rafmacalaba/datause-encoder-data huggingface dataset:
panel A<sup>5</sup> . All the South Asian countries (except Pakistan) are big positive outliers both in 1995 and 2006 controlling for other relevant factors. China as expected is the biggest positive outlier. Thus all these economies were investing more than the norm both in 1995 and in 2006.

To gauge the impact of reduced capital flows on investment, we need to examine how this huge investment is provided for. Is it through domestic savings or external capital? How does South Asia do on domestic savings front? Are they saving too little or too much?

We report the cross-country level regression on savings rate for 1995 and 2006, controlling for the level of per capita income, non-linearities in development (captured by the square of per capita income), and country size (proxied for by population) in Table 4, panel B. It shows that amongst the South Asian economies, India and Bangladesh are positive outliers in both 1995 and 2006<sup>6</sup> . China as expected is a significant positive outlier too in both these years. So South Asian countries are saving much more than the rest of the world controlling for the relevant factors.

South Asian economies are investing and saving at a much higher rate than other developed and developing countries. A significant portion of the investments are coming from domestic savings. Even when capital flows have increased in significance in the last decade, high investments in South Asia have been largely driven by domestic savings. So even in the face of greatly reduced capital inflows the region will suffer less as compared to some other regions whose investments were driven by external capital. High domestic savings are going to rescue the investments (though it does not mean that investments are not going to be hit at all) and help the region to recover and sustain growth in the medium term.

South Asia entered the period of crisis with a very high savings rate. How does this high domestic savings likely to affect recovery in South Asia? We run an OLS on GDP growth and savings rate for the period 2000 – 2007. We use a variety of controls which seem to have been related to growth. We use controls for financial development (proxied for by M2 as a ratio of GDP), openness (proxied for ratio of exports and imports to GDP), initial level of development (proxied for by initial GDP per capita) and demographic features (proxied for by life expectancy at birth). The results are presented in Table 5.

> 5 We picked 1995 and 2006 to see how the saving and investment behavior changed before and after the East Asian financial crisis.

> 6 We picked 1995 and 2006  to see how the saving and investment behavior changed before and after the East Asian financial crisis.

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the stratified survey data, we employ weighted least squares (WLS) estimation using the sampling probabilities as weights. WLS estimation ensures that the proportional contribution to the parameter estimates of each stratum in the sample is the same as would have been obtained in a complete census enumeration (cf. DuMouchel and Duncan 1983; Wooldridge 2001).

# **4. Student Background and Educational Achievement**

This section reports the results of regressing students’ educational performance on a host of student characteristics, which mainly measure their family background. Because this section is interested in the total impact of student background on educational performance, including any effect that might work through families’ differential access to schools with different endowments and through their influence on the institutional features of schools, the estimation of the student-background effects does not control for school characteristics, such as resource endowment and institutional characteristics, as in equation (2): While this specification omitting school characteristics should be preferable for the research question considered in this section, we also tested the robustness of our student-background results to estimating equation (2), which includes the school characteristics. None of our qualitative results are sensitive to the alternative specification.

Tables 3a and 3b report the results of estimating equation (5) for each of the considered countries. Fourth-grade reading performance as tested in PIRLS is strongly positively related to reading performance at the start of primary school in each of the countries. The measure of pre-school reading performance has the highest _t_ -statistic of all variables in Argentina and Colombia, and also one of the highest _t_ -statistics in all other countries. This provides some confidence in the value-added specification employed.

Kindergarten attendance is not statistically significantly related to reading performance of Argentine students, and in Colombia, students who attended kindergarten for a considerable time even performed significantly worse. In the other considered countries, there is similarly very little evidence of positive relationships between kindergarten attendance and later student performance. In interpreting this result, it should be borne in mind that children are not randomly assigned to attending kindergarten, so that significant selection patterns may influence this result. However, assuming that students from superior backgrounds are more 15
12a

# TABLE 6: EXPORTS BY KOREAN SMEs IN FOUR SUBSECTORS (MILLION OF USS)

|1-<br>~<br>~|~~<br>-.<br>.<br>WOVEN<br>TEXTILES|,<br>AUTO PARTS|**T**<br>ELECTRONIC<br>jPARTS|.<br>METAL CUTTING<br>|EQUIPMENT|
|---|---|---|---|---|
|19 83|_<br>_<br>_<br>_<br>_<br>_<br>_|<br>_<br>_<br>_<br>_<br>_<br>_<br>_|_<br>_<br>_<br>_<br>_<br>_<br>_|_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|SME Export Value<br>|(USMilions)|-<br>349|4|199<br>_|21<br>_|
|SME Shares of<br>Total Exports|21.3%|9.6%|19.5%||
|-1988.|||||
|SMEExport Value<br>= (USsKllions)|1,454<br>-|83|134%|68|
|SME Shares of<br>Total Exports|42.8%|25.9%|22.9%||
|I991|||||
|SMEExportValue<br>(USMMIIion|3,453<br>s)|-<br>184|2380|123|
|SME Shares of<br>Total Exports -|60.6%|44.1%|24.8%|=|

.
where there is ongoing rapid economic and political change and others that are firmly in the status quo category. Sweeping averages of aggregate variables - such as those reported above - thus mask an enormous amount of variability in the data. One of the recurring themes throughout this paper is that the firm data show a similar pattern of a very wide range of outcomes across firms _within countries_ in Africa. While most firms have not fared well during the last 10-15 years, some have actually performed extremely well. We think this is one of the most striking general results that have come out of the work on these data. Because the heterogeneity in outcomes is so strong, understanding its causes and consequences is, we think, of central importance. To this end, firm data must be available.

In recent years there has been a rapidly growing interest in documenting differences in the “investment climate” across regions and countries, and investigating if such differences can be linked to differences in outcomes, both across and within countries (e.g. Batra, Kaufmann and Stone, 2003; World Bank, 2004b, 2005).<sup>4</sup> The poor investment climate in Africa results in high transaction costs and particularly disadvantages the manufacturing sector and its ability to export, as manufacturers are intensive users of investment climate services (Collier, 2000). This is problematic, because exporting presents a promising route to growth and development in Africa. There are two reasons for this. First, the domestic markets for manufactured goods are typically very small. African industry must therefore orientate a substantial share of its output towards exporting if the sector is to grow, generate new jobs and contribute to poverty reduction in the continent. Second, there is evidence from the data that firms become more productive as a result of exporting. This is the so-called learningby-exporting effect, first documented for Africa by Bigsten et al. (2004) and subsequently confirmed on a larger sample by van Biesebroeck (2005a). We will return to this below. Since Africa represents only a tiny fraction of world trade the potential for expansion is significant, and experience from Ghana and Uganda suggests that export recovery can generate large gains quite quickly.

> 4 The investment climate as a concept is very broad, its key components being the institutional, policy, and regulatory environment in which firms operate (Dollar, HallwardDriemeier, and Mengistae, 2003). The quality of infrastructure, the nature of business regulations and their enforcement, the prevalence of credit constraints, the quality of governance, general conditions for private investment and enterprise growth, economic freedom, country credit ratings, human development, environmental sustainability and civil rights are all examples of recently studied dimensions of the investment climate.

3
provides information on the median price of properties in the project sold during every trimester.<sup>9</sup> By combining the information of different projects and different years, we are able to construct an unbalanced panel data set in which the unit of analysis is the project and the number of times it appears in the data set depends on the number of years it takes for the last unit to be sold.<sup>10</sup> The number of properties in each project varies, as does the number of observations per year, since not all the projects are in the market for the same amount of time. By summing up the average number of properties sold in each development per month, we are able to calculate the monthly average housing supply for each year. Table 1 shows the number of projects available on average each year, the estimated average housing supply, and the mean housing price for each year.

The median price experienced a positive growth of 2.8 percent during the 11-year period, suggesting that the reduction of the housing supply (from 2002 to 2013) was reflected in scarcity of housing units and consequently, an increase in housing prices. Every year in which the average housing supply had negative growth, the median price experienced positive growth (with the exception of two years in which the rise in the median price was less than 1 percent, and 2009, a year in which both the housing supply and the median price experienced negative growth of 2 percent).

|**Table 1:**<br>|**Number of obser**<br>|**vations (projects), esti**<br>|**mated housing supply and me**<br>|**dian price (2002- 2013)**<br>|
|---|---|---|---|---|
|**Year**|**Average**<br>**number of**<br>**projects on**<br>**sale each year**|**Average number of**<br>**properties sold per**<br>**month per project**|**Estimated housing**<br>**supply (total number of**<br>**properties sold per year)**|**Median price (MX$)**|
|2002|821|5.43|4,458.0|882,084|
|2003|919|3.77|3,464.6|1,140,472|
|2004|1,067|4.21|4,492.1|1,114,346|
|2005|1,233|4.05|4,993.7|1,114,212|
|2006|1,515|3.06|4,635.9|1,131,260|
|2007|1,542|2.52|3,885.8|1,154,502|
|2008|1,303|2.85|3,713.6|1,184,655|
|2009|1,235|2.94|3,630.9|1,160,689|
|2010|1,141|3.07|3,502.9|1,154,515|
|2011|1,014|3.46|3,508.4|1,140,002|
|2012|997|3.68|3,669.0|1,131,161|
|2013|967|3.50|3,384.5|1,158,109|

> 9 SOFTEC gathered information regarding the representative property of each project, including its sale price. Therefore, for each project, information regarding the median price is obtained. 10 This value varies between one and eleven years.

9
Implementing the above approach is the object of this section. Before presenting the results, however, it is important to understand its limitations. First of all, we can only test the effect of observed endowment diffarences. In the absence of panel data, unobserved differences such as activity specific skills or preferences cannot be controlled for.<sup>2 0</sup> Second, the validity of the test rests on _Hj_ being exogenous. Suppose that this is not the case, i.e., that factors are either perfectly mobile across space or that they can be accumulated instantaneously, so that factor prices are equalized across space. Further assume that prices vary systematically with location. Profit maximization dictates that: <!-- Start of picture text --> Pij 9Q(Lj,") = r<br>Pi Hi,<br><!-- End of picture text --> which implies that _Lij, Hij,_ and _Hj = Hij_ vary systematically with _Pij_ and thus with distance. The endogeneity of _Hj_ generates multicollinearity with distance since _Hj_ depends on relative prices and by assumption _Pij_ = EJ T1kDk + _EhH= 'Yhph_ +gij where eij represents factors other than distance that affect relative prices - and thus Lij _and Hj._ When Eij is large, Hj is much more correlated with Lij than distance itself. Including _Hj_ in the regression thus biases the distance coefficients toward zero.

Since we do not have instruments for endogenous placement of households, we cannot control for the possible endogerLeity of _Hj._ We nevertheless propose to do two things: (1) regress household characteristics _Hj_ on distance and see whether _Hj_ vary systematically with distance; and (2) add _Hj_ to all regressions presented in section 4. Given that _Hj_ is potentially endogenous, it is important to understand the limits of this procedure. If distance coefficients become 20Plans are underway to collect a second round of NLFS panel data in Nepal but the data will not be available for several years.

28
Table 3: Fiscal (pro)cyclicality of expenditure, whole sample (2000-2016): Impacts of development level, tax base, credit constraints, corruption, rule of law and order

|Dependent variable: Cyclicality of real general<br>government primary spending|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Cyclicality of real GDP|0.425**<br>(0.199)|2.124***<br>(0.706)|0.107<br>(0.378)|0.279***<br>(0.092)|1.070***<br>(0.313)|-0.089<br>(0.310)|-1.320**<br>(0.653)|-0.829**<br>(0.400)|1.730***<br>(0.118)|-0.114<br>(0.414)|
|**Development level**|||||||||||
|Crgdp*dvpt.level (polytomic)||-0.612***<br>(0.229)|||||||||
|**Tax base effect**|||||||||||
|Crgdp*tax<br>Crgdp*tax.base.variability|||0.041<br>(0.057)|0.091***<br>(0.002)|||||||
|**Credit constraints**|||||||||||
|Crgdp*credit.ratio<br>Crgdp*kaopen|||||-0.013***<br>(0.004)|1.178<br>(0.920)|||||
|**Perceived corruption**|||||||||||
|Crgdp*pol.corruption<br>Crgdp*pub.service.corruption|||||||2.808**<br>(1.106)|2.452***<br>|||
|||||||||(0.927)|||
|**Rule of law and order**|||||||||||
|Crgdp*law.order|||||||||-0.350***<br>(0.042)||
|Crgdp*rule.of.law||||||||||1.213<br>(1.153)|
|Constant|-377.001|152.592|-1,680.682|-1,180.377|-66.903|-8.790|-1,044.820|-1,185.272|575.096|-158.364|
||(340.819)|(518.802)|(1,848.808)|(956.057)|(461.523)|(582.628)|(1,388.965)|(1,020.610)|(859.336)|(467.021)|
|Adjusted R-squared|0.067|0.135|0.083|0.114|0.148|0.105|0.150|0.157|0.122|0.108|
|Rmse|5,531|5,324|9,347|9,188|5,339|5,607|5,497|5,437|5,823|5,629|
|Joint significance (p-value)|<br>-|<br>0.000|<br>0.074|<br>0.000|<br>0.003|<br>0.000|<br>0.001|<br>0.000|<br>0.000|<br>0.000|
|<br>CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|<br>Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)/I(1)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|1,954|1,954|663|663|1,802|1,707|1,639|1,639|1,569|1,639|
|# countries|116|116|42|42|115|115|112|112|99|112|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.1 in appendix A for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

11
**Figure 2. Numbers and Shares of Exporter-Importers in Peru Panel A. Number of Exporter-Importers**

<!-- Start of picture text --> 3,000 250<br> 2,500<br> 200<br> 2,000<br> 150<br> 1,500<br> 100<br> 1,000<br> 50<br> 500<br> ‐ ‐<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>All sectors Agribusiness Apparel<br><!-- End of picture text -->

**Panel B. Exporter-Importers vs. Exporters-Only**

<!-- Start of picture text --> 7,000<br> 6,000<br> 5,000<br> 4,000<br> 3,000<br> 2,000<br> 1,000<br> ‐<br>2000 2006 2012 2000 2006 2012 2000 2006 2012<br>Agribusiness Apparel All<br>Exporter‐Importers Exporters only<br><!-- End of picture text -->

**-** **<u>Panel C. Share of Exports by Exporter Importers</u>**

||Agribusiness|Apparel|All|
|---|---|---|---|
|2000|86%|93%|93%|
|2006|78%|85%|92%|
|2012|79%|59%|87%|

While the focus in our analysis is on imports of intermediates by Peruvian exporters, Panel A of Figure 3 shows for all exporter-importers and for exporter-importers in agribusiness and apparel in selected years the composition of their entire import portfolio in terms of the different categories of the BEC classification: capital goods, intermediates, final goods, and others. The same classification is applied to their export portfolio in Panel B. Exporter-importers import mostly intermediate goods. Considering all sectors, exporter-imports export a mixed portfolio of different types of goods, but in the agribusiness and apparel sectors, exporter-importers export mostly consumption (final) goods.

8
particular, we are able to investigate disruptions in economic activities and food market supply chains, mechanisms through which violent conflicts can affect food security and related welfare outcomes.

We focus on three sets of household outcomes: (1) food security, measured using the Food Insecurity Experience Scale (FIES); (2) access to food and food markets; and (3) household participation in major economic and livelihood activities (farming, non-farm business, and wage employment). To quantify the impact of violent conflict on these outcomes, we use a Differencein-Differences (DID) approach, where the periods (months) before and after the outbreak of the war are collapsed into pre-war and post-war-onset periods for aggregate analysis, and a two-way panel fixed effects model is implemented for disaggregated analysis using (monthly) householdlevel exposure to conflict data. The first stage of the war (November 2020 to June 2021) was mostly confined to Tigray, spilling over to neighboring Afar and Amhara regions thereafter. Thus, our analysis focuses on the first period of the war, so that we can precisely define the spatial extent of the conflict. To account for potential intermittent conflicts elsewhere, we relax this treatment assignment in the analysis by using monthly household-level exposure to conflict from the ACLED database.

The high-frequency nature of the data facilitates our identification strategy in two useful ways. First, the monthly follow-up and hence comparison of outcomes across affected and unaffected regions allows us to minimize compounding trends. The existence of multiple pre-war and post-war-onset waves also allow us to indirectly test for parallel trends, the main identifying assumption for a DID estimation strategy. Second, the relatively large sample size due to highfrequency data allows us to probe the robustness of our results using alternative definitions of control groups. For example, in some of our estimations, we restrict our sample to the Highland regions of the country, with Amhara, Oromia and SNNP regions assigned as the controls.

We find that the outbreak of the civil war increased the probability of moderate or severe food insecurity by 38 percentage points. This is a substantial impact, but not surprising given the massive disruptions to livelihoods and services as well as the scale of the war. Our analysis of the granular conflict data in the ACLED database indicates that exposure to an additional battle leads to 1 percentage point increase in the probability of moderate to severe food insecurity. Some of the ultimate effects on food security are driven by disruptions in markets and supply chains, destruction of livelihoods and income losses while some may have simply been consequences of 5
**Figure 3: Growth Poverty Correlation in South Asia**

<!-- Start of picture text --> Barisal<br>RajshahiBalochistan<br>Orissa Khulna<br>Mid-Western Region<br>B ihar<br>Far-Western Region<br>Madhya PradeshJharkhand<br>Uva<br>Chittagong<br>Uttar PradeshChattisgarhSylhetUttarakhandSabaraga- muwaDhakaMaharashtra<br>Eastern RegionCentral RegionWestern Region NWFP North WesternSouthern<br>West BengalKarnatakaCentral<br>Tamil Nadu<br>Sindh<br>Rajasthan North Central<br>Assam Punjab<br>Gujarat<br>Andhra PradeshKerala<br>Haryana<br>Western<br>Himachal Pradesh<br>Punjab<br>200 400 600 800 1000 1200<br>GDP per capita US$ at constant prices, 2000<br>50<br>40<br>30<br>20<br>Poverty Rate (%)<br>10<br>0<br><!-- End of picture text --> Source: Staff estimates using data in figures 4 and 5 accelerate growth. It has the highest population density in the world, and the second largest proportion of population living in the border areas after Europe. High population density and better access to markets can benefit growth by allowing South Asian firms to take advantage of agglomeration economies.

Despite this benefit of geography—density and distance—South Asia’s true growth potential has not been realized due to the lack of market integration within countries and across countries. South Asia accounts for only 3 percent of the world surface area, but it sustains a “whopping” 20 percent of the world population, nearly 1.5 billion people. It has the highest population density in the world, yet it has one of the lowest urbanization rates. There are indeed large differences across countries in South Asia. In 2005, India (which accounts for 74% of the regional population) produced close to 80% of the South Asian GDP. Pakistan (13 % of South Asian population), Bangladesh (10%), Sri Lanka (1%) and Nepal (2%) accounted for 11%, 6%, 2.3% and 0.7%, of the regional GDP, respectively. Bhutan, Maldives and Afghanistan collectively accounted for less than 1% of South Asia GDP. The differences in per capita income are large, ranging from a high of $2700 for Maldives (for the year 2006 measured in current US dollars) to a low of only $250 for Afghanistan (figure 4). Even if one were to exclude the small economies of Maldives and Bhutan, the per capita income gaps are pretty large.

4