesinos in Peru, _Journal of Economic Perspectives,_ 18(4), 69-92 _._

28
Table 1 OLS Estimates of the Determinants of Dispersion in Firm Sizes

|Equation (15):<br>C4i�|<sup>4</sup><br>Ni<br>=b0|(1�REN|i)<sup>b1</sup>(1�|DEPi)<sup>b2</sup>|RES<sup>b3</sup><br>i <sup>eui</sup>|
|---|---|---|---|---|---|
|Parameter Estimates|b0|b1|b2|b3||
|1982 cross-section|56.83<sup>���</sup>|6.89<sup>���</sup>|7.53<sup>���</sup>|-.09<sup>���</sup>|R<sup>2</sup>=:25|
||(1.83)|(1.32)|(1.50)|(.02)|�R<sup>2</sup>=:25|
|1987 cross-section|25.28<sup>���</sup>|5.03<sup>���</sup>|4.37<sup>���</sup>|-.29<sup>���</sup>|R<sup>2</sup>=:30|
||(1.02)|(1.10)|(1.57)|(.03)|�R<sup>2</sup>=:29|
|1992 cross-section|21.76<sup>���</sup>|3.83<sup>���</sup>|2.93<sup>�</sup>|-.32<sup>���</sup>|R<sup>2</sup>=:28|
||(1.06)|(.82)|(1.58)|(.03)|�R<sup>2</sup>=:27|
|Equation (18):<br>Vi(s)|=c0(1�|RENi)<sup>c1</sup>|(1�DE|Pi)<sup>c2</sup>RES|<sup>c3</sup><br>i <sup>e�i</sup>|
|Parameter Estimates|c0|c1|c2|c3||
|1982 cross-section|1.17<sup>���</sup>|11.74<sup>���</sup>|17.19<sup>���</sup>|-.24<sup>���</sup>|R<sup>2</sup>=:23|
||(.22)|(2.84)|(3.56)|(.06)|�R<sup>2</sup>=:22|
|1987 cross-section|3.00<sup>���</sup>|11.61<sup>���</sup>|10.67<sup>���</sup>|-.72<sup>���</sup>|R<sup>2</sup>=:31|
||(.87)|(2.63)|(3.75)|(.07)|�R<sup>2</sup>=:30|
|1992 cross-section|1.93<sup>�</sup>|8.55<sup>���</sup>|(6.10)<sup>�</sup>|-.79<sup>���</sup>|R<sup>2</sup>=:29|
||(1.02)|(1.87)|(3.66)|(.08)|�R<sup>2</sup>=:28|

Number of Observations = 400 in 1982, 430 in 1987, 429 in 1992 Standard Errors in Parentheses;<sup>���</sup> p < :01;<sup>��</sup> p < :05;<sup>�</sup> p < :1 The signi...cance of the e¤ect of contestability in tightening the size distribution of ...rms is also con...rmed by the analysis of a more accurate measure of dispersion that is derived from (4). The estimates from equation (18) indicate that the factors that determine the sunkenness of capital are also important determinants of ...rm size dispersion, as measured by the variance of the market shares of the largest 50 ...rms in each industry. We ...nd again that the lower is the sunkenness of the capital that is employed in an industry, the smaller is the dispersion and hence, the tighter is the distribution of ...rms around the optimum. The parametric restrictions that are implied by the model are con...rmed by these data. Indeed, the maintained hypothesis H02 : c1 = c2 is not rejected at the 5 percent level in all three temporal cross-sections.<sup>12</sup> Table 2 presents estimates of (15) and (18) using the pooled data from the 1982, 1987, and 1992 censuses. Both OLS and the asymptotically more e¢ cient

> 12 For the 1982 cross-section, F = :07 < F :195;396<sup>=3:87,andtheprobabalitylevelatwhicha</sup> standard F-test rejects the restriction is .79; for 1987, F = :03 < F1<sup>:95</sup> ;426<sup>= 3:86;andtheprobabality</sup> level at which a standard F-test rejects the restriction is .87; for 1992, F = :25 < F1<sup>:95</sup> ;425<sup>=3:86,</sup> and the probabality level at which a standard F-test rejects the restriction is .62.

14
**Pollcy, Research, and External Affairs**

**World Development Report**

**WPS 665**

This papera product of the Trade Policy Division, Countrv Economics Department -was prepared as a background paper for the _1991 World Development Report._ Copies are availablc free from the World Banik, 18XI 11 Street NW, Washington, DC 20433. Please contact the Worid Development Report office, room S13-060, cxtenision 31393 (36 pages).

In Ihc past decade the developing counitries have tried much harder to achieve macroeconomic stability than they have to eliminate inefficiencies from microeconomic distortions.

L6pez has pursu a relatively new line of in-quiry in cxamining measurement of the social income losses induced by thc reduction of the investment efficicncy caused by tradc distortions.

Empirical findings of the study suggest a strong negativc cffect ol trade distortions on the social efficiency of investment. Even a moderate, uniform tariff of 50 percent could cause a reduction in the efficiency of investment of up to 23 percent compared with a 0 percent tariff.

The (social) income losses caused by the reduced investment efficiency are considerable. Countries that havc a moderate investment ratio (about 20 percent of GDP) can experience social income losses in excess of 18 percent in 30 years if tariffls are about 50 percent.

The existence of labor market distortions causing unemployment may increasc the social valuc of capital. Capital accumulation moves **tliC** economy closer to the production possibility frontier by increasing employment.

This study confirms earlier findings about the relativelyn modest efficiency losses caused by the indcpcidenit effects of specific distortionis. L6pez also found, however, a significant synergistic cfiect when trade and wage distortions coexist and lead to larger efficiency losses.

The key issue is the combination of price distortions favoring capital-intensivc activity with wage distortions that cauise unemploymcnt and underemployment. This patteni of distortion is pervasive in developing countries.

The PRE Working Paper Series disseniinates **diec** findings of **work** under **wsay** in **the** Bank's l'olicy, Researcil, and External AlffairsComplex. Anobjective of tile scries is to get **thesc** findings **out quickly,** even ifprescntations arc **less** than fullv polished. The Findings, interprctations, and conclusions in thcse papers do not necessarily represent official Bank policy.

Produced by **thc** PRE Dissemination Center
Table 5: Correlates of men’s training choices in ICT and energy

||C|hose the IC|T or the e|nergy voca|tional trai|ning|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|**Sociodemographic characteristics**|||||||
|Age of the respondent (z-score)|-0.011|-0.038**|-0.037**|-0.037**|-0.038**|-0.036**|
||(0.013)|(0.009)|(0.010)|(0.011)|(0.011)|(0.011)|
|Number of dependent children (z-score)|-0.006|-0.000|0.000|-0.002|-0.002|-0.002|
||(0.012)|(0.012)|(0.013)|(0.012)|(0.013)|(0.013)|
|Household wealth index by cohort (z-score)|0.031|0.015|0.013|0.010|0.010|0.009|
||(0.020)|(0.015)|(0.015)|(0.015)|(0.016)|(0.017)|
|**Education and training**|||||||
|Years of education (z-score)||0.148*|0.147*|0.147*|0.147*|0.147*|
|||(0.013)|(0.013)|(0.013)|(0.012)|(0.013)|
|Already received training in EICT in the past||0.063|0.057|0.057|0.060|0.060|
|||(0.031)|(0.030)|(0.029)|(0.032)|(0.032)|
|**Employment and revenues**|||||||
|Had a paid work in the last 30d|||-0.036|-0.037|-0.034|-0.033|
||||(0.022)|(0.024)|(0.023)|(0.023)|
|Worked in EICT in the last 30d|||0.038|0.043|0.042|0.041|
||||(0.033)|(0.034)|(0.032)|(0.032)|
|**Network**|||||||
|Network size (z-score)||||0.031**|0.029**|0.029**|
|||||(0.011)|(0.011)|(0.011)|
|Proportion of males in the network (z-score)||||-0.035**|-0.033**|-0.033**|
|||||(0.017)|(0.016)|(0.016)|
|**Role model and support**|||||||
|Male role model|||||-0.031|-0.030|
Specifically, we regress the log value of employment and/or of the specialization index of locality _m_ and year _t_ on the log of the market access (ma) or of the market potential (mp), so that our coefficients estimates can be interpreted as elasticities. Standard errors are clustered at the locality level to adjust for heteroskedasticity and within-locality correlation over time. The regression sample is a balanced panel of 2,094 localities with employment data for the years 1986, 1994, 1999, 2004, 2009, and 2014. The specialization and concentration indices are calculated at the establishment and locality levels. Since the latter micro-geographic data only span the 2004-2014 period, we will therefore restrict our analysis on specialization to this time period.

In addition to the ’market access’ specification, we also estimate an ’infrastructure model’ where we substitute market access with our measure of efficient roads within a fixed radius surrounding a locality’s centroid. In that model, we also incluse a measure of access to international markets (measured by the minimum time or travel cost to a port or port of entry into the u.s.).<sup>15</sup> Table 5 presents the summary statistics for the key variables of our analysis and the overall, between and within standard deviations.<sup>16</sup>

# **.2 4 Identification issues**

The panel structure of the data allows us to control for location-specific time-invariant factors and general macroeconomic trends. However, we need to account for the three following problems: (i) omitted variables bias, (ii) the non-random road placement of roads, and (iii) the structural recursion problem with the use of market access regressors.

**Omitted variables bias** . In our framework, the problem of omitted variables is mitigated by the panel structure of the data. We include locality fixed effects in the panel estimation, which absorb all time-invariant local characteristics such as initial wealth. Therefore, the estimated effects of market access and market potential cannot be attributed to time-invariant locality differences. This is an advantage over a cross-sectional framework, where unobservable location characteristics that explain outcomes could be correlated both with surrounding population or income and transport infrastructure that enter into the calculation of MA and MP indicators.

Despite the use of panel data and the different controls included, our model is still potentially subject to problems of simultaneity bias or reverse causality. In this respect, the two main empirical challenges with our model specification are the non-random placement of roads

> 15It is not desirable to introduce our measures of access to external markets in the market access specification because of their potentially high correlation with our market access or market potential indicators.

> 16The latter decomposition confirms the presence of time variation in our data (although most of the variation remains cross-sectional).

17