rafmacalaba/datause-encoder-data huggingface dataset:
being manufactured are very heterogeneous, and include processed food products, books and media, metal products, chemicals and detergents, and a range of other products. Other industries includes a variety of different activities such as advertising, teaching, event management, photography and film-making, as well as some activities that were a mix of other categories and which owners likely found hard to classify.

# **3.2 Data Collection**

Our follow-up surveys targeted a total of 3,139 individuals comprised of four groups who had applied to the first round of the YouWiN! competition:

- 475 individuals selected as national or zonal winners of the competition, based on having the highest absolute scores on their business plans, or highest scores for their region. These are referred to as the _national and zonal winner sample._ Note this consists of all national and zonal winners except for the 5 disqualified plans.

- 729 individuals who were selected as ordinary winners of the competition through a random draw among individuals with the next highest scores. These are referred to as the _experimental treatment sample._ This does include individuals originally allocated to treatment who were subsequently disqualified.

- 1,112 individuals who had similar business plan scores as the ordinary winners, but were not selected in the random draw. These are referred to as the _experimental control sample._ Note 9 of these actually received treatment.

- 823 individuals who applied for the YouWiN! competition and had first round application scores just on either side of the cut-offs used to select people to attend the 4-day business plan training course. This group is referred to as the _regression-discontinuity booster sample._ Appendix 4 discusses this sample in more detail, which is used to examine whether the business plan training by itself had any effect.

# **3.3 Follow-up Surveys and Attrition**

Three follow-up surveys were taken, approximately at yearly intervals after individuals had applied for the program. Surveys took place nationwide via face-to-face interviews, supplemented by telephone interviews and web interviews when security concerns prevented travel, and to boost response rates. Surveys were conducted by TNS RMS Nigeria. In addition, an attempt was made 13
In Table 5 we apply the decomposition method to the (sizeable) ethnic group disparities in schooling found in Vietnam, while in Table 6 we give the corresponding decompositions over time for each ethnic group. In 1993, the primary-level school enrollment rate for minorities was 23% points lower than for the majority (“Kinh”) group.<sup>12</sup> The enrollment rates for both groups rose over time, and the gap narrowed appreciably to about 15% points (Table 5). At the earlier date we find that over half of the gap in the primary-school rate is accountable to the difference in mean consumption between the Kinh and the (poorer) minorities; the proportion is higher for boys than for girls. The difference in distribution tended to narrow the gap. Non-income factors accounted for as high a share as the difference in mean consumption. This pattern changed noticeably by 1998. The non-income differences between the two ethnic groups continue to play an important role, but the difference in mean income is no longer an important factor in accounting for the difference in school enrollment rates at the primary level. However, this has switched to the higher (“secondary”) age group. For this group, the non-income factors work in the opposite direction — reducing the enrollment gap. From Table 6 it can be noted that the share of the growth component in explaining changes over time for each group is larger at the primary level in the case of the minority group than for the majority, while the reverse is true for the secondary level. This suggests that the positive impact of growth benefited all groups, but that since the Kinh were already close to full primary enrollment, for them all of the improvement is concentrated on the secondary level.

Finally, in Tables 7 and 8, we give the results for a decomposition of the disparities in

> 12 The majority ethnic group comprises the Kinh and the Han Chinese.  In 1993, our data includes 2833 and 2711 majority group 11 to 16 and 12 to 18 year olds, respectively, while the numbers for 1998 are 2729 and 3473.  The ethnic minorities represent all 52 other ethnic groups and account for 486 and 380 observations in 1993, and 551 and 560 observations in 1998 for the primary and secondary aged groups respectively.

20
supporting the discovery of profitable choices of products, perhaps via tax incentives for applied research, subsidized public R&D and startup incubation centers, or laws and regulations that provide greater access to risk finance.

Furthermore, our findings advocate a more phased approach toward introducing trade reform, with countries pursuing an expanded production base and export diversification strategies prior to broad tariff removal. This sequencing of liberalization efforts— especially for countries that currently have a very concentrated export base—may be important for minimizing the disruptive effects that expanded trade may imply with regard to growth volatility.

This approach does not mean protecting domestic producers with “infant industry” tariffs—a classic inward-oriented strategy—but rather an outward-oriented one where barriers to domestic market entry are removed, which would then encourage innovation and development of new markets by companies at home. There is also strong evidence that better trade facilitation (through the reduction of fixed and variable costs of moving goods across borders) can be highly effective in promoting export diversification (Dennis & Shepherd 2007). Focusing on removal of red tape affecting exports and imports, and promoting the development of trade-related infrastructure and services sectors, can therefore make a major contribution to diversifying exports and helping manage outward orientation.

# **References**

Acemoglu, K. Daron & Fabrizio Zilibotti (1997). “Was Prometheus Unbound by Chance? Risk, Diversification, and Growth”. _Journal of Political Economy_ 105(4) (August): 709–751

- Ahmed, Shaghil (2003). “Sources of Economic Fluctuations in Latin America and Implications for Choice of Exchange Rate Regimes”. _Journal of Development Economics_ 72(1) (October): 181–202

- Al-Marhubi, Fahim (2000). “Export Diversification and Growth: An Empirical Investigation”. _Applied Economics Letters_ 7(9) (September): 559–562

- Arellano, Manuel & Stephen R. Bond (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”. _Review of Economic Studies_ 58(2) (April): 277–97

- Arellano, Manuel & Olympia Bover (1995). “Another Look at the Instrumental Variable Estimation of Error-Components Models”. _Journal of Econometrics_ 68(1) (July): 29–51

- Baldwin, Richard E. & Simon J. Evenett (editors) (2009). _The Collapse of Global Trade, Murky Protectionism, and the Crisis: Recommendations for the G20_ . London, England: Centre for Economic Policy Research/VoxEU.org

29
# Increased Tax Net for Business Income

In many countries, business income earned by the self‐employed is more frequently underreported than is wage and salary income, due to the lack of third‐party information accessible to tax authorities (Pissarides and Weber 1989; Lyssiotou, Pashardes, and Stengos 2004; Gërxhani 2004; Wangen 2005; Martinez‐Lopez 2012). Korea was no exception. According to Sung (1999, 69), between 1994 and 1998, before the introduction of TIETP, only about half of business income earned by the self‐employed was reported to the NTS. Such low tax compliance among owner‐operated businesses often led to a low number of active taxpayers as a percentage of relevant income earners.

As shown in Figure 5, the ratio of taxpayer to business income earners in Korea lingered aground 30 percent from early 1980s to the mid‐1990s, but it began to increase gradually and substantially from the late 1990s onward. It rose to about 50 percent in the early 2000s and dramatically to 86.4 percent in 2014. Such an increasing trend in the business income taxpayer ratio may be largely due to the introduction of TIETP, but it is also attributable to increasing nominal income due to economic growth and, in some part, to inflation (the inflation rate is low in Korea and thus has a moderate effect). Since Korea increased the PIT exemption threshold only once, in 2008 (from 10 million won to 12 million won), after introducing TIETP in 1999, and since it does not adopt inflation‐adjusted PIT brackets, income increases may result in a higher active taxpayer ratio. However, given the economic growth and inflation effects at all times, TIETP seems to have changed the trend from stagnant at around 30 percent to around 80 percent since its inception.

Figure 5 Personal Income Taxpayers as Percentage of Relevant Income Earners (%) <!-- Start of picture text --> 90 86.4<br>Business Income Tax<br>80.5<br>80 Wage and Salary Income Tax 77.9<br>71.8 72.4 73.0<br>70 68.6 66.8 67.6<br>62.6 63.8<br>60.8<br>59.7<br>60<br>59.6<br>57.8 56.7 51.9<br>50 52.4<br>46.4 46.8 48.3 50.0 51.2 52.2 51.1<br>40 37.8 39.4<br>36.0 36.6 38.7<br>33.9<br>31.3 31.9 33.7<br>34.1 35.1<br>30 33.0<br>29.3<br>20<br>1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text --> 33
# **Figure 3.1. Actual and Equilibrium REER**

<!-- Start of picture text -->
1.3<br>1.2-<br>1.1<br>1.0<br>0.8<br>/~~~~~~-0  Actual REER (1995=1)m<br>0.7  -0-  Equilibrium REER<br>. . . ~-*  b  Avera,qe  1960  - 2001.  . .<br>0.6<br>1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  2000  2001<br>Source: Alberola,  L6pez and  Serven  (2003)<br><!-- End of picture text -->

This evolution of the REER was duly reflected in Argentina's export performance. While real exports did show positive growth over the decade, they grew less than in comparable countries, and their rate of expansion was closely associated to the evolution of the REER. During the initial real appreciation at the time when the currency board was established, Argentina's exports stagnated. As the REER depreciated after 1993, exports expanded vigorously, at rates sirnilar to, or higher than, those experienced by other countries. When the REER started appreciating again in 1997, export performance fell significantly behind that of comparable countries. (Table 3.2).

Real appreciation is not necessarily a symptom of imbalance in need of correction. Indeed, during the 1990s - especially in the early part of the decade -- a number of reasons were offered by different observers in order to explain the persistent real appreciation of the peso as an equilibrium phenomenon. Most importantly, it was argued that the efficiency-enhancing reforms of the early 1990s had led to a permanent productivity increase in the tradable sector of the Argentine economy, which would have justified a permanent REER appreciation. Nevertheless, over the final two or three years of Convertibility an increasing number of independent observers and financial market actors expressed the view that the peso was overvalued - although the precise extent of the overvaluation was disputed, depending on the measure of the equilibrium REER used as benchmark of comparison. 13

> 13 For example, Deutsche Bank perceived the peso to be some 20 percent overvalued in real terms in mid2000. See also Sachs (2002), Rodrik (2002) and Hausmann and Velasco (2002) for various assessments of

> the degree of overvaluation of the peso.

18
Table 5. Regression Results Accounting for Large and Small Dams, Dependent Variable: CP

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|**D(LARGE)**|77.40|67.22<sup>*</sup>|−73.32<sup>*</sup>|−95.79<sup>*</sup>|−75.52<sup>*</sup>|−91.08<sup>**</sup>|190.6|
||(42.48)|(29.26)|(34.52)|(35.29)|(29.70)|(31.81)|(118.6)|
|**UD(LARGE)**|246.5<sup>*</sup>|184.6<sup>*</sup>|271.0<sup>**</sup>|234.2<sup>*</sup>|229.0<sup>**</sup>|258.9<sup>**</sup>|−2.805|
||(97.42)|(82.28)|(93.79)|(89.14)|(47.19)|(86.80)|(5.198)|
|**D(SMALL)**|10.67<sup>**</sup>|9.533<sup>**</sup>|6.749<sup>**</sup>|6.448<sup>**</sup>|5.593<sup>**</sup>|6.707<sup>**</sup>|−2.178|
||(2.255)|(2.493)|(2.065)|(1.919)|(1.535)|(2.059)|(9.590)|
|**D(SMALL)×D(LARGE)**|||17.35<sup>**</sup>|16.73<sup>**</sup>|13.05<sup>**</sup>|16.72<sup>**</sup>|0.565|
||||(2.893)|(2.669)|(2.231)|(2.830)|(16.40)|
|**UD(LARGE, NB)**||212.4<br>(128.2)||||||
|**UD(LARGE, U2)**||−84.75||||||
|||(134.2)||||||
|**UD(SMALL)**||2.256||||||
|||(1.500)||||||
|**UDAM(SMALL, NB)**||−9.617||||||
|||(16.61)||||||
|**UDAM(SMALL, U2)**||0.768<br>(0.867)||||||
|**SPI**|53.95<sup>*</sup>|<br>53.71<sup>*</sup>|52.92<sup>*</sup>|47.91<sup>*</sup>|32.41|49.64<sup>*</sup>|51.91<sup>*</sup>|
||(23.64)|(23.77)|(23.41)|(22.57)|(16.66)|(22.29)|(22.86)|
|**EVAP**|−76.56<sup>*</sup>|−77.63<sup>*</sup>|−79.65<sup>*</sup>|−90.19<sup>**</sup>|−48.10<sup>*</sup>|−76.54<sup>*</sup>|−81.34<sup>*</sup>|
||(32.81)|(31.02)|(31.45)|(29.76)|(21.52)|(31.33)|(31.36)|
|**RFL**|−47.88<sup>*</sup>|−46.73<sup>**</sup>|−45.28<sup>*</sup>|−43.23<sup>**</sup>|−38.70<sup>**</sup>|−44.85<sup>*</sup>|−42.35<sup>*</sup>|
||(17.30)|(16.09)|(16.59)|(13.48)|(11.81)|(16.50)|(16.06)|
|**RFH**|0.0810|−0.133|0.598|−0.706|11.04|2.564|0.176|
||(15.44)|(12.51)|(15.75)|(15.89)|(10.59)|(15.15)|(15.67)|
|**CL**|CROP|CROP|CROP|CLOSE|LC|MOSAIC|CROP|
|**IV**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|4,460|4,460|4,460|4,460|4,460|4,460|4,460|
|**Basins**|223|223|223|223|223|223|223|

_Note:_ Driscoll and Kraay (1998) standard errors corrected for spatial and second order autocorrelation are given in parentheses;<sup>**</sup> and<sup>*</sup> are 1 percent and 5 percent significance levels. CP (dependent variable) denotes cropland productivity. Regressions include river basin fixed effects, former homeland year interactions, interactions of homeland basin predicted dams with river basin gradient, perennial and ephemeral river lengths, river basin area, river basin elevation, river gradients, and river gradient-year interaction terms. In the first column, DAM and UDAM refer to the total of own-vicinity and upstream large and small dams together, whereas in the second and third columns, they refer to large dams only. LC excludes cells that had NPP of more than two standard deviations in 2000 compared to 1981. MOSAIC includes cropland mosaic. CLOSE indicates within 5 km of dams. In the last column, reported coefficients are for nonirrigation dams.

33
portfolio decelerated in 2004, on the basis of macroeconomic variables, the model would have justified a decline in proportion to GDP. This deviation from the equilibrium path, however, can be still explained by convergence. In turn, it is hard to explain a decline in the equilibrium path driven by high nominal interest rates in the case of a transition economy. Consequently, in the review period there was no credit boom in the case of households; however, the risk of its emergence was greater in this sector than in the corporate sector.

**Chart 4. Net change of loans outstanding to households by currency breakdown**

<!-- Start of picture text --> HUF Bn HUF Bn<br>400 400<br>350 350<br>300 300<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>-50 -50<br>Loans from banks - HUF Loans from banks - FX<br>Loans from financial enterprises - HUF Loans from financial enterprises - FX<br>1995Q1 Q2 Q3 Q4 1996Q1 Q2 Q3 Q4 1997Q1 Q2 Q3 Q4 1998Q1 Q2 Q3 Q4 1999Q1 Q2 Q3 Q4 2000Q1 Q2 Q3 Q4 2001Q1 Q2 Q3 Q4 2002Q1 Q2 Q3 Q4 2003Q1 Q2 Q3 Q4 2004Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB. Note: ―Banks‖ includes banks, branches, and cooperative credit institutions.

**Chart 5. Estimation of equilibrium credit/GDP of households**

<!-- Start of picture text --> Per cent Per cent<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Loan to GDP actual level<br>Equilibrium level based on median of country constants<br>Estimated equilibriums with the different county constants<br><!-- End of picture text --> Source: Kiss et al. (2006); authors‘ calculations.

# **_II. 2. Period of excessive credit expansion (2004–2008)_**

# **_Corporate segment_**

The year 2004 marked the end of the era of state-subsidized, forint-based housing loans previously dominant in household lending, and banks started to offer new, foreign-currency7
WPS4208 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 12</mark>

# **Civil War, Crop Failure, and Child Stunting in Rwanda ***

Richard Akresh Philip Verwimp Department of Economics European Centre for Advanced University of Illinois at Research in Economics and Urbana Champaign Statistics (ECARES) Université Libre de Bruxelles Tom Bundervoet Department of Mathematics, Operational Research, Statistics and Information Systems for Management (MOSI) Vrije Universiteit Brussel

## **Abstract**

Economic shocks at birth have lasting impacts on children’s health several years after the shock. We calculate height for age z-scores for children under age five using data from a Rwandan nationally representative household survey conducted in 1992. We exploit district and time variation in crop failure and civil conflict to measure the impact of exogenous shocks that children experience at birth on their height several years later. We find that boys and girls born after the shock in regions experiencing civil conflict are both negatively impacted with height for age z-scores 0.30 and 0.72 standard deviations lower, respectively. Conversely, only girls are negatively impacted by crop failure, with these girls exhibiting 0.41 standard deviation lower height for age z-scores and the impact is worse for girls in poor households. Results are robust to using sibling difference estimators, household level production, and rainfall shocks as alternative measures of crop failure.

## _Keywords:_ Child health, economic shocks, civil war, rainfall shocks, Africa _JEL classification:_ I12, J13, O12, O15

World Bank Policy Research Working Paper 4208, April 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We would like to thank the Department of Agricultural Economics at Michigan State University for making the data available and Ilana Redstone Akresh and seminar participants at Harvard’s Weatherhead Center, University of Illinois at Urbana Champaign, NEUDC, World Bank/PRIO 2006 Oslo, Norway conference and USAID/HICN Poverty Reduction in Conflict and Fragile States conference for helpful suggestions.

Corresponding author’s contact information: Richard Akresh, University of Illinois at Urbana Champaign, Department of Economics, 1206 South Sixth Street, Wohlers Hall, Room 470E, Champaign, IL 61820. Telephone: (217) 333-3467. Fax: (217) 244-6678. Email: akresh@uiuc.edu
household groups, it continues to rise for urban, non-farmer and female-headed households when prices increase by 100 percent.

We finally turn to the long-run scenario results shown in Table 5. In this scenario we assume capital and labor to be fully mobile, allowing for potentially larger output responses and stronger wage impacts. For a uniform 10-percent food price shock, poverty is expected to fall in 24 of the 31 countries included in our sample. For greater price shocks, the number of countries for which we estimate a poverty reduction declines—for a 50-percent shock, only 19 countries are expected to experience a decline in poverty. This figure drops to 16 for a 100-percent price shock.

The global estimate of poverty change resulting from food price changes in the long run—shown in the last set of columns in Table 6—is estimated to be favorable to the poor. We estimate a reduction of 1.4 percentage points in the global poverty rate for a 10-percent shock. This reduction grows to 5.8 percentage points for a 50-percent shock and 8.7 percentage points for a 100 percent food price shock. Looking at the results for different household groups, we find that most household groups experience poverty reduction even for large food price increases. The only exceptions are female-headed households, which do not benefit from very large price increases, and urban households for which the poverty rate is likely to increase with large food price increases. For smaller food price increases, however, we find no household group whose poverty rate would be significantly negatively affected in the long run.

# **Poverty implications of individual food price increases**

Our earlier simulations considered a uniform change in food prices, which is common in the discussions of the implications of food prices for poverty which often focuses on the changes in general food price indices, such as the World Bank’s Food Price Index. However, focusing on an index like this raises two important concerns. First, only those commodities that are internationally traded— and hence have well defined international prices—are used in the construction of many such indexes, including the World Bank index, which often ignore important consumption items, such as dairy products and many fruits and vegetables. Second, we frequently observe that different commodities’ prices rise at different rates, which means that the same changes in the food price index can be caused by very different underlying changes in the prices of the component commodities, which may have quite different impacts on poverty.

18
_-_ _<u>Table 2: Program impact on reported village level food prices</u>_

||Egg Price<br>Reported by<br>Household|Rice Price<br>Reported by<br>Household|Sugar Price<br>Reported by<br>Household|
|---|---|---|---|
|_Panel A: Program Impact_||||
|Program village|0.017<br>(0.014)|-0.000<br>(0.006)|0.000<br>(0.011)|
|_Panel B: Interaction with saturation _||||
|Program village|-0.083*|-0.009|-0.020|
||(0.046)|(0.020)|(0.037)|
|Saturation|-0.007|0.017|0.004|
||(0.062)|(0.027)|(0.049)|
|Program village* saturation|0.164**<br>(0.072)|0.014<br>(0.031)|0.034<br>(0.057)|
|_Panel C: Interaction with above median satura_|_tion_|||
|Program village|-0.011|0.003|-0.004|
||(0.019)|(0.008)|(0.015)|
|Above median saturation|0.007<br>(0.022)|0.013<br>(0.010)|0.005<br>(0.017)|
|Program village *Above median saturation|0.060**|-0.007|0.009|
||(0.027)|(0.012)|(0.022)|
|_Panel D: Interaction with fourth quartile satur_|_ation_|||
|Program village|-0.011|-0.001|0.005|
||(0.027)|(0.006)|(-0.012)|
|Above median saturation|0.030<br>(0.032)|0.014<br>(0.015)|0.027<br>(0.021)|
|Program village *Above median saturation|0.054|-0.003|-0.018|
||(0.038)|(0.017)|(0.025)|
|Control observations|65|65|65|
|Treated observations|65|65|65|
|Control Mean Price (in Philippine_pesos_)|6.015|33.392|38.977|

note: *** p<0.01, ** p<0.05, * p<0.1 All specifications include municipality fixed effects. All prices are in natural logs. Source: 2011 _Pantawid_ impact evaluation survey 45