rafmacalaba/data-use-mentions-extended huggingface dataset:
lockdown measures were imposed due to Covid-19 and therefore cannot speak to changes in poverty headcounts in the aftermath of the pandemic.

The rest of the paper proceeds as follows. Section 2 provides a detailed overview of the known differences between the survey instrument and sample design of CPHS and NSS and sets up both datasets to achieve closest possible comparability based on this knowledge. Section 3 examines the results of the reweighting exercise while Section 4 introduces our two approaches to estimating NSS-consistent measures of consumption. Section 5 reports headline poverty and inequality estimates and reports the results from robustness checks. Section 6 corroborates our findings using a range of independent data sources. We conclude in Section 7.

# **2 Data**

## **2.1 Consumer Pyramid Household Survey (CPHS)**

The CPHS is a stratified multi-stage survey with towns and villages from the 2011 population census as its primary sampling units (PSU) and households as its ultimate sample unit (USU). CPHS’ first stage stratum is a spatial unit called Homogeneous Region (HR), which is a set of contiguous districts with similar agroclimatic conditions, urbanization levels, female literacy rates and number of households. The latest round of CPHS consists of 102 HRs spread over 28 states and 514 districts in India (out of total of 36 states and 718 districts in India), with each HR further divided into rural and urban sub-strata. The latest round of CPHS’ rural sample comprises 63,430 households selected randomly from 3,965 villages and 110,975 households from 7,920 urban census enumeration blocks (CEBs).

The CPHS’ consumption module contains monthly household expenses for about 115 unique items. A quarter of these relate to food, while others include expenditures on clothing, footwear, cosmetics, toiletries, appliances, restaurants, utilities, transport, communication, education, health, monthly loan repayments and other miscellaneous items. CPHS interviews households three-times a year, at four-month intervals referred to as waves. Households report item-wise consumption for each of these
# **_C. Additional robustness tests_**

In this section, we present results from two additional strategies aimed at countering concerns of endogeneity from the selection of treatment and control countries that undertake CB reform – propensity score matching and the RZ methodology.

First, in columns (1) to (4) of Table 7, we present results from estimating the same specification as before (equation 2), but modifying the sample of control countries as a result of matching. The one-to-one nearest neighbor matching with replacement reduces the number of control countries used from 14 to 8 in the Orbis data. The regressions make use of frequency weights to produce an overall sample that is analogous to having an equal number of treatment and control countries. A test of the balancing property shows that the balancing property of the estimated propensity score is satisfied, i.e., countries with the same propensity score have the same distribution of observable (and unobservable) characteristics independently of whether or not they undertake CB reform.<sup>26</sup> In estimating the propensity score, we impose the Common Support condition, which ensures that matching is only conducted over the overlapping region of the distributions of propensity score in the treated and untreated groups, thus ensuring that conditional on observed characteristics, the probability of undertaking a CB reform or not is nonzero.

The regressions results with matching are qualitatively identical to the unmatched results in Table 6 above. Columns (1) to (4) of Table 7 show that the point estimates for the overall effect of the introduction of CB on job growth are slighter lower than earlier, as are the effects among SMEs and MSMEs. Increased access to finance resulting from the introduction of CBs

> 26 Rosenbaum and Rubin (1983) show that to ensure a bias reduction the propensity score should satisfy two important properties: _Balancing Property_ which ensures that given the propensity score, the treatment and observables are independent and the _Unconfoundedness Property_ which ensures that given the propensity score the treatment and the potential outcomes are
women in urban and rural areas in two points in time, 2001 and 2018, using a Probit estimation: where **X** i includes age, squared age, number of kids under 10 years old in the household, a 4- categories education variable, education level of head of household, a dummy variable that equals “1” if the woman has never been married, number of inactive women in the household and its square. These data come from the LFS of 2001 and 2018. We report the marginal effect of each covariate using the average values in the sample.

We also estimate two models to shed light on the role that values play in determining female labor force status. The first one is also a Probit model as in Equation 1. The data source is the World Values Survey. **X** i includes 10- Moroccan regions fixed effects, a dummy that takes the value of “1” if the individual is married, age and squared age, dummies for a 3- categories educational variable, and number of children. Our variable of interest is a dummy that equals “1” if an individual agrees with the claim “Men should have more right to a job than women if jobs are scarce,” which we associate with conservative or traditional traits.<sup>9</sup> We estimate this model for men and women separately, and we report the marginal effect evaluated at the means of the regressors in our sample.

> 9 We drop from the estimation those individuals who do not answer and those who do not know. We set the dummy to a value of zero for those individuals who disagree or whose answer is “Neither.”

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transition toward a future without oil. Most macroeconomic aggregates grow at real rates quite close to the rate of GDP at factor cost. The main exceptions are exports (which suffer from the decline in the oil sector) and private investment (which are hurt by the combination of high import intensity and depreciation).

**Figure 6:** Real sectoral GDP growth rates (2010-2025, percent)

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>Agriculture Industry - oil<br>Industry - non-oil Services - non-government<br>Services - government<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text --> On the fiscal side, government spending increases from 28 to 32 percent of GDP in 2025, mainly due to an increase in consumption caused by a rapid wage increase. Government revenues decline significantly to 21 percent of GDP. The fall in oil revenues, however, is mitigated by a rise in domestic non-oil taxes, both direct and indirect, relative to GDP.

GDP growth is accompanied by a relatively rapid expansion of employment. The unemployment is subsequently reduced from 19.4 percent (last country-wide assessment in 2005) to 13.5 percent in 2025. Real wages grow relatively evenly across the educational levels; between 4.1 and 5.0 percent per year on average. Wage growth is quite even across educational levels, reflecting the combined impact of differences in productivity and supply growth between labor types.

Poverty declines substantially and the other social indicators improve as well. In the baseline scenario, the poverty rate falls to about 24 percent in 2025, down from 51 percent measured by a household survey in 2005 (see Table 6). The
.483|0.124|
|Number of frm|223|152|220|149|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data 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 the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table estimate whether results are driven by anticipation and total trade volume. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

The last robustness check test is for misallocation effects on how surviving firms and entrants/exiters respond to trade openness. To this end, the sample was divided into two groups: surviving firms (i.e., firms active in all the 12 years of the survey and hence active 25
Figure 10: Formal and informal employment distribution by age cohort in manufacturing: Burkina Faso (2015) <!-- Start of picture text --> Entrant 1−5 years<br>6−10 years 10+ years<br>Employment in formal establishments Employment in informal establishments<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>Share of employment (%)<br>80<br>60<br>40<br>20<br>0<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the selected countries; see section 3. _Note:_ The reference is the total employment for each age cohort and formality status. The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

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owners but below median revenue per worker, irrespective of size (6%), and older firms with three or more workers, irrespective of revenue per worker or owner’s education (5%).

The EcC results for informal firms with high predicted probability of formality also highlight older 3-24 person firms that hire wage workers, irrespective of value added per worker (10% of informal firms), and those 3-24 person firms that do not hire wage workers but have high value added per worker (0.3%) as having the highest probability of formality. It further highlights older, two-person firms with value added per worker in the top quartile (3%). Their counterparts in the second and third quartile of value added per worker come next in priority (5%).

As before, cluster analysis provides a different lens to characterize the informal firms that are most amenable to formalization. According to the analysis using ELMPS data, shown in Figure 14, these include two clusters we describe as “larger professional” (8% of informal firms in ELMPS) and “larger nonprofessional” firms” (7% of informal firms). The latter cluster in particular is also disproportionately made up of firms with collective forms of ownership and relatively high capital. Both of these clusters disproportionately hire workers from outside the household. The first hires mostly non-relatives and the second hires relatives. These are followed by clusters we describe as “shops with older owners” (14%), and “workshops” (11%). These two clusters tend to skew older in terms of firm age and the latter is more likely to hire wage workers than average. The four clusters that have the lowest share of the lowest quartile of predicted probability of formality (5%-25%) still have only 14-26% of their firms in the top quartile of predicted probability.

Cluster analysis using the EcC data, shown in Figure 15, also highlights the larger professional (2% of informal firms in the
LOE=Lowlands Enset growing region; LOR=Lowlands reliable region. 05,11 refer to the datasets collected in 2005, 2011 respectively (and defined above).

We then repeat the exercise, but this time considering the whole dataset for 2005/2011 as the training/testing dataset respectively. In this way one might imagine going back in time to 2005, predicting consumption with that dataset for 2011, and assessing the results. As a though exercise we also repeat the process but with the training and testing roles reversed. When 2005 is the training dataset, Model 2 is the best predictor (lowest MSE). However when 2011 is the training dataset, the baseline model performs best.

Comparing how useful the two datasets are as “training” datasets. For all the models the MSE is smaller when 2005 is the testing dataset (& 2011 is training) than when 2011 is the testing dataset (& 2005 is the training dataset). This means that the 2005 dataset fits the model predicted by the 2011 dataset better than the 2011 dataset fits the model predicted by the 2005 dataset In other words, the model fitted on the 2011 dataset predicts the 2005 data more accurately that the model from 2005 predicts the 2011 data. This is a somewhat unexpected result, given that the 2005 dataset has more variation in the rainfall/drought variable. However, it must also be remembered that the PSNP safety net was introduced in mid‐2005, just after the survey was completed, which may have changed the structural relationship between drought and consumption in ways that are not captured perfectly by the regression model.

In all cases there is actually fairly small difference in the fit of the model, which suggests that the relationship between drought and consumption is actually fairly homogenous, and stable.

**Table 5.2: Testing the models with 2005 and 2011 as training and “holdout” data – squared model**

||Baseline|Model 1|Model 2|Model 3|Model 4|
|-
of 5%). Unemployment is defined more broadly than in official statistics to include un- and under-employment. In the simulations, a broad definition of unemployment increases the scope for the existing labor force to generate a larger (smaller) amount of effective labor if the incentives to work were to improve (deteriorate); typically, this seems realistic. For non-labor factors, the supply curves and total employment are vertical in any single year.

The above discussion refers to the functioning of model economy in a single year. In MAMS, growth over time is endogenous. The economy grows due to accumulation of capital (determined by investment and depreciation), labor (determined by share of the population in labor force age, the labor force participation rate, and the educational system), other factors (on the basis of exogenous growth data), as well as because of improvements in total factor productivity (TFP). The educational system is important as it drives the evolution of the educational attainments of the labor force, influencing their productivity, but keeps the population who is enrolled in schools away from the labor force. Apart from an exogenous component, TFP depends on the levels of government capital stocks.

> 8 For example, starting from a balanced situation, a balance of payments surplus could arise from increases in foreign exchange receipts (perhaps due to an increase in foreign aid or the world price of an export). In this situation, the exchange rate would tend to appreciate, reducing the domestic prices of exports and imports relative to the prices of domestic output sold domestically. This relative price change would encourage domestic producers to switch part of their outputs from exports to domestic sales and induce domestic demanders to switch part of their demands from domestic sources to imports. This process would continue until the balance of payments surplus is eliminated. The opposite would happen in the case of a balance of payments deficit.

> 9 Individual production activities do not respond to changes in relative prices for exports and domestic sales if their output
# III. Methods

This section describes the three methods that are evaluated: Direct survey estimates, the Fay-Herriot area-level model, and the household-level model. It also discusses important methodological considerations involved when estimating the household-level model, such as differences between software packages, transforming the dependent variable, the model selection procedure, and benchmarking. The final subsection considers the criteria used to evaluate the performance of different estimators.

## A. Direct survey estimates

Direct estimates obtained from the survey serve both as a benchmark against which to assess the increase in precision from incorporating satellite data, and as an input into the Fay-Herriot area-level models. We use two methods to obtain direct survey-based estimates **.** For both methods, the mean poverty estimate for each area is the weighted average, across households, of the dummy variable indicating whether each household is poor. Each household is weighted according to corresponding population weight in the survey. The two methods used to obtain direct estimates differ in how they estimate the variance of the means. The typical method of obtaining variance estimates clusters the residuals by enumeration area, which accounts for positive correlation within enumeration areas but assumes that disturbances across enumeration areas are independent. This assumption underestimates the variance of area-level poverty estimates, particularly if welfare is highly correlated across enumeration areas within target areas.

The Horvitz-Thompson (H-T) approximation offers an alternative, more conservative, approach to estimate the variance of the area poverty rates (Horvitz and Thompson, 1952). This approach is more conservative because it relaxes the standard assumption that households’ poverty statuses are independent if they live in different enumeration areas within the same target area. This comes, however, at the cost of imposing an alternative assumption, which is that the probability of one household appearing in the sample is independent of the probability of any other household appearing in the sample. This assumption is clearly violated in standard two-stage sample designs, as the probability of a household appearing in the sample is greater if a household in its same enumeration area also appears in
warrant further

> 7According to the V-Dem codebook, electoral autocracies have “De-jure multiparty elections for the chief executive and the legislature, but failing to achieve that elections are free and fair, or de-facto multiparty, or a minimum level of Dahl’s institutional prerequisites of polyarchy” (Coppedge, Gerring, Knutsen, Lindberg, Teorell, Alizada, Altman, Bernhard, Cornell, Fish et al. 2021).

8Electoral democracies are defined as “De-facto free and fair multiparty elections and a minimum level of Dahl’s institutional prerequisites for polyarchy as measured by V- Dem’s Electoral Democracy Index, but either access to justice, or transparent law enforcement, or liberal principles of respect for personal liberties, rule of law, and judicial as well as legislative constraints on the executive not satisfied as measured by V- Dem’s Liberal Component Index” (Coppedge et al. 2021).

9We use the Civil Liberty Index, which V-Dem describes as “Civil liberty is understood as liberal freedom, where freedom is a property of individuals. Civil liberty is constituted by the absence of physical violence committed by government agents and the absence of constraints of private liberties and political liberties by the government.” It is a scale that runs from 0 to 1, with 1 indicating higher levels of civil liberties.

> 10In particular, we use the Physical Integrity Index, which V-Dem describes as: “Physical integrity is understood as freedom from political killings and torture by the government. Among the set of civil liberties, these liberal rights are the most relevant for political competition and accountability. The index is based on indicators that reflect violence committed by government agents and that are not directly referring to elections.” It ranges from 0 to 1, with 1 signifying the highest levels of physical integrity protection and 0 representing the highest levels of violence.

6
are also used by Bahety et al. (2021) and Athey et al. (2021).

**Estimation.** In the IVR application, we focus on Bayesian estimation to obtain final parameter estimates. In Kasy and Sautmann (2021a), the outcome of each arm _k_ is Bernoulli distributed with a Beta prior, so that the posteriors after _t_ have closed forms. In the IVR experiment, we generalize the approach and estimate Bayesian hierarchical models with school-specific effects and a Binomial outcome distribution (Normal for reading fluency), described in detail in section 4. The Bayesian approach with updating between waves is internally consistent<sup>7</sup> and naturally produces _p_<sup>_k_</sup> _t_<sup>thatweneedforexplorationsampling.Bayesianinference</sup> is valid with adaptively collected data.

However, users may be interested in frequentist inference about the parameter estimates. Frequentist estimates that do not account for the data being generated by an experiment for policy choice are subject to potential biases (Melfi and Page, 2000; Xu et al., 2013). First, observations from an adaptive experiment cease to be iid draws – intuitively, adaptivity introduces sampling bias because random fluctuations in early observations in a given treatment arm _k_ affect the weight of these observations in the overall sample assigned to _k_ (by changing the assignment shares of this arm in future waves). Second, inference on the best arm out of a set, where the ranking is based on the treatment effect estimates, creates an upward bias and invalidates standard confidence intervals even with non-adaptive sampling (Andrews et al., 2021).

Inference from adaptively sampled data is an active field of research, with particular focus on algorithms targeting in-sample regret, which exacerbate selection bias by quickly focusing on high-performing arms. Adaptively weighted estimators can correct sampling bias and produce asymptotically normal estimators (Hadad et al., 2021; Zhang et al., 2021). Andrews et al. (2021) propose corrections for the “
on South Africa provides more persuasive evidence of a substantial, statistically significant, gender differentiated, causal impact of electrification on employment.<sup>38</sup> Specifically, female employment increases by 9 to 9.5 percentage points, or between 30 and 35 percent from baseline, in the wake of an electrification of a village in rural KwaZulu-Natal, as compared to no significant impact on men.<sup>39</sup> This result is strongest for women in the 30-34 age group, most likely because they have fewer childcare responsibilities.

Employing a similar strategy to Dinkelman (forthcoming), Grogan and Sadanand (2011) find that ―household electrification causes rural women to be about 23% more likely to work outside the home, but that there are no such effects for men in Nicaragua.‖<sup>40</sup> They find the greatest impacts in a younger cohort of women (20-35). In Guatemala, Grogan and Sadanand (2009) find women in villages with electricity are 9 percentage points more likely to be employed (marginal effect at the mean), and they see a five-fold increase in time spent working, versus no impact on men‘s probability of employment

> 37 Chowdhury (2010) only reports coefficients from probit and poisson models, not marginal effects.  The coefficient on electricity in 2000 is not as large or as statistically significant as the effect of a public phone in the village, for example, but is more significant than the effect of a paved road in the village.  Using a fitted value for electricity in the home (predicted from village price of electricity and land owned by household), Chowdhury (2010) also finds that electricity reduces the time spent on unpaid households work (but it is unclear how or whether she accounts for pre-estimation of the electricity variable in this regression).

> 38 Employment in South Africa is determined by a census question on whether a person (age 15 – 59) works, including formal work for salary or