rafmacalaba/data-use-mentions-extended huggingface dataset:
3 in part our analysis of new surveys (or adaptations thereof) since releasing the 2018 database, but also refinements we have made to out-of-pocket expenditure estimates for some surveys included in the 2018 database.

# 2.1. <u>New indicators</u>

The HEFPI 2018 database included five financial protection indicators: the incidence of (i.e. fraction of households experiencing) ‘catastrophic’ health expenditures, defined alternately as expenditures exceeding 10% of consumption or income, or as expenditures exceeding 25% of consumption or income; and the incidence of (i.e. fraction of households experiencing) impoverishing expenditures, defined as expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line, with two absolute poverty lines ($1.90-a-day and $3.20-a-day in 2011 purchasing power parity (PPP) dollars) and one relative poverty line (50% of median consumption or income).

The HEFPI 2019 database includes these five indicators plus nine new financial protection indicators:

1. The incidence of impoverishment for two other international poverty lines proposed by Joliffe and Prydz (2016), namely $5.50-a-day (relevant to upper middle-income countries) and $21.70-a-day (relevant to high-income countries).

2. The incidence of impoverishment using the societal poverty line (SPL) proposed by Joliffe and Prydz (2017), equal to the higher of two poverty lines: the $1.90-a-day line or 50% of median consumption.

3. For all four $-a-day poverty lines, the HEFPI 2019 database also includes not just the incidence of impoverishing expenditures but also the addition to the per capita poverty gap (in $ terms) attributable to out-of-pocket expenditures.<sup>1</sup>

> 1 This is computed (for the $1.90-a-day poverty line) as 1.9 times the difference between the per capita poverty gap for consumption (or income) net of out-of-pocket expenditures and the
|2243|
|Public utilities|22.2|7.4|2.3|10.1|0.0|6.3|335|
|Post & Telecom|8.4|7.2|3.8|3.7|0.0|4.5|443|
|Public Administration|14.5|5.4|2.6|4.3|1.1|6.1|2055|
|Education|18.7|7.5|2.1|4.8|2.0|6.1|2644|
|Health & Social Care|10.8|9.1|2.1|4.8|1.9|4.1|2808|
|<br>Personal & HH Services|<br>39.5|<br>38.6|<br>8.3|<br>13.3|<br>5.2|<br>17.7|<br>1471|

_Notes:_ For Italy and Austria, results refer to 2006 and 2007, respectively. Countries weighted equally (i.e. a respondent from any country is equally likely to be sampled.<sup>a</sup> Country-specific (and year-specific) rules on free movement of labor within EU are taken into account. _Source:_ Calculation with ESS data.

52
and Qatar) that managed to switch from fiscal procyclicality to countercyclicality over the past two decades. This confirms previous findings in the literature suggesting that escaping the fiscal pro-cyclicality trap is possible and demonstrates that this is feasible even for countries in a region like MENA with very high output volatility.

When looking at different subcomponents of government revenue, we find that tax revenue and non-tax revenue in MENA countries featured similar cyclicality patterns but of different magnitudes, with non-tax revenue typically moving more strongly with the business cycle than tax revenues (which is a desirable outcome for revenues under countercyclical fiscal policy). On the spending side, we find that subsidy expenditures in MENA countries tend to be less countercyclical (more procyclical) than other fiscal expenditures (an undesired outcome for revenue under countercyclical fiscal policy). Our findings give rise to implications for policies aimed at making fiscal policy more countercyclical, which we discuss in detail below.

# **3. Data and methods**

The existing literature uses various policy variables to analyze fiscal cyclicality, including government spending, revenue, and fiscal balance (or deficit). On the revenue side, it is often argued that (tax) revenues are endogenous to the business cycle and tend to support countercyclical fiscal policy simply as a result of the widened tax base during times of expansion (Vegh & Vuletin 2015, Bashar et al. 2017). This suggests that, in evaluating the performance of fiscal policy decisions on the financing side, tax rates rather than tax revenue should be considered. However, comparable cross-country data on tax rates are not readily available (Bashar et al. 2017).<sup>1</sup> To ensure the robustness and greater granularity of our conclusions, and to shed light on how some countries managed to escape the fiscal pro-cyclicality trap, our study analyzes multiple fiscal policy variables side-by-side. Specifically, the main fiscal policy variables considered in our empirical analysis are government total expenditure, government revenue, and overall fiscal balance. We also consider revenue as a share
We note first that 24% of all private facilities in treatment markets that were open at our baseline were closed by the government at some point in time. These facilities were mostly unlicensed (45% of unlicensed facilities were closed by the government compared to 7% of licensed facilities), and even though _all_ unlicensed facilities were supposed to be closed, actual closure rates were much higher (61%) among facilities in the lowest quintile of JHIC scores compared to the top quintile (11%). Among licensed facilities, facilities in the bottom two quintiles experienced a 11% to 21% rate of closures, compared to a negligible 1% to 3% among facilities in the top quintiles. Finally, overall exit rates in treatment markets are smaller than the closure rate: this is because many facilities reopened after being closed by the government and most of them do so without obtaining the required licenses. Both because the patterns of exits in treatment markets are very similar to what we see in the control group and because closed facilities seem to re-open, the impact of the treatment on exit rates will be smaller than the rate of government closure—emphasizing the difference between the impact of regulation from its proximate effect, which is what regular monitoring data would provide.

In Table 5, we use the census of facilities at randomization to estimate a 3.4-percentage point increase (p-value = 0.238) in exits among treated private facilities. This impact is not statistically significant and it is zero for public facilities. It is only when we look at variation by licensing status that significant differences arise, with unlicensed facilities 8.9 percentage points (37%, p-value = 0.046), more likely to exit in treated compared to control markets. Coding all outpatients as zero for inactive facilities shows that facilities that were unlicensed at randomization also see a decline in their outpatient load of 3.1 patients (p-value _<_ 0.010) or 43% compared to an average of 7.1 in
(US$122), and 39 percent are below the national poverty line.<sup>7</sup> The mean firm is making profits of US$125 per week on US$1041 of weekly sales.

Table 1 shows that the randomized treatment and control groups look similar on most baseline observables for the full sample, and for the separate sub-samples of interested and uninterested firms. The last column then tests whether interested firms differ from uninterested firms on these owner and firm characteristics. Owners are more likely to be interested in Agruppa if they are female (which qualitative research suggested was in part due to the advantage of not having to deal with transporting heavy sacks of vegetables), and if they are younger, more educated and have higher household and business incomes (possibly reflecting a greater willingness to try new technologies or higher opportunity cost for their time). Firm owners who have a greater distance to travel to Corabastos are more interested (they have more to gain from saving on travel time), but owners who rely on the core Agruppa products for more of their sales and who travel most frequently to Corabastos are less interested (perhaps because they value the selection at Corabastos more). Column 1 of Table 2 considers these variables jointly in a probit and confirms that women with young children are more likely to be interested, as are owners who sell more Agruppa products and who live further from Corabastos, but those who shop at Corabastos every day are less likely to be interested.<sup>8</sup> We also did a one-time survey of 650 customers in 20 of the blocks (10 treatment and 10 control) to understand the characteristics of customers, and the extent to which our neighborhood blocks corresponded to their shopping locations.<sup>9</sup> This was done by randomly sampling customers shopping at stores in our sample to interview. Seventy-four percent of the customers are women, with a median age of 48, and 98 percent lived in the neighborhood they were
been restored in all markets.

# B. Simulation Results for Chile

The domestic production data for Chile contained information on 69 domestic production sectors for Chile. Of these sectors, 41 produce traded goods and 28 sectors produce non-traded goods.<sup>5</sup> In order to economize on the number of endogenous variables and allow the equilibrium to be calculated on an EXCEL spreadsheet, the 69 production sectors were further aggregated into a 19
people joining and dying for the Nationalist party army, which put much less emphasis on ideology.<sup>14</sup> We obtain data on the Nationalist war casualties from the website of the National Revolutionary Martyrs’ Shrine of R.O.C in Taiwan, China, which records the names of about 140,000 people who died as members of the Nationalist forces before 1949.<sup>15</sup> We trace the places of birth of these Nationalist soldiers, and see if the number of Nationalist soldiers is related to famines in the 1920s in their home prefectures. In columns 1 and 2 of Table 9, we report the OLS and the 3SLS estimation of the effects of famines in the 1920s on Nationalist war casualties before 1949. The instrumental variables are the same as in the previous section. The number of famines during the warlord period does not have significant impact on the number of subsequent Nationalist soldiers killed in wars. This suggests that Nationalist military forces were unable to—or did not try to—draft more people from regions hit hard by prior famines. We conclude that state failure in the 1920s in China did not have a general impact on people’s choice of joining armies for livelihood, but it provoked strong support of the Communist ideology.

# **6.5. Consideration of Nationalism**

Apart from economic hardship and perceived injustice, another factor which also helps explain the ability of CCP to recruit many peasants is peasant nationalism in response to Japanese invasion (Seldon 1971). To see if our explanation remains valid, we drop casualties during the Second World War, known as the Sino-Japanese War (1937-1945) in China—the war casualties during this period could be more motivated by nationalistic sentiments. We still obtain a positive and significant relationship between famines and the Communist war

> 14 The difference between the CCP and the Nationalist ideology is evident from the official statements that a new party member of each party has to declare joining. See <u>
firms. This is done in two steps.

First, we calculate the five moments for formal firms as described above: based on the average firm size among formal employers, own-account workers, and employees, the other three percentiles are calculated by imposing the relative position of each percentile to the mean as found in the WBES<sup>10</sup> .

Second, we produce the same calculation for the mean and percentiles of informal firms based on the average firm size of informal firms and then re-scale the percentiles of the informal distribution concerning the same percentiles of the formal size distribution. For this second re-scaling, we use microdata from Burkina Faso, Cameroon, Ghana, Rwanda and Kenya to extract the mean and four percentiles for both formal and informal and obtain the relative size of each percentile from the informal firm size distribution with respect to the same percentile from the formal firm size distribution. For instance, the p25 of the formal firms’ size distribution is 2 in Ghana, and 3 in Kenya, while the same percentile among informal firms is 1 both in Ghana and Kenya. This means that the p25 of the informal firms in Ghana is 1/2 of the p25 of formal firms and 1/3 in Kenya. We produce this calculation also for percentiles p50, p75, and p90 and keep the mean across the five micro-datasets. In the example, this means using 1/3 as the adjustment scalar for the p25 of . informal firms<sup>11</sup> The method we followed has several limitations. First, estimates of the number of firms using employer statistics might not be consistent with estimates of the number of firms from establishment censuses. Second, we are using only five countries and thus imposing the shape of the firm size distribution based on limited information. In further revisions, we expect to include more countries and characterize the differences in the percentiles between formal and informal.

# **3.4 Number of workers by size and formality**

By dividing the
**-** **<u>Table 4: Encompassing model using the pre pandemic dataset</u>**

||Linear OLS|PLREG|Linear OLS|PLREG|
|---|---|---|---|---|
|Country fixed effects|No|No|Yes|Yes|
|Log GDP per capita|0.659||-0.987||
||(0.918)||(0.805)||
|Voice and Accountability index|0.694|0.490*|-0.191|-0.553|
||(0.807)|(0.250)|(0.404)|(0.354)|
|Government Effectiveness index|-0.655|-1.330***|-0.574|-0.221|
||(1.045)|(0.323)|(0.473)|(0.348)|
|Tax revenue over GDP|0.165**<br>(0.067)|0.183***<br>(0.022)|0.008<br>(0.036)|0.020<br>(0.020)|
|Percentage of population that uses the internet|0.037**<br>(0.017)|0.016**<br>(0.008)|0.045***<br>(0.012)|0.049***<br>(0.006)|
|Share of the population age 65+|0.689***<br>(0.084)|0.619***<br>(0.033)|0.006<br>(0.
equation 4). Applying this type of estimator to the evaluation data for _PROGRESA_ , Djebbari and Smith (2005) find that they can convincingly reject the common effects assumption in past evaluations. When there is such heterogeneity, one will often want to distinguish marginal impacts from average impacts. Following Björklund and Moffitt (1987), the marginal treatment effect can be defined as the mean gain to units that are indifferent between participating or not. This requires that we model explicitly the choice problem facing participants (Björklund and Moffitt, 1987; Heckman and Navarro-Lozano, 2004). We may also want to estimate the joint distribution of _Y T_ and _Y C_ , and a method for doing so is outlined in Heckman et al. (1997a).

However, it is questionable how relevant the choice models found in this literature are to the present setting. The models have stemmed mainly from the literature on evaluating training and other programs in developed countries, in which selection is seen as a largely a matter of 57
Secondary 856.0 1 086.3 78.8 1.9 2.0 Note: Own elaboration. Survey estimates correspond to authors’ simulation of the fiscal incidence model; for indirect taxes, survey estimates correspond to tax collected from West Bank since the PA only collects taxes in this region. For transfers, survey estimates include both West Bank and Gaza since the PA distributes public expenditure in both regions. Fuel excise numbers are sourced from Ministry of Finance monthly reports, Personal Income Tax numbers are sourced from the Income Tax Department of the Ministry of Finance, while pension contribution estimates are from 2014 and imputed based on information on number of each scheme’s number of active contributors, average wage, and rate, sourced from the World Bank (2016).

* Bifurcated amount of government spending/allocation for fuel subsidy is not available. IMF estimates (2017) for the fuel subsidies indicates an amount of NIS 251 million which includes both implicit and explicit subsidies and is therefore larger than the estimate of total indirect subsidies discussed elsewhere in the report.

*** Bifurcated amount of government spending/allocation for domestic electricity subsidy is not available. The closest estimate of the size of the implicit and explicit subsidy is from 2015. This includes direct and indirect effects, however, and so our estimate is expected to be substantially smaller given that it only includes domestic subsidies.

# **9.2 Limitations**

## **9.2.1 Direct taxes and pension contributions**

## **Personal Income Tax (PIT)**

**The main challenge in modeling the PIT was the complexity involved.** Two different tax systems were modeled depending on region and worker status, and each with numerous tax exemptions and deductions. In both systems, information on the order and size of the deductions and exemptions was lacking.

**The PECS 2016/17 also does not specify whether income data is net or gross.** It was assumed that employees would provide
sources are not well diversified, especially in rural areas. Across the five countries, 55.5 percent of the population live in a household in which the main income earner engages primarily in agriculture, raising animals, or fishing. At the same time, relatively few Sahelians hold savings, and neither borrowing nor selling assets is widely used as a coping mechanism: 28.9 percent of the population live in a household in which any member held an account with a financial institution, just 16.1 percent of the population live in a household in which any member actually held savings, while livestock sales were not clearly associated with seasonality in the EHCVM sample. Despite this exposure and widespread poverty, government support for households is insufficient, with just 10.5 percent of Sahelians living in a household that received food during the 12 months prior to being interviewed, and 3.6 percent living in a household that received cash transfers.<sup>6</sup> Comparing the waves of the EHCVM data demonstrates the dramatic effects of seasonality on Sahelian households’ welfare. The share of the population living below the national poverty line in the wave corresponding to the lean season was 13.7 percentage points higher in Burkina Faso, 6.6 percentage points higher in Niger, and 8.1 percentage points higher in Senegal compared to the wave corresponding to the non-lean season. Pooling the data from these countries, mean real monetary consumption is estimated to be around 10.5 percent lower in the lean season. Controlling for region fixed effects and other household-level controls has little effect on these estimates. Moreover, this drop is concentrated in reduced consumption of staple foods (such as cereals), rather than the more ‘typical’ reduction in dietary diversity that might be observed as households try to adapt. Seasonality therefore brings about extreme forms of deprivation. Suggestive evidence also indicates that the welfare losses begin relatively early in the lean season, even as early as April.

Turning to heterogeneity, the effects of seasonality appear
first available time-varying dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015. We exploit the experience of Indonesia because it fits remarkably well the general pattern of trade policy over the past three decades: import tariffs liberalization since the 1990s and a contemporaneous increase the application of NTMs (Patunru and Rahardja, 2015).

Our novel dataset improves on existing NTM data in three crucial aspects. First, instead of looking at industry aggregates or categories of goods (e.g. food _vs_ durable), we focus on ten-digit _Harmonized System_ (HS-10 digits) products. Second, we exploit the most disaggregate available classification of NTMs to assign narrowly defined measures

> 1The United Nations Conference on Trade and Development (UNCTAD) defines NTMs as _"policy measures other than ordinary customs tariffs that can potentially have an economic effect on international trade in goods, changing quantities traded, or prices or both"_ .

2
were unemployed and 5 percent left the labor force, relative to approximately 15.5 percent of other age groups becoming unemployed (Table 5-17).

**Nationality:** Conclusions based on reported nationality should be interpreted with caution due to the small sample size for national minorities and potential biases in data collection. Nonetheless, for Jordan, our method predicts the general pattern. As shown in Table 5-14, our model predicts higher rates of PFPS job loss for Palestinian and “other” workers, and lower rates of job loss for Syrian workers. This pattern is confirmed, indeed magnified, in the CMMHH data.  However, the survey-measured projections do not project this general pattern. Once again, the model-based prediction gives a closer approximation to the demographic pattern of job losses observed in the CMMHH, lending support to that approach of assessing jobs at risk.<sup>51</sup>

# **5.5.2 Georgia**

**Gender:** In Georgia, our model similarly projects impacts disfavoring men more than women (Table 5-18). The model shows a 17.4 percent rate of PFPS job loss for females compared to 20.0 percent for men in wave 1 of the CFUWBES and a 2.8 percent rate of PFPS job loss for females compared to 7.9 percent for men in the Wave 2. However, the projected gender gap narrows when we utilize the survey-measured job loss rates.

_Table 5-18 Georgia: Comparison of PFPS Job Losses, Modeled, and Survey-Measured (including jobs loss due to firm closures), by Demographic Group_

||CFUWBES1<br>|CFUW<br>|BES2 (Cumulative)<br>|
|---|---|---|---|
||Modeled|Model-predicted|Survey-measured|
|By gender||||
|Female|17.4|2.8|6.4|
|Male|20.0|7.9|8.
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data modeling (MIMIC) approach. These approaches have been applied widely in the shadow economy literature but primarily for macro data (for the indirect and modeling approach) and without a proper economic foundation for the MIMIC approach.

We argue that our MIMIC approach provides the more accurate estimate of the extent of underreporting because it incorporates firm-level incentives to comply with taxes and a production function technology linking true sales with observable firm-level production characteristics. It also controls for measurement errors and allows for underreporting in both official tax and survey data.

The next table compares the results from the three approaches. For the direct approach, the predicted true sales is calculated by dividing the sales reported to the tax-office by the percentage of sales that are reported to the tax office by a typical firm according to the same firm (multiplied by 100). For the indirect approach, the predicted true is assumed to be equal to the survey sales and for the MIMIC approach it is equal to the conditional expectation. Comparing the predicted true sales with the actual sales reported to the tax office, we have a total of 186 firms for which we have an estimate of underreporting from each of the approaches (Table 5).

The indirect approach gives the smallest estimates of underreporting to the tax office. This low estimate reflects the sensitivity of the indirect approach to measurement error.<sup>36</sup> It

> 36 Measurement error and convexity in survey sales implies that ~~,~~ and therefore  the expected level of underreporting ( ) is biased downward.

33
were indeed widely debated in the media and consumers might have falsely expected those provisions to enter into effect imminently. After realizing that the rebates would not enter into effect until August, they conducted in May the purchases they had initially postponed in April. To account for this, we average these two outcomes over April and May 2014 in Figure 1. No change is applied to the data used in the regression discontinuity estimations, as these are run on weekly data.

49