rafmacalaba/datause-encoder-data huggingface dataset:
TABLE A.8 Testing Treatment Effect Heterogeneity

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||||Pa|nel A: Student|test score data|measured at _Y_|2|||
||Fraction<br>target<br>teachers|Total target<br>teachers|Student<br>asset index|Number of<br>students|Size relative<br>to biggest<br>school|Log number<br>of students|Log size<br>relative to<br>biggest<br>school|School-level<br>_Y_0 score|Teacher’s<br>age|
|Treatment|-0.03|-0.03|-0.06|0.00|-0.01|0.10|-0.02|-0.01|-0.11|
||(0.07)|(0.04)|(0.08)|(0.04)|(0.04)|(0.18)|(0.06)|(0.02)|(0.07)|
|Covariate|0.02|0.02<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|0.00<sup>_∗∗∗_</sup>|0.39<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.21<sup>_∗∗∗_</sup>|-0.00|
||(0.08)|(0.00)|(0.02)|(0.00)|(0.09)|(0.03)|(0.03)|(0.03)|(0.00)|
|Treatment - covariate|0.05|0.00|0.02|-0.00|0.01|-0.02|-0.02|0.04|0.00|
|interaction|(0.13)|(0.01)|(0.02)|(0.00)|(0.12)|(0.04)|(0.04)|(0.04)|(0.00)|
|Observations|279,066|279,066|279,066|279,066|279,066|279,066|279,066|275,183|267,792|
|_R_<sup>2</sup>|0.28|0.28|0.29|0.28|0.28|0.28|0.28|0.28|0.28|
- 7 -

expenditures will increase as the labor force participation rate increases. If they are substitutes, the opposite will hold. This relationship is likely to differ for various categories of labor and different types of infrastructure, so our empirical results will simply capture the net effects. To the extent that the labor force participation variable also may be correlated with the population growth rate (i.e., high growth rates may imply a relatively low proportion of the population in the economically active age cohorts), the estimated regression coefficient may reflect even more than just the net substitutability or complementarity of infrastructure and labor.

The size of the labor force participation rate (LFPER) is computed as the total labor force (the "economically active" population including the employed, the unemployed, and the armed forces, but excluding homemakers, care givers, and students) divided by the total population, all multiplied by 100. The data source for both component variables is WDR91SD.

# _2. Other Variables Reflecting the Structure of the Economy_

No theoretical model is complete enough to provide a full list of variables that may be important in the empirical investigation of many economic phenomena. The stylized model that we use as departure point for our analysis does not pretend otherwise. Therefore, in addition to the above variables implied by the model, based on the previous empirical studies, we also included the following variables defining the domestic economy: the level of development, internal government budget balance, external balance, the size of the foreign sector, terms of trade shifts, debt obligations and institutional development.

2.1. Level of Development: Most government services and goods are expected to be normal goods. Accordingly, as income increases, the demand for infrastructure is similarly expected to increase, although possibly at a diminishing rate. Further, as the level of development increases, the structure of the economy is expected to change in ways that increase the productivity of infrastructure investment. For example, industrialization accompanies development and infrastructure may well be more productive to industry than agriculture.

Level of development is measured as real GDP per capita. Sensitivity analysis is conducted to ascertain the sensitivity of the results to two altemative formulations of real GDP per capita. The first measure is GDPCAPK. Here, currency conversions are accomplished using dollar exchange rate conversions and all values are deflated to 1980 US dollars. GDPCAPK is computed as total GDP in constant 1980 US dollars divided by total population. The second measure, GDPCAP2, measures real per capita income at international prices using the Summers-Heston data. The data series used to construct GDPCAPK along with GDPCAP2 are contained in WDR91SD.
# **Table A.8. Income Inequality, Reclassification: Top Performers Are Firms in the Top 10 Percent of the Distribution by Value Added per Hour Worked**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Top-pe|rformers|Constrain|ed gazelles|Survi|valists|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.000105|1.000|0.00122***|1.001|0.00200***|1.002|
||(0.000306)||(0.000355)||(0.000433)||
|Age owner|0.0147<br>|1.015|0.00162<br>|1.002|-0.0574***<br>|0.944|
||(0.0357)||(0.0195)||(0.0150)||
|Age squared owner|-0.000218<br>(0.000411)|1.000|-0.000<br>(0.000242)|1.000|0.000652*** <br>(0.000168)|1.001|
|Male(=1)|-0.0195<br>(0.207)|0.981|-0.0981<br>(0.0990)|0.907|-0.287**<br>(0.115)|0.750|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|-0.286<br>(0.456)|0.751|0.110<br>(0.237)|1.117|0.0420<br>(0.161)|1.043|
|Some secondary|0.305<br>(0.423)|1.356|0.753***<br>(0.220)|2.123|0.407***<br>(0.155)|1.502|
|Post secondary|1.324***<br>(0.491)|3.757|1.376***<br>(0.248)|3.959|0.805***<br>(0.215)|2.237|
|Other post primary|0.724|2.062|0.677**|1.967|1.048*|2.852|
||(0.621)||(0.315)||(0.578)||
|Can read|0.356|1.428|0.603***|1.828|0.283**|1.327|
||(0.357)||(0.163)||(0.133)||
|No opportunity in large firms|0.115<br>|1.122|0.194<br>|1.214|0.0155<br>|1.016|
||(0.263)||(0.123)||(0.235)||
|No opportunity in small firms|-0.362<br>|0.696|0.189<br>|1.208|-0.114<br>|0.892|
||(0.311)||(0.131)||(0.128)||
|Firm size|0.127<br>|1.135|-0.0429<br>|0.958|0.00233<br>|1.002|
||(0.228)||(0.0788)||(0.0918)||
|Firm age|-0.0290<br>|0.971|0.0181<br>|1.018|0.0152<br>|1.015|
||(0.0561)||(0.0280)||(0.0356)||
|Trade register|0.405|1.499|1.056***|2.874|0.422|1.524|
||(0.829)||(0.324)||(0.538)||
|Financial knowledge|0.336|1.400|0.528***|1.696|0.359***|1.432|
||(0.224)||(0.121)||(0.136)||
|Business association|0.311|1.365|-0.169|0.844|0.116|1.123|
||(0.352)||(0.163)||(0.186)||
|Lack of customers|0.230|1.258|0.134|1.143|-0.117|0.890|
||(0.186)||(0.0898)||(0.0932)||
|Lack of credit access|-0.335*|0.716|-0.111|0.895|-0.226**|0.798|
||(0.202)||(0.0948)||(0.100)||
|Management problems|0.230|1.259|0.108|1.114|0.0204|1.021|
||(0.241)||(0.116)||(0.122)||
|Lack of qualified workforce|0.375<br>|1.455|0.812**<br>|2.251|-0.488<br>|0.614|
||(0.899)||(0.373)||(0.439)||
|Discipline problem|-0.262|0.769|-0.103|0.902|0.319|1.376|
||(0.957)||(0.290)||(0.376)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|<br>Pseudo-R2|0.1255||0.1155||0.0918||
|Observations|470|470|1,862|1,862|1,680|1,680|
|Standard errors in parentheses **|* p<0.01, ** p<|0.05, * p<0.1|||||

36
way of calibrating the labor market demographic factors with this ”steady-state assumption” is to choose that _SW,t_ +1 = _SW,t_ = _SW,_ 0 and _SE,t_ +1 = _SE,t_ = _SE,_ 0 (so that _S_<sup>�</sup> _W,t_ +1 = 0 and _S_<sup>�</sup> _E,t_ +1 = 0) for all periods.

Suppose that a policy maker in Korea made this set of ”steady-state assumptions” in 1970, and then applied the benchmark growth model to simulate the GDP per capita for the future period of 1971-2014. Suppose that the data available for this policy maker in 1970 are the 19601970 period data. Once deciding to take the ”steady-state” approach, the best way to calibrate the constant growth rates of _gAL_ , _gh_ , and _gN_ would be to form an adaptive expectation and the best fits for the constant growth rate parameters would be the long-term average growth rates, represented by the annual average growth rates of the corresponding variables for the dataavailable period, i.e., the 1960-1970 period. In selecting the constant values for the investment rate, working-age population share, and labor force participation rate, we may want to take the average values for the past sample period to smooth out the shocks. However, if taking the averaging period too long, the average values would not represent the true values of the parameters for the simulation period. Thus, the average values for the initial five-year period prior to the starting date of simulation, for example, the 1966-1970 period values for the 1970 simulated prediction, are to be used to calibrate the investment rate, working-age population share, and labor force participation rate.

We can repeat the above simulation exercise by changing the starting year to 1980 (using the 1970-1980 data) or to 1990 (using the 1980-1990 data) instead of 1970 using the same calibration method. Comparison of the three sets of prediction results would be informative because Korean economy has evolved from a transition economy toward a steady-state economy. The calibrated values for the three sets of simulated prediction exercises, labeled as ”Pred ~~7~~ 0”, ”Pred ~~8~~ 0”, and ”Pred ~~9~~ 0”, respectively for the 1970, 1980, and 1990 simulation, by the above steady-state calibration method are summarized in Table 2. For the purpose of referencing with other countries, in Table 2, we also indicate the average purchasing-power-parity adjusted real GDP per capita level for each period when the parameter values of _γ_ 0, _SW,_ 0. and _SE,_ 0 are chosen.<sup>12</sup> For example, Korea’s average PPP-adjusted real income level was $1,466 in 1960s 12Note that this real income measure is obtained from the ”rgdpe” in PWT 9.0 divided by the WDI population 22
Figure 2.3 illustrates one such case. Using data for 27 sub-Saharan African countries, the graph purports to show how much of the improved growth performance of the late 1980s may be attributed to changes in macro policies, and is accompanied by the statement that 'the effect of changes in macroeconomic policies on growth is positive and statistically significant". Indeed, the regression coefficient is 1.91 with a t-statistic of 2.88.1" Yet this result depends crucially on one observation - that in the botom left, which is for Cameroon. Once this observation is excluded the line looks rather different - the sIope is now only 1.31, with a t-statistic of 2.24. If Rwanda is also dropped from the sample the slope coefficient falls to 1.14 and is no longer significantly different from zero.

**_fggwe 2.3_**

<!-- Start of picture text --> Macro policies and turnaround in GDP growth<br>Sensitivity to sample<br>Charge in GOP growth<br>10.0<br>. . .- .. . O . ................... . .... ........... .. . ................. ......... ..... _ .....<br>0.0~~~~~~~~<br>-10.0 .. *.....................~~ ~~~.....................<br>-15.0 -2.0 _ ____________________________ -1.0 _ 0.0 1.0 2.0 3.0<br>cnange ln macro policles<br>Full Restricted<br>sample sanple<br><!-- End of picture text --> One further example of the importance of visual data analysis is shown by Figure 2.4, drawn using data from Holmqvist (1992). The figure shows cross-country data for 38 developing countries - plotting educational expenditure per capita against aid receipts for educational purposes, also per capita.

The author is concerned to test if aid for education does indeed increase educational expenditure. Accordingly he regresses educational expenditure on aid for education, other aid and GNP (all expressed per capita), with the following result: 19
Table 6 The Gains From Cereals Tariff Liberalization

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|0.42|0.26|0.67|-0.25|0.53|0.30|0.23|
|Armenia|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Azerbaijan|0.38|0.13|0.46|-0.08|0.44|0.24|0.20|
|Bangladesh|0.03|0.03|0.08|-0.05|0.01|0.05|-0.04|
|Benin|0.30|0.20|0.58|-0.28|0.07|0.46|-0.39|
|Bhutan|1.83|0.73|2.39|-0.56|2.61|0.90|1.71|
|Bolivia|0.89|0.28|0.99|-0.11|1.19|0.53|0.66|
|Burkina Faso|0.63|0.15|0.79|-0.16|0.67|0.62|0.05|
|Burundi|-0.22|0.33|0.13|-0.35|-0.34|-0.02|-0.32|
|Cambodia|0.07|0.13|0.28|-0.21|0.15|0.07|0.08|
|Cameroon|0.96|0.45|1.36|-0.41|1.31|0.46|0.85|
|Central African Republic|-0.99|0.37|0.30|-1.28|-1.03|-0.72|-0.31|
|Comoros|-0.36|0.25|0.03|-0.39|-0.55|-0.17|-0.38|
|Cˆote d’Ivoire|0.54|0.18|0.72|-0.17|0.37|0.45|-0.08|
|Ecuador|1.07|0.64|1.34|-0.27|2.04|0.30|1.73|
|Egypt, Arab Rep.|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|
|Ethiopia|0.49|0.09|0.49|-0.01|0.59|0.44|0.15|
|Gambia, The|0.81|0.38|0.86|-0.05|0.41|0.94|-0.53|
|Georgia|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Ghana|-0.02|0.18|0.32|-0.34|-0.10|0.10|-0.20|
|Guatemala|0.75|0.26|0.85|-0.10|1.09|0.41|0.68|
|Guinea|0.54|0.21|1.07|-0.52|0.55|0.43|0.12|
|Guinea-Bissau|1.92|0.70|1.97|-0.05|2.31|1.00|1.31|
|Indonesia|-0.00|0.03|0.03|-0.03|-0.02|0.00|-0.03|
|Iraq|-0.03|0.08|0.16|-0.20|-0.17|0.03|-0.20|
|Jordan|0.00|0.00|0.00|-0.00|0.00|0.00|0.00|
|Kenya|0.26|0.59|2.23|-1.97|-0.28|0.55|-0.83|
|Kyrgyz Republic|0.08|0.04|0.11|-0.03|0.12|0.05|0.07|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the (country by country) elimination of import tariffs on cereals.

36
1999); increased foreign competition that reduces rents at the margin (Ades and Di Tella, 1999); and more international investors who are particularly sensitive to the costs imposed by corruption (Wei, 2000). However, the opposite case has also been proposed. Ades and Di Tella (1999) argue that the cost of corruption is higher in less open societies, implying that such societies devote more resources to combating corruption. Similarly, Tanzi (1998) reports that trade liberalization has created new opportunities for corruption and paying bribes gives greater advantages in obtaining privileged access to markets. Empirical evidence on the issue largely favors a negative relationship between corruption and trade openness (see for example, Krueger 1974, Ades and Di Tella 1999, Wei 2000, Gatti 2004, Gokcekus and Knorich 2006), although a few studies find a positive or no significant relationship between the two (see for example, Tanzi 1998, Torrez 2001, Majeed 2014). Thus, if the degree of trade openness also varies systematically with size of the country, the possibility of our main results suffering from the omitted variable bias problem cannot be ruled out. We guard against this possibility by controlling for trade openness defined as the ratio of merchandise exports plus imports to GDP (Merchandise Trade). The data source is WDI.

Our next set of controls includes two historical factors, ethnic fractionalization and the legal origin of the country. The existing literature suggests that countries with higher ethnic fractionalization might be more corrupt (Shleifer and Vishny 1993, Mauro 1995). One reason for this is lack of obedience with respect to the state as civil servants and politicians aim to exploit their positions to favor members of their own ethnic group. Another reason suggested in the literature is that divided societies tend to under-provide public goods, and this in turn creates opportunities for corruption rents (Pellegrini and Gerlagh 2008). There is a large literature that argues that the kind of legal codes that are in place in a country affect the quality of governance including corruption. Specifically, theories trace the effort of property owners to limit the 16
Policy Research Working Paper 6796

# **Abstract**

What kinds of changes in foreign competition lead domestic industries to seek import protection? To address this question this paper uses detailed monthly U.S. import data to investigate changes in import composition during a 24-month window immediately preceding the filing of a petition for protection. A decomposition methodology allows a comparison of imports from two groups of countries supplying the same product: those that are named in the petition and those that are not. The same decomposition can be applied to products quite similar to the imports in question, but not subject to a petition. The results suggest that industries typically seek protection when faced with a specific pattern of shocks. First, a persistent positive relative supply shock favors imports from named countries. Second, a negative demand shock hits imports from all sources just prior to domestic industries’ petition for protection. The relative supply shock is a broad one; it applies both to named commodities and to the comparison product group. The import demand shock, by contrast, is narrow, hitting only named products. The latter shock is also large: import growth over the two-year window is 15 percentage points lower in named products than in reference products, with most of this gap arising in the final two quarters before the petition. The negative import demand shock appears to be a key event in the run-up to the filing of a petition. It has been missed by previous studies using more aggregated data.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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 authors may be contacted at rhillberry@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 1: Just a surge from subject countries or from all sources?

||(1)|(2)<br>|(3)|
|---|---|---|---|
|VARIABLES|ln(_SMit_) =|ln(<sup>_SMit_</sup><br>_Mit_ <sup>)</sup><br>+|ln(_Mit_)|
|1 Qtr before fle|0.11***|0.19***|-0.08***|
||(0.030)|(0.023)|(0.022)|
|2 Qtrs before fle|0.16***|0.19***|-0.03|
||(0.029)|(0.023)|(0.021)|
|3 Qtrs before fle|0.14***|0.14***|-0.00|
||(0.029)|(0.023)|(0.021)|
|4 Qtrs before fle|0.13***|0.08***|0.05**|
||(0.028)|(0.022)|(0.020)|
|5 Qtrs before fle|0.11***|0.10***|0.01|
||(0.029)|(0.023)|(0.020)|
|6 Qtrs before fle|0.04|0.08***|-0.03|
||(0.029)|(0.023)|(0.021)|
|7 Qtrs before fle|-0.04|0.02|-0.06***|
||(0.030)|(0.024)|(0.021)|
|Constant|12.68***|-1.43***|14.11***|
||(0.033)|(0.026)|(0.024)|
|Observations|16596|16596|16596|
|R-squared|0.839|0.805|0.889|

Estimates include month, year, petition and HS10 fixed effects Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 increase in the level of imports from subject countries over the 24 month window preceding a filing.

While increased subject country imports might be suggestive of a damaging import surge, without a benchmark it is not obvious that this increase in imports is associated with an increase in import competition. To provide some context column (3) reports the average behavior of imports from all sources of the HS-10 products named in a petition. As is evident, there is no tendency for total imports to rise. Indeed, in the quarter before the filing there is a general tendency for imports to decline, falling 8 percent below the benchmark level, on average. Since total imports from all sources is a market level characteristic it provides insight into conditions for the market as a whole. One potential interpretation is that the products in which AD petitions are filed are likely to have experienced a negative demand shock which would account for the pronounced drop in spending on total imports of subject commodities immediately before a petition is filed.

The contrast in outcomes in columns (1) and (3) is very stark. Column (2) confirms that the underlying compositional change is very dramatic, with the subject countries’ imports growing over 14
Droughts and Deficits: The Global Impact of Droughts on Economic Growth Esha D. Zaveri<sup>1</sup> , Richard Damania<sup>2</sup> , Nathan Engle<sup>1</sup>

**Keywords:** rainfall, droughts, GDP growth, soil moisture, land use

**JEL Codes:** O13, O44, Q5, Q25, Q24

> 1 Water Global Practice, The World Bank, Washington, D.C., 2Office of the Chief Economist for Sustainable Development, The World Bank, Washington, D.C.
incomes of metropolitan areas command services of better quality and this, in turn, changes also the expectations of smaller urban centers, regarding the levels and types of services considered appropriate.

|Table|1.9:<br>GE|NERAL GO|VERNMEN<br>-In|TEXPENDIT<br>US Dolla|URES IN T<br>rs-|HE SOCIAL|SECTORS|al|||
|---|---|---|---|---|---|---|---|---|---|---|
||1975|<br>1976|<br>1977|<br>1978|<br>1979|<br>1980|<br>1981|<br>1982|<br>1983|1984|
|LAC SAMPLE b/|||||||||||
|Millions<br>Variat.Index 1975=100|41970<br>100|43177<br>103|43999<br>105|49765<br>119|52006<br> <br>124|54586<br> <br>130|62046<br> <br>148|54410<br> <br>130|<br>43188<br> <br>103|<br>40303<br> <br>96|
|Dollars Per capita|180|181|180|198|<br>202|205|228|<br>195|<br>151|<br>138|
|Variat.Index 1975=100|100|100|100|110|<br>112|<br>114|126|<br>108|<br>84|<br>77|
|MARKET ECONOMIES_cI_|||||||||||
|Millions<br>Variat.Index 1975=100|728097<br>100|787019<br>108|868617<br>119|1039564<br>143|1201856<br>165|1403882<br>193|1410800<br>194|1459511<br> <br>200|1496096<br>205|1188182<br>163|
|Dollars Per capita|1645|1672|1722|1904|1964|2006|1811|<br>1753|1731|1484|
|Variat.Index 175=100|100|102|105|116|119|122|110|<br>107|105|90|

al Social expenditures comprise education, health, social security and welfare, housing, recreation and other community services. Road construction and maintenance are not included.

bl Argentina, Colombia and Panama include central and subnational governments expenditures while only central government expenditures are reported for the other countries.

_cl_ Australia, Austria, Denmark, Iceland, Luxembourg and Switzerland include central and subnational governments expenditures, while only central government expenditures are reported for Canada, France, FR Germany. UK and USA.

Source: IMF, Government Finance Statistics Yearbook, various years.

1.10 Variety of Institutional Responses. The performance of certain tasks (e.g., garbage collection or urban transport) may be shared differently by the public and the private sector, the formal or the informal sector of urban economies. In different places, the citizens expect fulfillment of different needs by the central or the local governments or may rely to a varying extent on independently organized action and individual initiative.Z1 Most urban

- Z/ In Lima, about 60 percent of urban services (including trade) are managed by the informal sector (de Soto), while this percentage may be as low as 10 percent in Buenos Aires.