rafmacalaba/datause-encoder-data huggingface dataset:
CEFTA countries (except Kosovo) and all EU members as well, and (ii) that it includes consistently constructed domestic trade flows, which are crucial for the implementation of our methods. We offer further details on the dataset in Section 3.2.

Several main findings stand out from our partial equilibrium estimates. Without going into details, we note that we obtain estimates of the effects of all standard gravity variables that are readily comparable to corresponding estimates from the existing literature. This establishes the representativeness of our sample. In addition to the standard gravity covariates, we use border variables to allow for differential impact of borders on trade within EU vs. trade between EU and CEFTA. We find that, without any exception, the additional barriers to trade between CEFTA and EU are larger as compared to the barriers to trade within the EU in each of the four main sectors in our sample, including Agriculture, Mining, Manufacturing and Services. We also document significant variation in the border differential across sectors. The differences between the CEFTAEU border and the EU-EU border are the largest in Agriculture, followed by Services, Mining, and Manufacturing.<sup>4</sup> Capitalizing on the structural properties of the gravity model, we transform our border estimates into tariff-equivalent effects to find that the border tariff-equivalents that we obtain are significantly larger as compared to the existing differences in applied tariffs for each of the three goods sectors in our sample. Specifically we obtain a border tariff equivalent of 19.65% vs. 11.58% actual weighted average tariff difference in Agriculture, 10.62% border tariff equivalent vs. 1.68 actual weighted average tariff difference in Mining, and 7.92% border tariff equivalent vs. 5.22 actual weighted average tariff difference in Manufacturing. This result supports our assumption that, in addition to tariffs, there are other barriers to trade between CEFTA and EU. Comparison between the tariff data and our border differential estimates suggests that Mining and Agriculture are subject to more significant NTMs as compared to Manufacturing, where the difference between the tariffs and the

> 4We find this heterogeneity intuitive. The border between CEFTA and EU is likely to impose the most trade costs on agricultural products for two reasons: (1) most tariffs between EU and CEFTA countries have gone to zero for all industrial products, but for some agricultural products there are still import tariffs for CEFTA products into the EU and vice versa, and (2) even with low tariff barriers, the EU imposes high SPS conditions which results in traders facing long delays at the borders crossing into EU (for e.g. from Serbia into Croatia). The large border differences in services trade can be due to various unified standards within the EU, which impose barriers to services trade with outsiders. Another possible explanation is that trade in services is highly localized consumption. Finally, a possible explanation for the relatively small borders in Mining and Manufacturing is that the barriers to trade in those sectors have been decreased.

4
**Table 1: World Foodgrain Demand-Supply Situation, 1999-2009**

**<u>(millions of metric tons)</u>**

|Year|**W**|**heat**|**Rice,**|**milled**|**Food**|**Grain**|
|---|---|---|---|---|---|---|
||Production|Consumption|Production|Consumption|Production|Consumption|
|1999/00|586.7|585.0|408.9|399.8|1,873.60|1,867.60|
|2000/01|582.9|585.0|398.9|395.3|1,844.80|1,864.80|
|2001/02|583.1|587.1|399.7|413.4|1,877.90|1,908.00|
|2002/03|568.7|605.3|378.3|408.0|1,822.80|1,917.20|
|2003/04|553.8|588.6|391.9|414.0|1,862.40|1,948.10|
|2004/05|625.7|606.9|401.3|409.3|2,043.60|1,995.60|
|2005/06|620.9|624.4|418.3|415.8|2,018.90|2,033.60|
|2006/07|596.3|616.9|420.2|420.6|2,005.30|2,053.50|
|2007/08|610.5|622.1|429|426.6|2,115.50|2,114.90|
|2008/09|670.8|649.8|430.8|427.7|2,190.60|2,170.60|
|_Source:_US|DA||||||

**Figure 2: Global Wheat and Rice Stocks**

<!-- Start of picture text --> 400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09<br>Wheat Rice Total<br>Mllion MT<br><!-- End of picture text --> _Source: USDA database_ To what extent do these trends explain the rising prices? The drawdown of reserves during1999-2008 is a clear indication of a global tightening of food markets with obvious implications for rising prices. The acceleration of wheat price increases during 2006-2008 is partly explained by the large imbalance between supply and demand in this period, accentuated of course by adverse market expectations and the generally low short-term supply-demand elasticities. But the behavior of rice prices and its acceleration in 20072008 is not explained by demand-supply imbalances. As noted earlier, unlike wheat overall rice production exceeded consumption during 2006-2008 and yet prices hit the roof. One needs to look at other factors. Two critical factors are trade policies and cost of production.

4
# DC: The World Bank.

- Rodriguez, F., & Rodrik, D. (2001). Trade Policy and Economic Growth: A Skeptic’s Guide to the Cross-National Evidence, _NBER Macroeconomics Annual_ , _15_ , pp.261-325.

- Sawada, Y., Kohama, H., & Kono, H. (2003, October). _Aid, Policies, and Growth: A Further Comment._ Paper presented at the experts research workshop on “Quantifying the Impact of Rich Countries’ Policies on Poor Countries” at the Center for Global Development (CGD), Washington DC.

- UN. (2013). _The Millennium Development Goals Report 2013_ . New York: United Nations.

- Williamson, O. E. (2000). The New Institutional Economics: Taking Stock, Looking Ahead. _Journal of Economic Literature, 38_ (3), 595-613.

World Bank. (1998) _. Assessing Aid: What Works, What Doesn’t and Why._ Washington DC: The World Bank.

- ______. (2012). _World Development Indicators (WDI): 1990-2010_ [Data file] _._ Washington DC: The World Bank. Retrieved August 7, 2012, from http://data.worldbank.org/

- ______. (2013). The World Bank Group Goals: End Extreme Poverty and Promote Shared Prosperity. Retrieved August 12, 2013, from The World Bank Web site:

http://www.worldbank.org/content/dam/Worldbank/document/WB-goals2013.pdf 26
**Table A.13: Cost in the public sector**

|**Panel A: Staffper facility**|**(1)**<br>**N**|**(2)**<br>**Average monthly wage (Rs.)**|
|---|---|---|
|Medical Officer in Charge/Medical Officer|1.92|Rs.32,245|
|GNM/ANM/VHN/LHV|3.24|Rs.16,305|
|MPW/MNA/Assistant/Compounder|1.43|Rs.16,657|
|Pharmacist/Chemist/Lab Assistant/Technician|0.8|Rs.16,571|
|Paramedic/other|6.08|Rs.13,387|
|All|13.47|Rs.17,315|
|Number of facilities|115||
|**Panel B: Visits to thepublic facilitiesper month**|||
|Year 2008|111,039||
|Year 2009|113,230||
|Year 2010|111,473||
|**Panel C: Average perpatient cost**|||
|Year 2008|Rs.241.87||
|Year 2009|Rs.237.66||
|Year 2010|Rs.241.61||

Notes: We use an extremely conservative measure of per patient cost in the public sector facility. We assume that salary cost are the only cost in running a public health facility. Furthermore, we assume that every patient that visits the public health facility visits for a primary care visit, while people also visit public health facilities for preventative services such as vaccination. Wages data were collected in the year 2010, note that we use 2010 wage data to compute cost per patient in 2008 and 2009. Wages in 2008 and 2009 could have been lower.

73
# **1. Introduction**

The re-allocation of productive assets to more efficient uses is an important means of promoting higher aggregate output in any economy. The most important (non-labor) asset in most poor countries is land. The economic, social and political means by which a country allocates its land can thus have bearing on aggregate economic efficiency.

Much of the literature has argued that well-defined individual land rights are crucial for efficiency, though there have been dissenting views.<sup>2</sup> The main means of changing land ownership rights studied in the literature are market purchases and administrative land reallocations, both of which have been studied extensively.<sup>3</sup> Yet, in many developing-country settings it may well be the case that land inheritance is a more common means of re-assigning land-use rights, although this has received very little attention. With imperfect credit markets, inheritance may have bearing on the efficiency of farming and also promote new non-farm economic activity by providing the kind of lumpy boost to individual wealth that can allow new investment opportunities to be pursued—opportunities that would otherwise be impossible to take up given borrowing constraints. Thus there is interest in better understanding inheritance.

Equity is another reason we want to know more about inheritance. It is now understood that many poor developing countries are characterized by relatively high inequality (World Bank, 2005; Ferreira and Ravallion, 2009). However, we know far less about how much of that inequality is transmitted across generations. This can create inequalities of opportunity that come with costs to efficiency and growth, as argued in World Bank (2005). Are these economies characterized by a high degree of inter-generational persistence of poverty and affluence, or is there churning associated with successes and failures for adults taking up new economic opportunities? How much do family circumstances matter to adult outcomes? While there has been much debate about these issues in developed countries, they have received relatively little attention in poor, primarily agrarian, economies. Inheritance of agricultural land—the main nonlabor factor of production—is the first mechanism one thinks of for the inter-generational transmission of inequality in such settings.

> 2 For a dissenting view on the importance of individual land rights _per se_ see Platteau (1996). More supportive views can be found in, for example, Feder and Noronha (1987) and Deininger (2003). Also see the discussions in Barrows, and Roth (1990), Besley (1995) and Jacoby and Minten (2007).

> 3 See, for example, Deininger (2003) and Ravallion and van de Walle (2008).

2
**Table 1: Greenhouse gas emission by region, 1990 - 2005**

|||Emissions|(GtC02e)||P|er Capita Em|issions (tCO2|e)|
|---|---|---|---|---|---|---|---|---|
||1990|1995|2000|2005|1990|1995|2000|2005|
|Australia & New Zealand|0.5|0.5|0.6|0.6|22.5|22.6|25.0|25.9|
|E. Europe & Central Asia|6.5|4.7|4.3|4.5|14.0|10.0|9.1|9.6|
|Latin America & Caribbean|2.1|2.3|2.8|3.0|4.8|4.9|5.3|5.4|
|Middle East & North Africa|1.2|1.5|1.7|2.2|4.4|4.9|5.2|5.9|
|South & SE Asia Pacific|6.6|8.3|9.1|12.3|2.4|2.8|2.8|3.6|
|Sub-Saharan Africa|1.4|1.5|1.6|1.8|2.8|2.5|2.4|2.4|
|US and Canada|6.6|6.9|7.5|7.7|23.6|23.4|24.0|23.3|
|Western Europe|4.2|4.1|4.1|4.2|11.1|10.8|10.6|10.6|
|World|30.0|31.1|33.2|37.8|5.7|5.5|5.5|5.8|

Source: WRI (2011) and authors‘ calculations.

**Table 2: Greenhouse gas emissions in ECA by gas and sector over time, 1990-2005**

|||Type of g<br>|as<br>|||Secto<br>|r<br>||
|---|---|---|---|---|---|---|---|---|
||CO2|CH4|N2O|Other|Energy|Agriculture|Industry|Other|
|1990|4,890.0|1,212.2|384.3|37.2|5,530.7|623.7|173.6|195.7|
|1995|3,466.8|959.8|254.6|23.4|3,985.5|439.8|105.0|174.3|
|2000|3,230.8|824.4|230.8|25.0|3,673.5|364.6|105.7|167.2|
|2005|3,372.2|885.1|249.3|31.5|3,843.8|394.1|132.4|167.8|
|**ECA’s shar**|**e of global emissi**|**ons**|||||||
|1990|0.23|0.21|0.13|0.16|0.25|0.12|0.17|0.15|
|1995|0.16|0.16|0.09|0.09|0.17|0.08|0.09|0.13|
|2000|0.14|0.14|0.07|0.07|0.15|0.06|0.08|0.12|
|2005|0.12|0.14|0.08|0.06|0.14|0.06|0.07|0.12|

Note: all gases are expressed in MtCO2e.

40
17 The growth of Indonesia's garment industry was fuelled by very rapid entry of new firms, some fairly large but most small and medium in size. As Table 4 shows, between 1981 and 1988 the number of establishments registered with the Bureau of Statistics rose from 157 to 798, with the size composition changing somewhat in favour of the largest categories. In this relatively labour intensive industry, however, the output share of firms of 100-999 workers was still 65.4% in 1988, down from 73.5% in 1981.

Field interviews were conducted in 1992 with 35 garment exporters, 27 in the Jakarta area and 8 in Bandung. Of the 33 firms reporting year of founding, S date from 1968-77, 10 from the short interval 1979-82, 3 from 1983-86 and the other 14 from 1987-91. Nearly half of all the firms began with So workers or less; at the other extreme three began with more than 500, one of them back in 1971, the other two in 1985 and 1987; median size at start-up was 112 workers and the mean was 218 (Table 5). Median current size is 475 workers and the mean is 1109 so the typical firm has grown 4-5 fold over a period of about 9 years. Average annual growth has been fastest for the set of firms created since the mid-1980s (about 251 per year). The reported level of sales'ranges from under $100,000 to $57 million, with many firms falling in the $1-5 million range.

Over three quarters of the surveyed firms export all of their output directly or indirectly (26 of 33), with only 1 below 70%. A little under half of the sampled firms began exporting in their first year of existence; this share was much lower among the producers who had their start before the recent export boom. Of the 8 firms founded in the 1970s, two began to export immediately; for the others the minimum wait was 4 years and the median about 8B
# 9 References

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32
_A k_ (4) ln _TRADEij_ , _t_ = _K_ +α ln _GDPi_ + βln _GDPj_ − δ _t_ ln _DISTANCEij_ + ln(∑ _DISTANCEt_ γ _k_ ) . _ij_ But, the standard gravity equation, which is estimated on aggregate trade data is (5) ln _tradeij_ = _K_ +α ln _GDPi_ + βln _GDPj_ +γ ln _DISTANCEij_ + ε _ij_ .

This implies that the estimated coefficient on distance is biased, and that it makes little sense to compare the coefficients on distance in gravity equations over different periods. The problem is twofold. First, the equation is misspecified, distance does not enter log linearly in a bilateral trade equation. Second, if the importance of some industries is changing over time—the _A_<sup>k</sup> s are changing—the coefficient on distance will change, even if the elasticity of trade for each industry remains constant.

The increase in the importance of distance suggested by gravity estimates could therefore be due to an increase in the share of trade accounted for by distance-sensitive products, by an increase in the elasticity of trade to distance for a relatively large number of industries, or simply to misspecification. We refer to the trade-share movements as the _compositional_ effect, and the change in distance elasticities among industries as the _distance-sensitivity_ effect. Our goal is to estimate the contribution of these two effects to the change in the aggregate distance coefficient.

To decompose the change in the elasticity of trade to distance into these two components, we start by defining<sup>γ</sup> _tk_<sup>as the distance elasticity of trade of industry</sup><sup>_k_at</sup> period _t_ . The average-distance elasticity for all industries, γ _t_ , is thus _k k_ (6) γ _t_ = ∑ _sharet_ γ _t_ . _k_ Where _sharetk_ is industry _k_ ’s share in total trade at time _t_ .

12
|_Industrial Countries_|||||||
|---|---|---|---|---|---|---|
|Canada|0.09|5|0.18|6|0|1|
|France|0.66|73|0.44|56|0.88|76|
|Germany|0.56|61|0.63|79|0.5|40|
|Italy|0.57|63|0.38|39|0.75|63|
|Portugal|0.60|70|0.70|81|0.5|40|
|Spain|0.62|71|0.32|27|0.92|78|
|Sweden|0.36|38|0.48|63|0.25|32|
|United Kingdom|0.09|5|0.19|8|0|1|
|United States|0.07|2|0.13|2|0|1|
|_Latin America and the Caribbean_<br>_(LAC)_|||||||
|Argentina|0.51|53|0.43|53|0.58|50|
|Bolivia|0.56|61|0.37|38|0.75|63|
|Brazil|0.68|79|0.52|70|0.83|70|
|Chile|0.28|30|0.31|26|0.25|32|
|Colombia|0.48|48|0.21|11|0.75|63|
|Costa Rica|0.35|36|0.19|8|0.5|40|
|Dominican Republic|0.22|28|0.44|56|0|1|
|Ecuador|0.37|39|0.41|47|0.33|38|
|El Salvador|0.67|75|0.43|53|0.92|78|
|Guatemala|0.49|50|0.24|16|0.75|63|
|Haiti|0.14|9|0.29|21|0|1|
|Honduras|0.07|2|0.13|2|0|1|
|Mexico|0.74|81|0.57|72|0.92|78|
|Nicaragua|0.16|13|0.32|27|0|1|
|Panama|0.73|80|0.53|71|0.92|78|
|Peru|0.58|67|0.57|72|0.58|50|
|Uruguay|0.35|36|0.20|10|0.5|40|
|Venezuela, RB|0.54|57|0.25|18|0.83|70|
|_Middle East and North Africa_<br>_(MENA)_|||||||
|Algeria|0.40|42|0.22|12|0.58|50|
|Egypt, Arab Rep.|0.31|33|0.62|78|0|1|
|<br>Tunisia|0.57|63|0.47|61|0.67|60|
|_South Asia_|||||||
|Bangladesh|0.19|19|0.38|39|0|1|
|Bhutan|0.63|72|0.35|31|0.92|78|
|India|0.51|53|0.51|69|0.5|40|
|Pakistan|0.75|82|0.57|72|0.94|83|

See Annex 1 for an explanation of the construction of these indexes.

_Employers’ perception of the stringency of employment protection legislation_ Micro level data on firm characteristics, employers’ perception about labor regulations and their behavior are drawn from two separate business establishment surveys. The World Business Environment Survey (WBES, World Bank 2000) contains a large 12
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21