rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Impact of Diaspora on Exports**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|Ln(Exports)|Ln(Exports)|Ln(Exports)|Ln(Exports)|
||OLS|IV-OLS|OLS|IV-OLS|
|**Migrants_ij (log)**|**0.155*****<br>**(21.65)**|**0.0513***<br>**(1.692)**|**0.214*****<br>**(15.05)**|**0.455*****<br>**(5.725)**|
|**Migrants_ij (log)*Africa_i**|**0.0244***<br>**(1.953)**|**0.125*****<br>**(4.904)**|||
|**Migrants_ij (log)*Institutions**|||**-0.0138*****<br>**(-4.641)**|**-0.0506*****<br>**(-7.951)**|
|Institutions|||0.126***<br>(4.872)|0.321***<br>(8.281)|
|GDP_i (log)|0.913***|0.927***|0.949***|0.919***|
||(23.94)|(24.41)|(18.37)|(17.20)|
|GDP_j (log)|0.483***|0.522***|0.581***|0.587***|
||(13.99)|(14.55)|(12.12)|(11.72)|
|FTA|0.239***|0.247***|0.200***|0.214***|
||(5.794)|(5.871)|(4.541)|(4.500)|
|Distance (log)|<br>-1.201***|<br>-1.342***|<br>-1.245***|<br>-1.104***|
||(-46.67)|(-27.09)|(-44.04)|(-9.216)|
|Contiguity|0.340***|0.415***|0.354***|0.109|
||(4.144)|(4.469)|(4.009)|(0.613)|
|Common colony|0.543***|0.649***|0.429***|0.328***|
||(8.022)|(8.634)|(5.806)|(2.753)|
|Colony 1945|1.212***|1.377***|0.978***|0.716***|
||(11.41)|(11.32)|(8.379)|(3.295)|
|Common language|0.321***|0.400***|0.388***|0.274***|
||(7.179)|(7.388)|(8.074)|(2.812)|
|Constant|-20.23***|-20.74***|-21.00***|-16.53***|
||(-29.33)|(-29.81)|(-24.81)|(-25.06)|
|MR terms|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|
|Importer and Exporter FE|yes|yes|yes|yes|
|Sargan (p-val.)||0.174||0.277|
|<br>Underidentification test||0.000||0.000|
|(Kleibergen-Paap p-val.)|||||
|Observations|31,207|31,207|23,279|23,279|
|R-squared|0.748|0.745|0.766|0.761|

_Not_ es: Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Observations are clustered by country-pair. In column 2, we instrument both the endogeneous variable _Migrants_ij_ and the interactive variable _Migrants_ij*Africa_i_ with our instruments and the interactive terms of each instrument with Africa_i. In column 4, we resort to the same technique.

In column 2, estimation results with the instrumental variable technique (IV-OLS) are presented. The statistical tests confirm the validity of our instruments. The underidentification test of Kleibergen–Paap confirms that our instruments are strongly correlated with the endogeneous variable, namely _Migrants_ij_ and _Migrants_ij*Africa_i_ . Moreover, we do not reject the null hypothesis of the Sargan over-identification test which means that our 12
|2 month lagged autocorrelation of monthly outgoing duration (after first month with activity)|-0.039|(1.098)|
|---|---|---|
|Magnitude of strongest repeating temporal pattern in daily outgoing calls (Fourier transform)|0.004<sup>***</sup>|(0.001)|
|Magnitude of third strongest repeating temporal pattern in weekly outgoing calls (Fourier transform)|0.007<sup>***</sup>|(0.002)|
|2 month lagged spearman autocorrelation of monthly outgoing calls (after first month with activity)|13.496|(325.088)|
|Correlation of monthly outgoing SMS with 1 month lagged outgoing duration|-5.137<sup>*</sup>|(2.899)|
|Correlation of monthly outgoing SMS with 2 month lagged outgoing calls|13.600|(320.260)|
|Missing: correlation of monthly outgoing SMS with 1 month lagged outgoing duration|-4.941<sup>*</sup>|(2.898)|
|Constant|6.980<sup>**</sup>|(2.922)|
|Observations|7,068||

_Note: Standard errors computed based on the final logistic model; they do not adjust for model selection._ *p**p***p<0.01 Source: authors’ analysis from telecom data.

# **(b) Stepwise OLS FE**

|**Stepwise OLS FE**|||
|---|---|---|
||Coefficient|<br>SE|
|Fraction of outgoing duration during working hours|0.098***|(0.025)|
|SD of daily outgoing calls|0.556|(0.453)|
|SD of daily outgoing calls, after first activity|-0.573|(0.449)|
|SD of weekly outgoing calls|0.001|(0.001)|
|SD of monthly calls out|-0.001***|(0.0003)|
|SD of daily SMS out, after first activity|-0.011***|(0.003)|
|Q80-Q20 of weekly calls out|-0.026***|(0.008)|
|Q70-Q30 of weekly outgoing calls, on weeks it is above zero|-0.001|(0.001)|
|Q80-Q50 of daily outgoing SMS, on days it is above zero|-0.007***|(0.002)|
|Median daily outgoing calls, on days it is above zero|-0.020***|(0.004)|
|Mean weekly outgoing calls, on weeks it is above zero|0.004***|(0.001)|
|Q60/(Q70-Q30) of daily duration out|0.057***|(0.015)|
|Magnitude of strongest repeating temporal pattern in daily outgoing SMS (Fourier<br>transform)|0.0003***|(0.0001)|
|Missing: daily autocorrelation in outgoing duration|-0.411***|(0.134)|
|Observations|7,068||

_Note: Demeaned by week prior to estimation. Standard errors computed based on the final_ *p**p***p<0.01 _model; they do not adjust for model selection or demeaning. Source: authors’ analysis from telecom data._ 8
the 2001 Population Census of India, 11% of India’s population consisted of individuals who had moved permanently to their current district from a different district at some point in the past. In the 2011 Census, this share had increased to 14 percent.<sup>6</sup> Using railway data, the Government of India’s 201617 Economic Survey Report estimates a doubling of internal migration rate from 1991-2001 to 2001-2011 (Government of India, 2017).<sup>7</sup> India’s reputation as a low internal migration country is also being reconsidered by economic historians (Cohen, 1995; Tumbe, 2018; Rajan and Sumeetha, 2019). Migration in the past two centuries consisted mostly of young men migrating via caste networks to areas of economic opportunity, returning every few years, and eventually settling back into their home villages. This type of migration was common place: Tumbe (2018) estimates that 150 districts experienced mass migration during the past 150 years (defined as more than 20% of households affected). Migration has increased in the last few decades and is more permanent in nature. The high-skilled are increasingly dominant in internal migration flows (Docquier and Rapoport, 2012). Yet, this recent increase in migration builds on the migration networks established in the past century and a half (Tumbe, 2018).

The contribution of this study is to link migration to changes in land tenancy. This link first became clear to us during a field visit in rural Utter Pradesh, North India, in 2008, when we spoke with landlords about their contract choice. We then confirmed these patterns with historical accounts from other parts on India. Reddy (1996), for instance, collected land contracts and interviewed farmers in Andhra Pradesh, South India, spanning the period 1850 to 1980. He repeatedly notes the unique role that absentee landlords play in the rural economy. His analysis suggests that concerns about moral hazard cause absentee landlord to prefer contracts involving simple, once-per-

> 6The increase is larger when accounting for within-district migrants, who constitute an estimated 60% of all internal migrants in 2001. The Census does not cover temporary migration, which is also on the increase due to factors such as changes in weather and National Rural Employment Guarantee Act (Rosenzweig and Udry, 2014; Kaur, 2019; Morten, 2019).

> 7See Hnatkovska and Lahiri (2015) for comparable statistics using National Sample Survey data.

8
and employment growth rates, as well as on states’ poverty and income inequality rates. Finally, we identify the impact of public debt ceilings on bank lending to local governments as well as to private-sector firms. That is, we study whether the restrictions to local public debt introduced in the FD Law altered the credit allocation of banks away from the public sector and into privatesector firms. Bank lending dynamics to private-sector firms in states below and above the median ex ante public indebtedness suggest this is the case (Figure 4). While credit volumes to private borrowers in states below and above the median public indebtedness followed a similar trend before the FD Law, bank lending volumes to private firms in states with higher public debt begin increasing relatively more afterwards (Panel A). The increased lending volumes experienced by firms in states with ex ante higher public debt were further met by relatively lower interest rates (Panel B), hinting that these changes in lending were the result of an expansion of credit supply to private borrowers in ex ante more publicly indebted states.

One key identifying assumption to estimate the causal effects of the introduction of the FD Law using a difference-in-difference approach is that in the absence of the Law, the trends in outcomes between treatment and control states would have been the same.<sup>30</sup> While this assumption cannot be tested, we test for differences in the trends of our outcomes of interest across states of varying indebtedness before the introduction of the Law. To do this, we use the regression outlined in equation 2, where we restrict the sample to the pre-reform period.

The variable _Trendt_ in equation 2 is a linear time trend, and as before, _DebtStates,16Q1_ captures the indebtedness measure of states in the first quarter of 2016. The coefficient _β1_ captures the average trend over time of the outcome of interest _ys,t_ prior to the Law. The coefficient _β2_ , the main coefficient of interest, tests for differences in the trends of _ys,t_ across states of varying indebtedness levels in the pre-reform period. Included in the regression are fixed effects at the state level γ _s_ and in some specifications, we further include time-level fixed effects. The results of this test for the state-quarterly, state-yearly, state-biyearly, and state-monthly variables are displayed

> 30 This assumption is more plausible when the outcomes in the pre-reform period are similar in levels across states of varying ex-ante public indebtedness. In Table A3A, we test for differences in mean outcomes in the pre-reform period between states below or above the median public indebtedness. We find that across a series of economic and financial measures, states with varying ex-ante indebtedness were statistically similar prior to the implementation of the Law.

17
One difference is that private institutions may only be willing to share negative information voluntarily even though it may be in a wider social interest to share positive and negative information – see Japelli and Pagano (1993). The intuition is relatively simple. Sharing information has a cost and a benefit. The cost for a bank is that a competitor may learn something about that bank’s portfolio and the benefit is that the bank may be able to make more informed lending decisions and to predict default probabilities more accurately. It is more likely that the private benefits exceed the costs for negative information as revealing information about bad clients is not so costly. However, there may be net private costs to revealing positive information even though it may be beneficial to society to have that information shared.<sup>11</sup> And a regulator may compel regulated institutions to share both positive and negative information. In more than 80% of the PCRs surveyed, institutions are required to report on loans in good standing as well as loans in arrears.<sup>12</sup> While private credit bureaus may be able to develop some of this positive credit history data, it is often a slow process and they are unlikely to be able to achieve full reporting from supervised institutions as in a standard PCR.

A second important difference between PCRs and private credit bureaus is the type of information collected and the type of services offered. PCRs in general only supply very basic information and many do not supply a true credit history, but only a snapshot of the current status of a borrower with the financial system. Private credit bureaus tend to supply a greater quantity of information and finer information and also develop other products using that information, including, for example, credit-scoring products. One view is that PCRs should only provide very basic information and at very low cost while private credit bureaus can take this information, add to it, and develop value added information services. In this way a PCR may enhance the competitiveness of the private credit bureau industry.<sup>13</sup>

> 11 A counter argument is that society may wish banks to retain some positive information as private. We consider the merits of this argument in the discussion on competition below.

> 12 In some PCRs the positive information is reported but not shared or is shared subject to some cut-off.  For example, in the case of Argentina, information on loans with ratings 1 and 2 of less than 200,000 pesos are not shared in database format and there are restrictions on the number of individual hits someone can make on internet inquiries regarding this type of information to protect to some degree banks’ rents and hence their incentives to gather information on smaller clients.

> 13 We come back to this point in the discussion regarding competition.

14
Table 10 Continued: IV regression IT use on firm-level productivity considering China’s competition

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Share of labor with computer|-0.713|-0.557|-0.197|-0.417|-0.784|-1.186|
||(1.351)|(1.237)|(1.458)|(1.503)|(1.568)|(1.819)|
|Chinese competition*IT|14.489**|13.979***|12.014**|11.947**|10.963**|12.013**|
||(5.719)|(5.248)|(5.282)|(5.210)|(5.036)|(6.001)|
|Chinese competition|-3.216*|-3.179**|-2.697*|-2.711*|-2.275|-2.700|
||(1.642)|(1.589)|(1.572)|(1.544)|(1.485)|(1.895)|
|F-frst stage IT|6.5|7.9|6.6|6.3|5.2|5.3|
|F-frst stage IT*competition|10.3|12.5|13.4|14.0|13.6|13.3|
|Sargan|0.62|0.60|0.84|0.93|0.93|0.91|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI

# **5.4 Robustness tests**

In order to analyze the robustness of these results, all the equations were estimated using pooled regressions instead of fixed effects to test the sensitivity of our results to unobserved heterogeneity. Even though coefficients from the pooled regressions are slightly higher in all cases, the conclusions and statistical significance do not change.<sup>11</sup> With the purpose of analyzing what previous studies, such as Bloom et al. (2012b) have found regarding the relation between management and IT use and how these two factors combined enhance productivity, we included an interaction of IT use and a proxy for management in the productivity equation.

> 11Results are not shown here but are available upon request.

26
# **Table 6. Percentage of firms in each quartile of predicted probability of formality by group, ordered by quartile one, pooled ELMPS 2012 and 2018**

|**Profil**<br>**Group**|**e**<br> <br>**Firm**<br>**Size**|<br>**Firm Age**|**Capital**|**Non-rel. out of**<br>**HH hired**|<br>**Quartile of Ln(Reven**<br>**per Worker)**|**ue**<br>**Owner's Education**|**N**<br>**(Obs**|**.)**<br>**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firms in Each**<br>**Quartile of**<br>**Predicted**<br>**Probability**|
|---|---|---|---|---|---|---|---|---|---|
||||||||||Q1 Q2 Q3 Q4|
|1|1|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Below secondary|72|6.7|80.9 13.9 3.6 1.7|
|2|1|Less than 5<br>years|<5K|No|1|Secondary|28|2.6|67.6 32.4 0.0 0.0|
|3|2|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Below secondary|20|2.6|62.6 26.5 10.8 0.0|
|4|1|Less than 5<br>years|All capital (Aggregate)|No|3 & 4 (Aggregate)|Below secondary|34|3.4|57.4 36.2 6.4 0.0|
|5|1|Less than 5<br>years|<5K|No|2|Secondary|16|1.4|51.2 31.6 17.3 0.0|
|6|2|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Secondary+ (Aggregate)|25|2.6|50.7 40.3 9.0 0.0|
|7|1|5 years or abo|ve <5K|No|2|Below secondary|53|5.5|47.4 39.8 8.7 4.2|
|8|1|Less than 5<br>years|<5K|No|1 & 2 (Aggregate)|Above secondary|23|2.2|47.2 41.5 11.3 0.0|
|9|1|5 years or abo|ve <5K|No|1|Below secondary|60|6.0|44.9 38.8 16.3 0.0|
|10|1|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|Below secondary|10|1.0|43.7 32.4 18.1 5.7|
|11|1|5 years or abo|ve <5K|No|1|Secondary|30|2.9|42.2 42.1 12.3 3.3|
|12|1|5 years or abo|ve <5K|No|3|Below secondary|22|1.9|36.6 28.3 35.1 0.0|

41
6

**Table 2**

# **Complementary Roles of Debt Ratios and Primary Balances as Spreads' Determinants**

Dependent Variable: EMBORLAT Method: Least Squares Sample(adjusted): 1995:02 2002:01 Included observations: 84 after adjusting endpoints Newey-West HAC Standard Errors & Covariance (lag truncation=3)

|Variable|Coefficient|Std. Error t-Statistic|Prob.|
|---|---|---|---|
|C|-0.26|0.06<br>-4.45|0.00|
|DEBTY(-1)|0.01|0.00<br>4.48|0.00|
|PRIMBAL(-1)|-0.02|0.01<br>-2.40|0.02|
|R-squared|0.454|Mean dependent var|-0.011|
|Adjusted R-squared|0.441|S.D. dependent var|0.083|
|S.E. of regression|0.062|Akaike info criterion|-2.698|
|Sum squared resid|0.309|Schwarz criterion|-2.611|
|Log likelihood|116.295|F-statistic|33.723|
|Durbin-Watson stat|0.362|Prob(F-statistic)|0.000|

EMBORLAT= Brazil EMBI spreads orthogonalized from Latin EMBI average DEBTY= Debt to GDP ratio PRIMBAL= Primary fiscal balance The second implication of the DP model, namely the changing nature of the signaling role of primary balances, is captured by two alternative approaches. First, a dummy variable is defined for a specific signaling period and interacted with the primary balances. The original regression is augmented with this new auxiliary variable, and the sum of both coefficients has to be larger than the primary balance coefficient by itself. For the second approach, an auxiliary variable is constructed by the interaction of the primary balances with the debt ratio. If this variable is significant, then the hypothesis of the difference in the signaling role cannot be rejected.

For the first approach, we defined the signaling period from June 1999 to the time when the inflation-targeting approach was adopted and the Fiscal Responsibility Law was enacted. Given the significance of this auxiliary variable (Table 3), we cannot reject the hypothesis that primary balances affected spreads more forcefully during this signaling period. The alternative approach (Table 4) shows that the primary balance coefficient rose with the debt ratio, implying that signaling takes time and is not a once-and-for-all event. Drudi and Prati obtained the same result for Italy and Belgium.
managed to maintain strong export growth. This reflects improved productivity in key sectors and the resilience and sound fundamentals of the economy.

But strong economic growth and increased productivity have not yet been reflected in a structural improvement in labor market conditions. Employment has declined sharply during the first decade of independence, broadly similar to the experience in transition economies in general, reflecting in part a larger initial output loss. Starting from virtually zero at independence in 1991, unemployment in Latvia rose to a peak of 20.7 percent in 1996 (ILO definition). Figure 1 shows only a gradual decrease over recent years, in spite of sustained GDP and productivity growth.

**Figure 1: GDP Growth, Productivity, and Unemployment in Latvia**

<!-- Start of picture text --> 25 12000<br>20 10000<br>15 8000<br>10 6000<br>5 4000<br>0 2000<br>1995 1996 1997 1998 1999 2000 2001 2002 2003*<br>-5 0<br>GDP growth, % (LHS) Unem ploym ent rate, % (LHS) P roductivity, LVL (RHS)<br><!-- End of picture text --> Monetary policy has been consistently tight as the Central Bank concentrates on maintaining price stability in the country. Since its inception in 1994, Latvia’s fixed exchange rate regime, tied to the SDR, has served the country well. After reaching 109 percent in 1993, CPI inflation has been at single digit levels since 1997. In 2003 the CPI grew by 2.9 percent. Interest rates have come down significantly since the 1998 Russian crisis that affected the banking system. The annualized yield on a 12-month Treasury bill, which was 9.8 percent at the beginning of January 1999, was 3.5 percent in December 2003.

Fiscal policy has been conservative from the outset of the stabilization program with a few notable exceptions caused by domestic or external shocks. Owing to its generally good fiscal discipline, Latvia has low levels of public debt, and its credit ratings have improved, giving the country easier and less expensive access to international capital markets. However, the fiscal situation in 1999 and even in 2000 showed the consequences of the Russian crisis. From a surplus of 0.1 percent of GDP in 1997, the fiscal deficit rose to 3.9 percent of GDP in 1999 and remained relatively high in 2000 at 3.3 percent. This was above the target agreed with the IMF. In 2001, the situation improved somewhat and the fiscal deficit decreased to 1.4 percent of GDP. Still the budget for 2002 was for an increase in the fiscal deficit to 2.7 percent, significantly above the target of 1.5 percent. More worrisome is the fact that the 7
# **Figure 1**

Forecasted share of fossil energy in 2010 by energy project and according to year of publication Note: US scenarios are depicted as large squares

|45<br>50<br>55<br>60<br>65<br>70<br>75<br>80<br>85<br>90<br>95<br>**nergy share (%x100)**|
|---|
|35<br>40<br>**Fossil e**|
|30|
|25|
|20|
|15|
|10|
|5|
|0|
|1970<br>1975<br>1980<br>1985<br>1990<br>1995<br>2000<br>2005<br>2010|
|Department of the Interior<br>EUOGEP<br>Energy Research and Development Agency<br>European Union / DGTREN|
|Ford Foundation<br>Greenpeace<br>Harvard Business School<br>IEA|
|IIASA<br>IPCC<br>OECD<br>PEW|
|Shell International BV<br>Union of Concerned Scientists<br>WEC<br>WEC and IIASA|
**Table 7: Drought Impacts on Child Stunting by Country and Wealth Status OLS with Province  and Year of Survey Fixed Effects and Full Set of Controls**

||**Prob**|**ability of Stunti**|**ng (OLS with**|**Province and Su**|**rvey Year F**|**E)**|
|---|---|---|---|---|---|---|
||**All**|**Mozambique**|**Malawi**|**Zimbabwe**|**Tanzania**|**Zambia**|
|||**_Lowe_**|**_st(Poorest) W_**|**_ealthQuintile_**|||
|Drought: Birth Season|0.06***|0.11**|-0.00|0.12***|0.06**|0.09***|
||[4.79]|[2.46]|[-0.07]|[3.58]|[2.34]|[3.65]|
|Drought: Season after Birth|0.04***|0.07|0.07**|0.04|0.07**|0.00|
||[2.97]|[1.32]|[2.34]|[1.20]|[2.58]|[0.13]|
|Observations|11,605|1,517|1,941|2,179|3,302|2,666|
|||**_Second_**|**_and Third W_**|**_ealthQuintiles_**|||
|Drought: Birth Season|0.04***|0.08***|-0.00|0.09***|-0.02|0.11***|
||[3.70]|[2.84]|[-0.25]|[3.50]|[-1.16]|[5.13]|
|Drought: Season after Birth|0.04***|0.04|0.06***|0.03|0.02|0.09***|
||[4.04]|[1.09]|[3.07]|[1.27]|[0.80]|[3.55]|
|Observations|20895|3238|4097|3282|6328|3950|
||,|, <br>**_Fourth_**|, <br>**_and Richest W_**|, <br>**_ealthQuintile_**|,|,|
|Drought: Birth Season|0.01|-0.01|-0.02|0.10**|-0.00|-0.00|
||[0.31]|[-0.17]|[-0.93]|[2.05]|[-0.03]|[-0.08]|
|Drought: Season after Birth|0.03|0.04|0.03|-0.02|-0.01|0.14**|
||[1.51]|[0.70]|[1.24]|[-0.39]|[-0.29]|[2.24]|
|Observations|8,317|1,601|2,338|877|2,859|642|
|||**_Full S_**|**_ample: All We_**|**_althQuintiles_**|||
|Drought: Birth Season|0.04***|0.08***|-0.01|0.10***|0.01|0.10***|
||[4.53]|[3.32]|[-0.80]|[5.33]|[0.57]|[6.56]|
|Drought: Season after Birth|0.04***|0.05*|0.06***|0.03|0.03*|0.07***|
||[4.33]|[1.74]|[3.97]|[1.38]|[1.89]|[3.95]|
|Observations|40,817|6,356|8,376|6,338|12,489|7,258|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. This table uses the first specification in Table 5 and applies it different countries and wealth quintiles. Refer to the notes in Table 3 for all the other details.

30