rafmacalaba/datause-encoder-data huggingface dataset:
Bank PovcalNet’s interpolation methodology.<sup>27</sup> This alternative interpolated trend closely resembles the backcasted series described in Section 5 and further reinforces the findings of this analysis. In turn, this casts further doubt over the 17-percentage point drop implied by simply comparing the HNLSS 2009/10 and the 2018/19 NLSS: progress towards poverty reduction appears to have been much slower.

Figure 6 Comparison of imputed, backcasted and interpolated poverty rates for the period 2009-2019 <!-- Start of picture text --> 60.00%<br>56.40%<br>55.00%<br>50.00%<br>45.00%<br>46.3% 43.54%<br>41.53%<br>40.00% 42.8% 40.49% 41.88%<br>41.8%<br>38.9% 39.1%<br>35.00% 38.0%<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>HNLSS 2009/10<br>Backcast, pass-through=1<br>Interpolated series without 2009/10<br>Imputed poverty rate at US$1.90, GHS Original model<br><!-- End of picture text --> Note: the figure compares the different results of this analysis over the decade 2009/2019 and compares them to the HNLSS 2009/10-based poverty headcount rate estimate. The backcasted series uses sectoral GDP growth rates to backcast household consumption from the 2018/19 NLSS using household’s head sector of employment to map macro and micro-data. The interpolated trend applies PovcalNet’s methodology and interpolates between the 2018/19 NLSS and 2003/04 NLSS using growth rates in GDP per capita, excluding the HNLSS 2009/10 estimate. Imputed series use survey-to-survey imputations, data from 2018/19 NLSS household consumption and GHS non-monetary indicators to impute consumption in each of the GHS survey years. All estimates are available in Table 15 (see the Appendix).

# _<u>7.2. Caveats</u>_

Notwithstanding the robustness of the results to different checks, some standard caveats remain to this analysis.

27 The interpolated trend uses PovcalNet’s methodology to interpolate between two survey-based estimates using GDP per capita growth rates (Prydz et al. 2019). In this exercise, we drop available information from the HNLSS 2009/10 survey (currently available in PovcalNet) and interpolate between the 2003/04 NLSS data and the 2018/19 NLSS. In a nutshell, the interpolation applies the same growth rate as GDP per capita to the household consumption vector and allows to calculate poverty rates in each reference year. The estimate of poverty from these two distributions is the weighted average poverty rate from both distributions where whereby each poverty estimate is weighted by the inverse of the relative distance between the survey year and the reference year (Prydz et al. 2019).

22
potential migrants easier in intercept point surveys, but is not a necessary condition for the success of this method. In terms of migrant-sending countries, the stratified survey with listing can again be applied without much conceptual difficulty. It may be more difficult to think of intercept point where non-ethnically identified families of migrants congregate, but locations such as festivals, transportation hubs, money-transmitting branches, churches, social support networks may be starting points.

So what do we conclude from this experiment? The first is that, in practice, intercept point and snowball surveys are unlikely to provide a representative sample of the whole population of migrants or migrant-sending families. In particular, they are likely to oversample individuals more closely connected to the community. Secondly, as the results here and those in Bilsborrow (2006) show, snowball surveys of migrants or their families may be quite ineffective in practice at creating the long referral chains needed for this method to capture the target population. In our case, the snowball sample gave quite different (misleading compared to the stratified sample) pictures of the gender and educational selectivity of return migrants. Furthermore, the snowball method is not that much cheaper than a representative sample where the sampling weights are known. Thirdly, while the intercept method does not provide a representative sample of the whole population, surveying many locations and using reweighting does help make it more representative. Moreover, such a survey is much cheaper than the stratified sample, albeit at the cost of much less data being able to be collected. The intercept survey is therefore most likely to be of use for exploratory analysis, and for situations where the target population of interest are those who attend community locations. This may be the case when policy interventions will rely on these same types of locations to reach migrants. However, there appears no very close substitute to the more time-consuming and expensive two-phase stratified sampling in obtaining truly representative surveys.

37

-  -
Our paper is related to a recent series of papers that build new data sets with a view to improve understanding on the evolution of financial deepening (Dembiermont, Drehmann, and Muksakunratana 2013; Jordà, Schularick, and Taylor 2016; Müller 2017). These show that the main driver of the rapid growth in private credit to income in advanced economies since World War II is the rise of household credit, mostly of mortgages. Over time, credit to households has increased faster than corporate credit in both developing and developed countries. Further, in many countries the amount of credit to households has surpassed credit to corporations. In developing countries, consumer credit is the main driver of household credit growth, while mortgages play a more prominent role in developed countries. Our data set cannot verify such long-term trends, but our paper explores credit structure by maturity as well as credit by purpose and delves into the differences in the allocation across regions and income levels with an emphasis on understanding these patterns in LAC.

Our work is also related to the empirical literature on the determinants of financial development and on the determinants of long-term credit in particular. Several papers have explored the effect of the quality of institutions (La Porta et al. 1997; Beck, Demirgüç-Kunt, and Levine 2003), cultural factors (Stulz and Williamson 2003), political economy considerations (Rajan and Zingales 2003), and geography on the degree of financial development. Macroeconomic factors have also been found to have a positive effect including maintaining low inflation (Boyd et al. 2001) as well as trade openness (Baltagi, Demetriades, and Law 2009) and financial liberalization (Chinn and Ito 2006) albeit the latter is not without risks (Demirgüç-Kunt and Detragiache 1998).

Many papers have also explored the effect that those same variables have on the provision on long-term credit, which is thought to be particularly susceptible to macroeconomic and institutional environment (Booth 2001; Caprio 1997; Demirgüç-Kunt 1999; Fan 2012).<sup>6</sup> Due to the lack of comprehensive data on bank credit maturity, most studies use firm balance sheet data. These papers, in general, find that macroeconomic stability and the quality of institutions (with a

> 6 In the presence of uncertainty about the true quality of the borrower due to information asymmetries and macroeconomic and political volatility and when enforcing debt collection is cumbersome due to weak legal systems and creditor rights, lenders will be more prone to monitoring and disciplining borrowers through short-term contracts.

5
currency, on the exchange rate; while γ(ξ) denotes the effect on the exchange rate of occasional interventions, buying (if ξ > 0) or selling (if ξ < 0). Bt(ω) for **ω**  **Ω** denotes Brownian motion.

Assume that the discount rate is ρ > 0 and that the cost of large deviations of the exchange rate from its mean is K(Yt – _Y_ ) for K(x) ≥ 0 for all x. Let R(m) be the cost of applying the continuous control (i.e., not enough economies of scale), and the costs of discrete interventions are denoted by L(ξj) > 0 (i.e., large sterilizations create conflicts with monetary policy).

Taking into consideration the above, the total and discounted expected cost of applying the _combined intervention control w_ is as follows: T ≤ ∞ is a given (fixed) future time and E<sup>s,y</sup> denotes expectations with respect to the probability law of Yt with initial value y and mean value _Y_ . We also assume that the costs of selling foreign currency are not necessarily the same as those of buying foreign currency.

The optimization of equation (3) determines the characteristics of the stochastic process of the sol/USD and the variables governing such a process.<sup>1</sup>

> 1 Further details can be found in Mundaca and Oksendal (1998).

8
Overall, the analysis leads to five main conclusions. First, the literature on infrastructure has diversified hugely since the 1980s. From an almost exclusive initial focus on the output-elasticity and the use of public capital data, it has extended to specific sectors, many different outcomes, and a large variety of data sources and types. This has led to increasing heterogeneity in the literature, and has implications for the way we conduct this meta-analysis, leading us to focus mostly on specific sub-samples.

Second, as is generally the case for many other economic topics, it is subject to significant publication bias in favor of positive and significant results. Third, as the literature recognized the limitations and moved away from its initial focus on public capital, the estimates it has produced have become relatively smaller. We report average estimated elasticities that range between zero and 0.06 for most of the sector-outcome categories we consider. We are however careful in interpreting these magnitudes. As we discuss in the concluding section, given the differences in the nature of infrastructure indicators used, it is unclear whether lower elasticities necessarily entail smaller marginal product of capital.

Fourth, we look at the average elasticities specifically for developing countries samples, finding that they are generally larger than those for developed economies for the digital and transport sector, but not for energy. A few large estimates are found in very specific sector-outcome categories, such as digital-micro-output, and transport-labor. Finally, we discuss how these estimates could be leveraged to produce marginal rates of return for specific sector-outcomes, highlighting the lack of relevant data in this area, which appears to be the next priority in terms of research.

This study contributes to the literature on infrastructure by providing a systematic assessment of the body of research reporting elasticities of infrastructure for a large set of outcomes and across three sectors. In that sense, it aims at providing updated reference points regarding the quantitative effect of infrastructure, useful for both researchers and practitioners engaged in infrastructure projects. A number of review papers have been written on infrastructure, including the early essay by Gramlich (1994), the 1994 World Development Report (WorldBank, 1994), more recently Straub (2008) and Straub (2011), contribute to overall sector meta-elasticity estimates.

4
<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>it) 45 6 7 8 9 10111213141516171819 212223 627<br>-0.05<br>0.1<br>-0.15<br>——=G Wald s==EP World 9 -==DR World<br><!-- End of picture text --> <!-- Start of picture text --> oe<br>03<br>0.2 ee - Tt tee el<br>“<7 itedied<br>: on 2<br>of Ny _-- -_--3}rN Ce<br>ral ow”<br>7 Qoe a 2 “we” 4 5 8 3 10 11 12<br>as<br>7 Wao— <2 -=-=- “7” “7<br>0.2 “a+ cor ~ =<br>0.3<br>a4<br>5<br>a6<br>——G Mexico =EP Mexico —-DR Mexico<br>= —GWorld =— —EPWorld — — DRWorld<br><!-- End of picture text -->
# **in Countries with a Contractual Savings Small Financial Sector**

Gregorio Impavido, Alberto R. Musalem, and Dimitri Vittast

## The World Bank

**JEL Classification Code:** G18, G23, G28.

**Keywords:** Contractual savings, Small financial systems, Private pensions provision.

t Gregorio Impavido (2impavido@worldbank.org), Alberto Roque Musalem, (amusalem@worldbank.ori), and Dimnitri Vittas (dvittas@worldbank.or=) are Financial Economist, Advisor and Senior Advisor, respectively, in the Financial Sector Development Department of the World Bank. The authors are grateful to Giovanni Majnoni, Roberto Rocha and participants to the World Bank seminar on small financial systems for useful comments.
in policy simulations<sup>11</sup> . Although we use estimators which are traditionally reduced form, each coefficient in the regression equations corresponds to a structural parameter. Using the structural parameters, it is possible to simulate the model presented in the previous sections under different policy scenarios. However, in this paper, we are not interested in particular effects of policies per se: Our goal is to show the performance of this new estimation method using simulated data. We expose the system to policy shocks and illustrate robustness of the estimation method under non-stationary conditions. In a sense, we create aggregate shocks artificially.

For an illustration, we consider an open economy model with trade shocks, exogenous prices and endogenous wages. To simulate trade shocks, we need to define equilibrium real wages as functions of labor supply and prices. Assume that sectors are perfectly competitive with simple Cobb-Douglas production functions. We assume that workers are paid their real marginal products. Then, the following real wage equation closes the model where _p_<sup>_i_</sup> _t_<sup>istheexogenouspriceofsector</sup><sup>_i_output,</sup><sup>_Pt_istheconsumerpriceindex</sup><sup>_Pt_=</sup> Π _i_ ( _p_<sup>_i_</sup> _t_<sup>)</sup><sup>_bi_withbasketshares</sup><sup>_bi_,and</sup><sup>_A_ ̃</sup><sup>_i_isaconstantthatiscalibratedfromthedata.</sup> We calculate Cobb-Douglas labor shares and consumer basket shares from Bureau of Economic Analysis data. Then, we calibrate _A_<sup> ̃</sup><sup>_i_</sup> to match average wages in given sectors. The calibration exercise is similar to Artuc Chaudhuri McLaren (2010). The production function and consumer price index parameters are reported in Table 2 along with wages and labor allocations. We normalize all prices to one at steady state, _p_<sup>_i_</sup> _t_<sup>= 1for</sup><sup>_i_= 1</sup><sup>_, ..,_16,and</sup> fix the deterministic part of moving cost to be constant<sup>12</sup> over time _ct_ = 4 _._ 5. We assume

> 11As an example, consider a policy experiment of reducing the moving costs, _Ct_ , by 50 per cent. Assume that we would like to know the effect of this change on workers’ mobility decisions. We cannot simply change the resistance coefficient Ψ<sup>_ij_</sup> _t_<sup>andkeepothercoefficientsastheywere.Becauseafterachangeinthemoving</sup> cost, the values would also change, thus the Γ<sup>_i_</sup> _t_<sup>andΛ</sup><sup>_i_</sup> _t_<sup>parameterswouldchangeaswell.So,itisimpossible</sup> to use reduced form parameters Γ<sup>_i_</sup> _t_<sup>andΛ</sup><sup>_i_</sup> _t_<sup>forsimulations.BecauseofLucascritique,onehastoknowthe</sup> underlying structural parameters.

> 12 We assume that moving costs are constant over time for cosmetic reasons, so that the results are easy

16
the two sectors, and a different distribution of earnings across sectors affecting overall withingroups wage inequality. Schooling is a risky investment and a measure of risk is differences in wage effects across quantiles. Public sector employment involves less risk as the impact of schooling on within-groups wage dispersion is substantially smaller in the public sector (and in the case of the East Asian economies examined, more schooling has an equalizing effect); furthermore, the public sector provides more job security. Recent evidence shows that in Pakistan (Hyder 2007) and Cambodia (Lall and Sakellariou 2010), a year of education yields a greater increase in wages in the private sector than the public sector (despite more educated workers being attracted to the public sector of both countries) and that there is a different reward system in the private sector (where the earnings gap is small) compared to the private sector.

While it would be difficult to provide a complete explanation for the lower within-group dispersion, the distribution of unobservable skills and other characteristics between the two sectors will likely figure in any explanation. Assume for example that workers located at higher <!-- Start of picture text --> Figure 10a: Public Quantile Figure 10b: Private Quantile<br>Returns by Country Returns by Country<br>24<br>24<br>22<br>22<br>20<br>20<br>18<br>18<br>Cambo 16 Cambo<br>16<br>dia dia<br>14<br>14<br>China China<br>12<br>12<br>10 Indone 10 Indone<br>8 sia 8 sia<br>Philippi Philippi<br>6 6<br>nes nes<br>4 4<br>2 2<br>0 0<br>Q10 Q25 Q50 Q75 Q90 Q10 Q25 Q50 Q75 Q90<br><!-- End of picture text --> 21
Finally, it is worth re‐emphasizing that research suggests that incentives often need to complement one another in combination—rather than being perceived as substitutes—and be supported by other policy measures (some of which are briefly described in Annex 4.2). Critically, the success of any incentive program is intrinsically tied to how the overall economic and policy climate supports gender equality.

# **3.2 Suggested areas for further research**

Suggested areas for further study on investment incentives targeting gender equality cover four main pursuits:

**1. Compiling a comprehensive set of incentive scheme case studies for evaluation of their role in promoting gender equality** . Case studies on this topic tend to be limited in detail on both the design of the incentive scheme, and critically, its effectiveness. Compiling a more comprehensive repository would require pulling together examples that currently exist in various formats and across various sources, as well as developing new case studies. A useful repository would include examples of incentive schemes from low‐, middle‐, and high‐income countries, and include both successful and unsuccessful outcomes.

**2. Conducting rigorous comparative analysis of the effectiveness of different incentive schemes in achieving target policy outcomes** . While some individual incentive schemes have been evaluated, there is little existing comparative analysis on the relative effectiveness of, for example, tax incentives for childcare provision as compared to tax incentives for parental leave on increasing female labor force participation.

**3. Performing analysis of the interaction of different types of incentive schemes, or the interaction of an incentive scheme when paired with another type of policy intervention** . In addition to evaluating incentive schemes in isolation and against one another, it would be beneficial to evaluate the interaction of different types of gender‐related interventions. Since incentive schemes are often most effective when paired with other policies, further research should be undertaken to more precisely understand the role and extent of these interactions.

**4. Expanding research on incentives that address entrenched gender discrimination or gender norms on workplace‐related inequality** . While some of the incentives covered in this paper can help to reduce discrimination or harmful gender norms, it is worthwhile to further understand how behavioral incentives can address not just firms’ behavior and actions, but also the conscious and unconscious biases and beliefs that lead to gender inequality. Such research could also look at the relationship between inequalities that arise earlier in life, such as childhood education, and inequalities in the workplace, to unpack how these early‐in‐life disparities affect a woman’s opportunities throughout her professional career.

24
Thus, with a new data-set covering a different period (the 1990s) and using a different index of institutions and policies, we find the same basic pattern that we identified in Burnside and Dollar (2000a,b): the combination of good institutions/policies and external aid appears to lead to more rapid growth.

# 3. **_Conclusions_**

In conclusion, our original finding that aid spurs growth conditional on the quality of institutions and policies is quite robust. We find the relationship in a new data set focusing on the 1990s and using an overall measure of institutional quality. Our strongest conclusion from the cross-country work is that there is far more evidence that aid spurs growth conditional on institutions, than for the competing hypothesis that aid has the same positive effect in all institutional environments. On the other hand, because all cross-country statistical results are fragile, we cannot completely reject the hypothesis that aid never works anywhere. Like most economists we believe that institutions and policies matter for growth, but it is possible to find specifications in which the institutional quality variable is not significant, so a limitation of the cross-country approach is that it cannot definitively settle some debates.

Fortunately, policy makers do not form judgments based simply on cross-country regressions. There are other types of information that are useful for those trying to establish effective aid policies. First, one should not underestimate the importance of theory. Given that institutions and policies affect growth, it is difficult to write down a coherent growth model—unless one assumes international capital markets are perfect—in which the impact of aid would not be conditional on the same institutions and policies.<sup>6</sup> For aid to have no impact in a low-income country, regardless of the quality of institutions, would require a degree of perfection in international capital markets that we find implausible. So, based on theory, it is quite plausible that aid would promote growth in poor countries that manage to put good institutions into place.

A second type of information that is relevant comes from case studies. There is fairly broad agreement that the Marshall Plan accelerated European growth after World War II: this is the ideal example of the model we have in mind, with a significant volume

> 6 See Burnside and Dollar (2000c).

19