rafmacalaba/data-use-mentions-extended huggingface dataset:
firms without surveying their owners provides one approach, but at the cost of including some already formal firms and some closed firms or hard to interview firms in the study. One alternative would be to conduct a baseline survey to identify which firms are informal and willing to be interviewed, and then for the government to randomly assign all geographic blocks throughout the city to treatment or control status without reference to this baseline information. A downside of this approach is that it would make it harder to measure treatment spillovers, since one could not assign firms to different treatment status within blocks, although potentially the use of GPS coordinates (Gibson and McKenzie, 2007) on each firm could allow some measurement of spillovers.

# **References**

Alcázar, Lorena, Raúl Andrade, and Miguel Jaramillo (2010). “Panel/tracer study on the impact of business facilitation processes on enterprises and identification of priorities for future business enabling environment projects in Lima, Peru – Report 5: impact evaluation after the third round”, Report to the International Finance Corporation, Mimeo.

Alm, James, Gary McClelland and William Schulze, (1992). “Why do people pay taxes?” _Journal of Public Economics_ 48: 21–38.

Almeida, Rita and Pedro Carneiro, (2012). “Enforcement of Labor Regulation and Informality”, _American Economic Journal: Applied Economics_ 4(3): 64-89.

Andreoni, James, Brian Erard and Jonathan Feinstein, (1998).“Tax Compliance”, _Journal of Economic Literature_ 36: 818-60.

Benjamini, Yoav and Yosef Hochberg (1995) “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing”, _Journal of the Royal Statistical Society Series B_ , 57(1): 289300.

Bertrand, Marianne and Francis Kramarz, (2002), "Does Entry Regulation Hinder Job Creation? Evidence From The French Retail Industry," _Quarterly Journal of Economics_ , 117(4): 1369-1413.

Bruhn, Miriam (2011) “License to
and covering more recent years. It also concurs with a World Bank report on India (World Bank, 2002), which, in addition to labor regulation, identifies power shortages as a factor in economic performance gaps across states.<sup>7</sup> There are two important methodological differences between the analysis of the role of labor regulation presented in this paper and that of Besley and Burgess (2004). First, Besley and Burgess (2004) measure labor regulation in terms of a state level labor dispute law index registering “pro-employer” or “pro-worker” reforms over the 34 year period covered by their study.<sup>8</sup> A possible drawback in using legislative events to measure the degree of labor regulation is that it would entail suppressing possible differences across locations and regions in the implementation or enforcement of the same laws. It is also possible that firms respond differentially to the same enforcement effort due to differences in the nature and magnitude of other binding constraints. One way to minimize the bias in the estimation of local effects of labor regulation would seem be to measure it in terms of each firm’s evaluation of how far its choices are constrained by labor laws and regulation.

The second, and more important, methodological difference between Besley and Burgess (2004) and our analysis is that we estimate the short run effects of business environment on productivity, while their analysis is of a long run nature. Our data are such that we observe the business environment in a cross section of plants over a certain year, but along side inputs and output for the same year plus the previous two years leading up to it. In the analysis we assume that that the business climate indicators observed at the time of the survey also characterize a plant’s environment over the preceding two years. This enables us to use the longitudinal observations in productivity

> 7 Dollar et al (2002) also provide some evidence that differences in power shortages help to explain some of the observed regional gaps
Table 7: Impact of Gender on Absenteeism

|Ab|sent from Sch|ool_>_1 Week|
|---|---|---|
||(1)|(2)|
|Female|_−_0_._035<sup>_∗∗∗_</sup>|0_._018|
||(0_._013)|(0_._017)|
|Travel Time to School||0_._002<sup>_∗∗_</sup><br>(0_._001)|
|Female X Travel Time to School||_−_0_._002<sup>_∗∗∗_</sup><br>(0_._001)|
|Outcome Mean:<br>**Fixed Effects**|0.063|0.063|
|Year|X|X|
|Enumeration Area|X|X|
|Observations|5,398|5,393|
|R<sup>2</sup>|0.337|0.343|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is whether school going students (5th grade and above) in the LSMS household survey were absent from school for more than a consecutive week in the past 6 months. The regressors identify whether or not the student was female and what the travel time to school (in minutes) for the student was. Errors are clustered at the enumeration area level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

26
For annual data we use first-differencing and bandpass filtering (Baxter and King, 1999). Spectral analysis is used to assess business cycle synchronization with monthly data.

# **2.1.1 Annual Data: 1965 –2002**

Band pass filtered data, our preferred method for business cycle extraction in this section, shows that in Central America business cycle synchronization is highest between Costa Rica, Guatemala, El Salvador and Honduras. Nicaragua and Panama appear to be follow a different 4
five practices. However, given the maximum possible practices score is 29, the treated firms are still clearly a long way from implementing all the practices taught in the SIYB training. Columns three through seven pool together the different rounds and show that the training programs significantly improved each subcomponent of the overall business practice score, with improvements in marketing, stock control, financial planning, and record-keeping.

# **5.2 Impact on the business outcomes of existing business owners**

Eighty-three percent of the current business owners remain in business by the round 5 survey, and we cannot reject that survival is unrelated to treatment status (p=0.37). Moreover, the treatments do not appear to have affected which firm survived: treatment status is unrelated to 17
# **4 Results**

The sub-sections below present (a) the results comparing publicly available “top-down” population estimates with each other and with the census; (b) the predictive accuracy of satellite imagery-based indicators for census density, using LASSO-based Poisson regression models; and (c) out-of-sample density predictions based on a model using the survey (HIES), and comparing their accuracies with other estimates.

## **4.1 Validation of population products**

The validation exercise indicates that publicly available population count estimates are accurate at the sub-district level but not necessarily at the village level. At the sub-district level, except LandScan and WorldPop 2010, the _R_<sup>2</sup> for all other products with the census are above 0.9 (panel A in Table 4). At the village level, panel B in Table 4 shows that WorldPop 2015 has the highest association with the census ( _R_<sup>2</sup> of 0.988), followed by Facebooks HRSL (0.841), GHSL (0.672), GPW 2015 (0.595), GPW 2010 (.404), WorldPop 2010 (0.107) and LandScan (0.0006) in that order. These results qualitatively hold if we use correlation coefficients as a measure of association (see supplementary tables S2 and S3). Clearly, the products using the most recent census and using higher resolution imagery-based covariates are performing better than the others. For example, GPW and WorldPop 2010 estimates use the 2001 census for redistribution, and hence it is not surprising that they perform poorly in comparison to the 2012 census.<sup>18</sup> However, even the best performing products may themselves become inaccurate with the passage of time as their source data in the census becomes outdated.<sup>19</sup> 18 Similarly, an earlier unpublished version of Facebook’s population data which we worked with, only yielded a correlation
be exposed to the virus. ”Individuals who reduced income because of job or business losses to COVID-19” Those who answered they decreased income due to job loss or closure of business. “Individuals who reduced income due to reduced working hours:” Those who answered they decreased income due to reduction in working hours.

By wave 2, our projected sector estimates show a recovery for all sectors except _Garments_ (see Table 5-12), which remained by far the hardest hit. According to the Georgia High-Frequency Survey, conducted just after the CFUWBES2, among private sector workers, however, the greatest rates of job loss occurred for those previously employed in restaurants and hotels, 55 percent of whom reported being unemployed due to 35
# **4.  Relating Outreach Indicators to Household and Firm Data**

How well do our outreach indicators predict the actual use of savings and loan services by household and firms? To a large degree the usefulness of the macro-level banking sector outreach indicators we propose will depend on whether they track the micro data that we ultimately care about. Regressing user-based data from household and firm surveys on our indicators of deposit and loan use, we show the predictive power of our aggregate outreach indicators.<sup>8</sup> Specifically, we use country-level data on the percentage of households that have a bank account constructed from different household surveys and compiled by Claessens (2005) and Gasparini et al. (2005) and country-level data on the share of small firms with bank loans from the World Business Environment Survey (WBES).<sup>9</sup> While the household surveys are based on thousands of observations, WBES samples on average 120 firms per country, 40% of which are small.<sup>10</sup> We therefore expect a much lower degree of precision and predictive power when relating firm-survey based user data to our aggregate indicators than when using householdsurvey based measures. While we tried different empirical specifications, below we present the model with the highest R<sup>2</sup> .

A regression of the share of households with bank accounts (Household share) on the log of number of deposit accounts per 100,000 (Ln deposits per 100,000) and the log of average size of deposits in US dollars (Ln average deposit size) yields the following result (robust standard errors in parentheses):

> 8 We are grateful to Patrick Hohonan for this suggestion.

> 9 WBES is a database of firm level surveys, which we discuss further in Section 6.1.

> 10 Given the small sample size and the size-stratified nature of WBES – 40% small, 40% medium and 20% large enterprises, independent of the actual size
Lviv (2,148)<br>Mykolaiv (1,631)<br>Chernihiv (1,454)<br>Zhytomyr (1,404)<br>Kirovohrad (1,297)<br>Khmelnytsky (1,223)<br>Ivano-Frankivsk (743)<br>Zakarpattia (664)<br>Volyn (613)<br>Rivne (612)<br>Chernivtsi (489)<br>Ternopil (419)<br><!-- End of picture text --> _Notes:_ Panel 2a shows gross migration outflows from Donetsk and Luhansk oblasts between 2008 and 2012 by destination, with average annual numbers in parentheses. Panel 2b does the same by using annualized official internal displacement numbers (IDPs) between 2014 and 2019.

Like in other conflict situations, Ukraine faced major data constraints after the onset of the conflict. Most importantly, a complete record of the gross bilateral migration flows among Ukrainian regions is not available in the post-conflict period. Instead, we use information on the IDPs from Donbas as an indicator of the conflict-driven out-migration. By October 2019, the total number of officially registered IDPs were reported at 1,412,589 (21 percent of the combined population of Donetsk and Luhansk oblasts before the conflict). About 55 percent of all IDPs were registered within Donbas, while the rest was registered elsewhere in Ukraine. This outflow exhibited a pattern similar to the pre-conflict migration outflows, 12
# I Introduction

It has long been established that an arithmetic identity exists between changes in absolute poverty, changes in mean incomes, and changes in the distribution of relative incomes (Datt and Ravallion, 1992; Kakwani 1993; Bourguignon, 2003). Generally speaking, poverty reduction in a country can be decomposed into higher average growth, a reduction in inequality of incomes, or a combination of the two. Moreover, due to the non-linear nature of this identity, it has been shown that the sensitivity of poverty to growth in mean incomes is dependent on a country’s initial level of inequality: in general, the (absolute) growth elasticity of poverty is decreasing in a country’s initial level of income inequality.<sup>1</sup> The literature refers to this as the double-dividend effect of reducing inequality today: a reduction in inequality today leads to a reduction in poverty today and an acceleration of poverty reduction in the future (Bourguignon, 2004; Alvaredo and Gasparini, 2015).

Several papers have sought to empirically approximate this identity, and, in turn, quantify the role that growth in mean incomes and reductions in inequality play (and have played) in poverty reduction.<sup>2</sup> Despite the various specifications used to approximate the identity, these papers offer similar conclusions: over the past few decades, growth in mean incomes has been the primary driver of reductions in poverty, although changes in inequality have played a non-negligible role both through the direct effect of inequality on poverty and the indirect effect inequality has on the growth elasticity. Using the latest cross-country data, this paper seeks to update the empirical estimation of this identity, and, in turn, re-examine the role of income inequality for poverty reduction.

To begin, we re-derive the identity that exists between changes in poverty, changes in inequality, and changes in mean incomes. Using World Bank PovcalNet data from 1974-2018 for 135 countries, we then search for an empirical model that allows us to approximate the
shown in Figure 1.7, it is estimated that out of the over 1,700 currently exporting companies, 15% are microbusinesses and 58% are small and medium-sized enterprises (SMEs).<sup>5</sup> Equally important is the increasing number of products exported by Costa Rica, which amounted to 3,565 in the year 2003, according to official data from the Promotora de Comercio Exterior de Costa Rica (Costa Rica’s Foreign Trade Promotion Bureau).

<!-- Start of picture text --> Micro, 15%<br>Large, 27%<br><!-- End of picture text --> Source: Prepared by the authors based on figures from PROCOMER.

Finally, it should be noted that Costa Rica’s economic performance, linked to trade liberalization, has achieved three more major successes. First, it facilitated a reduction of the high levels of unemployment and underemployment that resulted from the foreign debt crisis of the late 1970s and early 1980s; second, it has made it possible to absorb a large increase in the supply of unskilled workers that was the consequence of the important migration flows of people from Central America, mainly Nicaragua, that occurred in the last few years; and finally, it has managed to bring down the poverty level from 29% in 1987 (a year after the liberalization process began) to 18.5% in 2003 (Figure 1.8).<sup>6</sup> In addition to the positive outcomes of the economic liberalization model regarding trade flows, foreign investment, employment and poverty, it should be noted that although Costa Rica’s income distribution did not improve over the liberalization period, it did not worsen. In fact, according to the family distribution of income based on the per capita income of Costa Rica’s households, Gini’s coefficient has not changed much between the years 1987 (0.401) and 2003 (0.425),
severity. More specifically, the change in poverty (measured by any poverty index P<sup>35</sup> ) over dates t and t+n<sup>36</sup> is decomposed according to the Datt-Ravallion (1991) method into three components: Pt + n – Pt = G(t, t + n; r) + D(t, t + n; r) + R(t, t + n; r) (1) with r being the reference date with respect to which the change in poverty is decomposed, and where: G refers to the “Growth component”, D refers to the “Redistribution component”, and R is a “Residual component for r=t”. The “growth” component is defined as the measure of the change in poverty due to a change in the mean of the welfare distribution while holding the Lorenz curve constant at a pre-defined reference level. The “redistribution” component is determined as the measure of the change in poverty due to change in the Lorenz curve of the welfare distribution while holding the mean consumption at constant reference level. The “residual” component typically exists when the changes in the mean of the selected poverty measure are dependent from the precise Lorenz curve, or vice versa<sup>37</sup> . Quantifying the contribution of growth vs. redistribution components in poverty reduction in Jordan enables the paper to reveal whether the growth in household welfare and shifts in income distribution has helped or hurt the poor during the period of strong economic growth between 2002 and 2010.

A three-fold decomposition of the overall Gini measure of inequality is also performed with the objective of delving further into the question of regional disparity. This follows a framework proposed by Lambert and Aronson (1993) and Essama-Nssah et al (2010) in which the overall

> 35 This paper will decompose the Poverty Headcount (P0), the Poverty Gap (P1), and the Squared Poverty Gap (P2) indices
|_R_<sup>2</sup>|0.288|0.294|0.327|0.289|0.295|0.328|
|Observations|4,393|4,393|4,393|4,393|4,393|4,393|
|Average outcome|0.712|0.712|0.712|0.712|0.712|0.712|
|Geography controls|Y|Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|Y||Y|Y|
|Province FE|||Y|||Y|

**Notes** The table presents OLS regression estimates measuring the effect of the share of refugees in 1928 on the presence of sport club in the early 2000s, at the community level. Small communities are those in the bottom two terciles of the distribution of the 1991, which corresponds to less than 1100 inhabitants in 1991. Standard errors are clustered at the province level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 Greek population census and General Secretariat of Sports’ inventory.

Table 5: Refugee resettlement and political fragmentation

|Dep. var:|20<br>|<br>12 electio<br>|Political Fr<br>n<br>|agmentation<br>|Index<br>2015 election<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)
0.73|(0.01)|0.74|(0.00)|
|Expenditures<br>|||||||
|**_Observations_**<sup>†</sup><br>||N||N||N|
|Households<sup>*</sup><br>||1,424||1,693||3,117|
|Individuals<sup>‡</sup><br>||6,995||7,907||14,902|
|Markets<sup>⸸</sup>||25||25||25|
|Food items||51||51||51|
|Nutrients||22||22||22|

Population statistics corrected using sampling weights. †Standard deviation in parentheses. † Excluded: 260 infants under 6 months who are assumed to be exclusively breastfeeding, 4 rural households unable to be matched to a market. * 1,081 are unique households observed at both time points, however the composition of those households changes in January 2016, so these are best thought of as two consecutive, but separate panels of households and individuals. ‡ Excludes individuals who reported eating no meals in the household in the prior week, allowing diet cost to be compared to reported food consumption expenditure. ⸸ List of markets and districts provided in Supplementary Table A.
50<sup>th</sup> round), 2004/05 (61<sup>st</sup> round), 2009/10 (66<sup>th</sup> round), and 2011/12 (68<sup>th</sup> round). The NSSO surveys began in the early 1950s and in terms of the international norms for consistency of expenditure or consumption surveys they have maintained a high degree of comparability over time, with the exception of the late 1990s (Datt et al, 2016).<sup>6</sup> For the four surveys that we use, a mixed-recall period (MRP) is used, with a 30-day recall for food and frequently

> 6 There is debate about the consumption and poverty estimates from the 55th round (1999/00) that is described in detail by Deaton and Kozel (2005). We do not use the results from this round.

6
This version: 15 Feb 2024 31 _<mark>The roads are pretty...but when I look at the long lines of vehicles in the morning, I get</mark>_ _<mark>a headache thinking about parents going to work and children going to schools...we</mark>_ _<mark>waste our time in traffic jams.</mark>_ <mark>[Multiple respondents, Bumiputera, Selangor]</mark>

**Table 5** - Individual Responses to the Question _: Compared to five years ago, is your life harder, easier or is there no difference?_

# **Individual Responses by %**

||**All**|**Sela**|**ngor**|||**Terengganu**|**Sabah**|
|---|---|---|---|---|---|---|---|
|||All|Bumi|Chinese|Indian|||
|Harder|55|61|59|66|58|46|51|
|Easier|19|14|09|29|05|24|25|
|No<br>difference|24|25|31|04|36|24|24|
|Total<br>participants|473|227|74|70|83|109|137|

Source: MLSQS 2019 data.

**Table 5** reports individual responses as to whether participants thought that

their lives were harder, easier or the same compared to five years ago. Fifty-five percent said that their lives were harder and only 19 percent said that it was easier. Residents of Selangor were more likely to say that life was harder (61 percent) compared to residents of Terengganu in eastern Malaysia (46 percent) or Sabah (51 percent).

*
Poverty.4 .6 .8<br>Inequality<br> Social inclusion Social cohesion Resilience Process legitimacy<br>.2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6<br>Note: Fitted values and 95% confidence interval shown<br>Source: Social Sustainability Global Database 2022.<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br><!-- End of picture text --> 2 In the specification reported in the main text, the social inclusion component includes access to labor force participation, one’s own bank account, and improved sanitation. The resilience component includes the average share of assets in a household, share of population that saves money, and share of population with several income sources. The social cohesion component includes trust in people, share of people who are active members of organizations, and share of people who voted. We tried other combinations of
mark>_ <mark>: Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).</mark> _Note:_ TFP = total factor productivity. AGR = agriculture. MIN = mining; O-IND = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. O-SER = services other than wholesale and retail trade; they include transport, communication, finance, commerce, government services, and private services. 5 Following Antras et al. (2012), input downstreamness is measured as = 1 × <u>i</u> + 2 × <u>j j j</u> + 5 5 5 5 VV ∑VV AA jii 3 × <u>j j j</u> + 4 × <u>j j j</u> + ⋯. DDii XXii XXii ∑∑VVjj AA AAjjii ∑∑∑VVjj 5ss AA AAjjssAAssii XXii XXii 24