rafmacalaba/datause-encoder-data huggingface dataset:
# 4. Data sources

As explained in the introduction, the paper relies on three data sets to explore the link between backyarding and job access: a count of backyard dwellings per parcel drawn from satellite data, job data at the level of transportation zones, and origin-destination trip-time matrices. The sources of these data sets are described below.

The satellite data, provided by GeoTerraImage (Pty) Ltd. is contained in the Building Based Land Use spatial data set, which provides a land-use classification per building. The data are captured from digital ortho-corrected aerial photography and/or high-resolution orthorectified satellite images. It differentiates between 17 classes of residential structures, including formal residential, informal residential and backyard structures. We overlaid the aerial GTI data on a map containing individual parcel contours from the City of Cape Town’s cadastre records, thus generating a count of backyard structures per parcel for 2014.<sup>12</sup> Employment at the transportation-zone level for 2013 is estimated as part of the City of Cape Town’s Land Use Model. The land-use model estimates the number of jobs by applying workplace density assumptions to the internal floor space of various types of non-residential buildings, as measured by the city’s Valuation Department in its non-residential valuation processes. The preliminary results per transport zone are reconciled with citywide job numbers (by occupation) as published in the Statistics South Africa Labour Force Survey.

The origin-destination matrix for commute-trip times is an output of the City of Cape Town’s four-step travel demand model, known as the EMME model. These four steps are (1) trip generation, (2) trip distribution, (3) mode choice and (4) route assignment. EMME was designed by INRO Consultants at the University of Montreal and adopted by the City of Cape Town in 1991. The model implements an equilibrium route assignment based on the distribution of trip origins and destinations in relation to the transport network and modal choice. On this basis, it estimates travel volumes, average trip distances and travel times between each transport zone, for each mode of transport, for the morning peak. The model is calibrated by means of General Household Transport Surveys, on-board surveys and cordon counts.

14
Alesina, Alberto and Paola Giuliano, 2010, The power of the family. Journal of Economic Growth, 15(2):93–125.

Associated Press, Feb 18<sup>th</sup> , 2008, ―Some Pakistani Women Warded Off Voting‖.

Banerjee, Abhijit V., Selvan Kumar, Rohini Pande and Felix Su, 2010, ―Do Informed Voters Make Better Choices? Experimental Evidence from Urban India‖, mimeo.

Bari, Farzana, 2005, ―Women‘s Political Participation: Issues and Challenges‖, prepared for the Expert Group Meeting on Enhancing Participation of Women in Development through an Enabling Environment for Achieving Gender Equality and the Advancement of Women, United Nations.

Beaman, Lori, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, and Petia Topalova, 2009, ―Powerful Women: Does Exposure Reduce Prejudice?", _Quarterly Journal of Economics_ Bratton, Michael, 2008, ―Voting Buying and Violence in Nigerian Election Campaigns.‖ Afrobarometer Working Paper No. 99.

Bruhn, Miriam, and David McKenzie. 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments." _American Economic Journal: Applied Economics_ , 1(4): 200–232.

Cardy, Emily A, 2005, ―An Experimental Field Study of the GOTV and Persuasion Effects of Partisan Direct Mail and Phone Calls.‖ _Annals of the American Academy of Political and Social Science_ 601: 28–40.

Chattopadhyay, Raghabendra, and Esther Duflo, 2004, ―Women as Policy Makers: Evidence from a Randomized Policy Experiment in India," _Econometrica_ , 72(5): 1409-1443.

Cialdini, Robert B., and Noah J. Goldstein, 2004, ―Social Influence: Compliance and Conformity.‖ _Annual Review of Psychology_ 55: 591–621.

CLRAE (Council of Europe‘s Parliamentary Assembly and Congress of Local and Regional Authorities of Europe), 2002, _Women’s individual voting rights: a democratic requirement_ . Council of Europe Publishing.

Collier, Paul and Pedro C. Vincente, 2008, ―Votes and Violence: Evidence from a Field Experiment in Nigeria.‖ University of Oxford, Working Paper.

Croson, Rachel and Uri Gneezy, 2009, Gender Differences in Preferences, _Journal of Economic Literature_ , Volume 47, Number 2, Pages 448-474.

Downs, Anthony. 1957. _An Economic Theory of Democracy_ . New York: Harper and Row.

25
Policy ReseaRch WoRking PaPeR 4644

# **Abstract**

This paper uses student answers to publicly released questions from an international testing agency together with statistical methods from Item Response Theory to place secondary students from two Indian states— Orissa and Rajasthan—on a worldwide distribution of mathematics achievement. These two states fall below 43 of the 51 countries for which data exist. The bottom 5 percent of children rank higher than the bottom 5 percent in only three countries—South Africa, Ghana and Saudi Arabia. But not all students test poorly. Inequality in the test-score distribution for both states is next only to South Africa in the worldwide ranking exercise. Consequently, and to the extent that these two states can represent India, the two statements ``for every ten top performers in the United States there are four in India'' and ``for every ten low performers in the United States there are two hundred in India'' are both consistent with the data. The combination of India's size and large variance in achievement give both the perceptions that India is shining even as Bharat, the vernacular for India, is drowning. Comparable estimates of inequalities in learning are the building blocks for substantive research on the correlates of earnings inequality in India and other low-income countries; the methods proposed here allow for independent testing exercises to build up such data by linking scores to internationally comparable tests.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to measure and understand inequality in the provision of education. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jdas1@worldbank.org.

_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
Table 4: Heterogeneous treatment effects by mother-in-law opposition to family planning at baseline

||**Sought c**|**ompany to v**|**isit the ADC from:**||**Visited t**|**he ADC with:**||
|---|---|---|---|---|---|---|---|
||Someone|Husband/<br>MIL|Non-husband/<br>MIL|**Visited**<br>**the ADC**|Husband/<br>MIL|Non-husband/<br>MIL|**Visited**<br>**any clinic**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Own Voucher|0.185***|0.051*|0.092***|0.220***|0.030|0.064**|0.180***|
||[0.040]|[0.027]|[0.031]|[0.038]|[0.026]|[0.032]|[0.052]|
|BAF Voucher|0.342***|0.037*|0.263***|0.151***|0.016|0.050*|0.083|
||[0.043]|[0.022]|[0.042]|[0.034]|[0.025]|[0.029]|[0.051]|
|Own Voucher _×_ MIL opposed to FP|-0.163**|-0.062*|-0.058|-0.123|0.093|-0.041|-0.061|
||[0.071]|[0.035]|[0.063]|[0.088]|[0.071]|[0.041]|[0.133]|
|BAF Voucher _×_ MIL opposed to FP|0.105|0.013|0.134|0.163|0.138|0.161**|0.229*|
||[0.119]|[0.045]|[0.108]|[0.102]|[0.090]|[0.080]|[0.123]|
|Observations|600|583|583|603|513|513|601|
|Endline control mean|0.034|0.016|0.022|0.015|0.018|0.022|0.193|
|p-values:||||||||
|Own = BAF|0.004|0.659|0.000|0.152|0.663|0.723|0.111|
|Own _×_ MIL opposed = BAF _×_ MIL opposed|0.041|0.163|0.105|0.019|0.668|0.014|0.080|
|Own + Own _×_ MIL opposed = 0|0.705|0.622|0.543|0.220|0.061|0.366|0.332|
|BAF + BAF _×_ MIL opposed = 0|0.000|0.203|0.000|0.001|0.074|0.005|0.006|

_Notes:_ All columns are fully-interacted regressions, where all covariates are interacted with MIL opposed to FP. The main effect of MIL opposition to FP at baseline is also included as a control variable. All columns include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-inlaw, FP denotes family planning, and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
**Table 1: Summary Statistics for Household- and Individual-Level Outcome Variables** **<u>by Covid-19 Status</u>**

||||||Standard|P-<br>|
|---|---|---|---|---|---|---|
||Full|Control|Treatment|Difference|error|value|
|**Panel A Household Welfare Outcomes:**|||||||
|log expenditure per capita|13.23|13.26|13.21|-0.05***|0.01|0.00|
|**Asset Type**|||||||
|Phone|0.88|0.91|0.86|-0.05***|0.00|0.00|
|TV|0.58|0.59|0.57|-0.01*|0.01|0.09|
|Motorcycle|0.02|0.03|0.02|0.00**|0.00|0.05|
|Car and/or truck|0.05|0.06|0.05|-0.02***|0.00|0.00|
|Bicycle|0.01|0.01|0.01|-0.01***|0.00|0.00|
|Radio|0.20|0.22|0.18|-0.04***|0.01|0.00|
|Furniture|0.95|0.96|0.94|-0.02***|0.00|0.00|
|Small appliances|0.36|0.41|0.32|-0.08***|0.01|0.00|
|Large appliances|0.36|0.37|0.35|-0.02***|0.01|0.00|
|Total number of different assets owned|6.76|7.04|6.54|-0.50***|0.05|0.00|
|Total number of assets owned (count)|11.79|12.26|11.44|-0.83***|0.09|0.00|
|Current value of all assets owned|469160|546326|416422|-129904***|11160.84|0.00|
|Log of value of assets|12.18|12.29|12.09|-0.20***|0.02|0.00|
|**Livestock Ownership**|||||||
|<br>has livestock|0.28|0.31|0.27|-0.04***|0.01|0.00|
|total number of different livestock|0.39|0.43|0.36|-0.06***|0.01|0.00|
|total number of livestock in herd owned by household<br>**Household Poverty Status**|1.80|1.72|1.88|0.16|0.17|0.35|
|Objective Poverty: Poor|0.45|0.42|0.47|0.05***|0.01|0.00|
|Objective Poverty: Multidimensional poverty index|0.39|0.38|0.39|0.01***|0.00|0.00|
|Subjective Poverty: I live well|0.24|0.27|0.23|-0.04***|0.01|0.00|
|Subjective Poverty: I live poorly|0.31|0.30|0.32|0.02***|0.01|0.00|
|Subjective Poverty: Rich social rank|0.32|0.34|0.31|-0.03***|0.01|0.00|
|**Panel B: Labor Market Outcomes**|||||||
|**Household Outcome:**|||||||
|Share of working individuals in household|0.30|0.31|0.29|-0.02***|0.00|0.00|
|**Individual Outcomes:**|||||||
|Daily hours worked|7.66|7.56|7.72|0.16**|0.08|0.03|
|Employed|0.49|0.51|0.47|-0.04***|0.01|0.00|
|Unemployed|0.05|0.05|0.04|-0.01|0.00|0.18|
|Discouraged worker|0.10|0.09|0.11|0.01**|0.01|0.04|
|<br>Formally employed|0.22|0.21|0.23|0.02**|0.01|0.02|
|<br>Works in agriculture sector|0.34|0.31|0.37|0.06***|0.01|0.00|
|<br>Works in industry sector|0.13|0.13|0.12|-0.01|0.01|0.33|
|Works in trade sector|0.05|0.06|0.05|-0.02***|0.01|0.00|
|Works in services sector|0.48|0.49|0.46|-0.03***|0.01|0.01|
|Log salary|11.08|11.06|11.09|0.03|0.04|0.43|
|**Sample size**|**29,192**|**17,480**|**11,712**||||

Note: “Difference” captures the raw difference between the post-Covid sample (treatment) and the pre-Covid sample (control). Statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1 Figure 2 shows the mean distribution of selected outcomes pre- and post-Covid-19 lockdown month. Per capita expenditure shows an immediate reduction in April, which is a month after the Covid-19 lockdown, with a slight recovery in the second month (May) but still below the January 2020 average (two months pre-Covid-19 lockdown).

15
subsidies, debt repayment moratoria, or encouraging loan restructuring.<sup>6</sup> This category also includes all prudential measures seeking to support and encourage the use of the flexibility embedded in global prudential standards (e.g. the use of capital and liquidity buffers,<sup>7</sup> the treatment of restructured loans, the treatment of non-performing exposures) while setting supervisory expectations about the use of such flexibility, for example by introducing payout restrictions (to ensure that released buffers are used to maintain the flow of credit) and transparency requirements. This category also includes measures aimed at bringing prudent flexibility to financial integrity requirements to help address COVID-19 related challenges (e.g., supporting digital onboarding, simplified due diligence). This category also includes crisis management measures, but few have been taken thus far (mainly introducing or modifying resolution tools and deposit guarantee funds so that they are fit for purpose in case of need).

# _Measures that focus on financial markets and non-bank financial institutions (NBFIs)_

Several countries banned short selling to curtail market volatility and some even decided to temporarily close their financial markets when circuit breakers were triggered. Market authorities also issued prudential and conduct measures to ensure the proper functioning of financial markets amid the crisis and to give guidance and support to market players other than banks such as asset managers and insurance companies (NBFIs). This category also includes public debt management actions, although few have been taken so far. However, strains in capital markets may prompt policy makers to adjust their debt management strategies including identifying funding from other sources to reduce pressure on traditional wholesale market borrowing.

# _Measures that focus on payments and financial market infrastructures_

Several countries took measures to ensure the smooth functioning of market infrastructures, notably the payment systems, including the relaxation of non-essential compliance requirements. Most importantly, financial authorities ensured the availability and acceptance of cash and digital payment methods. Among

> 6 See, for example, EBA (2020) for an overview of debt moratoria and public guarantee schemes in the European banking sector.

> 7 The estimates by Lewrick et al. (2020) suggest that the release of capital buffers (countercyclical capital buffers and other supervisory and management buffers) could unlock about US$5 trillion of additional loans, or 6% of total outstanding loans. In addition, restrictions on dividends can improve the effectiveness of the countercyclical capital buffer release as well as ensure provision of credit to firms and households (Muñoz, 2020; Beck et al., 2020).

7
C20a_providentfund2 Did your employer offer a Provident Fund in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C20b_gratuity2 Did your employer offer gratuity (lump sum) in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C20c_pension2 Did your employer offer a pension in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C21a_actualtermination2 Were you terminated from this previous MAIN job?

1. Yes

2. No à **Go to C21d** 888. Don’t know à **Go to C21d** 999. Refused à **Go to C21d** C21b_actualtermlength2 How many days of notice were you given?

**[If don’t know then put “Don’t Know”] [If refused then put “Refused”]**

C21c_actualtermpay2 How many days of pay did you receive on termination?

**[If don’t know then put “Don’t Know”] [If refused then put “Refused”]**

> à **Go to C23**

C21d_termination2 If you had been terminated from this previous MAIN job, would you have been given notice?

1. Yes

2. No à **Go to C23** 888. Don’t know à **Go to C23** 999. Refused à **Go to C23** C21e_termlength2 How many days of notice would you have been given?

**[If don’t know then put “Don’t Know”]** **<u>[If refused then put “Refused”]</u>**

6
regressions (Table A2 in Annex 1), panel C shows the results of the statistical cleaning by smoothing reported imputed rents over time, and panel D applies cleaning to 2005 only cleaning the initial position. Transition matrices “purged” of the measurement error and actual observed transition show remarkably consistent patterns. Everywhere the poor experience a lot of upward mobility with less than half of the lower decile staying in this position in three years, while the middle of the distribution is remarkably instable. The growth for each percentile with different cleaning assumption can be summarized also as a table (Table 6).

**Table 6 Change in welfare over 2005-08 (percent): actual and cleaning measurement error**

||change|**2005 a**|**ctual**||change|2005**m**|**odeled**||change|2005**mo**|**deled**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Deciles|**actual**|Se|95 confi|dence|**actual**|se|95 conf|idence|**modeled**|se|95 conf|idence|
|**1**|||||||||||||
|**poorest**|+33%|0.0084|+31%|+34%|+18%|0.0116|+16%|+21%|+27%|0.0060|+26%|+29%|
|**2**|+33%|0.0115|+31%|+36%|+20%|0.0152|+17%|+23%|+23%|0.0067|+22%|+24%|
|**3**|+34%|0.0141|+31%|+37%|+17%|0.0142|+14%|+19%|+21%|0.0071|+19%|+22%|
|**4**|+20%|0.0113|+18%|+22%|+16%|0.0160|+13%|+19%|+18%|0.0080|+17%|+20%|
|**5**|+16%|0.0119|+13%|+18%|+13%|0.0186|+9%|+16%|+13%|0.0081|+11%|+15%|
|**6**|+17%|0.0178|+13%|+20%|+20%|0.0243|+15%|+25%|+17%|0.0107|+15%|+19%|
|**7**|+7%|0.0177|+4%|+11%|+10%|0.0227|+5%|+14%|+16%|0.0113|+14%|+18%|
|**8**|+11%|0.0216|+6%|+15%|+13%|0.0323|+6%|+19%|+13%|0.0138|+10%|+16%|
|**9**|+9%|0.0319|+3%|+16%|+16%|0.0361|+9%|+23%|+12%|0.0164|+9%|+15%|
|**10**|||||||||||||
|**richest**|-30%|0.0764|-45%|-15%|+10%|0.0657|-2%|+23%|-3%|0.0274|-8%|+3%|

_Source: own estimates based on HIECPS 2005 and 2008_ Thus, the measurement error is not an explanation behind the observed pattern of pro-poor in the panel data. Indeed, replacing actual values with predicted on Figure 3, we would observe very similar shape to the observed.<sup>17</sup> We conclude from this, that panel data in Egypt convincingly show that the growth over 2005-2008 was pro-poor. But they also reveal a lot of mobility not only for the poor, but also for other parts of the distribution. The cross-section (i.e. if we look only at anonymous changes) might in fact hide tremendous amount of re-ranking that is taking place between the comparisons point and reveals only a tiny part of the actual turmoil associated with economic growth in the environment of high inflation.

> 17 Results available on request.

18
program’s effects provided that explicit scoring thresholds are used, but the number of firms close enough to the scoring threshold to enable such an approach is likely to be small in most cases.

Randomization is also likely to be a more cost-effective option than other quasi-experimental methods. Propensity score matching, for instance, would require firstly developing a sampling frame of businesses (which in itself is likely to be a challenge in many African countries) and then collecting enough information from a large enough sample to be able to select the most appropriate matching group of businesses to serve as a counterfactual – increasing the costs, time and logistics required to prepare a baseline survey. In contrast, a randomized experiment would not require a sampling frame (since all participating businesses in the study would come from the actual applicant pool) and baseline data for all of these businesses could potentially be collected cheaply through an application form that captures key outcome variables and indicators on which the randomization is likely to stratify on.

# **3.3 What are the Key Initial Issues to Consider for an Impact Evaluation of these Projects?**

As outlined in McKenzie (2010), there are a number of typical challenges in conducting an impact evaluation of policy operating at a firm level. First, the number of SMEs in many African countries is relatively small. In our group of matching grant impact evaluations, the DTI’s BBSDP project – a matching grant for black-owned SMEs<sup>7</sup> in South Africa - is a clear example of this challenge. The government was interested in supporting firms of a certain size because it already had a number of incentives for micro enterprises and wanted to shift focus from survivalists to established firms with potential for growth. In that vein, when preparing the project, the government had selected a minimum annual turnover of Rand 1 million (approximately USD 125,000) for firms to be eligible to apply. However, based on a set of representative firm-level surveys in South Africa, it seemed that less than 3 percent of the population of firms would qualify to participate in the program. This led to adjustments in the program, which culminated in a revised version of the project with a minimum annual turnover of Rand 250,000, increasing the chances of finding eligible enterprises.

Second, there is considerable heterogeneity in firm characteristics and performance, which affects the statistical power to detect the impacts of the programs and thus the sample size requirements. This is an important consideration, especially when one of the objectives of the study is to measure impact heterogeneity to understand for which type of firms the matching grant is most

> 7 The eligibility criteria imposed among other aspects the need for 51% of the ownership to be black-owned (African, Indian or Colored under the South African definition).

14