rafmacalaba/data-use-mentions-extended huggingface dataset:
The IMF is currently revising its Balance of Payments Manual. The new Manual is expected to help countries address their problems in measuring workers’ remittances. Moreover, at the time this paper is being written, an international group led by the IMF, World Bank and United Nations is reviewing the existing definitions of workers’ remittances across countries and preparing specific recommendations to improve mechanisms to collect remittance data.<sup>8</sup>

> 8 More information on the work of this International Task Force is available at: www.worldbank.org/data/remittances.htm.

14
of cash income to subsistence farmers. With respect to inputs, we would expect to find more evidence of decay in labor usage as opposed to fertilizer use, as labor can be used sporadically throughout the growing season while fertilizer purchase and application are generally singular events. We would also expect to find less evidence of recall decay in relation to less common events, such as labor use in places where outside labor is uncommon (such as Rwanda). Finally, when both are collected in a single visit, we expect to find more evidence of decay related to input rather than harvest data, as the recall period is several months longer.<sup>2</sup>

> 1 There is a large literature on reporting errors in consumption, and, to a lesser extent, in income surveys, primarily from the United States and other developed countries. Reviews of some of the evidence can be found in Gibson (2006), Deaton and Grosh (2000), and Scott and Amenuvegbe (1991).

> 2 In the data used here, farmers self-report production and input use. An alternative to self-reported production (which is subject to recall bias among other reporting problems) is to measure production through crop-cutting procedures conducted by field staff. Studies comparing crop-cut estimates to “wholeplot” harvests in Africa suggest that crop-cutting itself is not free from error and can result in large over-

6
HFPS. In Burkina Faso, Ethiopia, Malawi, and Nigeria response rates are lower (sometimes substantially) for rural and agricultural households, suggesting potential for nonresponse bias in these segments. However, as for the case of coverage bias, there are post-survey adjustments (described in Section 3.3) that can be implemented to at least partially correct for nonresponse bias.

_Figure 3: HFPS Response Rates_ <!-- Start of picture text --> Overall Rural Urban Agricultural Non-agricultural<br>BURK I NA F A S O E T H IOP IA MA LA W I NIGE RIA UGA NDA<br>93 94 92 94 92<br>83 81 83<br>79 76 74 73 73 76 78 72<br>70<br>67 65<br>60 63 59 62<br>52<br>50<br><!-- End of picture text --> _Source: Authors’ calculation based on HFPS data._

# _Sampling methods for phone surveys_

When considering the sampling approach to adopt for a survey, there are substantial trade-offs to each in terms of representativeness and methods available for correcting bias. Among the three common approaches (i.e., recontact, RDD, or telecom list-based), recontact surveys that use phone numbers from previous in-person, representative surveys may be considered the preferred sampling method and has also been the most commonly used approach in low- and middle-income settings (Ceballos et al., 2020; Dabalen et al., 2016; Glazerman et al., 2020; Himelein et al., 2020). The main advantage of this approach is that there exists a well-defined (often representative) sample selected for a previous in-person survey and for which there is a profile of characteristics available for the sample units (i.e., households or individuals). This permits a direct assessment of the representativeness of the selected and successfully interviewed phone survey sample and facilitates post-survey adjustments to reduce selection biases (Ambel et al
(2000), _Dominican Republic_ (1997), _Rwanda_ (1998), _Senegal_ (1995), and _Viet Nam_ (1989). The classification of fragility and the date of exit are based broadly on the CPIA index. Appendix 1 summarizes the experience of each of these countries and the circumstances of their exit from fragility.

# **The selection of countries was based on four criteria** :

- _Sustainable exit_ from fragility, i.e., the countries continued to be classified as non-fragile for at least ten years after exit.

- Broad _geographical representation_ .

- _Diversity_ of fragility experience, i.e., the sample covers cases where fragility emerged from a combination of different circumstances, such as an economic crisis, breakdown of the political system, violent conflict, etc.

- _Complexity_ of fragility experience, i.e., fragility did not emerge _solely_ as the result of war, while beforehand the state was otherwise stable and effective.

The purpose of these criteria is to ensure that any lessons learned would have the broadest possible applicability. The rationale for the last criterion, in particular, is to exclude cases (e.g., Cyprus in the 1970s or Lebanon in the 1980s) with functioning states and financial systems that descended into fragility only because of war, displacement, or the arrival of UN peacekeepers. In cases like this, financial reforms and policies would play little or no role in the exit from fragility, and there would be no lessons to be drawn for financial sector policy makers elsewhere.

**Data and information for the financial sector policies and reforms around the time of exit from fragility are sourced from World Bank and IMF documents for these countries** . Financial sector reforms are often undertaken with support of the World Bank and the IMF. Even when they do not form a major part of their respective adjustment programs, these country documents provide fairly detailed information about these reforms and policies. The main source documents for each country are listed at the end of
31

# _5.3 Protected areas_

A protected area is a protected geographical space to achieve long-term conservation of nature with the associated ecosystem services and cultural values (UNEP-WCMC, 2016). Urban growth over protected areas implies the loss of ecosystem services and cultural values and it jeopardizes the urban sustainability in the long term. We quantified urban growth over protected areas using the World Database on Protected Areas (WDPA) produced by the United Nations Environment Programme (UNEP-WCMC, 2016), which is the most comprehensive global database of marine and terrestrial protected areas. We considered all types of protected areas included in the WDPA and calculated the intersection with the new urban areas in 2000 and 2010 using geoprocessing tools in ArcGIS. Figure 14 shows the country average of percent urban growth on protected areas. Most countries in the LAC region show relatively low values (up to 6%). Bolivia has the highest values in both periods, both above 50%, which indicates a negative trend in terms of long-term sustainability. Venezuelan urban extents also show high values as well, up to 18%. According to UN (2014), it is expected that Bolivian cities will increase the urban population by 79% between 2014 and 2050; with the second largest annual rate of change in South America: 0.6%. This makes the detected trend an important issue to address in the near future.
very small industries: NIC 61 (Inland, sea and coastal water transport), 73 (Research on natural sciences, engineering, social sciences and humanities), and 90 (Sanitation services). We also drop NIC industries 37 (Recycling) and 50–52 (Wholesale and retail trade, repair of motor vehicles & household goods) due to inconsistent coverage across survey years. The latter exclusions substantially reduce the included establishment counts.

In our policy analyses, we also exclude plants from small and conflict states. These include the Andaman and Nicobar Islands, Dadra & Nagar Haveli, Daman & Diu, Jammu & Kashmir, Tripura, Manipur, Meghalaya, Mizoram, Nagaland and Assam. In some analyses, we drop observations for plants with negative value added, missing raw materials value and missing values of fixed assets. Lastly, we also drop any observation in the services sector surveyed from the states of Arunachal Pradesh, Lakshadweep, Mizoram and Sikkim because they are not covered by the organized manufacturing survey, with which we seek to compare trends. The latter step impacts less than 1% of our sample. We are left with about 352,000 observations corresponding to the year 2001, 186,000 from 2006, and 109,000 in the year 2010. These correspond to 421 districts in each year. In the regression analysis at the district level, we additionally drop a small number of observations that lack district identifiers.

# **IV. Empirical Strategy**

16
1985–2015 at 30x30m resolution was complemented with a layer on built area in 2019 at 10 m resolution made available by the WSFE team.<sup>8</sup> In the absence of time series 3D data on built volume, we also use this variable as a proxy for land-related investment in durable structures by overlaying it with the registered parcel layer to provide an estimate of built area on registered and nonregistered parcels for each block. Population data for 2000–19 is from the Center for International Earth Science Information Network (CIESIN) gridded population of the world (version 4) data set at a 1 km spatial and 5-year temporal resolution. It is used similarly throughout to generate annual values of block population density to serve as a control.

# **3.2 Descriptive Statistics**

Table 1 summarizes transaction-level variables for the 39-year period (1981–2019) for which data is available (column 1) and the 1981–2009 pre-reform period split into pre-and post-2000 (columns 2 and 3);

> 6 Figures and tables whose number is preceded by an A are all in the Appendix.

> 7 Parcels that cross block boundaries are assigned to the block containing the parcel centroid.

> 8 Annual time series data from 1985 to 2015 is available from <u><mark>https://doi.org/10.6084/m9.figshare.10048412.v1;</mark> data at 10m resolution for 2019</u> was kindly made available by M. Marconcini and missing annual values (2016–18) interpolated using a cubic spline. Results in Tables 3 and 4 are based on pre-2015 values and thus robust. Differences in resolution and the methodology used to compute built area between the 1985–2015 series and the 2019 values reduce the consistency of the time series so that results in table 5 should be considered only illustrative.

9
0.386|0.394|2.078|2.107|
|S.E. clustered|Municipality|Municipality|Municipality|Municipality|
|Controls|Yes|Yes|Yes|Yes|
|Block fixed-effects|Yes|Yes|Yes|Yes|
|R2 (Post)|0.132|0.138|0.166|0.167|
|R2 (ANCOVA)|0.170|0.170|0.297|0.297|
|N (Post)|1,024|952|1,019|946|
|N (ANCOVA)|940|940|931|931|

_Note:_ ***p < .01, **p < .05, *p < .1. Profits are in XOF, millions (XOF 1 million = US$1,820), and turnover is in log. Profit and turnover have been trimmed at the 99th percentile. The lower number of observations compared to Table 3 is due to missing values for profits and turnover. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned, and dummies for whether
mobile phone, fridge, electric stove|3 or 4|Grid connection|

# _3.2 Contingent valuation design_

In all three data sets, respondents were asked about their monthly WTP for one of

> 8 According to World Bank data, GDP per capita in 2013 was 1,072 USD in Senegal, 684 USD in Burkina Faso, and 633 USD in Rwanda. Based on purchasing power parity (PPP), the 2013 figures become 2,269 international $ for Senegal, 1,634 $ for Burkina Faso, and 1,452 $ for Rwanda.

17
# **References:**

Acemoglu, Daron, James A. Robinson, Simon Johnson (2001). "The Colonial Origins of Comparative Development: An Empirical Investigation American Economic Review, 91, pp. 1369-1401.

Arrow, Kenneth J. Partha Dasgupta, Lawrence H. Goulder, Kevin Mumford and Kirsten Oleson (2010) "China, the U.S., and Sustainability: Perspectives Based on Comprehensive Wealth" in _Is Economic Growth Sustainable?_ , G. Heal, ed. International Economics Association conference Volume 148, London: Palgrave Macmillan, pp. 92-144.

- Barro, R. J.  and Lee J. W. (2001) "International Data on Educational Attainment: Updates and Implications" _Oxford Economic Papers_ , 53 (3): 541-63.

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- Chong, Alberto and Calderon, Cesar. (2000). “Causality and Feedback between Institutional Measures and Economic Growth.” _Economics and Politics_ , 2000, 12(1), pp. 69-81.

- Cohen, Daniel and Marcelo Soto (2007) "Growth and human capital: good data, good results" _Journal of Economic Growth_ ", 12:51–76

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Fisher, I. (1906). _The Nature of Capital and Income._ London: Macmillan.

- Gundlach, E., Rudman, D. and Woessmann, L. (2002) "Second Thoughts on Development Accounting" _Applied Economics_ , 34, 11, 1359–1369.

- Hall, R.E., and C.I. Jones, (1999), “Why do some countries produce so much more output per worker than others
(0.067)|
|Controls||✓|✓||✓|✓|
|R-squared|0.002|0.011|0.012|0.004|0.001|0.002|
|P-value equal coef.|0.614|0.588|0.281|0.211|0.192|0.069|
|Observations|619|619|619|619|619|619|
|Wald F-stat|||117.0|||117.0|
|Mean Y (Control)|0.246|0.246|0.246|0.082|0.082|0.082|

Notes: Heteroscedasticity-robust standard errors in parentheses. Controls are described in the notes to Table 2. Independent variables Play Drama and Play Documentary take value 1 if the respondent has played half or more of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *** p < 0.01

# **4.5 Treatment effect heterogeneity**

Annex Tables A17 to A30 present heterogeneous effects across a series of social status indicators, where we hypothesized that effects would be smaller for groups with lower social status (World-Bank 2014). We find no evidence of heterogeneous impacts by individuals’ age, caste, educational achievement or membership in a social group in the short-term. However, both treatment arms generally were less effective for women and for individuals whose household-heads were on average more educated. Heterogeneous effects across gender persisted in the medium term, while those across household-head education vanished. Contrary to our hypothesis, in the medium-term we observe larger effects for less educated individuals in terms of the Global Index as well as the gender norms/roles and VAW indexes. This could be explained by the lower baseline
52.

- Keyzer, M., Merbis, M., Nubé, M., and van Wesenbeeck, C., 2008. “Food, Feed, and Fuel: When Competition Starts to Bite”. SOW-VU Brief, Center for Food Studies, VU University, Amsterdam.

- Mevel, S. 2008. “Inserting Biofuels into the GTAP Database”. Washington, D.C.: World Bank.

- Mevel, S. 2008. “Splitting the GTAP „other cereal grains‟ (GRO) sector into Corn and Other Cereal Grains”. Washington, D.C.: World Bank.

- Mitchell, D. 2008. “A Note on Rising Food Prices”. Development Prospects Group. World Bank: Washington, D.C. June.

- Runge, C., and B. Senauer. 2007. “How Biofuels Could Starve the Poor”. _Foreign Affairs._ 86 (3): 41-53.

- Taheripour F., Birur D., Hertel T., and Tyner W. 2008. “Introducing Liquid Biofuels into the GTAP Data Base”. GTAP Research Memorandum No.11.

- Taylor, J., and P. Van Doren. 2007. “The Ethanol Boondoggle”. _The Milken Institute Review_ , First Quarter.

- Timilsina,  G.R., Beghin, J.C., van der Mensbrugghe, D. and Mevel, S. 2010. “The Impacts of Biofuel Targets on Land-Use Change and Food Supply: A Global CGE Assessment.” Policy Research Working Paper 5513. The World Bank. Washington, DC.

- Timilsina,  G.R., Beghin, J.C., van der Mensbrugghe, D. and Mevel, S. (2012), The Impacts of Biofuels Targets on Land-Use Change and Food Supply: A Global CGE Assessment, Agriculture Economics, Vol. 43, pp. 313-330.

- van der Mensbrugghe, D. 2009. “The ENVironmental Impact and Sustainability Applied General
the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **3 Data**

## **3.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>7</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of
for other factors that might also be associated with both cabinet size and governance outcomes, in particular pressures to accommodate political allies and to buy off potential rivals.

# **3. A new data set**

To take a fresh empirical look at the relationship between cabinet size and governance and policy outcomes, we assemble a range of relevant data for Sub-Saharan African countries. The variables we include fall into three main groups, which we introduce below. The data appendix provides full variable definitions and sources and summary statistics are in appendix 1.

The main independent variable of interest is the number of cabinet ministers. No off-the-shelf data set exists that reliably captures this information. The _Cross-National Time-Series Data Archive_ initiated by Arthur Banks includes the size of the cabinet.<sup>6</sup> However, the sources used are not fully disclosed and the manual warns that “[i]n many cases, counts are approximate”. Instead, we build on Arriola’s (2009) data set on cabinet size, as it is coded on the basis of information on the membership of cabinets obtained from a single high-quality source, Europa Publications’ _Africa South of the Sahara_ , which is published annually. Focusing on political heads who are ultimately in charge of government departments, the data set records the number of ministers with full cabinet rank for each country and year in the sample. Excluded are members of the executive without cabinet rank, notably junior or deputy ministers, and individuals with lower-level executive functions, as their authority and discretion is limited. This

> 5 In addition, cabinet reshuffles can be used to create an atmosphere of ‘perpetual musical chairs’ that prevents individual ministers from amassing too much power (Roessler 2011: 309).

> 6 Cabinet size is captured by the _Polit10_ variable in the 2018 edition of the Banks data set.

4
# Reviewed Papers

Afridi, F., Mukhopadhyay, A., & Sahoo, S. (2016). Female labor force participation and child education in India: Evidence from the National Rural Employment Guarantee Scheme. _IZA Journal of Labor & Development_ , _5_ (1), 7. https://doi.org/10.1186/s40175-016-0053-y Agiro, B. T., & Huang, W.-C. (2020). Re-Examining the Effect of Maternal Employment on Child Overweight: The Case of School-Age Children. _Journal of Family and Economic Issues_ , _41_ (1), 140–157. <u>https://doi.org/10.1007/s10834-019-09658-8</u> Anderson, P. M., Butcher, K. F., & Levine, P. B. (2003). Maternal employment and overweight children. _Journal of Health Economics_ , _22_ (3), 477–504. https://doi.org/10.1016/S0167-6296(03)00022-5 Aughinbaugh, A., & Gittleman, M. (2004). Maternal employment and adolescent risky behavior. _Journal of Health Economics_ , _23_ (4), 815–838. https://doi.org/10.1016/j.jhealeco.2003.11.005 Baum, C. L. (2015). Does Early Maternal Employment Harm Child Development? An Analysis of the Potential Benefits of Leave Taking. _Journal of Labor Economics_ . https://doi.org/10.1086/345563 Bishop, J. (2011). The Effect of Maternal Employment on Youth Overweight in Australia. _Economic Record_ , _87_ (s1), 92–104. https://doi.org/10.1111/j.1475-4932.2011.00747.x Del Boca, D., Pasqua, S., & Suardi, S. (2016). Child Care, Maternal Employment, and Children’
# 3.2 Randomization

The GSO conducted the random assignment of 480 households to each arm. Households within an EA were randomly assigned to the interviewers working in the EA. Each interviewer was assigned an equal number of households from each treatment arm within the EA. Randomization at the interviewer-level was undertaken to reduce the potential for interviewer error that could bias FAFH and other responses. Randomization within the EA also reduces the potential for any bias stemming from variation across EAs. An additional sample of replacement households were likewise randomly assigned to each arm, and only surveyed after households from the main sample could not be reached or refused to participate.

After dropping households with missing information in critical areas for this study (mainly, FAFH consumption), there are over 475 households in each treatment arm, except for the 7-day individual diary, which has 473 households. About 20 percent of the final sample in each treatment arm consists of “replacement” households, with only T3 having a higher share (25.8%) than the other arms (see Table 1). In Section 5.1, we test for the effects of non-response by running the models only with the original sample and find that the results hold.

_Table 1 Samples of original and replacement households for each treatment arm._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||T1: 7-day<br>one-line<br>recall|T2: 7-day<br>individual<br>diary|T3: 7-day<br>individual<br>recall|T4: 7-day<br>HH<br>informant|
|Original|381|378|354|378|
|Replacement|97|95|123|98|
|Total|478|473|477|476|

By design, random assignment should result in groups that are statistically equivalent in both observable and