rafmacalaba/data-use-mentions-extended huggingface dataset:
higher-frequency industrial production (IP) data suggest that emerging economies did recover more strongly than advanced economies. Still, there was significant heterogeneity among emerging economies, with Eastern Europe and Central Asia faring the worst. Overall, lowincome economies were more isolated from the global shock.

Our findings put in perspective the concept of resilience to crises and how the arguments in the literature square with the evidence. The popular perception is that emerging economies fared, on average, substantially better than advanced economies during the 2008–2009 global crisis (Eichengreen, 2010). Researchers justify this claim by presenting evidence of a negative association between some indicators of economic performance around the crisis and the level of GDP per capita (Claessens et al., 2010; Frankel and Saravelos, 2010; Lane and Milesi-Ferretti, 2010; Rose and Spiegel, 2010, 2011a, and 2011b; Rose, 2011).

Our results differ from those in the literature for at least two reasons. First, when using GDP growth as an indicator of economic performance (as in Frankel and Saravelos, 2010; Rose and Spiegel, 2010, 2011a, and 2011b; Rose, 2011), it is true that advanced economies attained lower rates of GDP growth during the crisis. This measure, however, could be capturing that advanced economies grow slower on average than emerging economies independent of any crisis event. In contrast, the analysis of _collapses_ in GDP growth suggests that, on impact, the crisis was not more severe in advanced economies. Second, although other studies measure crisis incidence in terms of changes in GDP growth (Blanchard et al., 2010; Claessens et al., 2010; Lane and Milesi-Ferretti, 2010), we identify a non-linearity between the collapse in GDP growth and GDP per capita that was ignored in those studies. Collapses in GDP growth around the global crisis did not increase monotonically with GDP per capita. Instead, the largest collapses in 3
intervention. Without these building blocks in place, Duncan and Pollard suggest that there will be no growth in incomes. For example, investment projects in countries with no civil and social order have historically yielded dismal results.

The OECD (1999) reviews the lessons of experience on what works and what does not work in poverty reduction. It identifies three (3) issues generally regarded as critical: (i) the choice of the appropriate level of targeting; (ii) encouraging participation; and (iii) factors affecting sustainability. It reviews the pros and cons of targeted intervention; notes the “clear and demonstrable” benefits of stakeholders being in a position to influence the design and implementation of the intervention, while acknowledging the significant opportunity costs of participation for poor people; and lists rules for improving the sustainability of povertyfocused projects.

# _Typologies of Pro-Poor Interventions_

While acknowledging that there is no easy way to define “pro-poor spending,” Hentschel (2002) offers three alternative definitions: (i) spending that benefits the poor more than the non-poor; (ii) spending that actually reaches the poor; and (iii) spending that has an impact on the welfare of the poor over time. The merit of these definitions is that they begin to make explicit the concept of pro-poor, distinguishing between the relative and absolute impact on the poor and the long-term impact on poverty. Secondly, they implicitly underline the importance of empirical verification of the impact of public spending on the target group, using analytical techniques such as benefit incidence, service delivery surveys and expenditure tracking.

It is also important to acknowledge that the choice of public intervention must also take account of the nature of poverty being addressed. In a review of the social impact of its adjustment operations, the World Bank (1995) suggests that it is useful to distinguish between transient poverty and chronic poverty. Public expenditure policies are then designed differently depending on the nature of poverty being addressed. For example, with respect to transient poverty (poverty associated with abrupt changes
- heterogeneity in the “free or other” category, where significant number of relationships is likely to imply in-kind payments including labor;

- stigmatization of tenants in an environment of mass homeownership, which might lead to false claims of tenure status;

- sharing of residential dwellings, where two households, often related, occupy a dwelling. By definition only one will be registered as formal owner or tenant, so the other should be deemed as renting, even though without a formal contract and payments.

It is strongly recommended that countries interested in explicit rental housing policies should design systematic and consistent rental housing surveys, to avoid expensive incidental research efforts to distill the required data and information. An outline of such a survey is provided in Annex 2.

Bearing these limitations in mind and considering other data sources and anecdotal evidence allows the following preliminary rapid assessments for each country (Figure 4):

- According to the LSMS, the rental market in Armenia is relatively small at 7.3 percent, of which the lion’s share (5.8 percent) is non-privatized public apartments. Both ratios are substantiated by the 2001 census results. The private rental sector share of 1.3 percent is marginal, considering the country’s share of M-F stock, and likely has a significant element of under-reporting caused by tax evasion and tenants’ confusion about their status. This sector is concentrated in Yerevan. The public rental sector, in contrast, has remained relatively large, since non-privatized stock owned by the central government was transferred to the municipalities. The share is the highest in the earthquake zone of Gumri and Vanadzor.

- The 2002 household survey undertaken in Lithuania by the Lithuanian Free Market Institute<sup>22</sup> shows that 17.4 percent the households live in non-owner-occupied housing. A total of 10.5 percent of the households polled live in housing owned by other individuals; 3.8 percent in state-owned or municipal housing; and 1.7 percent in housing units that belong to an enterprise
to provide support to those falling outside of the contributory safety net to ensure that they are able to meet their basic needs and manage shocks has been critical.

Nevertheless, public social assistance has developed slowly in Turkey. The foundations of the current social assistance system were created in 1976 with the approval of Law 20224, which provided a small monthly benefit payment to the elderly or disabled poor, though coverage remained low. It

> 2 Camacho et al. (2014) and Gasparini et al. (2009) find similar results for the cases of Colombia and Argentina, respectively.

> 3 Household Labor Force Statistics, TurkStat.

> 4 Law on the Payment of Pensions to Old-Aged Persons [65 years old and over] who are Destitute.

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below that the key results are robust to using a more typical measure, namely the average predicted per capita expenditure across all households in each village.

The 20 percent census extract contains village, traditional authority (TA) and district names but not household or village geocoordinates. Obtaining information on the physical location of census villages is crucial for this exercise. Therefore, we match census village names with UBR administrative data, which contains the names of the administrative areas as well as the geocoordinates of interviewed households. This enabled us to calculate centroids based on the minimum and maximum latitude and longitude of households living in that village in the UBR administrative data as an approximation of the village centroid in the census for about 4,500 villages. These centroids were then matched to a set of grids constructed to cover the country, in order to link remote sensing information to the census.

The remote sensing indicators were obtained from Google Earth Engine, WorldPop, and Meta. For each village, we calculated the average of approximately 40 indicators: landcover indicators (e.g., percentage of vegetation, water, or build-up coverage), global precipitation measurement, soil moisture, nighttime data, and year of the transition from pervious to impervious areas. This was supplemented with gridded maps of building patterns (e.g., number, area, and length of buildings, among others) in 2017, population density indicators, build settlement growth, and distance to major roads taken from Worldpop. Grid-level averages of these satellite-derived features were then linked to the census data using the village centroids obtained from the matched UBR data.

This paper considers three main research questions: (1) How much does a hypothetical partial registry improve predictions of village-level welfare in this context, as compared with the existing UBR and two other feasible alternatives? (2) How much does increasing the size of the partial 3
of the Iraq Household Socioeconomic Survey (IHSES).

In practice, MoLSA does not observe potential household beneficiaries’ poverty status, as this would require administering an intensive, time-consuming and expensive household expenditure survey which is not practical to be conducted for all potentially eligible households. Instead, the MoLSA determination of eligibility is based on the PMT scoring system and the application process has the following steps:

1. Application:

- a. Apply on the MOLSA website, screening questions determine if a home visit is warranted;

- b. Applications process closed May 2016, with 340,000 waiting to go for MOP scoring.

2. Home visits:

- a. For those households passing the screening question, social workers visit the home and collect data on a range of indicators as advised by the PMT formula.

3. Household scoring:

- a. Ministry of Planning (MOP) estimates household per capita consumption using the PMT indicators;

- b. Models updated when new national household survey data available.

4. Eligibility determined:

- a. Households with estimated per capita consumption under the poverty line.

> 6 The 2017/18 Survey of Well-Being via Instant and Frequent Tracking (SWIFT) could not be implemented in 14 of the 120 districts due to continuous insecurity. The direct poverty estimate from the data without simulating for the missing districts is 18 percent.

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who had not attended school. For example, many children knew how to count and answer the simplest word problems even if they had not attended formal schooling. Short stories that were part of the original test were rewritten during the pretesting phase so that questions associated with auditory comprehension would be accessible to children who were illiterate.

# _SES and Sociodemographic Characteristics_

The key proxy of SES used in the analyses is a household wealth index in 2004 when children were 0–3 years, divided into quintiles. The index was constructed using principal component analysis of key durables, assets and dwelling conditions<sup>2</sup> (Filmer and Pritchett 2001). This SES proxy is less likely to suffer from the concern about measurement error that generally affects income variables. Furthermore, an index for wealth is often used in the analysis of SES gradients in child outcomes in both developed and developing country contexts, and commonly adopted in descriptive analysis of Demographic and Health Surveys. As described in greater detail below, a full set of dummy variables for wealth quintiles were included in our regressions, and the results translated back into English to ensure linguistic and functional equivalence. More information on each child test is available in appendix 1.

> 2. The wealth index is the first principal component of the following variables: walls, floors, roof, home ownership, whether the toilette is shared, whether household has access to safe water, ownership of chairs, phone, motor vehicle, cattle ownership, and poultry ownership. The sample is representative of rural and small urban villages (excluding large cities in all regions and the capital).
to permanent increases in income. One of the key insights from the model is that credit constraints are only an obstacle to productive entrepreneurship for a subset of individual types; less productive types are unable to sustain a business in any steady state. Nonetheless, savings constraints can also affect the investment decisions and occupational choices of lower productivity types who receive one-off infusions of funding or capital; though these individuals cannot sustain businesses, they may invest in capital and launch unproductive firms because enterprise capital is a technology for saving, albeit at a negative interest rate. Thus, short-term impacts of one-off transfers on entrepreneurship should not be taken as evidence that a program relieved a credit constraint or addressed a poverty trap; the critical issue is whether impacts on income persist over the longer-term.

We find that both the franchise treatment and the grant treatment led to substantial increases in income in the year after the interventions. Point estimates suggest impacts that are both economically and statistically significant: the franchise treatment increased weekly income by 30 percent, up 1.6 US dollars from a mean of 5.5 dollars in the control group (p-value 0.035); the grant treatment increased weekly income by 3.2 dollars (p-value 0.008) or 56 percent. As expected, these impacts appear to be driven by a shift from paid work to self-employment; women assigned to either the franchise or the grant treatment are approximately 10 percentage points more likely to be self-employed than those in the control group. Women assigned to the grant treatment also increased their labor supply (hours worked) substantially.

Though both interventions increased income in the relatively short-run, data from endline surveys conducted between 14 and 22 months after treatment indicate that the observed impacts on income disappeared in the second year after the program(s). At endline, women assigned to either the franchise treatment or the grant treatment are more likely to be selfemployed than women in the control group, but the treatments are not associated with increases in income or labor supply. In addition,
throughout the period of study. The reductions from the 630 baseline occur due to the following. First, the ASI surveys only record data for about 400 districts due to the lack of organized manufacturing (or its extremely limited presence) in many districts.<sup>8</sup> Second, we drop states that have a small share of organized manufacturing.<sup>9</sup> Last, we make an additional restriction for our regression sample that manufacturing activity in terms of plants, employment, and output in districts be observed at all points from 1994 to 2007.

The requirements with respect to continuous measurement of districts are motivated by a desire to have a consistent sample before and after the GQ upgrades. The requirements with respect to minimum share of states in organized manufacturing are motivated by a desire to have reasonably measured plant traits, especially with respect to labor productivity and plant TFP. With respect to the latter, we also exclude plants that have negative value added, which accounts for 6%-7% of employment. These restrictions are again not very significant in terms of economic activity, with our final sample retaining more than 90% of Indian manufacturing activity.

Our next step is to measure the distance of districts to various highway networks. We calculate these distances using official highway maps and ArcMap GIS software. Our reported results use the shortest straight-line distance of a district to a given highway network. We find very similar results when using the distance to a given highway network measured from the district centroid. The empirical appendix provides additional details on our data sources and preparation, with the most attention given to how we map GQ traits that we ascertain at the project level to district-level conditions for pairing with ASI data.

Our empirical specifications use a non-parametric approach with respect to distance to estimate treatment effects from the highway upgrades. We define indicator variables that take a value of one if the shortest distance of a district to the indicated highway network is within the specified range; a value of zero
# **Utility from shopping**

Perhaps people make frequent, small-quantity purchases because there is a utility value from shopping—e.g., from the socializing and community engagement that one enjoys in the market. An obvious problem with this hypothesis is that people can visit shops and markets without making purchases, and do so frequently. That said, if one believes in a very specific social contract that relies on frequent purchasing in small quantities, that could generate the observed patterns in the SHWALITA data.

It is straightforward to show that the data contradict this hypothesis. Many households make purchases both above and below _qmin_<sup>_∗_.</sup> By rearranging purchases to always purchase exactly _qmin_<sup>_∗_, households could increase the total number of purchases while always</sup> buying in bulk.

To illustrate, define _Khi_<sup>_∗_=</sup><sup>_e_ˆ</sup><sup>_hi/e∗_</sup> _min_<sup>.Thisisthemaximumnumberoftransactions</sup> that household _h_ could make on item _i_ at the lowest available unit price, without changing total expenditure on the item. The actual number of transactions that household _h_ makes on item _i_ , _Khi_ , could be smaller or larger than _Khi_<sup>_∗_.Thevalue</sup><sup>_K_</sup> _h_<sup>_∗_=�</sup> _i_<sup>_K_</sup> _hi_<sup>_∗_tellsushow</sup> many purchases the household could make if its goal were to maximize total purchases—to shop as much as possible—while only buying at the lowest available unit prices.

Table 8 shows the mean values of _Khi_ , _Khi_<sup>_∗_, and the difference</sup><sup>_K_</sup> _hi_<sup>_∗−Khi_, as well as the<
Table 1: Critical debt level _B_<sup>_∗_</sup> , % of GDP

|Country|AUS|AUT|BEL|CAN|DNK|FIN|FRA|DEU||
|---|---|---|---|---|---|---|---|---|---|
|_B_<sup>_∗_</sup>|78.9|58.9|56.1|75.9|76.7|54.1|59.5|58.1||
|Country|IRE|ITA|JPN|NLD|PRT|ESP|SWE|GBR|USA|
|_B_<sup>_∗_</sup>|56.4|54|79.1|57.1|58.3|57.4|77.2|78.8|79.2|

The calculation of these thresholds are based on prudential debt targets advocated by the OECD and meant as part of a simulation exercise. The numbers should not be interpreted as policy suggestions.

data with panel PVAR is performed before the main empirical analysis. Main empirical results are presented with two alternative definitions of high-debt regime, a model-based from the last section and a conventional one.

# **3.1 Overview of the narrative data**

Cross-country empirical studies of the effects of fiscal policy have suffered from a lack of data: quarterly narrative series exist for a number of advanced economies, such as Romer and Romer(2010) for the United States and Cloyne (2013) for the United Kingdom. Devries et al. (2010) constructed cross-country narrative series at annual frequency, but such data were not available at quarterly frequency until recently. In this section, I use a newly-constructed IMF quarterly panel dataset of narrative tax series to study expansionary austerity.

The narrative dataset is compiled by the
correct for biases due to non-random selection of respondents within the household (Brubaker et al, 2021; Kugler et al., 2021). Therefore, while household level outcomes such as self-reported income declines are plausibly representative, individual level outcomes such as employment mainly apply to household heads. See World Bank (2023) for more information on the HFPS.

The outcome variables taken from the HFPS are listed in Table 1 and include income change, employment, food security, and prevention measures such as social distancing and quitting schools. Work stoppage is measured in the first wave of the phone survey in each country, and is constructed based on the questions “Was the respondent working at the time the survey was conducted?” and “Was the respondent working before the pandemic?”. In particular, work stoppage is set equal to 1 if a respondent reported that they were working prior to the pandemic but not currently working at the time of the survey, 0 if they were working prior to the pandemic and were currently working at the time of the survey, and missing if they were not working prior to the pandemic.

Income change is measured by asking respondents whether their household’s income has increased, decreased, or stayed the same since the start of the pandemic (first wave only) or since the last survey wave (all subsequent waves). When looking at the evolution of the crisis over time, we combined these two questions into a single measure, in order to include all waves. Income change is further asked separately for each income source including farm, nonfarm, wages, and pensions. The question for total income change is “Has your Total Household Income changed since the pandemic started?” There are 5 categories of answers: 1 = Increased, 2 = Stayed the same, 3 = Decreased, 4 = Not received, 9 = Do not know. We create a variable on income loss, where “Decreased” is recoded as 1, “Do not know” and “Not
per square kilometer and per year over the period 1995-2010.<sup>8</sup> This is a strong instrument as is evidenced by the Kleibergen and Paap (2006) test (henceforth KP) statistic of 66 reported in the bottom panel of column 7 of Table 2. As was shown in Figure 1b areas buffeted by more intense lightning strikes have worse internet access. The 2SLS estimate of the impact of the internet on Daesh recruitment is statistically significant at the 10 percent level and is three times as large as its OLS counterpart. Our exclusion restriction hinges on the assumption that lightning strikes affect the propensity to join Daesh only through the availability and quality of internet. As lightning is correlated with rainfall ( _ρ_ = 0 _._ 63 in the data), which could affect economic development (e.g. through higher agricultural productivity), column 7 controls for averages of nighttime lights and rainfall over the period 1995-2010, which is also the time interval the instrumental variable is constructed from. Choosing lagged variables further mitigates the possibility that internet access has had an impact on nighttime lights, which could bias the first stage. In column 8, we revisit these results by controlling for delegation-level measures of radicalization into violent extremism used in column 5; if anything, doing so produces larger estimates for the of internet access.

**Robustness** Table 3 shows the results of a number of robustness checks. Where applicable, we present both OLS regression results (with delegation, age, and education fixed effects as in Table 2, column 6) in panel A and 2SLS estimates in panel B, using lightning strikes as instrument for internet access rates (as in Table 2, column 7).

We start by estimating three alternative specifications. In Table 2, column 7, we argued that satisfying the exclusion restriction requires controlling for GDP proxies and climatic data. We therefore added rainfall and nighttime light intensity variables as controls, and averaged these variables over the same time span as for the instrument,
are at the bottom of the income distribution.<sup>6</sup> According to 2000 Census, 44 percent of indigenous groups are in the bottom 20 percent of the overall distribution of income, and 80 percent in the bottom 50 percent (Hall et al 2004). Hence, indigenous peoples account for about a fifth of the extreme poor that is, over twice their population share. Moreover, indigenous groups typically suffer higher levels of deprivation in terms of access to public services.

> 6 The ENIGH does not include a question on ethnicity.

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