rafmacalaba/datause-encoder-data huggingface dataset:
ˆ ˆ ˆ α 0 = α 2 = 0 and α 1 = 1 is rejected at any level of confidence. Once we move to model B, which includes the EA-level aggregated variables, the intercept becomes insignificant for all weighting schemes and the predicted income is statistically equal to 1 in all specifications. The spatial correlation coefficient is still generally significant although not necessarily at all significance levels and for all weighting schemes. The null hypothesis of no spatial correlation is still rejected at all levels of significance with the 5 and 10 neighbor weighting schemes, but fails to be rejected at the 1% significance level for the Euclidian distance weighting scheme.<sup>13</sup> Table 5 - Spatial regression of the observed value on predicted and error component to measure any remaining spatial <u>correlation.</u>

||||Model A: No loc<br>|al contro<br>|ls||||
|---|---|---|---|---|---|---|---|---|
|**Weighting Matrix**|**5-Neig**|**bors**|**10-Neig**|**hbors**||**Euclidia**|**n distanc**|**e**|
||Coefficient|s.e.|Coefficient|s.e.||Coefficient|s.e.||
|α0|-0.748|0.109|**<br>-0.662|0.095|**|-1.145|0.174|**|
|α1<br>1|1.135|0.019|**<br>1.118|0.016|**|1.209|0.032|**|
|α2|0.406|0.029|**<br>0.514|0.022|**|0.005|0.002|*|
||||Model B: With lo|calcontr|ols||||
|**Weighting Matrix**|**5-Neig**|**hbors**|**10-Neig**|**hbors**||**Euclidia**|**n distanc**|**e**|
||Coefficient|s.e.|Coefficient|s.e.||Coefficient|s.e.||
|α0|0.137|0.092|0.130|0.096||0.382|0.233||
|α1<br>1|0.976|0.015|0.978|0.016||0.929|0.040||
|α2|0.546|0.055|**<br>0.650|0.030|**|0.014|0.005|*|

Source: Authors’ calculation. Note: ** Significant at 1%;*significant at 5% 1 Test whether coefficient is equal to 1; ** means we do have evidence to reject that α = 1.

To summarize, the addition of local variables significantly diminishes enumeration area correlation in the deviation of the local welfare measure from its prediction. It is important to emphasize that the ELL approach depends on a model specification that is carefully chosen from a set of “matched” variables between the survey and census and that includes, in addition, EA, district, municipality and/or other aggregated level variables in order to reduce or capture effects of integrated small areas. Note, however, that applicability of the ELL method does not hinge on fully independent prediction errors at the enumeration area level. The approach may do quite well as

> 13 See Appendix 2 for an alternative perspective on local geographic correlation of the unexplained component of simulated income.

20
<!-- Start of picture text --> 100% Q 100%<br>—— Year 2005 —— Year 2005<br>—O— Year 2009 —O— Year 2009<br>730% — = Year 2012 730% =— = Year 2012<br>: a d v o ~ ~<br>3 \ E oi<br>> a\\' > s<br>£60% \ 260% ss<br>F | r\ “ ~ 2%<br>=<br><!-- End of picture text -->

<!-- Start of picture text --> = N 3 41%<br>220% 220%<br>a 10%<br>“—O<br>8%<br>0% | 1 0%<br>Ql Q2 Q3 Q4 Q5 Ql Q2 Q3 Q4 Q<br>Quarter of corresponding year Quarter of corresponding year<br><!-- End of picture text -->
17

# Table 2. Macroeconomic Impact of Real Exchange Rate Fluctuations.

Method of Estimation: OLS Regression 1.

X-M = -2735 + 0.715*(X-M).1 + 2234*REER - 533*D2 - 596*D? - 247*D4 (-5.7) (15.7) (6.6) (-^3.9) (-4.4) (-1.8) SMPL: 1980.1-1990.4 R<sup>2</sup> = 0.97 D-W 1.92 Regression 2.

Ip/Y = 0.0425 + 0.889*(Ip/Y)., - 0.02*REER (2.3) (10.7) (-2.4) SMPL: 1983.1-1990.4 R = 0.86 D-W = 2.02 Regression 3.

Cp/Yd = 0.0352 + 0.531*(Cp/Yd)1 - 0.0325*REER - 0.0329*D4 + 0.0184*BRADY (4.0) (4.1) (-2.3) (-6.7) (2.7) SMPL: 1983.1-1990.4 R<sup>2</sup> = 0.81 D-W = 1.92 Notation: X-M deflated non-oiL trade surplus REER real exchange rate of Mexico vis a vis US (Producer Prices) D2, D3, D4 seasonal dummies ip/Y private investment GDP ratio Cp/Yd ratio of private consumption to disposable income (defined as quarterly GDP minus taxes) BRADY a dummy which takes value 1 since the second haLf of 1989 to account for the foreign debt agreement for debt reduction t-statistics in parenthesis Note: real interest rates (measured as Cetes minus past inflation) were also tested in regressions 2 and 3 but found to be statistically insignificant.

The decline of private saving could put at risk MexicoIs economic recovery as the accumulation of external liabilities is not backed by additional resources to service them in the future. This problem could be exacerbated if the real peso appreciation shifts an increasing portion of investment, the other factor behind large current account deficits, to the non-tradable sector. While the sustainability of current account deficits ultimately depends on the availability of foreign capital, which in 1990-91 over-financed the current account deficit, the volatile nature nature of recent capital inflows makes Mexico extremely vulnerable to domestic and external shocks even in the short term. Besides, over the medium term other problems may arise. The initially expansionary impact of a real peso appreciation on domestic aggregate demand is likely, over time, to be offset by the adverse employment effects of a shift from goods produced domestically to foreign goods (precisely, due to the ongoing real peso appreciation), i.e., a J-curved response of aggregate demand to the peso
<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1995 1996 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2015 2016 2017 2018 2020<br><!-- End of picture text --> Source: Latinobarómetro Corporation, several years Participation in authorized demonstrations is also relatively uncommon, with the regional average reaching only 15 percent of the population (see Table 1). Chile leads in this regard, with almost a third of the population reporting having taken part in legal demonstrations—likely reflecting the mass demonstrations in 2019 and 2020 over the cost of public transport and education as well as economic inequality more broadly. Participation in demonstrations in the region has declined over the past twenty years, coming in line with the global average of 12 percent (Cuesta, Madrigal and Pecorari, 2022).

Another form of societal participation is working with others to solve problems at the community level on a consistent basis. In this sense, only about a quarter of Latin American and Caribbean citizens appear to be working on issues that impact them or their community regularly. National averages are more or less in line with the regional benchmark, with a narrow intercountry variation. The proportion of persons working to tackle community problems has remained stable since 2000, at roughly 20-25 percent of the population.

# 3.2. Respondents’ Demographic and Socioeconomic Status

Around 26 percent of the Latinobarómetro respondents have complete secondary education, while only 13 percent have completed tertiary education (see Table A2 in Annex A). In addition, almost 8 percent of respondents are illiterate. When it comes to respondents’ perceptions of their own socioeconomic status, the majority of Latin Americans and Caribbeans identify as middle (33.8 percent) or lower middle class (31.7 percent). Less than 2 percent of respondents say they are upper class, while 6 percent claim to be upper middle class.

In terms of employment, roughly one-fifth of respondents are salaried employees, while about a quarter report being engaged in caring for family and household responsibilities or not working. Notably, one-third of respondents report being self-employed, indicating that vulnerable employment is highly prevalent in the region.<sup>12</sup> See Table A2 in Annex A.

> 12 Although there is no direct inquiry into informality in the Latinobarómetro survey of 2020, the self-employment status effectively captures this well-known phenomenon in LAC.

12
# **<mark>Table A2C – Parallel Trend Test of States’ Poverty and Inequality Given Their Public Debt Before FD Law</mark>**

<mark>This table reports OLS estimates of the trends of bank lending to local governments across states of varying public indebtedness prior to the</mark> introduction of the FD Law. The variable _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend for the years prior to the implementation of the FD Law (2010, 2012 and 2014). Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

||Pove<br>|rtys,y<br>|Moderate<br>|Povertys,y<br>|Extreme Po<br>|vertys,y<br>|Gi<br>|nis,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Trendy|-0.01*||0.00||-0.01***||0.00||
||(0.01)||(0.00)||(0.00)||(0.00)||
|Trendy*DebtStates,16Q1|0.01|0.01|-0.01|-0.01|0.02|0.02|-0.01|-0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|Observations|90|90|90|90|90|90|90|90|
|R-squared|0.97|0.97|0.94|0.94|0.98|0.98|0.63|0.64|
|Bank-State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|No|Yes|No|Yes|No|Yes|No|Yes|
**Table 1: Commodity prices and fundamentals**

||1998‐2004|2005‐11|Change(%)|
|---|---|---|---|
|**Nominal commodity price indices (2010 = 100)**||||
|Agriculture|52.7|89.1|69.0|
|_Food_|53.0|91.3|72.1|
|Metals|37.0|92.0|148.6|
|Energy|35.4|98.6|178.5|
|_Crude oil (US$ per barrel)_|25.0|75.8|202.7|
|Fertilizer|36.9|107.7|191.5|
|**Real commodity price indices (2010 = 100)**||||
|Agriculture|65.9|90.6|37.5|
|_Food_|66.3|92.8|39.9|
|Metals|46.2|94.1|103.5|
|Energy|44.3|100.5|126.9|
|_Crude oil (US$ per barrel)_|31.3|77.1|146.2|
|Fertilizer|46.1|108.5|135.1|
|**Macroeconomic drivers**||||
|GDP (low and middle income countries, trillion real US$, 2010)|10.7|16.4|53.6|
|Industrial production (emerging economies, billion real US$, 2010)|351.4|564.3|60.6|
|Exchange rate (US$ against a broad index of currencies)|95.0|78.4|‐21.2|
|Interest rate (10‐year US Treasury bill, %)|5.0|3.8|‐23.6|
|Funds invested in commodities (US$ billion, nominal)|57.0|206|261|
|**Population (billion)**||||
|Global|6.18|6.73|8.9|
|China|1.27|1.32|4.2|
|India|1.06|1.17|10.9|
|**Sectoral drivers**||||
|Stocks (maize, wheat, and rice, months of consumption)|3.5|2.5|‐26|
|Biofuel production (thousands of barrels per day equivalent)|233|858|267|
|Growth in yields (% per annum)|1.2|0.8|‐36|
|_Yields (average of wheat, maize, and rice, metric tons/hectare)_|3.7|4.1|10|
|Natural disasters (droughts, floods, and extreme temperatures)|180|223|24|
|OECDpolicies(Producer NPC)|1.3|1.1|‐13|

**Source** : BarclayHedge, Center for Research for the Epidemiology of Disasters (www.cred.be), Federal Reserve Bank of St. Louis, International Energy Agency, International Monetary Fund, Organization of Economic Co‐ operation and Development, United Nations, U.S. Department of Agriculture, World Bank, and authors’ cal‐ culations.

_12_
log(1+landholdings) and log(wealth), both measured at the beginning of the Kharif. Both these measures are positively signed, and although neither is individually significant, they are jointly significant at the 2% level. (These variables are strongly collinear; in an unreported regression excluding log(wealth), the coefficient on landholdings increases by a factor of 4 and becomes statistically significant at the 1% level.) Our covariates also include a direct proxy for credit rationing, derived as described earlier from household self-reports about why they do not have one more loan. This coefficient is negatively signed as predicted, and statistically significant at the 1% level. Quantitatively, switching on this variable reduces the probability of takeup by 30% (1.4 percentage points).

**III. Heterogeneous beliefs.** We next estimate whether a proxy for beliefs about the insurance payout influences participation. We include a variable that measures the household’s expectation about the start date of the monsoon. Our prediction is that households that expect the monsoon to start later will expect a higher payout, because the insurance payout is inversely correlated with rainfall from a fixed calendar date. This measure of pessimism is positively correlated with takeup as predicted, although it is statistically insignificant.

**IV. “Early adoption,” limited cognition and networks.** Qualitative responses suggest a significant fraction of households do not fully understand the insurance product, and that many relied on recommendations from other farmers or village leaders for insurance participation decisions. Here we test three hypotheses described earlier about household behavior in this kind of “incomplete information” environment. The first hypothesis is that households with a greater degree of familiarity with BASIX, the insurance provider, will have higher participation rates. First, we include a dummy variable equal to 1 if the household is a member of a borewell user association (BUA). A BUA is a group of households that jointly use and maintain a water bore or set of bores. Historically, BASIX provides group lending to BUAs, and in 2003, when the insurance was first piloted, the insurance was explicitly targeted to BUA members. BUA members

23
Table 4 Ratio of primary education attainment to secondarv education attainment

||**Africa**|**East**<br>**Asia**|**Mena**|**| Latin**<br>**| America**|**South**<br>**Asia**|
|---|---|---|---|---|---|
|1965|31.72|18.02|9.28|10.52|21.00|
|1975|14.72|6.81|5.74|6.31|7.29|
|1985|8.60|4.08|4.07|4.89|3.99|

14
and industries. We then assess the extent to which the observed variation in average size, growth since birth, and productivity at different points in the firms’ lifecycle in different countries can be explained by two explanatory factors: First, we explore how much country-level institutional factors affect firm size, growth, and productivity at different ages since founding. Here we quantify the explanatory power of these country-level institutional factors on firm indicators. However, we recognize that institutions also affect the initial size distribution of firms and that their influence through this latter channel may be very significant.

Second, we estimate the extent to which these post-birth variables can be explained by the firm’s initial size at founding. As the Lucas (1978) and Rauch (1991) models suggest, and Klepper (2001) shows empirically, more capable entrepreneurs are expected to form larger firms.<sup>3</sup> Given that entrepreneurial talent is a likely predictor of firm performance, we condition on initial firm size when predicting the role of institutions on subsequent growth. Furthermore, as Klapper et al. (2006), Kerr and Nanda (2009), and Ayyagari et al. (2014b) show, initial firm size is influenced by institutions within each country by affecting the financing of entrepreneurial firms and the pool of potential educated entrepreneurs. Thus, the explanatory power of initial size in the performance regressions also places an upper bound on the effect of institutions through this channel.

Using a variance decomposition approach, we examine differences between countries as well as differences between different types of firms, and their interaction.<sup>4</sup> We focus on the

> 3 Maksimovic, Phillips, and Yang (2013) show initial size and productivity are predictors of future public firm status, and hence growth and productivity.

> 4 To examine the relative influence of firm effects compared with country effects, we use variance decomposition analysis. Similar methods have been used in finance to examine the importance of country vs. firm level effects in explaining property rights protection (Ayyagari, Demirguc-Kunt, and Maksimovic, 2008). The method is also well established in corporate strategy studies in the context of decomposing profitability into corporate and industry effects [Schmalensee (1985), Rumelt (1991), McGahan and Porter (1997, 2002), Khanna and Rivkin (2001)]. By using this method, we can focus directly on the general importance of these effects in explaining firm size and growth over lifecycle without any assumptions on structural analysis.

3
**Table 3: Model Performance**

||**M**|**ain Results**||**Check**|
|---|---|---|---|---|
|Dataset:|**Stan**|**dard Indicato**|**rs**|**Offset**<br>**Indicators**|
|Performance:<br>Sample:|O<br> <br>All<br>AUC|ut of Sample<br>(5 fold CV)<br>Has<br>Bureau<br>Records<br>AUC|<br>No<br>Bureau<br>Records<br>AUC|Out of Time<br>(train early<br>period, test<br>late)<br>All<br>AUC|
|**Baseline Model**|||||
|**Credit Bureau**|||||
|Random Forest|0.516|<br>0.509|-|0.507|
|Logistic, stepwise BIC|0.565|<br>0.565|-|0.550|
|**Our Models**|||||
|**Phone indicators (CDR)**|||||
|Random Forest|0.710|<br>0.708|0.719|0.631|
|Logistic, stepwise BIC|0.760|<br>0.759|0.766|0.595|
|**Phone indicators, within-week variation**<br>**(CDR-W)**|||||
|Random Forest Weekly Ensemble|0.616|<br>0.614|0.630|0.641|
|OLS FE, stepwise BIC|0.633|<br>0.634|0.631|0.593|
|**Combined**|||||
|**Credit bureau and phone indicators**|||||
|Random Forest|0.711|<br>0.708|-|0.642|
|Logistic, stepwise BIC|0.772|<br>0.770|-|0.616|
|**Credit bureau and phone indicators,**<br>**within-week variation**|||||
|Random Forest Weekly Ensemble|0.618|<br>0.616|-|0.639|
|OLS FE, stepwise BIC|0.645|<br>0.645|-|0.586|
|Default Rate (%)|11|<br>12|10||
|N|7,068|6,043|1,025|6,975|

Standard indicators evaluate out of sample performance using 5-fold cross validation, averaged over fold draws. Offset indicators are derived from only half of the data (the first half for early transitions; the last half for late transitions); the out of time model is estimated on the early half of transitions and tested on the late half. AUC represents the area under the receiver operating characteristic curve, and BIC the Bayesian Information Criterion. For middle two columns, model is trained on all individuals except the omitted fold, and performance is reported for the given subsample within the omitted fold. Source: authors’ analysis from telecom data.

28
|**Country**|**Government Agency Handling**<br>**Dumping Investigation**|**Government Agency Handling**<br>**Injury Investigation**|**Government Publications that**<br>**Report Antidumping Activity**|
|---|---|---|---|
|European<br>Union|Trade Directorate of the European<br>Commission|Trade Directorate of the European<br>Commission|_Official Journal of the European_<br>_Communities_<br>http://europa.eu.int/eur-<br>lex/lex/JOIndex.do?ihmlang=en|
|India|Directorate General of Antidumping<br>and Allied Duties in the Ministry of<br>Commerce|Directorate General of<br>Antidumping and Allied Duties in<br>the Ministry of Commerce|_The Gazette of India_<br>http://commerce.nic.in/ad_cases.htm<br>|
|South<br>Korea|Korean Trade Commission (KTC)|Korean Trade Commission (KTC)|Korean Official Gazette (Gwanbo)<br>http://gwanbo.korea.go.kr/<br>http://www.ktc.go.kr/eng/main.asp|
|Mexico|Unidad de Prácticas Comerciales<br>Internacionales (UPCI, Internatational<br>Trade Practices Unit) in La Secretaría<br>de Economía (Economic Secretary)<br>http://www.economia.gob.mx/?P=104|Unidad de Prácticas Comerciales<br>Internacionales (UPCI,<br>Internatational Trade Practices<br>Unit) in La Secretaría de<br>Economía (Economic Secretary)<br>http://www.economia.gob.mx/?P=<br>104|_Diario Oficial de la Federación_<br>http://dof.terra.com.mx/|
|New<br>Zealand|Trade Remedies Group at the Ministry<br>of Economic Development|Trade Remedies Group at the<br>Ministry of Economic<br>Development|New Zealand Gazette<br>http://www.med.govt.nz/buslt/trade_r<br>em.html|
|Peru|Comisión de Fiscalización de Dumping<br>y Subsidio (Commission Inspecting<br>Dumping and Subsidies) in INDECOPI<br>http://www.indecopi.gob.pe/|Comisión de Fiscalización de<br>Dumping y Subsidio<br>(Commission Inspecting Dumping<br>and Subsidies) in INDECOPI<br>http://www.indecopi.gob.pe/|_El Peruano_<br>http://www.elperuano.com.pe/|
|Turkey|“Board of Evaluation of Unfair<br>Competition in Importation” (the<br>Board) and the “Department of<br>Dumping and Subsidy Investigation”<br>(the Department)|“Board of Evaluation of Unfair<br>Competition in Importation” (the<br>Board) and the “Department of<br>Dumping and Subsidy<br>Investigation” (the Department)|_Resmî Gazete (Official Gazette)_<br>published in Turkish with links to<br>antidumping measures imposed and<br>current investigations at<br>http://www.dtm.gov.tr/ITHALAT/da<br>mping/dampgiris.htm<br>|
|United<br>States|International Trade Administration<br>(ITA) in the Department of Commerce<br>http://www.ita.doc.gov/<br>|US International Trade<br>Commission<br>http://www.usitc.gov<br>|_Federal Register_<br>http://www.gpoaccess.gov/fr/<br>|
|Venezuela|Comisión Antidumping y Sobre<br>Subsidios (CASS)<br>http://www.cass.com.ve/|Comisión Antidumping y Sobre<br>Subsidios (CASS)<br>http://www.cass.com.ve/|_Gaceta Oficial_<br>http://www.tsj.gov.ve/<br>gaceta/gacetaoficial.asp|
|South<br>Africa|Trade Remedies Directorates at the<br>Department of Trade and Industry<br>(DTI)|Trade Remedies Directorates at<br>the Department of Trade and<br>Industry (DTI)|International Trade Administration<br>Commission Reports<br>http://www.dti.gov.za/offerings/offeri<br>ng.asp?offeringid=82|

14
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