rafmacalaba/datause-encoder-data huggingface dataset:
# Table 14: Trade, Legal Origins <u>and Bureaucratic Quality, Rich Countries vs. Poor</u>

Dep. Var.: log of average trade flow

|||All|Rich|Partner|Poor|Partner|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||OLS|IV|OLS|IV|OLS|IV|
|Different Legal Origin|-0.1|-0.14|-0.28|-0.2|-0.12|-0.19|
||[2.19]|[2.89]|[5.27]|[3.44]|[2.43]|[3.43]|
|Bureaucratic Quality|0.38|0.47|0.32|0.12|0.38|0.52|
||[26.20]|[12.64]|[17.90]|[2.21]|[24.12]|[11.09]|
|log distance|-1.09|-1.07|-0.81|-0.85|-1.14|-1.14|
||[36.97]|[35.44]|[24.97]|[24.52]|[33.34]|[32.94]|
|log product of GDP|1.07|1.03|1.06|1.11|1.08|1.04|
||[95.80]|[63.25]|[79.65]|[62.34]|[89.92]|[56.13]|
|log product of land area|-0.2|-0.18|-0.18|-0.18|-0.2|-0.18|
||[21.75]|[18.07]|[16.62]|[16.49]|[20.43]|[15.69]|
|no. of landlocked|-0.33|-0.35|-0.28|-0.25|-0.3|-0.33|
||[7.39]|[7.82]|[5.99]|[5.44]|[6.40]|[6.93]|
|common border|-0.16|-0.21|-0.29|-0.16|-0.24|-0.31|
||[3.43]|[4.27]|[5.63]|[2.68]|[4.83]|[5.69]|
|common border|0.98|1.02|0.09|0.17|1.17|1.24|
||[6.77]|[6.93]|[0.59]|[1.00]|[7.44]|[7.74]|
|common language|0.51|0.49|0.38|0.44|0.52|0.48|
||[9.07]|[8.62]|[6.13]|[6.90]|[8.35]|[7.52]|
|currency union|1.9|1.86|1.11|0.98|1.81|1.74|
||[7.85]|[7.66]|[6.17]|[9.69]|[7.51]|[7.15]|
|Observations|77594|77594|39961|39961|69014|69014|
|R^2|0.67|0.67|0.78|0.77|0.64|0.64|
|First stage R^2||0.43||0.38||0.37|
|% increase in trade due to<br>1s.d. institution|88%|119%|52%|17%|84%|130%|

Notes: t-values in Brackets. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for Bureaucratic Quality. The Rich Partner subsample includes all country pairs where at least one is "rich". The Poor Partner subsample includes all country pairs where at least one is "poor". "Rich" countries are defined as those with PPP GDP per capita larger than 10,000 dollars in 2000 and the rest are "poor". The percent increase in trade due to 1s.d. institution is calculated as exp(coef*sd)-1 and takes into account different standard deviations in each subsample. Summary statistics for the institutional quality variable in each subsample are reported in Table 16.

49
**Table 7. Cyclicality: Percentage of Countries for which Inflow A is Negatively Correlated with GDP, 1980-2007, Hodrick-Prescott de-trending**

||Number of<br>countries|REM<br>|FDI|ODA<br>**A**|REM+FDI+ODA|
|---|---|---|---|---|---|
|ALL DEVELOPING COUNTRIES|116|35%|31%|43%|32%|
|East Asia and Pacific|15|20%|40%|53%|40%|
|Europe and Central Asia|20|15%|30%|35%|25%|
|Latin America and the Caribbean|28|50%|18%|50%|29%|
|Middle East and North Africa|10|40%|40%|40%|20%|
|South Asia|6|33%|17%|67%|50%|
|Sub-Saharan Africa|37|41%|38%|35%|35%|
|low income|36|44%|36%|33%|39%|
|lower middle income|45|27%|31%|58%|33%|
|upper middle income|35|37%|26%|34%|23%|

**Table 8. Stabilizing Impact: Percentage of Countries for which Capital Inflow A is Stabilizing, 1980-2007***

||Number of|||**A**|REM+FDI+|
|---|---|---|---|---|---|
||countries|REM|FDI|ODA|ODA|
|ALL DEVELOPING COUNTRIES|116|20%|11%|56%|30%|
|East Asia and Pacific|15|7%|13%|60%|27%|
|Europe and Central Asia|20|15%|10%|50%|10%|
|Latin America and the Caribbean|28|7%|4%|46%|11%|
|Middle East and North Africa|10|50%|0%|60%|50%|
|South Asia|6|17%|0%|83%|50%|
|Sub-Saharan Africa|37|30%|22%|59%|49%|
|low income|36|25%|17%|61%|44%|
|lower middle income|45|22%|7%|62%|31%|
|upper middle income|35|11%|11%|43%|14%|

* CV(A)<CV(GDP)

26
Exporter Dynamics, Firm Size and Growth, and Partial Year Effects<sup>_∗_</sup> Andrew B. Bernard<sup>_†_</sup> Jose-Daniel Reyes<sup>_§_</sup> Daria Taglioni<sup>_¶_</sup>

**Keywords:** firm size distribution, firm growth, variance of firm growth, export entry, export growth, heterogeneous firms **JEL codes:** F14, L11, C81, D22 **Sector Board:** Economic Policy (EPOL)

> _∗_ We thank Emily Blanchard, Teresa Fort, Ana Fernandes and seminar participants at the World Bank for helpful comments and discussions. Any opinions or conclusions expressed herein are those of the authors and do not necessarily reflect those of the IBRD/World Bank and its affiliated organizations, the Executive Directors of the World Bank or the governments they represent, the NBER or any other institution to which the authors are affiliated.

> _†Tuck School of Business at Dartmouth, CEPR & NBER_ , 100 Tuck Hall, Hanover, NH 03755, USA, _tel:_ +1 603 646 0302, _email:_ andrew.b.bernard@tuck.dartmouth.edu

> _‡World Bank_ , 1818 H Street NW, Washington, DC 20433, USA, _tel:_ +1 202 473 8519 , _email:_ rmassari@worldbank.org

> _§World Bank_ , 1818 H Street NW, Washington, DC 20433, USA _tel:_ +1 202 458 7052, _email:_ jreyes2@worldbank.org

> _¶World Bank_ , 1818 H Street NW, Washington, DC 20433, USA _tel:_ +1 202 458 5264, _email:_ dtaglioni@worldbank.org
distorted equilibrium’s average firm size coincides with the empirical one (i.e., until the dots in Figure 3 are all lined up in the 45-degree line).

Formally, given the estimate of _γi,_ we guess a value of _τi_<sup>_E_and solve for the</sup> distorted competitive equilibrium under distortion pair � _τi_<sup>_E, γi_</sup> �. We compute the equilibrium average size ratio with respect to the undistorted economy and compare it against the average size in the data relative to the US. Guided by the theoretical results, we update the guess of _τi_<sup>_E_</sup> upwards if the average size still falls below the data and update it downwards if it rises above. 30 The results are plotted in Figure 4.

Figure 4: Model-Based Entry Tax Rate versus GDP per Capita <!-- Start of picture text --> Kenya<br>India<br>Salvador<br>Ethiopia ColombiaChile<br>Ghana BulgariaPeruRomaniaHunPortugary gBelgiumal<br>FranceFinland<br>Latvia Italy<br>Spain<br>5 6 7 8 9 10 11<br>log GDP per capita<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>log(1+TauE)<br>0.0<br>−1.0<br><!-- End of picture text --> Note: The figure plots the model’s estimate for the entry tax _τ_<sup>_E_</sup> against the log of GDP per capita. Data for GDP per capita correspond to the year 2014 and are drawn from the Penn World Table version 9.0.

The figure shows that entry barriers are sizable in the least developed economies and decrease to roughly zero in the most advanced ones. The model backs out mild entry subsidies for Latvia, Spain, and Italy, which are the countries that lie above the 45-degree line in the left panel of Figure 3.

As a first attempt to give meaning to the magnitudes, Figure 5 plots the

> 30Notice from Figure 3 that countries for which the average size under idiosyncratic distortions only lie above the 45-degree line (Latvia, Spain, Italy), the model will back out a negative value of the entry barrier (i.e., an entry subsidy).

36
38 speculates that possible reasons for the strong<sup>impact of driver training include (i) a closer trminer-trainee</sup> relationship In driving courses, (ii) the fact that driving<sup>skills can be learned Independently of other factors</sup> such as English proficiency and basic academic skills,<sup>and (iii) the greater opportunity offered those wth</sup> driving qualifications for starting their own contracting businesses.

# Sweden

The opportunity to evaluate labor market programs<sup>like Sweden's national training system</sup> is severely constrained by the difficulty of obtaining<sup>a reasonable control or comparison group. Random</sup> assignment of eligible workers to treatment and<sup>control groups runs into the ethical issue of denying</sup> potentially beneficial services to individuals selected<sup>to be members of the control group. Similarly, a</sup> matched compadson group of individuals who did<sup>not have access to be program is not easily visualized</sup> for a program that is both nationwide and permanent.

These factors taken in conjunction with the intemal<sup>feedback generated through AMU</sup> boards probably explain the small number of<sup>Swedish evaluation studies. BiJorklund (1990, 1991)</sup> fumishes an overview of seven available studies<sup>examining the labor market effects of mobility grants,</sup> JSA, temporary employment, and job skills training.<sup>The two studies evaluating the effect of skill training</sup> yield mixed results. Although the estimated training<sup>effects are generally positive, they are frequently not</sup> statistically different from zero and their size appears<sup>to be quke sensitve to model specification. Of the</sup> remaining five studies, only the two evaluating JSA<sup>programs Indicate evidence of a positive effect on</sup> labor market opportunities. In the two communities<sup>studied, the assignment of addtional employment</sup> exchange personnel to assist the unemployed<sup>isfound not only to speed up reemployment but also to</sup> improve the quality of job placements as measured<sup>by monthly eamings, percentage of workers with</sup> permanent jobs, and scores on a test of job satisfaction.<sup>There is also evidence that JSA is cost effective.</sup>
24- Hussein Abdel-Aziz Sayed, Magued I. Osman, Fatma El-Zanaty and Ann A. Way (1989), Egypt Demographic and Health Survey 1988, Egypt National Population Council and Demographic and Health Survey Institute for Resource Development/Macro systems, Inc.

_25- Menofla governorate has previously been chosen forseveral health and integrated projects._ 26- SPACC (1991), The Care for Diarrheal Diseases in Children Under 3: An Ethnographic Study in 6 Communities of Egypt, Unpublished report submitted to NCDDP.

27- Veney, J.E. and A.D. Kaluzny (1984), Evaluation and Decision Making for Health Services Programs, Chapter 10, Prentice Hall.

28- Rashad, H. and Mohamed, A. (1989), 'Analysis of 1986 census data related to infant mortality' in Infant & Child Mortality Rates in Egypt 1980-87 published by the Population Studies and Research Center, CAPMAS, Cairo.

29- Ministry of Health and United Nations Children's Fund (UNICEF/Egypt), Executive Summa: National Cluster Survey for Vaccination Coverage Egypt - 1990.

26
Botswana (75 percent) and South Africa (90 percent) gross enrollment in secondary schools is higher. It is likely that a representative sample of children (enrolled and unenrolled) would place India below additional countries.

That the average child is performing poorly masks the considerable variation in the distribution. At the bottom, children score extremely poorly. There is no evidence that the distribution is more compressed at the bottom than for other low-performing countries. In fact, only three countries—Saudi Arabia, Ghana, and South Africa—score worse than Rajasthan or Orissa if ranked by the 5th percentile cutoff score (Figure 6). When the education system fails, it fails completely.

# **4.2 Inequality in the Learning Distribution**

Following Micklewright & Schnepf (2006), we report a simple statistic measuring test-score dispersion—the difference between 5th and 95th percentiles of the test score distribution. Figure 7 shows the significant educational inequality in the Indian learning distribution. In both the Indian states, the 5-95 percentile spread is greater than 300, and just below the most unequal country in the TIMSS sample—South Africa.

TIMSS 2003 also presents achievement benchmarks based on an intensive effort to anchor performance to objective criteria. Table 2, drawn from TIMSS 2003 (Exhibit 2.1), describes the low (400), intermediate (475), high (550), and advanced (625) international benchmarks; Table 3 shows the results. In Rajasthan and Orissa, 1 percent of children pass the advanced benchmark. This actually is above many other poor performing countries. At the same time, only 42 percent in Rajasthan and 50 percent in Orissa pass the lowest benchmark. Put another way, only 40 to 50 percent of Rajasthan and Orissa’s enrolled ninth graders have “some basic mathematical knowledge”—the description of the low international benchmark.

A second useful exercise that demonstrates the vast differences between tested children is to rank Table 3 by those who reach each of the different international benchmarks. Ranked by the low international benchmark, Rajasthan is 8th from the bottom and Orissa 9th; ranked by the intermediate benchmark, they are now 9th and 14th from the bottom respectively; ranked by the high international benchmark they are now 11th and 16th from the bottom. The advanced international benchmarks put both states at the respectable positions of 12th and 18th, although 15
private travel demand to the cost of driving. This has potentially important implications for the appropriate balance between road and mass transit infrastructure investment.

While the choice among alternative mass transit modes depends on specific circumstances, a robust conclusion is that in the absence of road travel pricing that adjusts for externalities, there will be the potential for too much investment in roads, too little investment in mass transit. While subsidies to mass transit users would help stimulate greater use of existing capacity, much more important, given the growing demand for travel generally, will be careful efforts to include the full social costs of travel in evaluating alternative infrastructure investments. A transit project that is “too costly” when considering direct costs could become more attractive when avoided pollution and congestion costs are taken into account. By similar reasoning, it will be very useful in future urban land use planning to leave corridors available for potential future expansion of mass transit, thereby increasing the economic returns by limiting damage to existing structures and public services.

# **References**

American Bus Association (ABA), 2007. _Comparison of Energy Use and CO2 Emissions from Different Transportation Modes_ . American Bus Association, Washington, DC.

Agostini, C. A. and G. A. Palmucci, 2008, “The Anticipated Capitalization Effect of a New Metro Line on Housing Prices”, Fiscal Policy 29 (2): 233-256.

Bell, Michelle L., Devra. L. Davis, Nelson Gouveia, Vı ́ctor H. Borja-Aburto, and Luis A. Cifuentes, 2006. “The avoidable health effects of air pollution in three Latin American cities: Santiago, Sao Paulo, and Mexico City.” _Environmental Research_ 100: 431-440.

Borja-Aburto, V.H., D. Loomis, S.I. Bangdiwala, C.M. Shy, and R.A. Rascon-Pacheco, 1997. “Ozone, suspended particulates, and daily mortality in Mexico City.” _American Journal of Epidemiology_ 145: 258–268.

Bovenberg, A. Lans and Lawrence H. Goulder, 2002. ”Environmental Taxation and Regulation.” In _Handbook of Public Economics_ . A. Auerbach and M. Feldstein eds. New York: North Holland.

> BTS, 2004. _National Transportation Statistics 2004_ . Bureau of Transportation Statistics, US Department of Transportation, Washington, DC.

40
3

# **Table 1: GDP, Consumption and Public Spending: Average Growth Rates and Volatility, 1961-2005**

||G|DP|Consu|mption|Public S|pending|
|---|---|---|---|---|---|---|
||μ<sup>a</sup>|σ<sup>b</sup>|μ<sup>a</sup>|σ<sup>b</sup>|μ<sup>a</sup>|σ<sup>b</sup>|
|Lowincome|1.9|2.0|1.1|2.0|5.3|4.5|
|Middleincome|2.7|1.7|2.7|1.7|4.6|2.9|
|High income|2.6|1.6|2.5|1.4|2.8|1.3|

Notes: a. Average. b. Standard deviation. Calculations are based on WDI data. All variables are growth rates. GDP and Consumption are in per capita terms. Government spending is the general government consumption spending .

**Table 2: Relative Volatilities of Consumption, Output and Public Spending**

||Con/GDP|PubSp/GDP|PubSp/Cons|
|---|---|---|---|
|**Low Income**|1.0|2.3|2.3|
|HIPC|1.2|2.8|2.6|
|LDC|1.4|2.8|2.0|
|**Middle Income**|1.0|1.7|1.6|
|**High Income**|0.9|0.8|0.9|
|EMU|0.9|0.7|0.7|

The fact that public spending volatility is higher than that of output and consumption in low and middle-income countries is not necessarily problematic. In fact, it could be interpreted as a successful use of a policy instrument to stabilize the target variable (output or consumption). However, the pro-cyclical nature of fiscal policy in most developing countries, explored elsewhere (Alesina and Tabellini, 2005; Talvi and Vegh, 2005), suggests that fiscal policy may be destabilizing. This is confirmed by the positive association between fiscal policy volatility and output and consumption volatility, as shown in Figures 1 and 2.<sup>2</sup>

> 2 Volatility is defined as the standard deviation of the per-capita GDP and consumption growth rates during 1961-2005.
and 50% in Shanghai). This is particularly true in high-tech manufacturing and creative content sector. Quite a few firms in the KIBS sector in Beijing called for improving the business ethics, for it is particularly difficult to protect innovation in this sector through IP law.

# **Cities in Knowledge Economy**

Generally, Shanghai is more efficient than Beijing from a business point of view according to the responses of the sample firms. As Table 34 shows, 78% of firms in Shanghai ranked the city efficient or very efficient, while only 40% in Beijing ranked the city efficient. No firms in Shanghai considered the city very inefficient, but 2% firms, all of which are high-tech manufacturers, ranked Beijing very inefficient.

**Table 34: Rank of business efficiency (Percentage of respondents)**

|How to rank the efficiency of the city from a<br>businesspoint of view?|High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Very efficient|7|5|-|4|
|Efficient|20|40|47|36|
|No opinion|66|45|53|54|
|Inefficient|-|10|-|4|
|Very inefficient|7|-|-|2|
|Total|100|100|100|100|
|_Shanghai_|||||
|Very efficient|28|-|15|18|
|Efficient|38|89|70|60|
|No opinion|28|11|15|20|
|Inefficient|6|-|-|2|
|Very inefficient|-|-|-|-|
|Total|100|100|100|100|

Generally speaking, firms in both cities rank the property of the city they are living in to be innovative and attractive to knowledge-based firms and industries, and no firms ranked cities “very low” in this question (see Table 35). Particularly, all sample firms of creative content sector in Beijing consider this city will be the most attractive to creative industries in China, while firms in high-tech manufacturing and in KIBS ranked Shanghai higher than Beijing.

44
using official CPI deflators to obtain a comparable poverty line in 2008 and its associated poverty rate of 19.5 percent.

Macroeconomic trends shown in Figure 1 appear to corroborate the poverty decline as shown by the household consumption data, since the downward sloping poverty trend is consistent with that of growth in real GDP per capita. The period between 2002 and 2007 sees rapid growth, which, however, slows down in the subsequent period between 2008 and 2010. Real GDP per capita grew by 3 percent and poverty was estimated to fall by about 5 percentage points in this latter period.

While poverty could be tracked between 2008 and 2010 with the consumption data from the HEIS, no consumption data exists after 2010 that can be used to monitor poverty trends. Projections show per capita GDP growth to be weak, but this alone does not say much about poverty trends. The recent subsidy reforms and the associated cash transfer could well impact poverty, as could the various economic stresses including a continued weak labor market, increased energy prices, and a large influx of war refugees from Syria.<sup>18</sup> Against the background of infrequent collection of consumption data, the country’s economically uncertain atmosphere provides an even stronger impetus for policy makers to track poverty with alternative methods like imputation-based estimates.

# **III.2.2. Data Description for the HEIS**

We use the most recent two rounds of Jordan’s Household Expenditure and Income Survey (HEIS) in 2008 and 2010, which are the nationally representative surveys used to produce official poverty statistics. The HEIS has been implemented nine times since 1966, and every other year between 2006 and 2010. In addition to household expenditures, it collects data on

> 18 According to the UNHCR (http://data.unhcr.org/syrianrefugees/country.php?id=107), in July 2012 there were about 29,000 registered Syrian refugees in Jordan; a year later the number of refugees rose to about 115,000, and by August 2014 the number further increased to slightly more than 600,000, which is roughly a tenth of Jordan’s population.

20
sold improves the probability of selling at the market by the farmers. This is expected when transportation cost declines with an increase in quantity sold. The e ff ect of wealth is negative and statistically signi fi cant at 1 percent level. The result with respect to wealth is thus consistent with the expectation that wealthy farmers face higher opportunity cost of labor, and are less likely to sell at the market. The results with respect to quantity sold and wealth level are consistent also with the fi ndings of Fafchamps and Hill (2005). The endogeneity of the quantity sold does not pose a serious problem in this regression. The null hypothesis of exogeneity of quantity sold can not be rejected as the fi rst stage residual has a t-value of 0.32. This result is not surprising given the fact that more than two-thirds of the farmers make only one sale during the year.

The regression in column 3 still ignored a number of explanatory variables that may be relevant for determining both opportunity cost of labor and transaction cost. In the next regression reported in column 4, we included a number of household speci fi c variables (household size, and composition). Since availability of family labor reduces the need for hiring outside labor for the transportation, handling and supervision of sales, the propensity to sell at the market should increase with an increase in the number of adult family members. To control for the opportunity cost of labor, we included the number of adult males as an indicator for family labor available for taking the product to market.<sup>12</sup> In addition, we included a dummy to indicate if household head belongs to the Scheduled Caste/Scheduled Tribe (SCST).<sup>13</sup> The log of age of the household head, and his/her education level are also

> 12 Women seldom take produce to wholesale market in India. Note also that district level dummies capture, in addition to unobserved heterogeneity, the e ff ect of cost of hiring labor as labor market often clears at local level.

> 13 Scheduled Castes and Scheduled Tribes are communities that are accorded special status by the Constitution of India. These communities were excluded from the caste system that was the social superstructure of Hindu society in the Indian subcontinent (Wikipedia 2006, http://en.wikipedia.org/wiki/Scheduled_Castes_and_Tribes)

18