rafmacalaba/datause-encoder-data huggingface dataset:
|X_ijt/Xjt_<br>(6)|0.372***<br>(0.078)<br>0.172***<br>(0.045)<br>0.251***<br>(0.040)<br>0.192***<br>(0.048)<br>-1.817***<br>(0.061)<br>-1.069***<br>(0.043)<br>-0.279***<br>(0.029)<br>e” PTAs<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br> in parentheses are clustered by country-pair. In<br>ure, implemented in Stata with _ppml_<br>_fe_<br>_bias_. In<br>n domestic sales are linearly interpolated and the<br>mn 1 and column 2 classifes: 23 PTAs in cluster<br>er #1) largely overlap with the ones identifed by<br> but was classifed as high elasticity agreement by|
|---|---|
|(5)|0.421***<br>(0.133)<br>0.211***<br>(0.052)<br>0.180***<br>(0.066)<br>0.191*<br>(0.107)<br>-1.170***<br>(0.096)<br>-0.606***<br>(0.058)<br>-0.169***<br>(0.053)<br>ify ”borderlin<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br>tandard errors<br>(2019) proced<br>issing values i<br>orithm in colu<br>ects (i.e. clust<br> cluster # 2)|
|(4)|0.380***<br>(0.103)<br>0.183***<br>(0.042)<br>0.165***<br>(0.056)<br>0.176**<br>(0.076)<br>-1.153***<br>(0.054)<br>-0.601***<br>(0.039)<br>-0.159***<br>(0.028)<br>Reclass<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br> included. S<br>r & Zylkin <br>ll columns m<br>ean++” alg<br>her trade ef<br>reement (i.e.|
|X_ijt/Xjt_<br>(3)|0.402***<br>(0.083)<br>0.248***<br>(0.048)<br>0.209***<br>(0.038)<br>0.211***<br>(0.047)<br>-1.815***<br>(0.061)<br>-1.067***<br>(0.043)<br>-0.275***<br>(0.029)<br>etup<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br>cts are always <br>ng the Weidne<br> imports. In a<br>orts. The “km<br>s with the hig<br>ium” depth ag|
|(2)|0.379**<br>(0.167)<br>0.309***<br>(0.081)<br>0.192***<br>(0.055)<br>0.151<br>(0.106)<br>-1.176***<br>(0.094)<br>-0.604***<br>(0.058)<br>-0.164***<br>(0.053)<br>n-iterative s<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br>_j_) fxed efe<br>orrected usi<br>ountry total <br>ry total exp<br>. Agreement<br>fed as ”med|
|(1)|0.347***<br>(0.128)<br>0.274***<br>(0.059)<br>0.167***<br>(0.047)<br>0.147**<br>(0.074)<br>-1.158***<br>(0.053)<br>-0.599***<br>(0.039)<br>-0.155***<br>(0.027)<br>No<br>1982-2017<br>153<br>153<br>156,789<br>it, jt, ij<br>er-Importer (_i_<br>stimates are c<br>g destination c<br>tion of a count<br> in cluster # 3<br>t is now classi|
|Dep Var:<br>X_ijt_|PTA_K_#1<br>_ij,t_<br>PTA_K_#2<br>_ij,t_<br>PTA_K_#3<br>_ij,t_<br>Transitory PTAs_ij,t_<br>INTL BRDR_∗_1980<br>INTL BRDR_∗_1990<br>INTL BRDR_∗_2000<br>Cluster defnition<br>Period<br>N. Countries (max)<br>N. Countries (end year)<br>Observations<br>FEs<br>_Notes_: Exporter-time (_it_), Importer-time (_jt_) and Export<br>column 2 and column 4 both standard errors and point e<br>column 3 and 6 the dependent variable is normalized usin<br>remaining missing values are extrapolated using the evolu<br>#1; 58 PTAs in cluster # 2 and the remaining 197 PTAs <br>the preferred routine. A notable exception is NAFTA tha<br>the preferred algorithm.|

18
||Exports to the<br>EU (1000Eur)|Exports of 0<br>duty products|Exports excluded<br>from Cotonou<br>preferences|Exports eligible<br>for Cotonou<br>preferences|
|---|---|---|---|---|
|Swaziland|<br>148741|2%|73%|25%|
|Tonga|<br>720|66%|1%|33%|
|Trinidad,Tob|<br>408025|24%|6%|69%|
|Zimbabwe|<br>778498|26%|5%|69%|
|TOTAL|37388365|64%|4%|31%|

**Table 3 Utilization rate by sectors (for eligible products)**

|sectors|Utilization<br>rate|Average duty<br>reduction|Value of exports<br>(EUR 1000)|Number of<br>observations|
|---|---|---|---|---|
|agriculture|62%|15%|4.674.556|4.576|
|clothing|83%|12%|903.713|2.723|
|footwear|51%|6%|37.605|431|
|machinery|29%|3%|1.449.037|5.108|
|mineral|47%|3%|212.573|85|
|textiles|70%|7%|203.393|1.009|
|wood|86%|5%|258.164|738|
|other|34%|4%|4.490.398|9.015|
|Total|49%|7%|12.229.438|23.685|

Table 3 depicts the utilization rate of Cotonou preferences and average duty reduction by sector. The average duty reduction was the smallest for machinery and mineral products, where on average the difference between MFN and ACP preferential tariff is 3%. The utilization rate in these two sectors is smaller than in most of the sectors, although there is an important difference between the two sectors since utilization is much higher in mineral than in machinery products. The sectors where the utilization rate was the highest are wood, clothing, textiles, and agriculture.

# **Econometric Model**

This part of the chapter examines empirically the driving factors for requesting preferential treatment for exports, and the impact of preferences on trade volumes. The analysis is limited to the exports which are eligible for preferences.

# **Methodology**

The exporter's decision whether to request preferential treatment or not and how much to export could be differently influenced by the preferences offered, the determinants of trade, and some 12
13 that the coefficient of the infrastructure variable now has the wrong sign, which may suggest that the effect of infrastructure on malnutrition could be largely through its interaction with health and education interventions.

# **5. Conclusion**

The importance of adequate, affordable, and universal or at least widely accessible health care is recognized as essential to improving child health outcomes. It is also well understood that mothers’ education is a key determinant of the well being of children – whether by reducing fertility or by facilitating the adoption of good nutritional and hygienic practices. What we have argued, however, is that achieving the health and education MDGs will require more than health and education interventions. In particular, infrastructure services have an important role to play, and a failure to recognize this in planning MDG strategies will risk undermining success. After a quick review of the literature as it pertains to the achievement of the child health MDGs, we have outlined and estimated a simple reduced form model for analyzing the determinants of child health outcomes using cross-country quintile-level data. The use of such data has enabled us to go beyond traditional cross-country regressions by exploiting some of the variability in outcomes and explanatory variables observed between asset quintiles within countries.

We find that at the national level, GDP per capita, female illiteracy and ethnolinguistic fractionalization all affect infant and child mortality. At the quintile level, the households’ asset levels (which is used to define the quintiles), the prevalence of diseases (as proxied by the rates of malnutrition and diarrhea), and the level of access to piped water also affect infant and child mortality. By contrast, the various direct measures of health interventions do not provide much explanatory power. This could be due either to the fact that we do not use the right measures of access to quality health care, or that measurement of the health care effect is made difficult by the presence of reverse causation – a child being treated could either mean that he/she has access to health care, or that he/she is severely ill possibly because of general weakness. It may also be that medical treatment has a larger impact on disease prevalence than on mortality, so that once we control for disease prevalence, we do not find large impacts.
implementation (such as for 2012 versus 2016). These figures are available in the appendix (figures A.1 to A.4).<sup>9</sup> For this purpose, we rely on individual, bank-level balance sheet data from Bankscope. For the analysis, we consider all the banks available in Bankscope for the countries in our sample. Consequently, for 2012, the data set consists of 389 banks from the treated group and 402 banks from the control group. The corresponding figures for 2016 are 408 and 306 banks for the treated and control group countries. As the data set is limited to only the balance sheet data, we do not conduct this analysis for the same variables as our banking system indicators studied earlier. Consider the results in table 8 first.

Table 8: Higher moments of bank capitalization: Treated versus control group

||**Treated**<br>|**group**<br>|**Contro**<br>|**l group**<br>|**Treated %**<br>|**Control %**<br>|**DiD in**|
|---|---|---|---|---|---|---|---|
|**Variable**|**2012**|**2016**|**2012**|**2016**|**change**|**change**|**p.p.**<br>**(%)**|
|Mean|21.25|21.69|20.46|20.46|1|0|1|
|Median|13.80|14.54|15.02|14.07|2|-3|5|
|Std. dev.|30.52|20.69|17.12|18.13|-17|2|-19|
|Skewness|-5.63|2.39|1.92|1.73|-|-5|-|
|Kurtosis|90.17|8.06|8.85|7.39|-105|-8|-97|

_Note:_ The bank capitalization is measured by the ratio of the bank’s capital to total assets. The distribution of bank capitalization in the figures is based on the univariate kernel density estimation. DiD is calculated as the difference between the percentage change for the treated and control groups. DiD = difference-in-differences; p.p. = percentage points.

The results suggest that the standard deviation decreases for all bank characteristics in the countries that implemented Basel III, while the opposite is the case for other EMDEs. This result suggests that Basel III contributed to a more harmonized banking sector environment and reduced the presence of extreme outliers (banks).

On the specific bank characteristics, we find that, unlike in other EMDEs, bank systems in Basel III countries became marginally more capitalized at the mean and around the median. Moreover, the dispersion around this higher average capitalization declined in Basel III countries, while it slightly increased in other EMDEs. In the latter countries, the probability mass in the tails of the distribution (kurtosis) also increased—that is, the relative number of banks at the extremes

> 9 All the plots of kernels are censored for extreme values for better readability, while the tables are not.

21
# **1.  Introduction**

A perennial concern among aid donors and recipient country policymakers is that aid inflows may have adverse side effects on recipient countries. One of these is that large aid inflows lead to upward pressure on the real exchange rate, and an associated loss of competitiveness in the export sector, which could in principle undermine the growth benefits of aid. In this paper, we provide new empirical evidence on the short- to medium-run impacts of aid on the real exchange rate, in a large annual panel of 104 developing countries over the period 1979-2010<sup>1</sup> . We use the local projections method suggested by Jordà (2005) to flexibly describe the dynamic response of real exchange rates to aid inflows. Our basic findings are easily summarized: a one percentage point increase in aid as a fraction of GDP leads to a small and statistically-significant real depreciation of about 0.5 percent on impact, which gradually reverses over time to a small, but statistically insignificant, real appreciation of about 0.5 percent after five years. These findings cast doubt on the view that substantial real appreciations are a likely adverse side effect of aid for recipient countries. If anything, they mirror the theoretical ambiguities about the potential effects of aid on the real exchange rate, that we discuss in more detail below.

An important methodological challenge in isolating the impact of aid on the real exchange rate is that aid inflows are not randomly assigned to recipient countries. Rather, fluctuations in aid within countries over time may very well respond to macroeconomic shocks that also matter for the real exchange rate. For example, a macroeconomic crisis might lead to a large depreciation in the nominal exchange rate, and, given some price stickiness, also the real exchange rate. If the country experiencing the crisis also receives aid inflows in response to the crisis, this would create a spurious correlation between real depreciations and aid inflows. Similarly, a period of poor macroeconomic policy performance leading to high inflation might lead to a real appreciation, and at the same time cause donors who tie aid to policy performance to reduce their aid.

We address these endogeneity concerns by constructing a predetermined component of aid associated with past approvals of loans from official creditors to developing country governments, that can be used as an instrument for overall aid. Official creditors include multilateral aid agencies such as the World Bank, African Development Bank, and Asian Development Bank, as well as major bilateral aid agencies. Disbursements on concessional loans from official creditors are an important form of aid to

> 1 The main data constraint is the multilateral trade-weighted real exchange rate (IMF) that is only available from 1979 whereas the other core variables are available from 1970 onwards.

2
banking sectors). In addition, import restrictions can hurt businesses that need imported inputs. Sanchez-Martin et al (2014) find expropriation risk and trade openness have an effect on FDI flows in Latin America. Actions that target particular sectors (export taxes) or firms (nationalization) reduce microeconomic credibility, as defined in the introduction of this paper. Brunetti et al (1998) use a survey of entrepreneurs to develop a measure of policy credibility and find a significant correlation with economic growth and investment.

# _Business climate_

One summary measure of the business climate is the Ease of Doing Business aggregate from the annual Doing Business report.<sup>51</sup> As in the macroeconomic performance section, a useful set of comparator countries is the emerging market members of the G20. Figure 3 below reports the “distance to the frontier” measure rather than the ranking. The distance to the frontier is how close the country’s performance is to the leading country in that indicator. In other words, Mexico’s score in figure 4 is a little over 70 percent of the leading country. This becomes particular interesting when one looks at subcategories of doing business (See Annex 2). It should be noted that many of the subcategories are based on the analysis of legislation in the particular area; i.e., the _de jure_ regulations. One issue related to government credibility is the degree and consistency of _de facto_ enforcement of _de jure_ regulations.

**Figure 4: Doing Business 2015, Comparison to G20 Emerging Markets**

<!-- Start of picture text --> Ease of Doing Business 2015<br>Distance to the Frontier<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: Doing Business database, 100 represents the score of the leading countries.

Figure 4 shows that the emerging markets of the G20 lag substantially behind the global leaders. In relative terms within the group, Argentina lags about 15 percent behind leader Mexico.

> 51 Also see World Bank (2005) for a discussion of the investment climate more generally.

23
world commodity prices, a current account deficit, and an overvalued real exchange rate. The country had a much more successful adjustment, however, and embarked on a decade of rapid growth.

As recounted in Demery and Demery (1992), Malaysia had high levels of economic growth and rapid rates of growth of manufacturing exports between 1968 and 1984, but was a major natural resources exporter as well. Key commodity exporus included tin, rubber, petroleum, natural gas, and palm oil. Commodity and oil prices boomed in 1979 and 1980, and the government expanded spending. These prices started decl ining significantly in 1981. The terms of trade reversals cost the country real income amounting to seven percent of GDP in 1981 and three percent in 1982. However, expecting the reversals to be temporary, the government maintained its fiscal expansion, relying on foreign borrowing to finance growing government deficits. The large inflows of debt finance, combined with government spending tilted towards the nontradables sectors, resulted in an appreciation of the real exchange rate.

Starting in 1984, the Malaysian government embarked on an adjustment program. It cut government spending to reduce the deficit.<sup>36</sup> To restore external competitiveness and profitability in tradables, it depreciated the nominal exchange rate and reduced external borrowing. Combined with lower domestic inflation, these policies led to a real depreciation between 1985 and 1987 ofjust under 30 percent.

The change in the real exchange rate, along with the other adjustment policies, had several beneficial effects. The current account returned to surplus in 1986 and 1987 and resources started to move back to the tradables sectors.'<sup>7</sup> The adjustment also restored growth. According to World Bank data (World Bank, 1999), aggregate real GDP growth averaged 8.3 percent annually from 1987 to 1991, well above the rates before the adjustment.'<sup>8</sup> And for the whole decade 1987 to 1996 aggregate real growth averaged 8.5 percent.

> 16 Lower tax revenues resulted in the deficit rising again in 1986 and 1987, though the government financed this mostly with domestic borrowing. Foreign borrowing was actually negative in 1987. Another result of the government's expenditure reduction policies was that private consumption and investment fell from 1984 to 1987. This kept demand for imports flat while the level of exports rose, helping bring the current account back into balance.

> 17 Between 1979 and 1984, these sectors (agriculture, mining, and manufacturing) fell from 56 percent of GDP to 52 percent, but recovered to 54 percent in 1987.

18 Although GDP grew by 6.8 percent annually between 1980 and 1984, Demery and Demery attribute this largely to government spending, which was unsustainable, given the current account deficit, the growth of government debt, and terms of trade reversals. During 1985 real GDP fell by 1I.1 percent, and then rose by 1.2 percent the next year.

14
11 being substantially modified in order to allow INTA to continue to provide some level of extension to those producers who are unable to pay for extension services.

Table 2: Actual and projected participation and extension personnel in ATP1 1995-1999

||1<br>Ac|995<br>tual|1<br>A|996<br>ctual|1<br>Ac|997<br>tual|1<br>Pro|998<br>jected|1<br>Pro|999<br>jected|
|---|---|---|---|---|---|---|---|---|---|---|
|Region|Groups|Producers|Groups|Producers|Groups||Producers|Groups||Producers|GroupsI|Producers|
|Al|12|<br>_55_|43|518|19|445||1990||3370|
|A2|9|78|101|927|29|244||2170||3142|
|B3|8|77|94|1179|<br>121|<br>2050||2930||5660|
|B5|11|<br>46|31|446|81|1205||1665||3000|
|C6|5|33|41|417|62|523||1760||3450|
|Total|46|289|310|3473|312|4477|525|10515|<br>930|18622|
|Extensionists|2<br> <br>|4<br>||78|9<br>|3|1<br>|20||120|
|GroupstExtensionist<br>|1<br>.9<br> <br>|<br> <br> <br>|<br> <br>||**3**43<br>3<br>|43.0<br>|43||7.7T||
|1rodu|cer/Ex<br>___|tensio<br>__J<br>12.0|nist<br> <br>|T44.5|___j4|8.1||80.3||124.1|

Source: Garcia, 1997b.

Figure 3 presents the participation trends during the first two years of ATPl's existence, while Table 3 presents recovery rates of producers' payments for extension. The exponential growth in the number of producers (and groups) that joined ATP 1 is explained by the "over capacity" of extension agents in INTA's regional offices that could absorb growing number of producers. However, given the present capacity--both professional and managerial--of INTA, these trends will decrease over time if INTA does not increase its professional and managerial capacities.
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