rafmacalaba/data-use-mentions-extended huggingface dataset:
p < 0.01.

We take a number of steps to assess robustness. First, we re-estimate our pooled effect dropping one matched triplet at a time (Figure E.4), dropping each possible pair of matched triplets (Figure E.5), or dropping each week (Figure E.6). Second, we estimate the effects by month to assess whether our results are driven by a particular moment in the crisis. The pooled coefficient is positive across every month, and we find significant effects in October, December, February and April (Table E.3). (We find large and significant effects for CM in October 2014 and April 2015; for NFA, in October and December 2014.) The spread of these effects across various months throughout our sample period verifies that the effects are not driven by any particular period. Third, we conduct a placebo test where we substitute the nearest out-of-sample neighbor for each section. We find no significant effects in Table E.12, alleviating concerns that our treated sections are spatially clustered in areas where reporting is higher for reasons unrelated to treatment (e.g., exposure). Finally, we adopt a number of alternative specifications to handle our count data. In the bottom panel of Table 3, we use the inverse hyperbolic sine transformation of the counts and find effects of similar magnitude; the effect of NFA on confirmed cases becomes significant in this specification. We expand upon this in Table E.8, which presents estimates using a linear probability model, the logged count (adding 28
production). In other cases, however, women dominate employment in export manufacturing (e.g., in Bangladesh, Cambodia, Nicaragua, and the Philippines) and highvalue agriculture (e.g., in Ecuador, Thailand, and Uganda) (Dolan and Sorby 2003; Kabeer, 2008).Women employed in these industries will likely suffer direct employment losses from the contraction in industrial countries’ demand for developing country exports. In Thailand, during the financial crisis of 1997, women were the majority of all retrenched workers in sectors such as garments, toys, knitting, electrical appliances, jewelry, plastic products, and shoes and leather products (Mahmood and Aryah 2000).

There is anecdotal evidence that, unlike in previous crises, the informal sector may have ceased to be a good safety net during this downturn. This is linked to falling demand for products in global supply chains that include informal sector producers. For example, in India, half of self-employed women in the garment sector in Ahmedabad saw their monthly earnings decrease to less than 1000 rupees by January 2009, while all earned above that amount in November 2008. During the same period, 31 percent saw significant decreases in the number of days worked.<sup>2</sup> Similarly, self-employed waste pickers, frequently women and among the poorest of the poor, have seen a significant reduction in the price of scrap/recycled waste material in Delhi and Ahmedabad, India (Chintan Environmental Research and Action Group 2009). Case studies of waste pickers, home-based workers, and street vendors in 10 countries reveal uniformly deteriorating working conditions and falling incomes as result of this financial crisis (Horn 2009).

> 2 Reported by Women in Information Employment Globalizing and Organizing (WEIGO) 2009, “Global Economic Crisis and the Informal Economy,” based on data from the Self-Employed Women’s Association.

12
tics were also elicited, along with the main income sources of the household. Households then responded to a series of social capital questions. In addition, the household survey asked questions about both housing characteristics and small assets. We used these to generate wealth indices that help shed light on the socioeconomic effects of the program generally (Alix-Garcia, Sims, Orozco-Olvera, Costica, Medina, Romo-Monroy & Pagiola 2018) and the distributional consequences of changing targeting approaches in this paper<sup>8</sup> . We examined a variety of methodologies for aggregation, including simple summation of the presence or absence of characteristics, principal components, polychoric principal components (which takes into account categorical variables), and inverse proportion weighting (which applies higher weight to very scarce items). Our analyses use the village level averages of the simple asset indices, though the other more complicated aggregations of the household data yield similar results.

# 3.2 Deforestation risk

In order to conduct targeting exercises below, we create a measure of deforestation risk<sup>9</sup> . We would like this measure to be specific to the risk affecting the types of properties that apply for the program. To this end, we use the national level program data to examine deforestation behavior within the polygons that were submitted for consideration during the study period (2011-2014) but were rejected from the program. We analyze polygons with unique histories – that is, if an applicant submitted different pieces of overlapping land in different years, we separate the submitted parcel into multiple non-overlapping units in order to avoid double-counting of areas.

To ensure that the deforestation index is exogenous to program participation during the period that we analyze and simulates an ex-ante information set, we create the index using measured deforestation between 2005 and 2010, prior to application. The deforestation data come from Hansen et al. (2013). This dataset provides information on annual forest loss beginning in 2000

> 8Housing characteristics included brick or wooden walls, concrete, metal, fiber, cement,
In terms of salient administration features, the testing is school-based, with students tested in their school during normal school hours. The test papers are written, formatted, sorted, counted, and sealed in envelopes by ADU. PEF also administered the tests at program schools in QAT 1−QAT 3. Starting from QAT 4, the administration of the test was competitively outsourced to independent testing agencies, namely the Board of Intermediate and Secondary Education (BISE), Bahawalpur and the Punjab University Education Testing Service, Lahore.<sup>11</sup> The testing agencies are expected to strictly follow the test administration guidelines prepared by PEF and are responsible for the administration steps starting from securely transporting the test materials to the schools and ending with hand-scoring the test following the provided scoring guides and the delivery of the student- and subject-wise test score database and filled-in and unused question papers, as well as brief test administration reports and test administration checklists for each school, to ADU. PEF is not completely divorced from test administration however—it sends a PEF staff member to be present at each test to monitor test agency compliance with the guidelines; if any problems arise, the staff member is authorized to take corrective steps. The staff member is expected to fill in and submit a monitoring report (accompanied by photographs and video footage [see Figure 2 for examples]) for each program school covered.

Although the QAT is a pre-announced test, it attempts to control for test pool selection by the school at the grade level. Prior to administering the QAT, PEF sends an intimation letter to the school some two-three weeks in advance of the planned test date, with key test planning information. The testing agency also communicates the same information to the school a few days before the planned test date, either by letter or personal visit. Importantly, schools are notified that 100% attendance is required on the test date, with attendance numbers checked against the grade-wise enrollment statement submitted to PEF for the latest subsidy benefit payment. Consistent
term financing and have less access to long-term financing (e.g. Demirguc-Kunt and Maksimovic,1999; Fan, Titman, and Twite, 2010).

Next we use data on US listed firms from Compustat to study recovery from the 2008 financial crisis in the US. We then compare the US experience with that of the 3S episodes. The comparison with emerging markets is particularly relevant since Calvo and Kung (2010) draw parallels between the recovery process in the US crisis and past emerging market episodes and suggest that the US recovery is a new Phoenix miracle. Boone and Johnson (2008) commenting on the 2008 US financial crisis argue that “the evolution of the current crisis seems remarkably similar to emerging market crises of a decade ago.” Others (e.g. Claessens, Kose, and Terrones, 2009) have made the case for Phoenix Miracles in developed markets as well, with Krugman (2009) noting the role of self-financing in the partial recovery of Japanese firms after the turn of the century.<sup>5</sup> We have the following main findings: First, a closer look at the macro evidence suggests that there is heterogeneity across countries even amongst the Phoenix Miracles identified by the macro literature. In some countries like Thailand, there is no evidence of credit recovery even two periods after GDP recovery, suggesting a true Phoenix Miracle. However, some of the other episodes identified as Phoenix Miracles do not appear to be such even on the basis of macro evidence alone. For instance, while private credit lags GDP recovery by one period in most cases, in the instance of Korea and two crisis episodes in Turkey, private credit recovers at the same time as GDP calling into question whether they are truly Phoenix Miracles. In both Korea and the first crisis episode in Turkey in 1992-1996 (as well as in Russia though credit lags GDP recovery there), private credit far surpasses original credit levels before the crisis. Overall, of the 9 crisis episodes in our sample that were
MONGOLIA'S INFORMAL SECTOR Size and Trends 10 household survey conducted by the SSO, add strong support to the key finding that _most Mongolian informals do not consider themselves employed, and are thus omittedfrom official statistics._ Some statistics on ablebodied people of working age, employment and "not-employment" are depicted graphically in Figure 1.

<!-- Start of picture text --> "Not-Employed" in Ulaanbastar<br>by Reason<br>100,000<br>60,000 --------------<br>40,000 .<br>20,000<br>0 1 1<br>199, 1992 1993 194 1995 199<br>jJ3clearly not in labor force seasoral<br>nv<br>mmnotfiedingprofessiw o rk nal [left anyorex-convic<br>*taidoff Jnor<br>Source: SSO<br>Able-bodied People of Working Age<br>and Official Employment in Ulnanbaatar<br>330,000 IS0,000<br>310,000 -------- ---- 1,00060---------------------------<br>290,000 - ------ 140,000<br>270,000 ------- 120,000<br>250,000 ------- 100,000<br>230,000 80,0------- ,<br>210,000 ------- 60,000<br>190,000 - -- - - - 40,000<br>170,000 ------- 20,000<br>150,00 1991 1992 1993 1994 1995 1996<br>-a- Able-bodied people of working age _- Officially
and Income Survey (HEIS) that is conducted annually since 1963 in rural areas and 1968 in urban areas. Unit record data are available in an electronic format since 1984.This paper constructs the trend for the most recent 2008-2014 period. Besides the knowledge gap, this particular time period was chosen to minimize the risk of comparability between surveys due to changes in instruments and data collection process.<sup>5</sup> The paper explores different analytical approaches for constructing the components of the welfare aggregate as well as price adjustments, and conducts various robustness checks of poverty trends. This work may be helpful to inform discussions about establishing official poverty measurement methodology and necessary data requirements.

Poverty rates in this paper are measured using different international poverty lines expressed in USD at 2011 purchasing power parity (PPP). Using an international line helps to avoid arbitrariness and sensitivity of establishing a line in local currency units. In addition, selected international lines in this paper are used only for illustrative purposes.

This paper consists of seven sections. Section 2 briefly discusses main methodological issues in poverty measurement. Section 3 explains construction of different components of expenditure based welfare aggregate. Section 4 discusses the methodology for price adjustment. Section 5 presents robustness tests of expenditure based poverty and inequality measures. A brief robustness test of poverty trends by using income data is done in section 6. Section 7 concludes.

# **2. Methodological issues in poverty measurement**

Measuring poverty requires two broad steps. The first step is to define an indicator to measure welfare or living standards. The second step requires setting a poverty line - the minimum welfare level below which a person is considered to be poor.

There are two most common welfare indicators for poverty measurement: consumption and income per capita derived from survey data. The choice between them is often made based on the social-economic conditions of the country chosen for the analysis and microdata collected. Income is usually used to measure welfare in rich and developed countries. For example, poverty in most of
country separately, we derive probabilities for an _increase_ , _decrease_ , or _no change_ in income based on certain household and demographic characteristics – namely, where they reside (urban or rural area), the number of members in the household, the education of the head of household, and the age of the head of household (Table B.1. reports the coefficients from these regressions).

Then, in the 2019 welfare distribution, we randomly assign each household an increase, decrease, or no change in income based on the probabilities derived from the multinomial logit regression on the phone survey. Suppose that the phone survey revealed that a particular household type in a country had a 75% probability of experiencing an income loss, 20% probability of experiencing no change, and 5% probability of experiencing an income increase. Then, all households in the 2019 welfare vector that are of this type are randomly assigned such that 75% have an income loss in 2020, 20% have no change, etc.

In the robustness checks section, we validate this matching procedure in Nigeria, where the phone survey sample was drawn from the 2019 survey. Therefore, we know which households experienced an income gain, loss, or no change. We show that the results are very similar whether we use the observed matching or the random matching.

# _(b) Estimating the size of the income gains and losses_

Whereas we now have an approximation of _which_ households experienced a gain, loss, or had no change in income in the 2019 distributions, we still do not know the size of the income losses and increases. We need the latter to estimate the 2020 welfare vector.

We first split the sample into rural and urban households. We then allocate sectoral growth rates from national accounts – growth in income from agriculture, industry, and services –to rural and urban areas, which requires several strong assumptions. Growth in the agricultural sector is allocated to rural households. Growth from industry is applied to urban households, which
Russia and China during the 1990s. Stiglitz (1999) emphasizes that, over the decade beginning in 1989, China‘s gross domestic product (GDP) doubled while Russia‘s GDP almost halved. Different explanations have been offered for these two performances. For example, Blanchard and Shleifer (2001) explain China‘s better performance on the basis of the existence of strong central control of the economy by the Communist Party, which kept centrifugal forces under check; in contrast, they claim that a weak federal government in Russia was unable to control destructive behaviors of the regions. Similarly, Fisher and Sahay (2000) argue that Russia lagged in the implementation of structural reforms and failed to solve its fiscal problems, which led to large fiscal deficits and ultimately to the financial collapse in August of 1998. Shleifer and Treisman (1999) blame Russia‘s poorer performance on the common pool incentive problem created in Russia by the revenue sharing of all main taxes between the federal government and the regions. In contrast, Montinola, Qian, and Weingast (1995) and Qian and Weingast (1996) argue that China‘s revenue assignments were ―market preserving‖ (unlike Russia‘s) in that the Chinese assignments stimulated local governments to become entrepreneurial and to seek the growth of their local economies. Whatever the explanation for the poorer economic performance of Russia, the fact is that large proportions of the Russian population suffered income declines throughout much of the 1990s. For example, Graham and Pettinato (2002) use data from the Russia Longitudinal Monitoring Survey, and find that 77 percent of the population sampled had income declines from 1995 to 1998. Interestingly, at the time of the third Longitudinal Survey in 1999 the Russian economy experienced positive rates of growth. Alm, MartinezVazquez, and Torgler (2006) observe significant regional differences in individuals‘ attitudes toward paying taxes. There are strong ethnic, economic, and fiscal disparities across the Russian regions. Each region generally shows a diverse picture, with significant changes between 1995
2015/16: Development Goals in an Era of Demographic Change.</mark>_ <mark>Washington DC: World Bank.</mark>

- ______. <mark>2015b.</mark> _Aging in East Asia and Pacific:  Capitalizing on the Demographic Transition_ . Washington, DC: World Bank.

- ______. <mark>2015c.</mark> _<mark>Golden Aging: Prospects for Healthy, Active, and Prosperous Aging in Europe and Central Asia</mark>_ <mark>.</mark> Washington, DC: World Bank.

- ______. World Development Indicators Database. <mark>Washington, DC</mark> : World Bank.

27
analysis. Andrews and Shapiro (2021) study the question of how to best communicate results if audiences come with different priors, and suggest that there may be cases where analysts should censor the estimates they report as a result. Abadie (2020) notes that the information content in non-significant results will depend on the priors decision-makers had about the null hypothesis being rejected. Our approach explicitly elicits these priors, and so provides a way to communicate how much these priors should be updated given the data. Finally, while Bayesian analysis has not yet been used for analyzing individual impact evaluations, Bayesian meta-analysis approaches have been used to aggregate evidence from different studies (e.g. Meager (2019); Vivalt (2020)).

Second, the paper contributes to a literature on improving management and export performance in firms. Several recent experiments have found management consulting can improve management practices and firm performance (Bloom et al. (2013); Bruhn et al. (2018); Iacovone et al. (2022)). However, these existing studies have not focused on export-oriented firms with exporting as a main outcome. Bloom et al. (2021) use data on American and Chinese firms, and find that better managed firms are more likely to export, sell more products to more destinations, and have higher export revenues. Despite this relationship, our results suggest that purely improving management practices may 4
of fuel spark changes in taxicab fares. Third, the return to driving a taxi represents a combined return to both labor and capital. These contrasts, and the apparent popularity of taxicab driving as an informal activity, motivated the survey of 308 taxicabs undertaken in February 1997. Some descriptive statistics from the survey are provided in Appendix B.

90. The rapid growth in the number of private vehicles in Ulaanbaatar has placed competitive pressure on the taxicab industry. Although the market-determined fares increase sharply each time the price of fuel is increased, the real return to driving a taxicab has been eroded by inflation and has fallen since 1993. Despite these competitive pressures, Ulaanbaatar's taxicab drivers, both part-time and full-time, make significant contributions to the local economy. The time that Ulaanbaatar's drivers spend operating taxicabs is equivalent to approximately 6,000 fulltime equivalent jobs. The value of that time is even more telling. On an annualized basis, Ulaanbaatar's taxicabs contribute more than 21 billion togrogs to value added at current (3/97) prices. This amounts to roughly 3% of GDP for the entire country for 1996, or 9% of the Ulaanbaatar economy, after adjusting for inflation. This fraction of GDP seems quite large given that it represents only taxicabs, most of whom are part-time moonlighters, and only in the city of Ulaanbaatar - it does not include the lucrative market for travel to the _aimags,_ trucks, buses, or any other informal activity.

# **Informal Freight Transport**

91. Informal participation in the trucking industry is arguably a direct result of reforms. During privatization many trucks were privatized directly to the drivers, sometimes with payment taking the form of an agreement to provide services for some period of time. The new owners suddenly had the freedom to seek out work on their own schedules, and the responsibilities of finding their own customers. (See Box 10.) 92. Relative to other transport sub-sectors, trucking is less dominated by informnals. The total number
& Sjursen, I. (2013). To pay or not to pay? Citizens' attitudes towards taxation in Kenya, Tanzania, Uganda and South Africa. _World Development_ , 64:828-842.

> 136 OECD. (2013). What drives tax morale? _Tax and Development_ . Paris: Organisation for Economic Cooperation and Development.

> 137 Paler, L., Prichard, W., Sanchez de la Sierra, R., & Samii, C. (2017). Survey on total tax burden in the DRC, Final Report. Kinshasa: Department for International Development.

35
with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed poverty rates for 2019/20 use the estimated parameters based on the 2016/17 data. 100 simulations are implemented. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in %. True rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

53
2019.

- Uganda Bureau of Statistics (UBOS) (2014b). Uganda National Panel Survey (UNPS) 2010-2011. Public Use Dataset. Ref: UGA ~~2~~ 011 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2059` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2019). National Panel Survey (UNPS) 2005-2009. Public Use Dataset. Ref: UGA ~~2~~ 005-2009 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1001` on 6 September 2019.

- Williams, T. M. and W. R. Travis (2019). Evaluating alternative drought indicators in a weather index insurance instrument. _Weather, Climate, and Society 11_ (3), 629–49.

- Wineman, A., N. M. Mason, J. Ochieng, and L. Kirimi (2017). Weather extremes and household welfare in rural Kenya. _Food Security 9_ , 281–300.

- Yeh, C., A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. _Nature Communications 11_ , 2583.

- Zandler, H., T. Senftl, and K. A. Vanselow (2020). Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia. _Scientific Reports 10_ , 22446.

56