rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 1 provides summary statistics from the quantitative data, focusing on the heads of households. Of the individuals in our sample, 87 percent were born outside Delhi. Education levels are relatively low: 4.5 years of schooling on average. More than half of respondents note that they knew a person from their home village in the city prior to migrating; on the other hand, only 12 percent knew a close relative. A remarkable percentage of respondents (54%) are from scheduled castes or tribes—historically underprivileged groups with preferential access to government jobs and education. A likely reason for this is that Delhi, as India’s capital, provides many such opportunities.

Respondents naturally had greater interaction with leaders from within the slum. 83 percent claimed to “know and regularly interact” with their pradhan, and 52 percent to know and interact with community or religious leaders. Yet, a striking finding lies in the extent to which governance networks extend beyond the slum’s boundaries. A remarkable 30 percent had interacted with elected politicians; in fact, one in four claimed to have interacted with a member of parliament. We were surprised by these figures at first and conducted further interviews to identify why this is the case. These suggested that Delhi slums are the source of “garrisons” for politicians who use them for boosting attendance at rallies. Politicians also pay careful attention to slum dwellers so they can be assured source of votes. The possibility of pure exaggeration seems unlikely given the contrastingly low percentages that report an acquaintance with bureaucrats (10%) and NGO staff (8%). These figures instead appear to be consistent with the limited incentives NGO staff and bureaucrats face to interact with the poor.

One concern with our identification strategy is that the first stage regression can only be estimated on the sub-sample that migrated to Delhi within the coverage of the climate data. Thus Table 1 also compares whether the migrant sub-sample is different in their observed 21
conservation”’ for conservation behavior change campaigns. _Applied Environmental Education & Communication_ , _21_ (1), 3–6.

**Data Availability:** All anonymized data are publicly available (Cisse et al., 2024).

**Code Availability:** All replication codes are publicly available (Cisse et al., 2024).

**Acknowledgments:** We thank Alexandra Jarotschkin, Megan Lang, John Loeser, James Sallee, and Juliet Wright for helpful comments, and Maximilian Auffhammer for Institutional Review Board assistance. Funding for the experiments provided by the Development Research Group of the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank

40
# **Annex V: Stamp Tax Revenue Data Across States**

Andhra Pradesh Figure 1a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 1b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Andhra Pradesh<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Year<br>Tax Revenue Stamp Tax Rate<br>Figure 2a: Annual Percentage Change<br>in Stamp Duty Revenues and Rates<br>Delhi<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>Year<br>Tax Rev Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>% change annually<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Andhra Pradesh<br>90000 10.1%<br>80000 10.0%<br>70000 9.9%<br>60000 9.8%<br>50000 9.7%<br>40000 9.6%<br>30000 9.5%<br>20000 9.4%<br>10000 9.3%<br>0 9.2%<br>Year<br>Tax Revenue Stamp Rate<br>Figure 2b: Stamp Duty Revenues<br>and Rates<br>Delhi<br>45000 14.0%<br>40000 12.0%<br>35000<br>10.0%<br>30000<br>25000 8.0%<br>20000 6.0%
In Cambodia and Ethiopia as well, there were no missing observations on valuation for financial accounts, mobile phones and durables. Aggregating missing data will lead to zeros within specific asset classes and could lead to an underreport of individuals’ total wealth.

(2) Among joint owners of the same asset (for example, the same land parcel), there will be some variation in asset valuation. If some individuals or groups tend to systematically underreport their asset valuation relative to others, their overall wealth will be lower even among the same set of assets owned.

Regarding the first source of measurement error, missing data are not uncommon in household surveys. We used the presence of missing data as an opportunity to understand how alternate approaches to treating this issue could lead to overall differences in findings on individual wealth. Specifically, we compared individual wealth when leaving missing values as is (and hence treated as zeroes when aggregating values across asset categories), with a multiple imputation model (Rubin, 1987). Multiple imputation is commonly used to address issues related to missing data or the need to replace sensitive data in large-scale surveys (see Reiter, 2012 for a discussion) and Kilic et al. (2017) also rely on multiple imputation to derive predicted GPS-based area measures for up to 1/3 of agricultural plots that are not visited for GPS-based area measurement in the context of LSMS-ISA-supported national surveys in Tanzania and Uganda.

The multiple imputation approach uses information on observed values of variables that are predictive of the missing values — for example, individual, household, enumeration-area and interviewer variables — and conducts a predictive mean matching model based on repeated imputations. In our analysis, following the approach used by Kilic et al. (2017), on the individual-level data, the imputation is based on running the 17
Table B.4: Probability of being a Monopsonist, Probit Estimate

|VARIABLES|Monopsony|
|---|---|
|_ωit_|-0.208***|
||(0.0771)|
|Small Size Firm|1.206***<br>(0.138)|
|Medium Size Firm|0.493***<br>(0.106)|
|Skill Ratio|-1.017***<br>(0.307)|
|Foreign Ownership|-0.361***<br>(0.107)|
|Unionisation of Workers|-0.0274<br>(0.113)|
|Firm Average Years of Education|-0.0170<br>(0.0172)|
|Number of Apprentices|0.138***<br>(0.0392)|
|Location: Kumasi<sup>_⋆_</sup>|-0.136*|
||(0.0798)|
|Location: Takoradi|-0.158|
||(0.147)|
|Location: Cape Coast|-0.357*|
||(0.204)|
|Time|0.101*|
||(0.0574)|
|Time Squared|-0.00570<br>(0.00409)|
|Constant|2.965***<br>(1.016)|
|Observations|1,531|
|Pseudo _R_<sup>2</sup>|0.2143|
|Log Pseudolikelihood|-817.288|
|Sector Dummies|Yes|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for
# **_Contexts, efforts and performances_**

The first thing is to assess the overall context. Of course, usual data on the business environment, the education level, the information infrastructure is important and generally available. More detailed information on innovation systems, as collected within the WBI KAM, is also interesting, although there is a lack of such data for a number of less developed countries.

_Box 8. Variables for innovation systems(WBI Knowledge Assessment Methodology – 120 countries included in the Data Base)._ Gross Foreign Direct Investment as % of GDP Royalty and license fees payments / mil. pop. Royalty and license fees receipts / mil. pop. Researchers in R&D Total expenditure for R&D as % of GNP University-company research collaboration Scientific and technical journal articles Admin. Burden for Start-Ups Patent applications granted by the USPTO High-Tech exports as % of manuf. Exports Royalty and license fees payments ($ mil) Royalty and license fees receipts ($ mil) Science & engineering enrolment ratio (% of tertiary level students) Researchers in R&D / mil. pop. Manuf. Trade as % of GDP Entrepreneurship among Managers Scientific and technical journal articles / mil. Pop Availability of Venture capital Patent applications granted by the USPTO / mil pop. Private sector spending on R&D With regard to innovation performances, it is important to identify appropriate proxy variables. For medium income countries, data relating to the evolution of the patenting activity (including deposits in national regimes), levels of business RD, the capacity of retention of educated workforces, and the rates of the creation and growth of new firms, are relevant.

For low income countries, some of those data are losing their relevance. In particular, the lower the development of a country, the less interesting and relevant is the data on research and development. It would be more interesting today to capture data on know-how and, notably, on local indigenous knowledge in the developing countries; however, this would require new types
can negatively affect investment and productivity of land use (Dillon and Voena 2018) and women’s bargaining power.

The cost of establishing and maintaining formal tenure systems is another factor. With first-time registration costs of US$30 and US$60 per title in traditional land projects (Brits _et al._ 2002) and up to US$1,000 per title in some cases (Jacoby and Minten 2007), it is easy to argue that costs well above any imaginable benefits undermine the economic rationale for titling. Advances in technology such as aerial photography and earth observation drastically reduced costs of surveying. If used together with participatory ways of demarcation,<sup>6</sup> these can drastically reduce the cost of tenure regularization, as in Rwanda where 11.5 million parcels were demarcated and registered at US$5 to US$6 per parcel (Nkurunziza 2015). Cloud computing and interoperability, e.g., with personal ID or building permit systems, can increase benefits from land rights documentation for public policy. Increased levels of cell phone penetration even in Africa allow notification of co-owners and updating of records in new, sustainable ways. Experiences also highlight the scope for

> 4 With credit-market imperfections, unrestricted transferability may trigger distress sales or myopic transactions with negative social impacts.

> 5 This is in addition to women being more likely to suffer from conflict (Deininger and Castagnini 2006; Joireman 2008), especially inheritance-related ones (Chapoto _et al._ 2011).

> 6 A system that relies on textual records only (documenting neighbors’ names rather than boundary coordinates) implemented in Ethiopia from 2005 achieved a cost of US$ 1 per title for first-time registration (Deininger _et al._ 2008). Its positive impact, including on gender (Melesse and Bulte 2015; Muchomba 2017) suggests that documenting social consent on boundaries is more important than high-precision maps.

7
fact that pollution abatement costs are a relatively small fraction of overall costs in most industries. The evidence suggests that factors such as capital abundance, labor abundance, location, institutions, and agglomeration effects are more important than environmental policy in determining firm location choice and

> 8 They looked at 14 pollutants tracked by the World Bank's Industrial Pollution Projection system (Hettige et al., 1995).  These include several air pollutants (such as SO2 and particulates), water pollutants (BOD and suspended solids) and toxic releases.

14
_nges betwee_<br>_ervations ar_<br>_c Lorenz cur_|70.40<br>_n benchm_<br>_e weighte_<br>_ve._|‐9.91<br>_ark years ar_<br>_d using pop_|60.49<br>_e in perce_<br>_ulation. U_|1.68<br>_ntage points_<br>_sing dataset_|62.17<br>_. For all o_<br>_of 100 pe_|‐10.14<br>_ther rows, changes_<br>_rcentile groups_|

New PPP exchange rates become available periodically, reflecting new collections of international price data. Recently, the World Bank adopted the 2011 PPP exchange rates for its measurement of global poverty. Ferreira et al. (2015) provide a detailed explanation of how the World Bank adopted these new PPP exchange rates, and in particular how the international poverty line was updated. The changes for the global extreme poverty headcount have been small. For example, in 2011, the global poverty rate falls from 14.5% to 14.1% when moving from the 2005 PPPs (with a poverty line of $1.25) to the 2011 PPPs (with a poverty line of $1.90).

According to Table A1, the revision of the PPP exchange rates only had a small effect on the level of regional inequality. But it could potentially lead to a re-ranking of countries, even though these effects cancel out in the aggregate. With the GE(0) inequality measure, African inequality consists of three components (see the formula in footnote 32): (1) a country’s population size; (2) its level of inequality; and (3) its position in the African distribution captured by the country mean. Because of a substantial change in relative price levels (Ferreira et al., 2015), the change to 2011 PPPs 25
economic contraction affects child survival is shorter than an annual frequency.<sup>23</sup> The empirical estimate of the mortality semi-elasticity with respect to GDP downturns will be mapped in the model to the increased mortality from lower consumption. However, the estimated coefficient may capture other mechanisms, such as lower availability or utilization of health services. Unfortunately, there is insufficient health system capacity time series data to isolate this effect empirically. The available health capacity data would be _de facto_ absorbed by the country fixed effects in estimation. Going further, we tested for heterogeneous semi-elasticities when stratifying by hospital capacity and found no statistically significant differences in the semi-elasticity estimates across countries with different levels of hospital capacity.

Further we believe it unlikely that the health capacity channel is an important one for the magnitude of the semi-elasticity estimate. This is because the semi-elasticity is estimated on past (pre-pandemic) data on “regular” economic downturns, rather than those brough on by pandemics. In a regular economic downturn, there is less reason to believe that the recession in and of itself leads the health system capacity to be overrun. To the extent that lower utilization of health services is responsible for part of the child mortality response in regular recessions, it is likely due to lower demand for health services by the households suffering negative income shocks, rather than sharp reductions in availability.

# **3.4 Demographic and Economic Parameters**

Country-specific age distributions are obtained from the 2020 World Population Prospects.<sup>24</sup> The age distribution is used to compute _s_ 0 _−_ 5, which is then used to rescale the semi-elasticity of under-5 mortality to the age group 0 - 15 using the formula in the previous section. In addition, we use the age distribution of the three age groups to compute the masses of the different age groups within the household ( _l_<sup>1</sup> _, l_<sup>2</sup> _, l_<sup>3<
line is set to Br 4,360 (in 2016 prices), which corresponded to the 40<sup>th</sup> percentile of consumption expenditure distribution in 2016.<sup>13</sup> Households that earn less than Br 4,360 per adult equivalent per year (in 2016 prices) are considered as poor households in each wave.

According to the recent three waves of the ESS, household consumption has been dropping in many regions. Among the untreated group of communities, the average per adult-equivalent annual consumption (expressed in 2016 prices) has steadily decreased from 6,347 birr in 2012 to 6,030 birr in 2014 and 5,383 birr in 2016 (Table 3). In tandem, the share of population in poverty, or poverty rates have been rising in those communities, reaching 43.7 percent in 2016. By contrast, the mean consumption level and poverty rates have been relatively stable in treated communities during the period.

This study employs an objective indicator of drought exposure. The 2015/16 drought is thought to be one of Ethiopia’s worst droughts in the past five decades. About 10.2 million people in Ethiopia needed assistance. The drought was a result of rain failure during both _Meher_ (the main harvest) and _Belg_ (spring harvest) seasons in 2015. In order to identify communities that were affected by the recent droughts, an objective measure is prepared.<sup>14</sup> The normalized difference vegetation index (NDVI) is a commonly used measure of vegetation, and its deviation in the period of the droughts in 2015/16 from the long-term average score—which is calculated as the z-score— indicates the effect of the drought for agricultural production: where X��� indicates the monthly value of NDVI in community _j_ in year _y_ ; X��� is the mean value over each month for the years 2000 to 2016; and SD�X��� is the standard deviation over the
# **Appendices**

In this Appendix I provide details on the sample construction and some additional tables and graphs.

Table A1 list the Census’ samples obtained from IPUMS-International and the size of the extract.

Table A2 reports sample size from raw data to samples restricted by age and by availability of information on education.

Table A3 reports the rates of co-residency by country for different ages.

Table A4 reports the rates of co-residency by country-sample for different ages.

Figure A1 displays rates of coresidence by urban/rural population and by gender.

Figure A2 displays a comparison of estimates of upward mobility for the same countrycohort with all children versus coresident children. The source of these estimates is Munoz and Siravegna (2021).

Figure A3 displays the educational attainment transition matrix for individuals aged 14-25 years.

Figure A4 displays the educational attainment transition matrix for individuals aged 14-25 years in selected countries.

Table A5 summarizes the education level by cohort using data on individuals at least 25 years old.

Table A6 reports district-level estimates of intergenerational mobility.

Figure A5 and A6 displays maps of mobility at the district-level for LAC.

Figure A7 and A8 displays maps of mobility at the district-level for LAC using secondary education.

Table A7, A8, and A9 report estimates of IGM that consider secondary education. Table A10, A11, and A12 report estimates of IGM that consider the likelihood of completing secondary education when parents completed less than primary.

33
for people with completed primary education. The lower likelihood of HE ownership for people with completed secondary education and above is consistent with secondary education being rare, and therefore providing opportunities in relatively high paying non-farm wage and salary jobs – often a more desirable alternative. Size of NFE is positively associated education, with self-employed HE owners without family help being the least educated, HE owners with family help being more educated and ME owners the most educated, almost as well educated as public sector wage workers (figure 12). And as shown below, education does increase earnings for HE owners. So while primary education may not be necessary to start a HE or ME, judging but the sorting among occupations be education, it seems to help.

Why do owners start HEs? Obviously, to make money. But are they “pushed” or “pulled” into the sector? Consistent with divergent views of informal enterprises over time, many analysts have discussed whether HEs are the „reserve‟ sector, where people end up because they cannot find other opportunities (a view associated with the “exclusion” school of thought) or a dynamic sector, which people enter to as a positive choice, to exploit an opportunity and/or to have the independence that self-employment brings - a view associated with the “new” view of informality (Maloney, 2004). Household surveys in two of our study countries (Tanzania and R. Congo) asked HE owners to report their main reason for starting a business and found that push factors dominated the list. Not being able to find a wage and salary job was the most frequently cited reason, but it was cited by less than 40 percent of respondents (multiple responses were permitted). The need for income came in a close second, and was the most common in rural areas. The most commonly cited pull factor in R. Congo was the desire for independence, while in Tanzania the opportunity to make a profit was the most common response (this responses was not included
health care provider or were hospitalized during 2013/14 is estimated based on the survey. The unit cost is determined by dividing total health spending by the total number of individuals. This assumes more-equitable distribution among health service users than happens in reality; health service users do not receive the same levels of benefits depending on the nature of the service received, but it is not possible to distinguish health spending for different types of services in the budget data. The calculation of the unit costs of education and health is refined here by estimating specific unit costs according to region.

Health and education spending are globally progressive in Mali and Niger. In-kind transfers in health and education are overall progressive in the two countries (figure 6). Their progressivity is 10
Policy Research Working Paper 9477

# **Abstract**

Many governments in developing countries have pursued policies targeting specific geographic areas over the past several decades. However, only a few have rigorously evaluated the causal impact of these interventions. This paper examines the effectiveness of a prominent place-based policy in India: the centrally sponsored New Industrial Policy for the state of Uttarakhand. Using georeferenced economic census data, the analysis applies a boundary discontinuity research design and zones in on the unique border between Uttarakhand and Uttar Pradesh, two states that were officially one before the implementation of the New Industrial Policy. The findings show that there was a significant and abrupt increase in employment at the town and village level when crossing the state border from Uttar Pradesh to Uttarakhand after the full implementation of the New Industrial Policy. The conclusion even holds for firms within the same sector. The increase is mainly due to larger firm sizes and expansions into new industries. A main component of the New Industrial Policy was excise tax incentives for certain industries. The paper finds that the increase in cross-border employment is higher for sectors receiving excise tax incentives than others. Additionally, exploring spillovers between industries, the paper shows that, controlling for the direct effects, the sectors with labor requirements similar to those receiving excise tax incentives also experience an increase in employment. Finally, the growth in the number of firms in Uttar Pradesh close to the border remained stable before and after the New Industrial Policy, which suggests the results are not fully driven by firms relocating from Uttar Pradesh to Uttarakhand.

This paper is a product of the Poverty and Equity Global Practice and the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at yli7@worldbank.org and ssinharoy@worldbank