rafmacalaba/datause-encoder-data huggingface dataset:
The second form of data comes from specialized firm panel surveys that have been collected as part of randomized controlled trials to assess different interventions designed to help micro and small firms in different countries. In each case we only use the control group data, in order to be able to assess firm death in the absence of any intervention. We searched publicly available datasets for impact evaluations targeted at microenterprises, but found most data made available only provided sufficient detail for replicating particular papers, and were not suitable for assessing survival dynamics. We use 11 panel surveys carried out in randomized trials conducted by one of the authors.<sup>6</sup> These include microenterprise surveys of informal firms used for formalization experiments in Benin, Malawi and Sri Lanka; surveys of microenterprises used in business training interventions in Kenya, Sri Lanka, and Togo; microenterprises that were part of cash grant interventions in Ghana and Sri Lanka; microfinance clients that were part of an insurance intervention in Egypt; business plan competition applicants in Nigeria; and microenterprises that were part of an intervention that gave wage subsidies, savings help, and business training in Sri Lanka. In addition, the one additional publicly available dataset which we were able to include was data from a program for female small businesses in Uganda from Blattman et al. (2016).<sup>7</sup> Many of these specialized firm surveys are representative of urban microenterprises of particular size cutoffs or informality status, having been found through door-to-door listing surveys. Some of the samples also screen on the gender of the owner. The main exceptions are the business plan competition applicants in Nigeria, the microfinance clients in Egypt, and the Uganda study which was targeted at women in marginalized villages in Northern Uganda.

These specialized firm surveys collect data at relatively high frequencies, with a number collecting data at quarterly or semi-annual frequencies. However, few of them follow firms for more than three and a half years. The exceptions are three surveys from Sri Lanka, which tracked firms at 5 to 6 years, and at 10 to 11 years in the case of the Sri Lanka Microenterprise Survey (SLMS). Table A.4. provides an overview of the follow-up periods provided by each survey.

> 6 We excluded data from surveys with high attrition rates for which death rates could not be accurately calculated, including data from a grants experiment in Mexico and a formalization experiment in Brazil.

> 7 The Uganda study targeted women, rather than firms, as the unit of observation. We take the sample of control group participants who started a firm and use this sub-sample to track firm dynamics.

8
economic performance, as measured by the<sup>performanceandgrowthofGDP.</sup> We therefore include lagged real GDP per capita in US$,<sup>RGDPPCt .,andthelagged1-year</sup> growth rate of real GDP, GDPGt.1.<sup>9</sup> We expect<sup>countries experiencingnegativegrowth to</sup> have higher rates of defaults. We also control for<sup>periods of systemic bankingcrises with</sup> data from Caprio and Klingebiel (2000), D_CRISISt.1<sup>, whichmay indicateperiodsof not</sup> only economic slowdown but also periods during<sup>which borrowersare more constrained</sup> in finding additional bank financing and more<sup>likelytofileforbankruptcy.Wealso</sup> include lagged real interest rates as a measure of<sup>thecostof financing,RINTERESTt.</sup> 1<sup>,</sup> expecting to find that higher real interest rates<sup>are associated with moredefaults.</sup> To measure the relative orientation of banks versus<sup>equitymarkets,weincludea</sup> dummy variable provided by Demirguc-Kunt<sup>andLevine(1999),D_BNKORIENT,that</sup> identifies countries as market- versus bank-oriented,<sup>dependingonthe relative</sup> importance of intermediated (bank) versus direct<sup>(capital) financialmarkets. Totest</sup> whether countries with more significant entry restrictions<sup>have fewerbankruptcyfilings,</sup> we use the data collected in Djankov, La Porta,<sup>Lopez-de-Silanes, and Shleifer (2002)on</sup> the restrictiveness of entry-the time to establish<sup>anewbusiness-totest whetherentry</sup> and exit rates tend to be correlated across countries.<sup>Thisvariableis calledTIME.We</sup> also test for the effect of a higher concentration of<sup>SMEs,measuredas the percentageof</sup> employment attributed to SMEs collected by Klapper<sup>andSulla(2002) and denotedhere</sup> by SME_SHARE.

9 The subscript, t, indicates a time series. All other<sup>variablesare constantovertime,but</sup> vary by country.

14
In addition, Aggarwal et al. (2006) have recently used cross-country panel data to show an association between remittances and financial development, with the argument being that the receipt of remittances paves the way for recipients to demand and gain access to other financial services, even if the funds themselves are not received through banks. However, they note that remittances may instead substitute for use of credit and other demands for bank accounts, so that the direction of causation is unclear. Furthermore, it is possible that household members who use the banking system are more likely to migrate, reducing household use of bank accounts when they leave. We will therefore also consider measures of access to bank accounts as another outcome measure.

# **3. Context and the Pacific Access Category**

# **3.1 Background**

The Kingdom of Tonga is an archipelago of islands in the South Pacific, a three hour plane flight from New Zealand. The population is just over 100,000, with a GDP per capita of approximately US$2,200 in PPP terms. One-third of the labor force is in agriculture and fishing, with the majority of paid workers in the manufacturing and services sectors, which are dominated by the public sector and tourism.

Emigration levels are high, with 30,000 Tongans living abroad, 94% of them in New Zealand, Australia and the United States. Migration to New Zealand began in sizeable numbers during the 1960s and 1970s, with Tongans arriving on temporary permits to take up work opportunities. After their permits expired, some returned to Tonga and others stayed on in New Zealand illegally. An amnesty in 1976 granted many of these individuals permanent residence. Migration for work continued in the late 1970s and 1980s. However, in 1991, New Zealand introduced a selection system for immigration, in which potential migrants are awarded points for education, skills, and business capital. Few Tongans qualified to migrate under this system, and so most Tongan migration since this time has been under familysponsored categories. For example, in 2004/05 only 20 Tongans were admitted as principal applicants under the points system, compared to 349 under family categories, the majority through marriage or as dependent children. Migration to Australia and the United States has also become much more restrictive and reliant on family reunification categories. Australia admitted 284 Tongans during the 2004/05 financial year. The United States admitted 324 Tongans in the 2004 calendar year, comprising only 5 under employment-based preferences and 290 under immediate relative or family-sponsored categories.

9
<!-- Start of picture text --> 4 :<br>Model B2 F<br>[i[J-o 288124<br>[HG -o288122- 0.000000<br>[HB occoo001- 0250001 0.25000<br>(HBB - 0 soo000 0 0 -f4—<br>(EB 0.s00001- 0760000 mu i a<br>GB ov7sn001-osrese Bf OIL<br>BN 0| . 228339102120 - 1 .275899.102119 a BE =HEE .<br>MMM -275000- 1.50000<br>HRHM +500001 - +.#50000 oe a PERE<br>HBB 2.00.75 0001 -- 20a c 00 000 0 ees oe ee<br><!-- End of picture text -->

Figure 7: Estimated Relative Risk of Conflict Events for Locations in Figure 3, based on Model B2
timeline).

First, we installed 41 outdoor air pollution monitoring devices throughout Tbilisi to improve the monitoring network of the city. These PurpleAir monitors provided a reliable and low-cost way to monitor PM2 _._ 5.<sup>6</sup> Another advantage is that they allow to have comparable results to other academic studies of air pollution (e.g. Burke et al. (2021); Krebs et al. (2021); Liang et al. (2021)). Figure 1E shows the location of the installed monitoring devices. They were installed by the Caucasus Research Resource Center (CRRC) in collaboration with MEPA and the Tbilisi City Hall. In most cases, outdoor monitors were installed in public schools to ensure reliable access to power and an internet connection. Installation took place during a two-week period at the beginning of January 2023.

The second phase of the intervention was a household survey that took place at the end of March 2023 and beginning of April following the installation of the outdoor monitors. CRRC surveyed 862 randomly selected households within 230 randomly selected census blocks in Tbilisi. Surveyed census blocks are shown in navy in Figure 1 in the bottom portion of Panel E. We describe the details of the selection of census blocks and households in Appendix 1.

Our information intervention was embedded in the survey design. The experiment contained a control and three treatment arms: _Control -_ Households were surveyed, but no information about air pollution was provided by the survey team.

_Treatment 1 (Leaflet) -_ At the end of the baseline survey, households were given a leaflet with information about air pollution: what it is, how it impacts individuals’ health, actions that people can take to reduce exposure to air pollution - like using ventilation while cooking or avoiding high traffic areas - and a scale explaining how health effects increase as the level of pollution rises. The information and scale in the leaflet were based on the U.S. EPA brochures on the dangers of air pollution. Households also received a magnet with a QR code that linked to an online version of the leaflet.<sup>7</sup> _Treatment 2 (Outdoor pollution) -_ In addition to the information provided to households

> 6In Figure A5 we plot the readings from the four Ministry of Environment monitoring stations against the readings from the nearest installed PurpleAir monitor. There is broad agreement in the readings, although the PurpleAir monitors appear to have slightly higher readings at high levels of pollution.

> 7An English version of the leaflet is shown in Appendix 7.

7
countries is probably more related<sup>to econometric difficulties than to the</sup> inappropriateness of the analytical framework.

# b. Additional Cross Country Evidence

We also estimated a single equation<sup>for the level of the parallel premium using</sup> annual data from a panel of twenty<sup>high premium countries. The objective of this</sup> exercise was to evaluate the robustness<sup>of the results obtained for the</sup> individual countries, and to investigate<sup>whether there are any behavioral</sup> difference in countries (or periods)<sup>where parallel markets are official and</sup> those where they are not. In addition, we looked in a crude way at the relative importance of restrictions in the<sup>capital and current account cn the pre,...,</sup> We use a random effects procedure<sup>as set out in Hausman and Taylor(1981) to</sup> account for unobservable, country-specific<sup>effects. Since this requires a</sup> balanced panel, a dummy was introduced<sup>for the period when exchange rates were</sup> unified in the countries in the sample.<sup>The fundamentals used were the real</sup> money stock and the fiscal surplus<sup>to GDP ratio. We included four dummy</sup> variables to account for countries or<sup>periods in which: (i) the foreign exchange</sup> market was unified, (ii) the parallel<sup>market was official, (iii, there were</sup> restrictions in the current account, (iv) there<sup>were restrictions in the capital</sup>
<!-- Start of picture text --> Figure 4 Figure 5<br>SPAIN PORTUGAL<br>SHARE OF IMPORT FROM EC10. 1970-1995 SHARE OF IMPORT FROM ECIO, 1970-1995<br>80.0 80.00<br>70.0~~~~~~~~~~~~~~~~~~~~00<br>eo.o 6~~~~~~~~~~~~~~~~~~0.00<br>40.0 50.0<br>/~~~~~~~~~~~00 . . . ., .w . .<br>40.0 2: ;hrs .: f<br>4OR ~ F0.00 -.-. - .-.- -u-;-M-FG-s-s-j(>5L-4.0<br>::. . . . . . .- . .: : :'. - .''' , 1 :<br>.000.00 *5*fsrsr-rst*5*5*;*7w300 4 -144 t s.<br>20.0<br>2.0 Ree; . i*fi es+400<br>10.0 . :: , ' T 1 ' s f } f '1' 10.00 tttRrwMvs+uf<br>I x , g '~CI<br>r g 5> ~~~~ ~ ~ ,, ~ ~ ~~~~~~~~~~~~~0.00 '<br>500~~~~~~~~ j-*-AoR r'r -- | +AGR MFG t | t<br>Fi1ure 6<br>EC1I0<br>SHARE OF IMPORT FROM SPAIN AND PORTUGAL, 1970-1995<br>6.00<br>t~~~~ 3 S r00ys<br>2 00.s , :s<br>> i g * .s + r , , U r<br>s.oo 54--f5s's;M5rii-+ -*r<br>-,*,,-AG- AF<br>s s.s q,, v,,W ,3 '1f<br><!-- End of picture text -->
<!-- Start of picture text --> Female Non-Indigenous Aymara<br>o>) =o o>)<br>eden ie _+-<br>3°? 2% -_—<br>‘oY OP] ge a<br>80 SY80 4e7 a oa<br>70+ 60-69 50-59 40-49 30-39 25-29 70+ 60-69 50-59 40-49 30-39 25-29<br>Cohort Cohort<br><!-- End of picture text --> <!-- Start of picture text --> oO oO<br>7 7 _—-<br>€9BOF, ee a 4 #Q per _*-—ee<br>gene S248 a ae<br>380 ee 80<br>aR iF<br>70+ 60-69 50-59 40-49 30-39 25-29 70+ 60-69 50-59 40-49 30-39 25-29<br>Cohort Cohort<br>——@-—- Male ----®---- Female<br><!-- End of picture text -->
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# Tackling Social Exclusion: Evidence from Chile

Pedro Carneiro<sup>_∗_</sup> Emanuela Galasso<sup>†</sup> Rita Ginja<sup>‡§</sup> January 28, 2015 Keywords: Social Exclusion, Social Protection, Chile, Extreme Poverty. JEL Codes: C26, I38, J08

## **1 Introduction**

Households in extreme poverty are generally deprived in multiple dimensions. The lack of material resources, coupled with little access to information, and other constraints to their decision making ability (Mullainathan and Shafir, 2013), limit the actions they can take towards improving their lives (Bertrand et al., 2006, Duflo, 2012). Although this is well known, most anti-poverty programs address single aspects in isolation of all other ones, and focus mainly on the lack of financial resources.

In 2002, Chile implemented _Chile Solidario_ (CS hereafter), an anti-poverty program which was progressive by the standards of most countries, even in the developed world. The target of the program were the 5% poorest families in Chile, who were perceived not only to be poor, but also alienated from the welfare services potentially available to them. The program had two main

> _∗_ University College London, CEMMAP, IFS. Email:p.carneiro@ucl.ac.uk

> †World Bank. Email: egalasso@worldbank.org

> ‡Uppsala University and Uppsala Center for Labor Studies. Corresponding Author: Uppsala University, Department of Economics, Box 513 SE-751 20 Uppsala, Sweden. Email: rita.ginja@nek.uu.se; Tel.: +4618471591.

§The authors are grateful to the Executive Secretary at the Ministry of Planning (now Ministry of Social Development) and FOSIS for granting access to the data used in this paper. We gratefully acknowledge the generous financial support of the Research Budget Committee of the World Bank (RSB), the Spanish Trust Fund for Impact Evaluation (SIEF) and the World Bank Gender Action Plan. Rita Ginja acknowledges the financial support from the Fundac ̧ ̃ao para a Ciˆencia e Tecnologia and the Royal Economic Society. Pedro Carneiro gratefully acknowledges the financial support from the Economic and Social Research Council (grant reference RES-589-28-0001) through the Centre for Microdata Methods and Practice), the support of the European Research Council through ERC-2009-StG-240910ROMETA and ERC-2009 Advanced Grant 249612. We thank the comments of participants at the Econometric Society Winter Meetings 2010, NEUDC 2011, 2011 World Bank CCT 2nd Generation Conference, LACEA 2011, IZA-World Bank Conference 2013, SESP-Northwestern and AEA Meetings 2014. We are indebted to Theresa Jones for her support in the evaluation effort and to Veronica Silva for sharing her knowledge about the design of the programs under study. These are the views of the authors and do not reflect those of the World Bank, its Executive Directors, or the countries they represent. All errors are our own.
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31
Supplemental Table 3. Association between contextual factors and child development scores at baseline using bivariate OLS regression

|||<br>|Receptive<br>|vocabu<br>|lary<br>||Expres<br>|sive<br>|A<br>|dapte<br>|d MDAT<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Engli|sh|Swah|ili|Luo||vocabu|lary|Fine m|otor|Langu|age|
||b|se|b|se|b|se|b|se|b|se|b|se|
|_Child characteristics:_|||||||||||||
|Height-for-age_z-_score|0.25***|0.03|0.19***|0.03|0.26***|0.04|0.3***|0.04|0.34***|0.03|0.33***|0.03|
|_Primary caregiver_<br>_characteristics:_|||||||||||||
|Caregiver completed<br>primary school|0.19*|0.09|0.22**|0.1|0.15|0.09|0.29***|0.1|0.23**|0.1|0.15|0.1|
|Caregiver is literate|0.13|0.1|0.11|0.1|0.03|0.09|0.23**|0.1|0.18*|0.1|0.19*|0.1|
|Caregiver working<br>memory (out of 20)|0.08***|0.03|0.04|0.02|0.03|0.02|0.06**|0.03|0.05*|0.03|0.07**|0.03|
|Caregiver depressive<br>symptoms (out of 26)|.|0.01|.|0.01|–0.01|0.008|0.01|0.01|–0.01|0.01|0.01|0.01|
|_Household_|||||||||||||
|_characteristics:_|||||||||||||
|Household size|–0.11|0.05|–0.08|0.05|–0.15***|0.05|–0.06|0.05|–0.07|0.06|–0.03|0.05|
|Household wealth index|0.03|0.02|0.04**|0.02|–0.01|0.02|0.04**|0.02|0.05**|0.02|0.02|0.02|
|Family care indicators<br>|0.03*|0.01|0.02|0.01|0.01|0.01|0.03*|0.02|0.05***|0.01|0.04**|0.01|
|score(out of 17)<br>Notes. Receptive English a<br>Picture Vocabulary Scale (<br>adapted MDAT are the_z-_s<br>were age adjusted. Baseli|nd Luo voc<br>BPVS). Expr<br>cores from<br>ne and follo|abular<br>essive<br>the Ke<br>w-up|y scores ar<br>vocabular<br>nya adapt<br>were cond|e_z-_sco<br>y_z-_sco<br>ation o<br>ucted a|res, measur<br>res were me<br>f the Malaw<br>pproximatel|ed using<br>asured<br>i Develo<br>y 5 wee|separate<br>using a too<br>pmental A<br>ks apart. *|assess<br>l deve<br>ssessm<br>_p_< 0.1|ments bas<br>loped from<br>ent Tool (<br>, **_p_< 0.0|ed on t<br>the P<br>MDAT<br>5, ***|he British<br>PVT. The<br>). All_z-_sco<br> _p_< 0.01.|<br>res|