rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The minimum wage is an increasingly popular policy tool globally. Studies on countries of various income levels and stages of development find that minimum wages boost labor income by raising wages in the formal sector, thereby lessening deprivation (de Janvry and Sadoulet, 1996; Devereux, 2005; Bird and Manning, 2008; Gindling and Terrell, 2010; Lustig and McLeod, 1997; Saget, 2001). Such labor wage regulations are intended to improve workers’ welfare, but a rise in labor costs can reduce the number of employees firms have the capacity to hire and retain, and can squeeze profit margins to such a degree that firms’ survival may be at risk (Draca, Machin, and van Reenen, 2011). The minimum-wage literature on firm-level responses has been growing in high-income countries in recent years, but it remains quite sparse for developing countries.<sup>1</sup> In addition, due to data limitations, firm-level outcomes are typically analyzed in isolation from worker-level outcomes. The issue warrants greater attention; firm-level responses and welfare effects of the minimum wage may be different in developing-country settings, given prevailing labor-market characteristics, such as weak allocative efficiency, low firm productivity, and high informality.

This paper addresses this gap by studying the effects of raising the minimum wage on formal firm destruction, and the accompanying impacts on informal and informal employment in the context of a developing economy. We analyze the repercussions of this increase by using an extremely rich, employer-employee-match dataset that covers the universe of registered private firms and formal workers in Turkey.<sup>2</sup> This enables us to look jointly at the impacts of the minimum wage on firms and workers. We supplement this with data from a nationally representative survey on labor-market outcomes of the Turkish population; these data include representative samples of those who are employed (in formal and informal work), and those who are unemployed or out of the labor force. We estimate the effects of the minimum
6 percent for the next five years on the public debt-to-GDP ratio is simulated using the IMF’s _World Economic Outlook_ (WEO) data for G7 countries. Compared to them, we use updated WEO data and expand sample countries. We also take account of the debt rollover ratio more explicitly and examine the effects of a temporary inflation shock as well as a persistent shock. Hilscher et al. (2017) and Equiza-Goñi (2016) also simulate the effects of inflation on public debt-to-GDP ratio based on the debt dynamics equation, but they use more detailed government bonds data (the former also uses option price data to estimate the distribution of inflation risks) focusing on the US and a few euro area countries, respectively. Aizenman and Marion (2011) simply use the US debt maturity in 2009 to approximate the effects of persistently higher inflation. End et al. (2015) uses a model of fiscal account dynamics for the euro area to simulate the effects of persistent inflation/disinflation shocks. Krause and Moyen (2016) uses a New Keynesian dynamic general equilibrium model with an imperfectly observed inflation target and simulate the effects of shocks to the inflation target. While our simulation approach is different

> 3 In some countries (most notably Japan but also euro area and the U.S.), central banks hold substantial amount of long-term government debt, and thus the debt-reducing effect of an inflation shock is partially offset by the loss of central banks. We assume that the loss of central banks caused by an inflation shock in our analysis is not so substantial to affect their credibility and monetary policy frameworks.

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, $730; Hungary, $1225; Latvia, $600; Lithuania, $870; Poland, $884; Romania, $1275; Slovak Republic, $1445; and Slovenia, $1075. All have lower border costs of exporting in 2010 than the $1731 of Armenia. But the border costs of exporting a container from Bulgaria suggests that EU membership is not a magic bullet for reducing the border costs of exporting down to the level of the Baltic countries. It appears reasonable,

> 32 For the raw Doing Business data see: http://www.doingbusiness.org/Data/ExploreTopics/trading-acrossborders.

> 33 Price index data are taken from the website of the Armenian Statistical Office. See http://www.armstat.am/en/?nid=126&id=07001.

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involvement in ensuring diary completion, including oral questioning and enumerator assistance, can blur the line between a diary and recall survey, but allow the method to act as a benchmark of reliable consumption (Beegle et al., 2012). Given these challenges and the importance of a highly accurate benchmark, we undertook strict protocols for implementation of the FAFH diary.

# **3. Experimental design and context**

To test different food expenditure data collection methods, we implemented a randomized survey experiment in Vietnam across approximately 2,400 households in urban Hanoi. In collaboration with the General Statistics Office (GSO) of Vietnam, we took the biennial VHLSS as a starting point for design of the experiment and implemented four additional treatments varying the recall period and FAFH collection method. The survey, fielded between August and October 2016, was conducted entirely using computer assisted personal interview (CAPI) methods utilizing the World Bank’s CAPI software Survey Solutions.

## 3.1 Experimental design

Households across 40 enumeration areas (EAs) in urban Hanoi were randomly assigned to one of five treatment groups. The randomization was performed within the EA so that in each EA there were 12 households assigned to each treatment group. The five treatment arms had several variations in terms of the recall period, the respondent answering the FAFH section, the number of times the household is visited, as well as the methods used in inducing accurate recall (see Figure 1). For the purpose of this paper – which focuses on variations in data collection methods to capture FAFH consumption rather than on the recall period - we omit analysis of the data collected in the “status quo” arm, T0, which was unrelated to FAFH.

The first arm, T1, followed the standard VHLSS methodology and asked for an aggregate of all FAFH for only three categories: (1) total FAFH of present household members, (2) total FAFH of temporarily absent household members, and (3) other FAFH. We expect that these household-level aggregated categories
Figure 2a: Africa’s production of metals and minerals, 1996–2012 <!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>.5<br>1998 2000 2002 2004 2006 2008 2010 2012<br>Year<br>Iron ore Manganese Chromite Copper<br>Source: Authors' construction using USGS data<br>Figure 2b: Africa’s production of metals and minerals, 1996–2012<br>1.7<br>1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>1.1<br>1<br>.9<br>1998 2000 2002 2004 2006 2008 2010 2012<br>Year<br>Aluminum Steel Petroleum Coal<br>Source: Authors' construction using USGS data<br>1000 metric tonnes<br>in constant 2000 levels<br>1000 metric tonnes<br>in constant 2000 levels<br><!-- End of picture text --> 26
# Table 3.  Panel fixed-effects regression of output growth on commodity prices

|||**D**|**ependent var**|**iable: GDP**|||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Explanatory variables**|**EMDE**|**EMDE**|**EMDE**|**Full**|**Full**|**Full**|
|**Commodity Export Price Index (EPI)**|**0.085 *****|**0.063 *****|**0.080 *****|**0.024**|**0.018**|**0.026**|
|**Terms of Trade**||**0.029**|||**0.026**||
|**GDP (-1)**|||**0.367 *****|||**0.366 *****|
|**Commodity Export Price Index x EMDE**||||**0.062 ****|**0.046 ***|**0.054 ****|
|**Observations**|**381**|**364**|**370**|**533**|**436**|**518**
household specific errors are generated as and, in the case of heteroscedasticity, as Construct the vector _yc_<sup>_∗_= (</sup><sup>_yc_1</sup><sup>_, . . . , ycN_</sup> _c_<sup>)</sup><sup>_T_of simulated welfare for every household within location</sup> _c_ of size _Nc_ , for all the locations _c_ = 1 _, . . . , C_ in the census.

> 12Interested readers should refer to Van der Weide (2014) and/or Nguyen et al. (2018) for an in-depth look at how these are obtained under ELL and Henderson’s method III.

> 13In the traditional ELL method of Section 2, _σ_ ˆ _η_ 2<sup>isassumedtofollowaGammadistribution,whichdoesnotholdin</sup> this case.

> 14Actually, Van der Weide (2014) does not offer a method for the estimation of the standard errors, but the approach described below was the one implemented on PovMap and consequently also in the Stata sae package from Nguyen et al. (2018).

> 15Note that not all clusters are expected to appear

> 16Note that, in each bootstrap replicate, the census is generated from a different model.

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distributional impacts of infrastructure investments, industrial policies, and financial shocks (see e.g. Acemoglu et al., 2012; Liu, 2019; Demir et al., 2022; Balboni et al., 2023; Castro-Vincenzi et al., 2024; Demir et al., 2024; Huremovic et al., 2024; Donaldson, 2025) and inform tax policy and the design of social protection programs.<sup>2</sup> Formalsector data may not be representative of the overall spatial distribution of trade, especially if formal firms are concentrated in specific sectors and regions and exhibit distinct linking patterns. Given the scarcity of granular data on the informal sector, we lack empirical evidence on the nature and degree of the bias caused by neglecting informality.

We address this question by combining transaction-level administrative tax records of over 76,000 formal firms and 5.8 million firm-to-firm relationships in Kenya, with micro and aggregate data on informal sector activity obtained from labor force surveys and national accounts.<sup>3</sup> We first

> 1Transaction-level survey data (see e.g. Startz, 2021) or administrative industry-specific data (see e.g. Hansman et al., 2020), which often cover a wider range of firm and buyer-supplier characteristics, are a popular, albeit costly, complement to data relying on tax records.

> 2An advantage of transaction-level firm-to-firm trade data over traditional sources like input-output tables is the possibility to track spatial heterogeneity in economic activity, rather than being limited to national aggregates.

> 3The labor force survey module was integrated in the 2019 population census. The public micro data covers

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associated with such an intervention, it amounts to around $5,300 per case. Compared to a typical benchmark for cost-eectiveness of $150, targeting all travelers would be 35 times as costly. Therefore, if a policy were to target travelers, only a subset should be targeted.

There are two ways that a policy can be targeted: (1) it can target certain destination areas for travelers or (2) it can target travelers from specic origin areas. For both cases there are two sets of costs: (1) xed cost, which consists of training community health workers, health post nurses, and district health supervisors in investigation of travel cases as well as costs for weekly electronic data transmission; and (2) variable cost, which are the costs associated with investigating and treating travelers.<sup>26</sup> I use the Fiscal Year 2015 Malaria Operational Plan for Senegal for the policy simulations. Based on this plan, $400,000 was allocated for case investigation and targeting individuals in districts with incidence less than 5/1000. Given there were 19 such districts in 2015, I assigned the funding proportional to district population in order to assign an amount to the ve districts that are part of the simulation. This leads to $127,850 dedicated to the ve low malaria districts studied here. I assume an even split between xed and variable costs. To calculate the variable cost per person, I use the information available from Richard Toll where 3,609 people were targeted in 2015. Variable funding for that district of $10,286 leads to a per person cost of $2.85.

The rst type of targeting focuses on destination areas. Given some set of resources R meant to target travelers, those resources can either be distributed across all health facilities or they can be concentrated in a smaller set of facilities in certain areas and in certain months. The most naive method for targeting would be to randomly select health facility areas and
the poor, particularly for FCi∗(x) ∈[10,40].

ˆ We also test for robustness with respect to the choice of α . For this purpose, we repeat ˆ the analysis using several values for α (Figure 6). Since the first derivative of the adjustment term e<sup>−α�(Li−LR)</sup> with respect to α� is negative for “high” supplier (−(Li −L<sup>R</sup> )e<sup>−α�(Li−LR)</sup> < 0, ∀Li > L<sup>R</sup> ), we expect to observe a greater impact on targeting performance on the lower part of the distribution when using higher α� (the lower e<sup>−α��Li−LR�</sup> the higher the impact). This is confirmed by Figure 6. The second derivative with respect to α� is positive ((Li −L<sup>R</sup> )<sup>2</sup> e<sup>−α�(Li−LR)</sup> ≥0) and we observe consequently a diminishing increase of the impact when increasing α�.

Redoing the same estimation but on the NSS Bihar sample and the Bihar Survey, we find that f(x) is flatter than f<sup>∗</sup> (x) for the poorer half of the distribution (Figure 5). In the Bihar

> 17 Recall the category un-rationed includes also households reporting that they do not want to participate in NREGS works.

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per capita consumption. We present the results by decile or quantiles of 5 than one survey year was available for a particular country, we used the most recent survey year.

9 This note of caution is particularly important for country-specific results. Some but not all LSMS surveys are selfweighting. In this analysis no weights have been used to adjust for sample design or non-response. Thus, the results are applicable for the sample population only.

**10** Purchasing power parity conversion would have been preferable, but consumption-heading conversion factors (which would have been used to convert information on expenditures) were not available for all sectors, all countries, or all survey years.

" The consumption aggregates used here were prepared by LSMS survey research teams. The aggregates combine information collected from households about their expenditures on and consumption of a host of food and non-food items.

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14 high-income country guidelines that arguably are more aligned with pure need. Specifically, we now use the rate of women in the 20-69 age group who had a pap smear in the three years prior to the survey interview. The three-year interval is used in the United States as well as in most countries in the European Union (International Agency for Research on Cancer 2017). There is more heterogeneity in the age-groups recommended for screening across high-income countries, but the guidelines typically recommend screening from a woman’s 20s to her 60s.<sup>12</sup> Whenever the raw survey data permit, we therefore use the 20-69 age-range, which is also used by OECD (OECD 2018).

We continue to apply the same method to standardize pap smear rates with different reference periods in the original survey data as for the 2018 HEFPI database.<sup>13</sup> Nevertheless, the shortening of the reference period from five to three years and the extension of our preferred agegroup from 30-49 to 20-69 years changes the values of all previously presented pap smear data points in the 2019 HEFPI version.

The 2019 HEFPI database makes a more minor change to the definition of two additional indicators: skilled birth attendance and having 4 or more antenatal care checks. Here, we change the age-range<sup>14</sup> of women for whom we compute the indicators from 18-49 to 15-49. Most surveys with maternal and child health modules like the DHS, MICS and Reproductive Health Surveys (RHS) use the 15-49 age-range to collect data on pregnancy outcomes. We nevertheless used the 18-49 age-

> 12 In the United States the recommended screening age-range is 21-64 and the commonest range in the European Union is 25-64, although only 28% of countries use this range (no European Union country guideline recommends ending screening at age 49) (International Agency for Research on Cancer 2017).

13 When a survey used a pap-smear utilization question with a reference period other than 3 years, we transform the survey utilization
aid-dependent groups. As a result, the multipliers are very imprecisely estimated, to the point of being meaningless, and it is not possible to draw any conclusion about differences in multipliers by the degree of aid dependence.

Summing up, the novel DRS loan-level data on which the instrument developed in this paper is based covers a very large set of developing countries. This makes it feasible to investigate a range of plausible hypotheses regarding various potential sources of heterogeneity in government spending multipliers in developing countries. This section has provided some suggestive (although not statistically significant) evidence that the short-run effects of government spending do vary with the state of the business cycle, the degree of trade openness, the exchange rate regime, and (to a limited extent) with sample. Consistent with the predictions of the Mundell-Fleming model with capital mobility, they find that multipliers are higher under fixed exchange rate regimes in their sample of countries. They also find higher multipliers in countries that are more open to trade, as I do here.

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"Infrastructure Coverage and the Poor: A Global Perspective" Komives, Whittington, and Wu connections. 22 Between 40 and 50 percent of the poorest urban households who had access to these three services in their communities had connections in their homes. This is much lower than the coverage rate among the richest urban households, but it does mean that nearly half of urban households with monthly household incomes around US$32 per month (as measured by our consumption aggregate) chose to install water, sewer, or phone service when these services were available to them. The percentage of households with access who connected was highest for electricity service in both urban and rural areas. Sewer, in-house tap, and telephone connection rates follow in that order.<sup>23</sup> These figures do not necessarily imply that households would prefer electricity over other services if they could choose from among all four services (electricity, in-house water, sewer, or telephone). Of the households for whom access information is available on the four services, only 30 percent had access to all four.'<sup>4</sup> Nearly all (98 percent) of these households had electricity, 82 percent had sewer connections, 75 percent had in-house water taps,<sup>25</sup> and 50 percent had phones. Because the LSMS surveys lack information on service prices, it is not possible to determine how differences in the price and connection fees for these services contribute to this outcome.

# **5. Household coverage: a multivariate analysis**

The results presented in sections 3 and 4 demonstrate that infrastructure coverage varies with household income, by country of residence, and between urban and rural areas. In this section we downside, the community data roughly divide households into two groups: those with no possibility of connecting to a network and those who may have a chance of connecting.

22 It is possible that the low connection rates in rural areas is more a reflection of problems with the access
of formal work positively associated with the level of productivity; and second, since pension systems (dis)incentivize savings and capital accumulation. Tkhir (2021) shows that the actual 2019 Brazil pension system reform—which reduced the replacement rate, increased the required years of contribution and raised the retirement age—led to a drop in the share of informal workers as well as to an increase in capital accumulation, both with likely positive productive effects. Reforms involving rises in payroll taxes, increase informal work and disincentivize capital accumulation, likely leading to negative effects on productivity. Reforms involving a reduction in the replacement rate or a rise in the required years of contribution or an increase in the retirement age, reduce informal employment and stimulate capital accumulation with likely positive effects on productivity.

Carta et al. (2021) show another mechanism through which raising the retirement age might increase productivity, i.e. maintaining productive older workers at work. The authors exploit a policy-induced sharp increase in retirement ages on workers of different ages using Italian matched employer-employee data to find that an increase in employment of older workers (as a consequence of the reform) leads to a precisely estimated rise in value-added. The findings suggest rising institutional retirement ages can help firms to retain valuable older employees.

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monetary poverty, would be required. This also serves to highlight the essential role public spending on health and education could have on children’s schooling and health access. Finally, a comprehensive analysis of the distributional effects of current fiscal policies on children is essential to estimate the potential impact of policy changes. Simulations of new policy approaches or changes to existing tax or spending regimes provide a clearer understanding of the policy’s impact on poverty and inequality and provide policy makers with essential information to guide evidence-based decision-making on public finance.

The Ethiopia case study offers an opportunity to examine the fiscal space in an environment with high child poverty and high child undernutrition. In 2016, approximately 88% or 36.2 million children in Ethiopia were multidimensionally poor, meaning they were deprived of the fulfillment of multiple rights or needs for basic food or services (CSA and UNICEF Ethiopia 2018). And despite progress over the previous decades, the most recent Demographic and Health Survey 2
parenting practices based on 24 items adapted from the Longitudinal Study of Australian Children (LSAC) (Zubrick et al. 2008). The primary caregiver of each child was asked a range of questions about their parenting practices that reflect different levels of warmth, consistency, and hostility. The resulting scores for parenting practices were standardized to have a mean of 0 and a standard deviation of 1.

# <u>ECED Characteristics</u>

Lastly, our data also included ECED characteristics averaged at the village level. The Early Childhood Environment Rating Scale-Revised (ECERS-R) – a classroom assessment tool designed to measure the quality of early education through classroom observations – was applied to two ECED centers in each village (see Brinkman et al. 2016 for details of the ECERS-R in Indonesia). Similarly, our data included information on the average monthly fees of ECED services. We used the monthly mandatory fee charged by the services directly to the families, which ranged from zero (free) to 67,500 Indonesian rupiahs, with an average of 10,779 Indonesian rupiahs. Only 303 villages out of 310 were sampled for ECED characteristics; as a result, we have missing observations for ECED characteristics for children in these 7 excluded villages.

# **5. Differences in enrollment patterns and gaps in test scores**

Table 2 documents the various enrollment patterns observed in the data and how they vary between children of different backgrounds. 39.7 percent of the children from the bottom quintile report having no ECED at all. This is in contrast to 10.5 percent of children from the top wealth quintile. 7.6 percent of children from the bottom quintile report having attended playgroup and then kindergarten before primary school. By comparison 22.7 percent of the top quintile follow this enrollment pattern. Enrolling in kindergarten before primary school is much more common among the top quintile – 50.4 percent of this quintile does so – while only 28.3 percent of the bottom quintile does so.

As Table 3 documents