rafmacalaba/datause-encoder-data huggingface dataset:
Ravallion (2022) argues that for some research on the top of the distribution, survey data will be adequate. The evidence we have discussed above suggests that in South Africa and Côte d’Ivoire, a divergence between survey and tax data starts quite far down the distribution and thus that tax data are required to accurately measure the top tail. Further work comparing tax (both new and already existing) and survey data will shed light on the extent to which surveys can be reliably used to measure top incomes.

Making use of other types of administrative data may also be useful. Further work could use house price data and improve the methods of Van der Weide et al. (2018). This is likely to be available only in urban areas in low-income countries, and many middle-income countries as well. House price data could be complemented with land record data for rural areas, where this exists. The extent to which the top tail is comprised of those who live in rural areas in low-income countries could then be investigated more comprehensively, a topic that there has not been much research on.

A second useful part of a research agenda using new data would link survey and tax microdata at the individual level, to provide further insight into the reasons for differences across tax and survey data, specifically the role of unit non-response, measurement error and under-reporting. We are only aware of one study in a LMIC that uses linked survey and tax data – Flachaire et al. (2021) for Uruguay. The links were only available for couples with children aged 0-3 so the extent to which this generalizes to the adult population in Uruguay is unclear. Further research would thus be extremely useful.

A third part of the research agenda would be to undertake future household surveys asking about incomes or wealth that oversample high-income or high-wealth households or areas. Oversampling rich households using tax or other forms of administrative data is not common, even in high income countries, probably because it requires coordination between various public agencies (for example national statistical offices and tax authorities). In addition, administrative tax data in LMICs may miss the rich whose incomes are outside the tax system, which may be a large proportion of those at the top of the income distribution (Haq, 1964, Ravallion, 2022). Nevertheless, it would be useful, perhaps as a pilot, for an LSMS to oversample high income households using tax data, where this can be obtained. A less ambitious method of oversampling would be to use population census data to oversample small areas with high incomes or with characteristics that are correlated with high incomes, if income data is not collected in the census. A version of this was attempted in the NIDS panel fifth wave in South Africa to increase the sample size of white and Indian South Africans, by drawing a top-up sample from enumeration areas with large proportions of these groups, using estimates from the most recent population census (Branson, 2019).

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|Grouping|Model|_ICp_2|_HQ_2|Standardized score|Rank|
|---|---|---|---|---|---|
|Adv, Eme,Dev|(1; 1,0,0; 2,0; 0,0,0,0,1,0)|-0.193|-922|0.969|1|
|Adv, Eme+Dev|(1; 1,0; 0,0; 0,0,1,0)|-0.205|-859|0.962|2|
|Adv, Eme,Dev|(1; 1,0,0; 1,0; 0,0,0,0,1,0)|-0.196|-878|0.951|3|
|Adv, Eme+Dev|(1; 1,0; 1,0; 0,0,1,0)|-0.202|-838|0.944|4|
|Adv,Eme+Dev|(1;1,0;2,0;0,0,1,0)|-0.195|-862|0.941|5|
|Single|(2; 0,0; 0,0,0,0)|-0.188|-628|0.797|>50|
|Single|(1;0,0;0,0,0,0)|-0.169|-556|0.711|>50|

Table B.1: Information criteria scores in the sample including developing countries. The numbers in the "Model" column correspond to the number of factors in each group ( _r_<sup>_G_</sup> ; _r_ 1<sup>_R, r_</sup> 2<sup>_R, r_</sup> 3<sup>_R_;</sup><sup>_r_</sup> 1<sup>_D, r_</sup> _−_<sup>_D_</sup> 1<sup>;</sup><sup>_r_</sup> 1<sup>_RD_</sup> _,_ 1<sup>_, r_</sup> 1<sup>_RD_</sup> _,−_ 1<sup>_, r_</sup> 2<sup>_RD_</sup> _,_ 1<sup>_, r_</sup> 2<sup>_RD_</sup> _,−_ 1<sup>_, r_</sup> 3<sup>_RD_</sup> _,_ 1<sup>_, r_</sup> 3<sup>_RD_</sup> _,−_ 1<sup>).Thestandardizedscore</sup> is computed as (IC _p_ 2/IC _p_ 2 _,max_ +HQ2/HQ2 _max_ )/2. The rank is based on the standardized score. The maximum number of factors considered was (2; 2,2,2; 2,2; 1,1,1,1,1,1) in the Adv, Eme, Dev grouping and (3; 3,3; 2,2; 1,1,1,1) in the Adv, Eme+Dev grouping.

|Grouping|Model|Glob|Fac<br>Adv|tors<br>Eme&Dev In|Glob|Loa<br>Adv|dings<br>Eme&Dev In|
|---|---|---|---|---|---|---|---|
|(Adv, Eme, Dev)|(1;1,0,0; 20; 000010)|0.993|0.985|0.984|0.993|0.964|0.981|
|(Adv, Eme, Dev)|(1;1,0,0; 10; 000010)|0.992|0.95|0.973|0.992|0.93|0.964|
|(Adv, Eme+Dev)|(1;1,0; 10; 0010)|0.993|0.969|0.919 (0.990)|0.993|0.956|0.853 (0.991)|
|(Adv,Eme+Dev)|(1;1,0;20;0010)|0.992|0.983|0.678(0.993)|0.992|0.949|0.606(0.995)|

Table B.2: Correlations of the factors and loadings of the different models with those of the (1;10;00;0010) model (the selected one), in the sample including developing countries. The emerging& developing inflow factor (loadings) of the selected model is compared with the first inflow factor (loadings) of the other models. In brackets is the correlation between the emerging and developing inflows factor (loadings) of the selected model and its projection over the (2 or 3) factors (loadings) of the corresponding model affecting only inflows.

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- MCGM, (2015), MCGM Draft Development Plan (2014‐34) and ELU maps. Municipal Corporation of Greater Mumbai. http://mcgm.gov.in/irj/portal/anonymous/qlddevplan

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- Sales, R. (2009). Vulnerability and adaptation of coastal communities to climate variability and sea‐level rise: Their implications for integrated coastal management in Cavite City, Philippines. Ocean and Coastal Management, 52(7), 395–404.

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> - 25 -

city is high, then internal migration may also cease to be viewed as an effective device for viilage households to achieve absolute income gains, even if there is a positive urban-rural income differential. This interesting possibility is ruled out by conventional, absolute-income models of migration.

The remaining variables in the decision model are included for their hypothesized influence on the returns to migration versus nonmigration activities and on the motivation of household members to contribute all or part of their earnings to their respective households. We would expect migrating household members to be those whose attributes are most likely to be associated with high differentials in returns to the household from migration versus nonmigration activities. In addition, certain household characteristics are likely to have an important effect on both the probability of migration and the choice of migrant destination.

The logit analysis reveals striking differences between migrants and nonmigrants as well as between the two groups of migrants. On average, migrants tend to be male, 20-30 years of age, not heads of households, and to possess past migration experience. However, two of these variables affect the migration categories in very different ways. Although males are significantly more likely than females to participate in Mexico-to-U.S. migration, sex plays an insignificant role in explaining internal migration. Household heads, in contrast, are very unlikely to engage in international migration but are no less likely to be internal migrants than are those who are not heads of households. The latter result no doubt reflects differences in opportunity costs between internal and international migration for household heads. For heads of households, administrative responsibilities on the family farm and
Aceh into Pidie (the last subdistrict surveyed is Meurah Dua) and moving south into Aceh Barat (the last subdistrict surveyed is Meuruebo).

# FIGURE A1 ABOUT HERE

Figure A1 shows a map of the survey area, with a blow-up (right side in figure) of the Banda Aceh area (upper-left part of coastal area). The map shows household survival rates by village (yellow being the worst). Unfortunately, the map is based on the government rendering, post-tsunami, of village boundaries. In that dimension the map is grossly inaccurate. We took GPS readings of the center (the mosque) of the living area of each village. In only 6% of the cases is that GPS reading within the village boundaries. In 15% of the cases, it is over 10 kilometers away. Coastal villages are drawn as non-coastal and vice-versa which explains why, in parts of the map, a yellow (low survival) village may be shown next to a supposed coastal village, which is dark (high survival). Nevertheless the map pictures the general survey area.

# _The Survey Area_

# _Sources of Population and Building Stock Numbers_

To cover our villages consistently, for pre- and post-tsunami populations of villages, we use government counts. For the 88 villages added to our survey after 2005, 2007 recall of pre-tsunami populations tended to be strategic or unreliable (the village head reporting those numbers is usually new to office). Official population counts pre-tsunami are from the P4B, a 2004 government pre-election census. The official survival rate is the 2006 PODES count divided by the count in P4B. The PODES is a tri-annual government 32
and changes in coefficients and unobservables contributed to the stagnation of FLFP between 1987 and 2011.

The probability of woman _i_ in year _t_ (1987, 1999, 2004, 2009, and 2011)<sup>11</sup> being in the labor force (including self-employment, unpaid family work, regular and casual wage employment, and unemployment) is modeled as where _F_ is the standard normal cumulative distribution function. The model is estimated separately for each year to allow for changes in behavior over time.

The first right-hand side term is a state fixed effect. Education is measured through dummies for the highest education level completed, _D_<sup>_E_</sup> ( _E_ =2, ..., 6), with illiterate ( _E_ =1) as the reference level. Xit is a vector of explanatory variables at the individual and household level, including household income excluding her own earnings, and education of the household head. Income is measured as total household earnings in the reference week excluding the woman’s own earnings.<sup>12</sup> We use income per capita to control for differences in the number of people depending on that income.<sup>13</sup> Instead of an asset index or similar measure of wealth, the education level of the household head is included to capture household wealth or permanent income beyond total earnings. If higher socioeconomic status leads to more restrictions on women and if greater wealth reduces the need for women to work, the education level of the household head should have a

> 11 Data for 1993 are not used in the econometric analysis because the 1993 data do not contain district identifiers, which are needed to construct district-level explanatory variables. Districts are administrative units at the substate level in India. Our sample covers 362 districts across India’s 18 main states.

> 12 For total household earnings, the earnings of self-employed household members are imputed based on the earnings of employees. Although this is a fairly rough approximation, it appears this imputation serves the purpose of measuring household income well: results are very similar when households with at least one self-employed adult are excluded from the sample.

> 13 Data on other income sources are not collected in the survey, nor are household assets.

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operator conditional on all the information available to the family at time t. The above expression measures the innovation to permanent income after the risk sharing agreement. A related object is the innovation to permanent income before the risk sharing agreement, denoted by Using equations (2), (4), and (5) into equation (3) delivers the following expression for the change in consumption as a function of the innovation to all families’permanent incomes before risk sharing The left side of this expression is the change in family i’s per-capita consumption. The term on the right side includes the deterministic drift in consumption plus the innovation to the family’s permanent disposable income. The latter term, in turn, can be written as which illustrates the risk sharing agreement. In particular, if family i’s has a bad income realization relative to that of the other families (if the term in parentheses is positive), it receives a transfer proportional to the di¤erence in the income shocks, where the factor of proportionality is the contribution of family i to the income pool. If family i has a good income realization relative to that of the other families (if the term in parentheses is negative), it transfers resources to the other families in proportion to its contribution �i. Furthermore, note that if �i = 1, so that country i shares all of its income with the pool, the idiosyncratic shock "it disappears as an isolated term from expression (6) and only enters through its impact on the innovation to the pool’s permanent income<sup>PN</sup> j=1 !!ji00<sup>�j"jt.</sup> Before proceeding, we discuss an important speci...cation issue. As noted in Mankiw and Campbell (1989,1990) and Crucini (1999), aggregate consumption and income time series 8
_Normsj_ variable encapsulates 4 different measures of progressiveness: men’s individual beliefs, women’s individual beliefs, men’s perceived community norms, and women’s perceived community norms. Men’s (Women’s) Individual Belief (0-1) is the proportion of men(women) in the country who agreed with the statement that _men and women should have equal opportunities_ measured prior to the COVID-19 pandemic. Men’s (Women’s) Community Norm is the country-level aggregate of the perceived gender norm as reported by men(women) in the sample towards gender equality rescaled to be a continuous variable between 0 and 1 (in increments of 0.1). Higher values of the norms measures all indicate more progressive or egalitarian views within the country i.e. a more liberal society. Alternatively, a lower value indicates more regressive or discriminatory views i.e. a more conservative society. The beliefs and norms were collected before the pandemic in December 2019 and aggregated at the country level among both men and women.<sup>28</sup> Moreover, since this sample is not nationally representative, we also show how these data correlate with a comparable gender equality statement collected in the World Values Survey (WVS). Overall, this correlation is strong, giving us assurance that, in this vein, our sample is not unrepresentative.

# **5 Results**

# **5.1 Gender Differentials in Firm Closure**

Before presenting results on the impact of school closure policies, we examine the overall gender gaps in firm closures and performance during the 2020-2021 COVID-19 pandemic. Tables 1a and 1b show that the COVID-19 pandemic did indeed impact female-led businesses differently than male-led businesses throughout the 2020 survey period and continues to persist into 2021.

The repeated cross-section results, following equation 1, are presented in Table 1a. The time path results suggest that more female-led businesses were closed during COVID-19 than male-led businesses for each wave of the survey. We define closure as when the respondent

> 28See Appendix D for a full description of the norms constructs used in the analysis.

18
outcomes include all the areas targeted by the project in its assessment of institutional performance—urban planning, fiduciary systems, infrastructure management, revenue generation and accountability and oversight by citizens. In addition, we examine impact on measures of state capacity emphasized in the growing research literature—ability to raise revenues; administrative capacity of state personnel; and culture and norms of performance in the organizations of local government.

# **3 Data**

## **3.1 Data sources and timeline of data collection**

Two rounds of data were collected as part of the impact evaluation. We present a timeline of the surveys’ implementation and the APAs in Figure 1. The first survey was completed in February 2016 when some project interventions had been implemented in most LGAs. As such, the first survey can be considered as capturing the situation at early stages of project implementation. Likewise, the second survey was conducted in April-May 2018, when not all project activities had been completed, and therefore can be considered as capturing late stages of project activity. Specifically, the temporal distance between the first and second survey rounds is of 26 months which is around 45% of the temporal distance between the 1st APA and the 6th APA ( _≈_ 60 months). During this period, about half of the project funding commitment was disbursed.<sup>10</sup> As part of the project, APAs were also collected at regular intervals.

## **3.2 Panel data**

The data used for this study are built into two panel datasets (i.e., two datasets with data from the same LGAs and/or households at two different times): one of households and one of government officials. Households and government officials were interviewed in all 40 original evaluation LGAs (both in the first and second rounds) and in 12 newly added LGAs (only in the second round). The 40 LGAs consisted of 18 project LGAs and 22 comparator LGAs. The process of selecting comparator LGAs, drawing on a mix of propensity score matching and local expert surveys, is detailed in Appendix A. Since the main goal of this paper is to assess changes in outcomes between the first and second rounds of surveys, for the remainder of the text we will only refer to the 40 LGAs originally present in the first round survey, ignoring the additional LGAs added at the second round.

The Household panel dataset includes 5,996 observations across the two waves - 2,998 households were interviewed in each wave. The sampling of households in the first survey round was

> 10This timing could present a problem if all the gains in the program took place at the very beginning, before most activities were implemented, and/or at the very end. According to the APAs, indicators kept growing over subsequent APAs which suggests that not all gains were achieved at the early stages and indicators do not present large jumps towards the end of the period, i.e. between 5th APA and 6th APA.

14
is in any case unpredictable, falls behind. It would avoid the need for extensive recourse to the technique of special accounts<sup>26</sup> for priority sector expenditures.

# **B. Improving the Expenditure Circuit and Management at the Central Level**

The system of expenditure cycle in place in Chad includes the usual stages of the budgetary systems inspired by France. Budget accounting covers commitments and orders to pay issued by public officials, while the Treasury records the commitments that it has taken charge of (“ _pris en charge_ ”), and in principle, of all other operations made by the State. Commitments must first be endorsed through a visa by the financial controller’s office, which is in charge of checking their adequacy. Treasury Department controls the payment orders (Ordonnancement) once they are transferred to the Treasury.

Generally the Direction of Payment Orders in the Ministry of Finance authorizes orders to pay. Thus, the Ministry of Finance oversights beforehand every stage of the expenditure process. This high degree of centralization should enable the Ministry of Finance to have detailed data on budget execution. However, existing data are not systematically reviewed, and the statements produced by the various departments are not cross-checked or reconciled. In addition, given that the centralization of the process of budgetary execution is combined with inadequate circulation of information between the Ministry of Finance and the sectoral ministries, credit managers at the ministries do not systematically get the information on the execution of the expenditures that they have initiated.

The circuit of the expenditure of material and investment financed on interior resources, which relates to approximately a third of the budgetary expenditure, constitutes the principal mode of expenditure currently in place in Chad. It is held in two administrative and accounting phases, as described in the Presidential Decree 118-F of June 29, 1963 bearing general payment of the public accounts. The administrative phase relates to engagement (commitment), liquidation (validation) and order to pay “ordonnancement” of the budgetary expenditure; and the accounting phase relates to mainly their payment.

The following administrative structures are involved in the circuit of the expenditure in Chad: (i) Administrators of appropriations, which exert the functions of Directors of the Administrative and Financial Businesses and Material (DAAFM) in the sectoral ministries; they issue the projects of purchase orders (engagement) for their ministry and make carry out the expenditure by the technical directions; (ii) The Management of Financial Control and Commitments Directorate (DCFE) which at the same time engages the budgetary expenditure and controls the committed expenditure ( _engagement et liquidation_ ); hierarchically attached directly to the Minister of Finance, and not to the Secretariat-general of the MEF likewise the other directions of the

> 26 Special accounts utilization should be limited because of their lack of transparency nature.

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