rafmacalaba/datause-encoder-data huggingface dataset:
Once again, this variation has little effect on the estimated impulse responses, which are very similar to those in the benchmark specification.

In our benchmark results, we use the IMF's trade-weighted multilateral real exchange rate. As a further robustness check, we investigate how our results change if we instead use the more widelyavailable bilateral real exchange rate, based on nominal exchange rates relative to the US and relative price levels as measured by GDP deflators. The results are shown in the bottom panel of Table 3. While qualitatively the results are similar in this larger sample of observations, quantitatively the main difference is in the contemporaneous effects which are substantially larger than in the benchmark. The estimated impact of a one percent to GDP increase in aid is close to a one percent depreciation in the exchange rate for the full country sample. Over time however, the impulse response becomes closer to the benchmark specification, indicating a small and statistically insignificant real appreciation after 5 years.

Figure 3 provides a visual summary of these first basic robustness checks, in the full sample of countries. It graphs the estimated impulse response and 95 percent confidence intervals in the benchmark specification, and then superimposes the estimated impulse responses for the four robustness checks reported in this section. A quick look at this graph confirms the discussion above -- across these four variations, changes estimated impulse responses relative to the benchmark specification are minimal. In fact, with the exception of the estimated impact effect using the bilateral real exchange rate, all of the estimated points along all impulse responses fall within the 95 percent confidence intervals for the benchmark specification.

# **6.  Monetary Policy, Exchange Rate Regimes, and the Composition of Aid-Financed Spending**

We next consider the interaction between monetary policy, the exchange rate regime, and the composition of aid in determining the effect of aid inflows on the real exchange rate. Hussain et. al. (2009) provide a comprehensive discussion of these interactions, which we briefly summarize here in order to motivate several refinements of our basic specification. The two key considerations in determining the effect of aid on the real exchange rate are the extent to which aid resources are absorbed by the recipient economy, and the extent to which aid resources are spent. First, low absorption occurs if central banks use the foreign exchange associated with financial (as opposed to inkind) aid receipts to accumulate domestic reserves, while high absorption occurs when central banks sell 13
# **2.7 Uncertainty Estimates for ML Estimators**

For EBP, we model welfare at the sub-area level, which is then aggregated up to the target area level. Because the model specification contains a random effect at the target area level, this procedure accounts for the hierarchical nature of the data. The procedure uses a parametric bootstrap to estimate uncertainty, drawing from the estimated distributions for the random effects and the error terms, and accounting for the arcsin transformation implemented for EBP. For the three ML estimators, we also estimate the models at the subarea level.

While Friedberg et al. (2020) prove a central limit theorem for local linear forests that allows for the construction of uncertainty estimates, variance estimation has not yet been implemented for Boosted Regression Forests. As an alternative, we propose a non-parametric bootstrap procedure that draws from previous work on residual bootstraps with hierarchical data (e.g. Luo and Lai (2021)).<sup>13</sup> ˆ For subarea _sa_ , consider the sample direct estimate of the outcome: _ysa_<sup>_direct_</sup> . In addition, there is the prediction from the machine learning algorithm, _y_ ˆ _sa_<sup>_ML_.With these two estimates, we calculate subarea-specific “residuals”</sup> as: We can likewise calculate residuals at the area level, by aggregating _y_ ˆ _sa_<sup>_ML_</sup> and _y_ ˆ _sa_<sup>_direct_</sup> to the area, weighting by estimated population from WorldPop: The proposed bootstrap continues in two steps. First, note that we can only calculate this residual for in-sample subareas and in-sample areas. After estimating predictions, we first calculate the residuals in Equation 2. Then, we randomly draw one residual from the vector _R_<sup>ˆ</sup> _sa_ for each in-sample subarea and add that residual to the prediction: _y_ ˆ _sa_<sup>_ML_</sup> + _R_<sup>ˆ</sup> _sa_ . We do this sampling with replacement, for in- and out-of-sample subareas.

We now have adjusted subarea predictions for all subareas. We aggregate all of these predictions to the area level, using estimated population from WorldPop as weights. At the area level, we pursue a similar strategy, but this time we draw area-level residuals, with replacement, for all areas, regardless of sample status. We

> 13We estimate the EBP model using REML as the default. We apply weights using the weights option in the _lme_ function of the _nlme_ package and we rescale the weights to sum to the number of observations within group. When estimating BRF, the R _GRF_ package does not allow for aggregate variance estimates.

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The control group is not completely without publicly provided health insurance. While those not in NHIS must pay fees at public healthcare facilities, those fees only have to cover supplies, maintenance, medicines, and diagnostic costs. Capital expenditures and, importantly, staff salaries are covered by the general government budget regardless of a user’s NHIS affiliation. While not called health insurance, this support to public healthcare facilities does function like insurance: General revenue is collected from all taxpayers to provide benefits to those who draw unfortunate health outcomes. Our comparison estimates the financial insurance value of NHIS relative to this “reduced insurance” state, not to no insurance at all.

The data we need from treatment and control groups are more detailed than those used in a typical evaluation in that we need to compare full distributions of OOP healthcare expenditures, not just their means. We create these distributions from the observed distribution of OOP healthcare expenditures for treatment and control groups, assuming that the cross-person distribution reflects each individual’s _ex ante_ distribution of possible expenditures.

Given these OOP healthcare-expenditure distributions for those with and without NHIS membership, we then calculate the risk premium associated with each distribution for each person in the sample using a stylized constant relative risk aversion (CRRA) utility function. The difference in those risk premiums is our estimate of the financial insurance value of NHIS membership.

The data come from the Ghana Living Standards Survey (GLSS) 2016/17, fielded between October 2016 and September 2017. GLSS 2016/17 is the latest available round of the national household sample survey administered by Ghana’s statistical agency (GSS 2019). The survey collected information on household expenditures, including expenditures for healthcare for each person in the household. Our data, then, are individual level for each person in the survey, with 58,844 people considered in all.

The remainder of the paper is organized as follows. Section 2 provides background information on government healthcare financing and spending in Ghana and for NHIS. Sections 3 and 4 describe how we calculate the distribution of OOP healthcare expenditures for those with and without NHIS membership. Section 5 calculates and discusses the risk premium associated with each distribution and their difference, which is the financial insurance value of NHIS membership. Section 6 concludes.

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former one controls for the import status of firms. Yet, neither study divides firms into four groups according to their trade orientation and hence cannot show the complementary relation between importing and exporting activities as clearly as presented here.

This study derives four important results on the relation between firms’ global engagement level and their performance: i) globally engaged firms are larger, more productive, more capital intensive, and pay higher wages than purely domestic firms; ii) among exporters, firms that import intermediate products are more productive and grow faster; iii) firms that only import intermediate products are more productive and grow faster than non-traders, however the distinction between exporters and non-traders is more significant; and iv) firms with foreign ownership grow faster but they are less innovative than domestically owned firms.

Importing and exporting can lead to better firm performance in different ways. Higher use of foreign inputs can increase firm productivity due to access to more variety of inputs or directly due to the higher quality of these products. On the other hand, exporting increases the market size of the firm which increases the future return of R&D investments. Eventually both activities decrease the cost of implementing technological innovations and spur growth. There have been recent studies that incorporate R&D decisions in a framework that relates firm evolution and foreign exposure. Using data from Taiwanese manufacturers, Aw, Roberts, and Winston (2007) find a significant role of R&D investments in explaining firms’ export patterns. They also analyze the effects of interaction between R&D investments and exporting on productivity growth. In this study, I explore whether investments in R&D affect evolution differently for firms with varying levels of global engagement. Introducing interaction terms of R&D with two-way traders, only importers, and only exporters, I show that investment in R&D significantly increases the growth rate of two-way traders and only importers. It also increases the innovation of two-way traders.

The rest of the paper is organized as follows. In section 2, I explain the analytical framework of the model. Following that I introduce the dataset and variables of interest. Then in section 4, I perform a descriptive analysis that relates firm performance with its trade orientation. In section 5, I elaborate on the relation between evolution of firm and its trade orientation controlling for factors that can potentially affect firm evolution. In section 6, I provide sensitivity analysis to check the robustness of the findings. Finally, I analyze the interaction between a firm’s trade orientation with its R&D investment and finish with some concluding remarks.

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6 Once enough progress has been achieved ixn reducing -- in a credible way -- the inflation and the balance of payments dAficit, other atructu~ral reforms aimed at improving resource allocation and achieving sustainable and equitable growth should be attempted. These reforms include: public sector reforms, trade reforms, financial reforms, reforms of the regulatory framework and labor market reform-.

# III. HAIN FINDTNGS OF RAL-2: EFFECTIVENESS OF PROGRAMS AND PROGRAM IMPLEMENTATION2

For countries experiencing acute macroeconomic imbalances and which suffer from deep rooted structural problems that impede a better use of resources and limit growth, the benefits from a structural reform program will take time to deveJop. Therefore, to assess adequately the effectiveness of adju-.tment programs, enough time should be allowed to elapse since the initiation of the program before it is examined. Intermediate evaluations could rely on indicators of progress in the adjustment. That is the route followed in the Bank's two reports on Adjustment Lending (RAL-1 and RAL-2). A. Effectiverness of Adjustment Programs--Macroeconomic Indicators To assess the contribution of adjustment lending to sustainable growth, the second Report on Adjustment Lending (RAL-2) examines performance in terms of intermediate indicators of structural transformation--saving ratios, investment ratios, and export ratios--along with the rate of growth of output. For the analysis, countries are grouped into three cat _ries: early 2 This section and the next draw heavily on World Bank (199Ob).
managers might perceive a tradeoff between maturity, on the one hand, and currency and inflation risks, on the other hand. When managers can reduce those risks, they seem more willing to invest more long term. Still, the evidence shown here suggests that mutual and pension funds hold a significant fraction of short-term assets even when risks are reduced. For example, the average maturity for holdings of indexed peso bonds is 7.15 and 5.31 years for mutual and pension funds, respectively (compared to the average maturity of insurance companies, of 9.77 years, when holding indexed and non-indexed instruments).

In addition to the risk of different investment instruments, there exist the risks of investing at different maturities. Available evidence from other emerging economies suggests that, if anything, investors in emerging economies should tilt their portfolios toward the long term relative to investors in developed countries.<sup>22</sup> Here, we complement the existing evidence by compiling new data on prices of inflation-indexed government bonds at different maturities, measured, alternatively, by indices of traded bonds at different maturity buckets and indices derived from a model-based estimation of the yield curve.<sup>23</sup> We compute average returns, standard deviations, and Sharpe ratios (average returns over standard deviations) for securities of different maturities over different holding periods. These estimates are useful because, assuming zero covariance across

> 22 Broner et al. (2013) compute Sharpe ratios of short- and long-term bonds in various emerging economies (excluding Chile) and show that, on average, the difference in the Sharpe ratio of long- and short-term bonds is higher in emerging countries than in developed countries. Moreover, those estimates have the advantage that they are computed with much longer time series. In our case, we only have a five-year sample, which might be short for this type of calculation. Still, the estimates in both papers lead to the same conclusions.

> 23 We focus on inflation-indexed securities because, as discussed above, they are the ones that better allow investors to reduce risk and invest more long term. On a more pragmatic note, bonds issued by the central bank at maturities beyond ten years are almost exclusively inflation indexed. The price information comes from RiskAmerica, a private company that provides fair value pricing for the Chilean fixed income market. These prices are widely used by institutional investors that mark their portfolios to market.

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**Table 1.  Standard economics and two strands of behavioral economics**

||**STANDARD**<br>**ECONOMICS**|**BEHAVI**|**ORAL ECONOMICS**|
|---|---|---|---|
||(1)|(2)|(3)|
|**Concept of**<br>**the actor**|**The rational actor**<br> <br> Fixed,<br>autonomous<br>preferences|**The quasi-rational actor**<br> Fixed preferences,<br>including a preference for<br>conformity<br> “Fast thinking”|<br>**The enculturated actor**<br> Peer influences and “fast thinking,” as<br>in column (2)<br> Endogenous preferences<br> Endogenous cognition<br> Perceptions that are not an unchanged<br>or literal copy of the environment, but<br>are simplified and transformed by social<br>constructs (cultural mental models)|
|**Disciplinary**<br>**foundations**|Rational choice<br>axioms|Evidence primarily from<br>psychology (cognitive and<br>social)|Evidence primarily from psychology<br>(including cultural), sociology, and<br>anthropology|
|**What are the**<br>**social drivers**<br>**of behavior?**|Incentives<br> Prices<br> Rules of the<br>game| Incentives, as in column (1)<br> Context in the moment of<br>decision-making, e.g., the<br>way in which choices are<br>presented and the norm<br>that is focal in attention| Incentives, as in column (1)<br> Context in the moment of decision-<br>making, as in column (2)<br> Experience and exposure, which<br>►Affect responses to particular primes<br>►Create mental models, including<br> Categories<br> Concepts<br> Social identities<br> Worldviews, and<br> Narratives.|

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emptive purchase, if successful in making heroin much harder to obtain, might drive many addicts into treatment<sup>40</sup> or otherwise lead them to desist from heroin use for a period of time.

There are two standard objections to this, one practical and the other conceptual. The practical objection is that it would be impossible to make this pre-emptive purchase discreetly. Traffickers would soon become aware of the new entrants in the market and would bid against them. The price of opium in Afghanistan would soar and the program would end up costing taxpayers a great deal more and still not prevent opium from continuing to flow into the illegal market, albeit at higher prices. The conceptual objection is that the intervention would exacerbate long-term problems. In face of the increase in demand at the farm-gate level, growers would now plant more, thus worsening the world heroin problem after the pre-emptive buying program ended.

Though both objections have some power, neither individually nor jointly are they decisive. The traffickers in the short run might not have access to funds to bid the prices very much higher than they are now; over time they can increase their sales revenues enough to do so but perhaps not in the first year. Nor does failure to buy the whole crop mean that users would be unaffected by the program; if the governments succeed in purchasing half the product, there will still be substantial hikes in export prices. These might be large enough to raise retail prices in some countries, motivating a large number of addicts to desist, with or without formal treatment.

The fact that there will be an increase in production, and presumably lower prices, in the following years, has relatively little consequence for the global market. A decline in the price of opium has minimal effect on the price of heroin in the major consumer markets. The claim here is of an asymmetry. A sharp reduction in physical availability might generate a price spike that would in fact affect final demand but that a glut cannot have the opposite effect because declines in opium farm-gate prices have minimal effect on retail prices. Thus the shortterm gain from the price spike may not be offset by any harm from the increased production that it generates, whether in Afghanistan or in some other nation that entered the market because of the perception that returns had increased.

I offer this not as a complete analysis of the effects of a pre-emptive purchase but rather to indicate the kind of innovation that needs careful analysis. The sudden rise in prices might lead another nation to enter the market, thus spreading the problem and eliminating one of the attributes that make pre-emptive purchase possible. If crops can be expanded rapidly, then the program might be so short-lived as to be not worth the effort. One would have to consider not

> 40 A number of U.S. studies have found that higher prices for cocaine have increased treatment seeking.

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Policy Research Working Paper 5874

# **Abstract**

Middle class values have long been perceived as drivers of social cohesion and growth. This paper investigates the relation between class (measured by position in the income distribution), values, and political orientations using comparable values surveys for six Latin American countries. The analysis finds that both a continuous measure of income and categorical measures of incomebased class are robustly associated with values. Both income and class tend to display a similar association to values and political orientations as education, although differences persist in some important dimensions.

Overall, there is no strong evidence of any “middle class particularism”: values appear to gradually shift with income, and middle class values are between the ones of poorer and richer classes. If any, the only peculiarity of middle class values is moderation. The analysis also finds changes in values across countries to be of much larger magnitude than the ones dictated by income, education, and individual characteristics, suggesting that individual values vary primarily within bounds dictated by each society.

This paper is a product of the Office of the Chief Economist, Latin America and Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jrigolini@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
TABLE II: Labor Supply Statistics of Children and Parents, 1997

|Parents with children aged 12-15|Mean|Std. Dev.|
|---|---|---|
|Has job (father)<br>|0.97|0.18|
|Has job (mother)<br>|0.14|0.34|
|Weekly income (father, inc_>_0)|174.2|129.2|
|Weekly income (mother, inc_>_0)|137.2|141.0|
|Boys aged 12-15|Mean|Std. Dev.|
|Enrolled|0.69|0.46|
|Has job (not enrolled)|0.63|0.48|
|Has job (enrolled)|0.075|0.26|
|Hours worked per week (not enrolled, has job)|42.4|13.3|
|Hours worked per week (enrolled, has job)|14.2|5.92|
|Weekly income (inc_>_0)|132.5|90.6|
|Weekly income (inc_>_0, not enrolled)|143.7|90.2|
|Ofered weekly transfer (1998, semester 1)|38.8|17.6|
|Girls aged 12-15|Mean|Std. Dev.|
|Enrolled|0.62|0.49|
|Has job (not enrolled)|0.20|0.40|
|Has job (enrolled)|0.029|0.17|
|Hours worked per week (not enrolled, has job)|45.6|16.8|
|Hours worked per week (enrolled, has job)|14.6|6.85|
|Weekly income (inc_>_0)|139.0|100.4|
|Weekly income (inc_>_0, not enrolled)|146.5|99.6|
|Ofered weekly transfer (1998, semester 1)|42.6|18.7|

_Note:_ Sample: all households in 1997 with two parents where one parent reports to be the head and with at least one child aged 12-15 years. Has job takes value 1 if individual reported to work in a paid or unpaid job last week, or reported to have a job but did not work last week. Weekly income is summarized for those reporting positive incomes (we remove those reporting positive incomes outside of the 1<sup>_st_</sup> _−_ 99<sup>_th_</sup> percentiles). All incomes are inflation adjusted to be in 1998 values (inflation is calculated as the median percentage change in reported annual incomes by state).

One may wonder what the 37% of boys and 80% of girls who report not being in school and not having a job do with their time. As noted in Parker and Skoufias (2001), domestic activities and some other unpaid activities are not included in the survey definition of having a job. Fortunately, however, Progresa carried out an additional survey, the June 1999 Time-Use Survey, where individuals were asked about how they allocated their time in the previous day, allowing one to investigate the extent that schooling and _all forms_ of work are substitutes. Using this survey, Parker and Skoufias (2001) find that conditional on going to school, girls and boys aged 12-17 spend on average 6.3 hours a day at school (prior to grants).<sup>18</sup> Moreover, they find that a) there is no change in hours spent at school after the introduction of Progresa grants (conditional on going to school), b) there is no 18. In addition, children spend time doing homework and traveling to and from school. Parker and Skoufias (2001) find that almost all children in school report doing homework, and that the average number of hours spent on homework is 1 hour per-day.

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