rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

**Aivazian, V., L. Booth, A. Demirgijg-Kunt, and V. Maksimovic, 1999. "Capital Structures in Developing Countries." Forthcoming** _Journal ofFinance._

Bhide, **A., 1993.** "The Hidden Costs of Stock Market Liquidity." _Journal of Financial Economics_ 34, **31-51.** Calvo, **G.** and **C.** Reinhart, **1999.** "When Capital Inflows Come to a Sudden Stop: Consequences and Policy Options," University of Maryland.

Caprio, **G.** Jr. and **A.** Demirgiig-Kunt, **1997.** "The Role of Long-Term Finance: Theory and Evidence." Manuscript. The World Bank.

Claessens, **S., S.** Djankov, and L. Lang, **1998.** "Corporate Growth, Financing, and Risks in the Decade before East Asia's Financial Crisis." Policy research working paper **2017.** The World Bank Demirgilg-Kunt, **A.,** and R. Levine, **1997.** "Financial Structure and Economic Development." Manuscript. The World Bank.

Demirgii-Kunt, **A.,** and R. Levine, **1999.** "Bank-Based and Market-Based Financial Systems: Cross-Country Comparisons." Manuscript. The World Bank.

Demirgiig-Kunt, **A.,** and V. Maksimovic, 1994. "Capital Structures in Developing Countries: Evidence from Ten Countries." Policy Research Working Paper **1320.** The World Bank Demirgiiq-Kunt, **A.,** and V. Maksimovic, **1995.** "Stock Market Development and Firm Financing Choices." Policy Research Working Paper 1461. The World Bank.

Demirgi9-Kunt, **A.,** and V. Maksimovic, 1998a. "Institutions, Financial Markets, and Firm Debt Maturity." Manuscript. The World Bank.

Demirgijg-Kunt, **A.,** and V. Maksimovic, **1998b.** "Law, Finance, and Firm Growth."' _Journal ofFinance,_ **_53,_ 2107-37.** Diamond, D.W., 1991a. "Debt Maturity Structure and Liquidity Risk." _The Quarterly Journal ofEconomics,_ **_709-737._** Diamond, D.W., **1991b.** "Monitoring and Reputation: The Choice between Bank Loans and Directly Placed Debt." _Journal ofPolitical Economy_ 99(4), **689-712.**

> Gallego, F. and **N.** Loayza, 2000, "Financial Structure in Chile," Manuscript. Central Bank of Chile.

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received cash transfers reported engaging in significantly fewer risk behaviors (Pettifor and others 2015). Another study found that cash incentives conditional on schooling led to a 30 percent reduction in HSV-2 incidence, but could not establish the impact of those incentives on HIV incidence (Abdool Karim and others 2015). Both studies might not have had enough statistical power to detect impacts on HIV incidence.

Because in these experiments the cash transfers were either unconditional or conditional on school attendance but not on sexually transmitted infection (STI) status, it is likely that the mechanism was not directly a price effect affecting the cost of unsafe sex, but rather an income effect putting girls in a better position to refuse or better negotiate risky sexual encounters or an “incapacitation” effect whereby the cash transfers keep the girls in school and therefore limit their exposure to risky environments.

Other studies evaluated conditional cash transfers in which the condition is directly attached to negative test results for sexually transmitted infections. In Malawi, Kohler and Thornton (2011) evaluated an intervention offering a single cash reward in one year’s time for individuals who remained HIV negative. They found no measurable effect on HIV status, but the number of sero-conversions in the sample was very small and statistical power was therefore low. The RESPECT study (de Walque and others 2012; de Walque, Dow and Nathan 2014; de Walque and others 2016) evaluated a randomized intervention that used economic incentives to reduce risky sexual behavior among young people aged 18-30 and their spouses in rural Tanzania. The objective was to prevent HIV and other sexually-transmitted infections by linking cash rewards ($20 or $10 dollars) to negative STI test results assessed every four months. The study tested the hypothesis that a system of frequent feedback and positive reinforcement using cash as a primary incentive to reduce risky sexual behavior could be used to promote safer sexual 10
There are many ways to estimate a multivariate density function. For example it is possible to generalize the one-dimensional technique for obtaining histograms. However, the resulting density estimate will not be differentiable. More popular techniques involve kernel estimators. Formally, the estimate of the multivariate density f(w) = f(y,x), where W. = (Yt,X 1 ) is given by: A n - - f(w) (1/nhq)E **K((W1** w)/h) (24) **i-1** where: h is the window width, n is the number of observations, q is the number of elements in any W. (equal to the number of explanatory variables, k, plus one for the dependent variable) and K() is the kernel function. The kernel function is typically taken from the set of many times differentiable multivariate density'functions. One popular kernel function is the multivariate normal or Gaussian density. Note, however, that the choice of a Gaussian kernel in no way restricts the estimated density. Indeed, it would appear that density estimates are much less sensitive to the kernel function than they are to the window width, h. The choice of the window width determines, in part, the degree of smoothing of the densicy estimate. Recent work by Racine (1989) has examined the optimal window width, h, in the sense of integrated mean square error. The results for the Peru Living Standards Survey data make use of this optimal window width and a Gaussian kernel.

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26 imForts of capital goods. q1 (eq. 51. Short-run choices can be be described as fcIlows. Output Isupplv (Yi depends on total capital stock and the real e<-hange rate, where the latter acts as a proxy for variable costs. A real depreciation, i.e. an increase in A, will boost wage and intermediate inputs costs and lowers supply. Given that capital stock data are not a,ailable In Morocco, we take a first quasi difference of the origina'l supply funxtion and estimate equation 6. In the estimation we take the primary and the government sector output levels to be exogenous. After output supply is determined, the demand for intermediate inputs (M ) and the supply of exports (X) can be simply described in eqs. 7 andn8 respectively as a function of Y, the wage rate (w) and the relevant price variable (p and p ). We do not however assume the country to be small in expoRnmarketX. The price of exports p depends therefore on exports volumes, world demand (WD) and foreign competitors' prices (p*). This completes the description of the firm's behaviour.

Households' choices are described in the second block of equations. We make a separability assumption where consumers first determine the level oF saving and aggregate consumption and then allocate total consumption between domestic and foreign commodities. Aggregate private consumption is modelled as in the previous section (eq. 10). In the present version of the model we take the seciFication where private consumption depends on disposable income (Y), real interest rate (r) and the real exchange rate ()\). The allocation oF aggregate consumption between domestically and Foreign produced commodities is described by a simple (constrained) linear expenditure system. We allow for the fact that a subset of foreign consumption goods cannot be freely imported and study the impact of these rationing measures on total Import of consumption goods. Our approach parallels the one used for investment imports and draws on Bertola and Faini (1990). As with investment goods, we do not allow for any rationing effect on aggregate consumption. The resulting equation is highly non-linear, but can be simply described (eq. 11) as stating that imports of consumption goods depend on total consumption, relative prices and the extent oF quotas on such import category (q_). Eq. 12 defines disposable income as the sum of GCDP, net factor irncome from abroad (NFI), interest on public domestic (GOINT) and foreign (OXINT) debt, transfer (TR) minus total taxes (Ti and monetary financing (MF). Note that interest payments on foreign debt (i XINT) are already included, with a negative sign, in NFI.
# **Panel B. Pairwise correlations**

|||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|[11]|[12]|[13]|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Made or received a digital payment|[1]|1|||||||||||||
|Overall Restrictions on Banking Activities|[2]|-0.44*|1||||||||||||
|Banks' assets|[3]|0.53*|-0.19*|1|||||||||||
|GDP per capita growth|[4]|-0.01|0.01|-0.13|1||||||||||
|log(population)|[5]|-0.15|0.19*|0.10|-0.08|1|||||||||
|Inflation, consumer prices (annual %)|[6]|-0.08|-0.04|-0.18*|-0.18*|0.02|1||||||||
|COVID growth differential|[7]|0.36*|-0.29*|0.04|0.02|-0.28*|0.21*|1|||||||
|Mean years of schooling|[8]|0.75*|-0.40*|0.39*|0.00|-0.29*|-0.06|0.55*|1||||||
|Access to electricity (% of population)|[9]|0.43*|-0.17|0.44*|0.17|-0.11|-0.27*|0.49*|0.72*|1|||||
|Individuals using the Internet (% of population)|[10]|0.78*|-0.44*|0.54*|-0.03|-0.27*|-0.20*|0.45*|0.86*|0.75*|1||||
|Mobile cellular subscriptions (per 100 people)|[11]|0.32*|-0.26*|0.49*|0.02|-0.13|-0.13|0.03|0.35*|0.37*|0.39*|1|||
|Enablers data economy|[12]|0.40*|-0.13|0.32*|0.01|0.34*|-0.02|0.11|0.35*|0.24|0.38*|0.17|1||
|Safeguards data economy|[13]|0.33*|-0.41*|-0.05|-0.06|-0.20|-0.13|0.07|0.25|-0.02|0.31*|0.16|0.18|1|

Notes: Pearson correlation coefficients. Asterisk indicates coefficient statistically significantly different from zero (p-value < 0.1).

34
proportion from South and East Asia (Kapiszewski 2006). Migration to Qatar and to the other Gulf countries is typically temporary, with work contracts determining the duration of stay (Shah 2008). These contracts are normally for two years, though renewable at the discretion of the employer. Only workers earning income above a minimum level are allowed to bring their dependents with them—in Qatar, the figure was QR 8,000 a month in 2010, the equivalent of US$ 26,300 annually (henceforth, all monetary measures are converted to US dollars).

Migration from India’s southern state of Kerala, where our surveyed migrants come from, accounts for more than half of the total Indian migration to the Gulf in the 1990s. Results from the population-representative Kerala Migration Survey 2011 indicated that 17.1 percent of households in Kerala received remittances which were estimated to make up 31 percent of the state’s net GDP (Zachariah and Rajan 2012).

Migration is typically a familial decision made with the expectation of improving the family’s economic conditions from the migrant’s remittances. Especially for temporary migrants, satisfying the remittance expectations of the family back home is of paramount importance and is often regarded as the price to pay for the option to return home in case of illness, temporary or permanent loss of income, or to inherit property (Banerjee 1981, Dustmann and Mestres 2010). Moreover, the threat of social sanctions or exclusion, including stigmatization from the family is a strong motivating factor for meeting expectations, which the anthropological literature has long emphasized (Philpott 1968, Goldring 2004, Peter 2010). Plateau (2014) points to a large body of ethnographic work that highlights the importance of shaming, disapproval, ostracism, and other means to pressure giving among (extended) family members in the sub-Saharan Africa. For the studied case of male migrants from Kerala to the Arabian Gulf, Osella and Osella (2000) describe the feared risks of migrants losing face and reputation if they do not meet the social 6
|VARIABLES|(1)|(2)|(3)<br>Height-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Singida|-0.173|-0.175|-0.130|-0.086|-0.169|-0.128|
||(0.599)|(0.598)|(0.591)|(0.579)|(0.603)|(0.585)|
|Tabora|0.198|0.199|0.232|0.219|0.226|0.130|
||(0.592)|(0.592)|(0.587)|(0.574)|(0.593)|(0.578)|
|Rukwa|-0.533|-0.532|-0.504|-0.489|-0.531|-0.494|
||(0.601)|(0.597)|(0.591)|(0.580)|(0.601)|(0.582)|
|Kigoma|-0.651|-0.648|-0.611|-0.613|-0.649|-0.668|
||(0.584)|(0.583)|(0.575)|(0.564)|(0.584)|(0.570)|
|Shinyanga|0.071|0.072|0.098|0.104|0.074|0.077|
||(0.587)|(0.586)|(0.579)|(0.567)|(0.587)|(0.572)|
|Kagera|-0.612|-0.609|-0.587|-0.638|-0.611|-0.629|
||(0.583)|(0.580)|(0.572)|(0.560)|(0.582)|(0.564)|
|Mwanza|0.323|0.327|0.360|0.337|0.326|0.270|
||(0.577)|(0.579)|(0.571)|(0.559)|(0.579)|(0.563)|
|Mara|-0.084|-0.080|-0.068|-0.077|-0.081|-0.180|
||(0.594)|(0.594)|(0.587)|(0.578)|(0.596)|(0.583)|
|Manyara|0.260|0.262|0.289|0.331|0.322|0.298|
||(0.622)|(0.621)|(0.613)|(0.601)|(0.629)|(0.611)|
|Kusini unguja|0.538|0.543|0.519|0.568|0.537|0.544|
||(0.565)|(0.566)|(0.564)|(0.582)|(0.563)|(0.567)|
|Mjini/Magharbi unguja|-0.269|-0.268|-0.272|-0.246|-0.269|-0.325|
||(0.571)|(0.570)|(0.566)|(0.552)|(0.571)|(0.556)|
|Kaskazini pemba|<br>-0.561|<br>-0.558|<br>-0.574|<br>-0.555|<br>-0.561|<br>-0.608|
||(0.710)|(0.710)|(0.707)|(0.685)|(0.709)|(0.701)|
|Constant|-0.997*|-1.000*|-1.020*|-1.072**|-0.997*|-1.023*|
||(0.546)|(0.546)|(0.539)|(0.524)|(0.547)|(0.530)|
|Observations|1,099|1,099|1,097|1,099|1,095|1,095|
|R-squared|0.077|0.077|0.079|0.085|0.078|0.087|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure on total expenditure.

91
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All the bilateral variables that capture various aspects of migration costs are highly significant at the 1% level, with sensible orders of magnitude relative to previous findings in the literature, which moreover vary sensibly over both skill groups. Geographical distance deters international migration and has a stronger impact upon the low-skilled, since typically highskilled migrants are better able to overcome higher international migration costs. Similarly, while migrants from both skill groups migrate more on average to bordering countries, this effect is much stronger for the low-skilled for similar reasons. Furthermore, both of these effects are similar in magnitude in both 1990 and 2000. Sharing a common language is more important for the high-skilled since language requirements are generally more relevant for this group. Diaspora networks conversely encourage the migration of low-skilled migrants more than their high-skilled counterparts, since this latter grouping typically has additional resources in order to migrate internationally. An alternative explanation is that networks will likely play a more important role in South-South migration, which comprises higher volumes of low-skilled migrants. Similarly, colonial links, which may be considered to also comprise an historical diaspora component are more relevant for the low-skilled, but across both skill groups the influence of colonial links declines between 2000 and 1990, a result in line with Head, Mayer and Ries (2010). All of these results on the role of the bilateral variables are consistent with other papers in the literature, such as Beine et al. (2011) and Grogger and Hanson (2011). The differences in the magnitudes of the parameter estimates are likely driven by the fact that previous papers used data from OECD destinations whereas we are able to include many non-OECD destinations.

23
regressions of growth on each of the five individual quintile shares, including them one at a time in their growth regressions. They find that higher bottom quintile shares are positively correlated with subsequent growth, while higher upper quintile shares are negatively correlated with subsequent growth. This latter finding is not very surprising given that top (bottom) quintile shares can be thought of as alternative summary measures of inequality that are almost perfectly positively (negatively) correlated with the Gini coefficient in their data.

Finally, Castelló‐Climent (2010) (hereafter, CC) also documents the effects of inequality on growth in a large panel of countries. This paper considers inequality in the distribution of human capital, in addition to the income or consumption inequality measures that are studied in the other three papers reviewed here. Human capital inequality is constructed using the methodology proposed in Castelló and Domenech (2002), which relies on the Barro‐Lee cross‐country data on years of schooling by age groups of the population, and then calculates inequality in education across age groups as the measure of human capital inequality. Although this measure captures only human capital inequality across (and not within) age groups, an advantage of this measure is that it has much greater country and time coverage than measures of income and consumption inequality from the UNU‐Wider World Income Inequality Database. Since the strongest results in this paper are for the human capital inequality measure, I focus on these here. CC finds a strongly significant negative relationship between human capital inequality and subsequent growth in a large panel of countries observed over five‐year periods.

# **2.2  Estimating Growth Regressions Using the SYS‐GMM Estimator**

All of the studies discussed above estimate variants on the following standard cross‐country growth regression: (1) where ��� is log real GDP per capita, ����� is a vector of explanatory variables, and the composite error term �� ��� ���� includes country and period effects as well as an idiosyncratic component. Note that subtracting lagged per capita income, ����� , from both sides of Equation (1) results in a regression of real per capita GDP growth on initial income and a set of control variables. In all four studies, the length of the time interval is five years. As noted above, the studies reviewed here differ in their choice of explanatory variables and sources of inequality data, but otherwise adhere to this basic setup. For 7
_Table 3: The impact of precipitation on GDP growth by agricultural activity_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Share of|No Cropland|>0 to 0.25|0.25 to 0.5|0.5 to 0.75|>0.75|
|Cropland||||||
|Rainfall|2.542|1.091*|1.003**|1.392**|2.151***|
||(2.391)|(0.454)|(0.364)|(0.447)|(0.346)|
|Rainfall<sup>2</sup>|-0.350|-0.139+|-0.135**|-0.325**|-0.453***|
||(0.504)|(0.082)|(0.047)|(0.114)|(0.094)|
|Temperature|0.369|0.721**|1.500***|0.392|-0.092|
||(0.537)|(0.239)|(0.386)|(0.268)|(0.205)|
|Temperature<sup>2</sup>|0.008|-0.021**|-0.040**|-0.011|-0.005|
||(0.007)|(0.006)|(0.013)|(0.010)|(0.006)|
|Observations|90,932|494,505|110,904|82,596|90,510|
|R-squared|0.140|0.301|0.344|0.336|0.456|

Specification is the baseline model from Table 2. Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1 14
Next, we review the country context in which the collected coefficients were estimated. We focus on macro-financial factors potentially relevant for the interest rate pass-through: GDP per capita ( _gdppc_ ), trade openness measured as exports over GDP ( _openness_ ), net foreign assets ( _nfa_ ), financial depth measured as credit to GDP ratio ( _credit_gdp_ ), market capitalization ( _capitalization_ ), turnover ratio for stocks ( _stock_turnover_ ), and claims on central government divided by GDP ( _claims_gov_gdp_ ). Each macro-financial variable is measured at the median year of the sample used in the original study. For example, if a research study uses a sample from 2006 to 2010, then the median year for this research is 2008. Table 3 displays descriptive statistics for all macro-financial variables.

# [Table 3 about here]

The third group of control variables covers monetary policy characteristics: a measure of central bank independence ( _cbi_G_ ), a dummy for inflation targeting framework ( _inf_target_ ), and a dummy for floating exchange rate arrangement ( _floating_exrate_ ). For the measure of central bank independence, we use the same approach as for macro-financial variables. That is, we choose the median year of the study’s sample and match it with the corresponding assessment of central bank independence from the Garriga (2016) data set.<sup>4</sup> Similarly, the median year of the sample determines the setting of the dummies for inflation targeting and floating exchange rate regime. Table A4 in the Appendix presents the descriptive statistics of the central bank independence variable.

The two dummy variables for the type of monetary policy framework and exchange rate arrangement are based on the Annual Reports on Exchange Arrangements and Exchange Restrictions from the IMF.<sup>5</sup> We differentiate inflation targeting from other monetary policy regimes. The corresponding dummy ( _inf_target_ ) takes a value of one if the country targets inflation, and zero otherwise. The dummy _floating_exrate_ then takes a value of one for a _de facto_ floating exchange rate arrangement as classified by the IMF, and zero otherwise. Figure 10 shows that a floating exchange rate

> 4 Garriga, Ana Carolina (2016). Central Bank Independence in the World: A New Data Set. _International Interactions_ , 42(5), pp. 849-868.

> 5 The reports are available online at the IMF website: https://www.imf.org/en/Publications/Annual-Report-on-Exchange-Arrangements-and- <u>Exchange-Restrictions/Issues/2017/01/25/Annual-Report-on-Exchange-Arrangements-and-Exchange-Restrictions-2016-43741.</u>

13