rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Destination income and firm average output prices**

|||de|p. var.: f|rm-average l|og real output|price||
|---|---|---|---|---|---|---|---|
|||O|LS|||IV||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|log avg. destination gdp/cap|0.08|0.04|0.04|0.03|2.06***|1.76***|1.75***|
||(0.06)|(0.08)|(0.08)|(0.08)|(0.64)|(0.66)|(0.66)|
|export share of sales||0.08|0.09|0.09|-0.95***|0.63|0.63|
|||(0.07)|(0.08)|(0.08)|(0.34)|(0.91)|(0.91)|
|log avg. destination distance|||-0.00|-0.00|0.00|-0.06*|0.01|
||||(0.01)|(0.01)|(0.01)|(0.03)|(0.07)|
|log sales||||0.04***|0.03**|0.03**|0.02|
|||||(0.01)|(0.01)|(0.01)|(0.02)|
|initial source interactions|Y|Y|Y|Y|Y|Y|Y|
|frm efects|Y|Y|Y|Y|Y|Y|Y|
|year efects|Y|Y|Y|Y|Y|Y|Y|
|N|45659|45659|45659|45659|45659|45659|45659|
|Kleibergen-Paap LM statistic (under-identifcation)|||||248.92|192.30|232.20|
|Kleibergen-Paap LM p-value|||||0.00|0.00|0.00|
|Kleibergen-Paap Wald rk F-stat (weak insts.)|||||2.65|2.09|2.32|
|Anderson-Rubin Wald test F-stat|||||2.98|2.99|2.99|
|Anderson-Rubin Wald test p-value|||||0.00|0.00|0.00|

Notes: Instruments are interactions of indicators for positive exports to destination in 1997 and log relative price level (reciprocal of real-exchange rate) for Portugal’s top 100 non-Euro-zone export destinations; see first-stage results in Table A4. Initial source interactions, included directly as covariates, are defined analogously to the instruments, using indicators for initial imports. Columns 5 treats only log avg. destination GDP/cap as endogenous; Column 6 adds export share of sales, and Column 7 adds log avg. destination distance to endogenous set. Petroleum exports and imports excluded. Euro-zone countries not included in instrument set. Robust standard errors in parentheses. *10% level, **5% level, ***1% level.
# _5. Results_

We discuss our results in three steps. First, we present the basic impulse response functions of our main variables of interest, the exchange rate and inflation. These indicate a strong dependence of the exchange rate on copper, as well as a moderate effect of the exchange rate on price levels. Second, we report variance decompositions over the sample periods in order to isolate the most important contributing factors on each of the variables, and the evolution of this pattern over time. Third, we analyze whether there is a differential ERPT depending on the shock that drives the exchange rate fluctuation, following Forbes et al. (2015). We further investigate whether and to what extent food and non-food inflation are affected differently, and also report differential ERPTs obtained using the respective disaggregated CPIs.

# _5.1 Impulse response functions_

Figure 3 depicts the cumulative impulse response functions (IRF) for the exchange rate as a reaction to a 1% positive shock in the other variables. The solid line reports the pointwise mean IRF, which is the recommended point estimate to be reported given a linear loss function (Baumeister and Hamilton, 2015). The dashed lines depict the 68<sup>th</sup> and 90<sup>th</sup> percentiles of the estimated IRFs (following the Bayesian convention of _credible_ intervals, in lieu of frequentist confidence intervals).

Panel _(a)_ summarizes one of the key results of this exercise: the exchange rate reacts strongly and quickly to an increase in copper prices. The mean response suggests that, at impact (within the quarter), an unanticipated 1% rise in copper prices leads to an exchange rate appreciation of 0.2%. The exchange rate continues to markedly appreciate for the next two quarters, up until 0.4%, and then stays at roughly that level. This is slightly lower than the 4.8% appreciation following a 10% increase in copper prices in the long run reported by Chipili (2015), although this estimate is within the bounds of the 68% interval.

An expansionary monetary shock of 1% (panel _(c)_ ) is estimated to induce a marked depreciation of about 0.7% within the quarter, reaching its peak in the next quarter at 0.9%. The point estimates suggest a slight overshooting here, and the persistent effect is of about 0.8%. Note that after three quarters, the estimates become less confident to the point where a zero effect cannot be excluded based on the 90% credible interval.

12
In _T_ 2 communities, the largest numeracy impacts were observed in ‘number recognition’ (0.67 _σ_ ), ‘missing number’ (0.47 _σ_ ) and ‘number discrimination’ (0.43 _σ_ ) when compared with the control group. These impacts are followed by addition and subtraction (both indicators with gains of 0.32 _σ_ ). For all these indicators, the difference between _T_ 2 and _T_ 1 is significantly different from zero.

Table 3: Treatment Effects on Literacy and Numeracy Test Scores

|||_Literacy_|_Skills_||_Numera_|_cy Skills_|
|---|---|---|---|---|---|---|
||Prop.|Zero|Z-s|core|Z-s|core|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any Treatment (T1 or T2)|-0.077**||0.187*||0.243**||
||(0.036)||(0.106)||(0.116)||
|Videos (T1)||0.024||-0.071||-0.117|
|||(0.043)||(0.108)||(0.132)|
|Videos + Mobile apps (T2)||-0.186***||0.462***||0.628***|
|||(0.034)||(0.132)||(0.134)|
|Observations|8,928|8,928|8,928|8,928|8,928|8,928|
|R-squared|0.163|0.183|0.248|0.253|0.264|0.280|
|Covariates|YES|YES|YES|YES|YES|YES|
|Enumerator FE|YES|YES|YES|YES|YES|YES|
|Control Mean in Baseline|0.976|0.976|0.000|0.000|0.000|0.000|
|Control Mean in F-up|0.438|0.438|-0.713|-0.713|1.063|1.063|
|T1 vs T2(p-value)||0.000||0.000||0.000|

Note: Column 1 and 2 show the results of linear probability models of the proportion of children scoring ”zero” in letter recognition. Column 3-4 show z-scores of literacy skills and Column 5-6 z-scores of numeracy skills. Test scores are normalized using the mean and standard deviation for the control at baseline. All group regressions have clustered standard errors at the school catchment area level. The covariates considered in all regressions are: age of target child, gender of target child, education of household head (baseline), availability of food (baseline) and enumerator fixed effects. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Treatment effects for _T_ 2 communities should be read as lower-bound estimates given that only 40% of households attending the aspirational videos received a smartphone with the m-Learning apps. Thus, our ITT estimates include both participants receiving and not receiving the mobile phone.

23
Policy Research Working Paper 5152

# **Abstract**

Using a database of 76,046 empirical economics papers published between 1985 and 2004 in the top 202 economics journals, the authors report two associations. First, per-capita research output on a given country increases with the country’s per capita gross domestic product (GDP). Regressions controlling for data availability and quality in the country, indicators of governance and the use of English yield an estimated research-GDP elasticity of 0.37; surprisingly, the United States (US) is not an outlier in the production of empirical research. Second, papers written about the US are far more likely to be published in the top five economics journals, even after the quality of research has been partially controlled for through fixed-effects for the authors’ institutional affiliations; the estimates suggest that papers on the US are 2.6 percentage points more likely to be published in the top-five journals. This is a large effect because only 1.5 percent of all papers written about countries other than the US are published in the top-five journals. The authors speculate about the interpretations of these facts, and invite further analysis and additions to the public release of the database that accompanies this paper.

This paper—a product of the Poverty and Inequality Team, and the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to analyze the geography of academic research. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at research@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
# <u>4.1 Statistical source and variables</u>

The statistical source used for this analysis is the World Bank‘s World Development Indicators (WDI). This database provides more than 800 development indicators, with time series for 209 countries and 18 country groups from 1960 to 2007. From the World Bank‘s World Development Indicators (WDI), we have temporal observations (T=10) for 16 Latin American countries for the period 1996–2006. We are able to form a balanced panel of data. (See descriptive statistics of the variables in Table 1.)

# [INSERT TABLE 1]

Table 2 presents a summary of the key variables used to empirically validate the combined stochastic-inefficiency model (and the control variables used in the second step analysis).

# [INSERT TABLE 2]

# <u>4.2 Empirical results</u>

The indices of TEC, TP, SEC and _G_ 0 are calculated using equations  7 a  14 and the average of these indices for the selected period (2001-2003) is presented in Table 3.

# [INSERT TABLE 3]

Table 3 shows that the major contribution to productivity growth in the Latin American countries is from technological progress. In contrast, the Technical Efficiency Change Indices are relatively low, suggesting that this component does not contribute much to productivity growth.

As to the negative contribution of SEC to productivity growth, we find several explanations with regard to this issue in Ventura-Dias, Cabezas and Contado (1999). These authors find that, with the exception of Mexico, the majority of Latin American economies are fully exploiting comparative advantage rooted in abundant natural resource endowments. Mexico firstly and Central American countries more recently have developer manufacturing activities oriented to the United States market based on a second source of comparative advantage, low-paid unskilled labor. Only Argentina, Brazil and Chile have developed competitive industries that can be classified as raw material processing (pulp and paper, nonmetallic minerals) and scale-intensive industries (steel, basic chemicals).

Finally, Figure 1 shows the indices of TEC, TP, SEC and _G_ 0 by country using the Hodrick and Prescott Filter.

8
service provision; also if the financing were available, it would be difficult to use it without significant losses in efficiency, reducing growth for services actually delivered.<sup>18</sup> At the macro level, GDP growth for the foreign-financing scenarios is around 0.7 percentage points higher than for the base, making it possible to reduce the unemployment rate, which by 2015 has decreased by as much as 7.3 percentage points. This boost to GDP growth spills over into stronger growth in household consumption, made possible by a stronger increase in the supply of skilled labor and increased employment generated by higher public and private investment. On the other hand, there is a strong decrease in GDP growth in the domestic borrowing scenario, from 5.1 percent in the baseline to 3.6 percent. The decrease in GDP growth is smaller under the tax financing scenario. As explained, GDP growth in the domestic financing scenarios declines as a consequence of the crowding-out of private investment.

In the scenarios that use foreign financing, the exchange rate appreciates at the same time as the trade deficit increases with a surge in imports and a decline in exports. As described in Sundberg and Lofgren (2006) for the case of Ethiopia, Dutch Disease effects can be a serious concern. In our case, aid-induced appreciation of the real exchange rate and the drop in exports are severe in view of the high cost of MDG financing. In fact, under the foreign transfer scenario, exports in 2015 are 37.8 percent lower than in the base scenario, while the real exchange rate appreciates by 35.4 percent.

In case additional financing needs are met with foreign loans, the foreign debt-to-GDP ratio increases to 125.9 percent in 2015 (see Table 4). This ratio is 92.3 percentage points higher than for the baseline. In the foreign transfer scenario, foreign grants to the government attain an average level of 12.8 percent of GDP throughout the simulation period, compared to 2.1 percent in the baseline. In the tax financing scenario, the average direct tax collection increases from 5.8 to 22.5 percent of GDP between 2004 and 2015.

> 18 In support of this assessment, note that Yemen consistently ranks among the lowest 25 percent among countries for most governance indicators, including government effectiveness (see < _http://www.govindicators.org_ >; Kaufmann et al. 2010). For the basic education sector, World Bank (2006) highlights issues that point to difficulties of scaling-up spending while maintaining or improving efficiency.

28
Figure 2 presents the distribution of answers to Q1 in the pooled global sample.7 The vast majority of respondents (73.5%) believe that attacks on civilians cannot be justified at all. Some 10.2% of respondents find such attacks to be mildly unjustifiable, whereas 7.1% of them are indifferent on this issue (they score 3). Although the overall picture suggests that most people do not support targeting civilians, nearly 10% (3.5% and 5.6%) of respondents believe that attacks on civilians are “mildly justifiable” or “completely justifiable”. We are interested in profiling the latter group of respondents with most extreme views; they find attacks on civilians completely justifiable. The proportion of such respondents varies substantially across regions and countries, ranging from 0.2% in Thailand to 33.7% in Djibouti (Figure 3) and averaging close to 6% worldwide. Overall, Sub-Saharan Africa (SSA) stands out with the highest incidence of individuals with extreme views as during most years in the period of interest, their share is above 10% (Figure 4). The region with the second highest rate of people with extreme views is South Asia (SA). In both regions, the prevalence of such individuals increased after 2007. It is important to note that in the Middle East and North Africa (MENA) the incidence of extremists was low throughout the period and increased only after the Arab Spring events in 2011.

Without controlling for other factors, the data suggest that there are no major differences of opinion on this issue between men and women (Figure 5). However, compared to other parts of the radicalization spectrum (categories 1-4), the data suggest that people with extreme views tend to live in rural areas (Figures 5), tend to be poor (Figure 6), and predominantly have elementary education or less (Figure 6). A more detailed breakdown by residence reveals that these people tend to live in small towns and villages (Figure 7). It is striking that all people with extreme

> 7 The number of observations in each figure varies depending on the data available for the combination of variables included in it and the sample includes all countries in the Gallup World Poll for which there is information on the radicalization question (Q1).

9
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10 Evolution of hidden costs associated with SODECI**

||Water production|Distribution<br>losses|Collection<br>ratio|Average total<br>cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(millions<br>ofm<sup>3</sup>/year)|(%)|(%)|($/m<sup>3</sup>)|($/m<sup>3</sup>)|($ millions/year)|(% revenues)|
|2001|146.1|17.47|100.0|0.83|0.45|45.3|94.1|
|2002|148.3|18.77|100.0|0.91|0.47|52.0|93.5|
|2003|156.2|20.25|100.0|0.92|0.57|44.0|72.7|
|2004|161.4|21.70|100.0|1.03|0.76|37.0|47.6|
|2005|164.6|21.73|100.0|1.03|0.76|37.5|48.5|
|2006|167.8|22.02|100.0|1.02|0.76|36.2|61.9|
|2007|170.9|22.32|100.0|1.01|0.83|26.5|39.8|
|2008|175.9|22.54|100.0|1.17|0.89|43.3|54.6|

**Figure 7 Evolution of hidden costs in Côte d’Ivoire’s water sector**

<!-- Start of picture text --> Collection Inefficiencies Under-Pricing Losses<br>Figure 8 Hidden costs of water utilities<br>unaccounted losses % of revenues underpricing collection inefficiencies<br>Percentage of GDP<br><!-- End of picture text --> 19
and greater political openness mitigate the adverse impact of moral hazard on bank fragility.<sup>2 4</sup> It is worth noting, however, that the generosity of the deposit insurance regime increases the probability of suffering a crisis even in countries with the highest Rule of Law values (e.g., the cross-over point is Rule of Law =7.4, but the maximum Rule of Law value is 6). Thus, while greater Rule of Law reduces the destabilizing effects of generous deposit insurance, it does not eliminate it.

# 5. Supervision

In contrast to the helping-hand view of government, the main message that emerges from our study encompassing a large number of official supervisory policies is that we were not able to identify a strong connection between bank performance and official supervision (see Tables 5-7). Specifically, the overall official supervisory power indicator is not related to bank development or bank efficiency or the level of nonperforming loans. Declaring insolvency power is also unrelated to development or efficiency. The prompt corrective power indicator is negatively related to bank development (but these results are not robust to changes in the conditioning information set nor do we get this negative relationship when controlling for endogeneity). There is some weak evidence that supervisory forbearance discretion is positively related to bank efficiency (but this is not robust either). There is, moreover, a positive link between supervisory tenure and bank development. Supervisory independence, loan classification stringency, liquidity requirements, diversification guidelines, and restrictions on making loans abroad are not related to bank development or efficiency or the level of nonperforming loans (see Appendix available on request). In sum, those features of official "core" supervision are not strongly linked to bank development, bank efficiency, and the level of nonperforming loans in a predictable, convincing manner.

In terms of banking crises, the same basic message emerges with only one exception (Table 8). Official supervisory power, declaring insolvency power, loan classification stringency, and supervisor are all unrelated to the likelihood of a crisis. In turn, prompt corrective power and provisioning stringency are unrelated to the likelihood of a crisis.

The one exception involves the diversification index (which aggregates diversification 24 The Rule of Law is an indicator of the degree to which the country adheres to the rule of law. It ranges from 0 to 6 with higher values indicating greater confidence in the legal system to settle disputes. It is obtained from the International 36
the ex-post negotiations costs. Cost aspects involve managing the risk of moral hazard and the difficulty of measuring GDP objectively. Making the instruments as simple as possible and some oversight over national statistics collection and production will be necessary.

In addition, international agencies can help introduce better risk management tools in a number of ways, among others by: providing objective price indexes on risks of interest to developing countries, e.g., commodity prices indexes, rainfall and other climatic indexes, etc.; developing markets in “zero-supply type” securities, such as a “low correlation” security, which third parties could trade in. The international agencies can provide the index, trading systems and/or settlement and clearing systems, and help start the market; issuing indexed liabilities and thereby jump-starting and creating markets in tools of specific interest to developing countries; and intermediating more risk management tools by lending and using derivatives innovatively.

32
uses this many panel data sets from multiple countries to validate cross-section poverty dynamics prediction methods.<sup>15</sup> Variation in poverty rates, aggregate conditions, and panel length provide an added value that allows investigating the performance, sensitivity, and robustness of the LASSO-PMM method. This knowledge is pivotal to understand whether this method can be applied more generally to monitor and predict household welfare dynamics using data sources from other regions and countries.

All 36 panels are harmonized to maximize comparability across countries and over time. The harmonization procedure follows the criteria established by SEDLAC (2018), which consists in using identical definitions in each data set. The advantage of this procedure lies in ensuring comparability across relevant dimensions, so that any observed differences across countries are due to specific changes in poverty dynamics, aggregate conditions, and panel length. The harmonization procedure requires cleaning and processing each panel to ensure that all samples, definitions, and variables are identical in each country and year. The full list of available covariates for each country and panel in the validation data are shown in Appendix Table A.1.

The decision to use harmonized panels is taken in order to test how the method would perform on cross-sectional data that applies some form of harmonization, such as SEDLAC, LABLAC, and other worldwide repositories commonly used to measure and monitor poverty, much like we use in our application in section 4. Satisfactory validation results would provide confidence to implement LASSO-PMM in environments where cross-sectional harmonized microdata exist to predict welfare dynamics. Moreover, the need to use harmonized data incorporates an additional restriction to the validation exercise, apart from the existing constraint

> 15 For instance, Herault and Jenkins (2019) use 31 panels from two countries (Australia and the UK) in their validations; Dang and Lanjouw (2013) use 11 panels from 5 countries; Dang et al. (2014) use 8 panels from 6 countries; and Bourguignon and Moreno (2018) use one panel from Mexico.

16