rafmacalaba/datause-encoder-data huggingface dataset:
plentiful in the agricultural tariff schedules of many high-income, high-protection countries), and the effects of agricultural tariff rate quotas. In addition, key trade policy changes to the start of 2005 have been added, namely, the commitments associated with accession to WTO by such economies as China and Taiwan (China), the implementation of the last of the Uruguay Round commitments (most notably the abolition of quotas on trade in textiles and clothing at the end of 2004), and the eastward enlargement of the European Union from 15 to 25 members in April 2004.

Hence what distinguishes the present study from the above 2004 studies and other books with similar titles is that (a) its _ex ante_ analysis focuses on the core aspects of the July Framework Agreement from the viewpoint of agriculture and developing countries, taking account also of what might happen to non-agricultural market access and the other negotiating areas, and (b) it does so in an integrated way by using the new GTAP Version 6 database (amended to account for key protection changes to early 2005) and the latest version of the World Bank’s global, economy-wide Linkage model, details of which are documented in van der Mensbrugghe (2004).<sup>4</sup>

# **What questions are addressed in this study?**

Among the core questions addressed in this study, following an intense program of integrated research during the latter half of 2004 by a complementary set of wellinformed scholars from four continents, are the following:

- What is at stake in this Doha round, in terms of efficiency gains foregone by the various regions of the world because of current tariffs and agricultural subsidies?

4 This analysis is vastly more sophisticated than the _ex ante_ analyses undertaken for the Uruguay Round. At that time there were very few economy -wide global models, so primary reliance was on partial equilibrium models of world food markets (see, e.g., World Bank 1986, Goldin and Knudsen 1990, Tyers and Anderson 1992); estimates of protection rates were somewhat cruder and less complete; and analysts grossly overestimated the gains because they did not anticipate that tariffication would be so “dirty” in the sense of creating large wedges between bound and MFN applied tariff rates, nor did they have reliable estimates of the tariff preferences enjoyed by developing countries or the ad valorem equivalent of specific tariffs. Some of these limitations also applied to _ex post_ analyses of the Uruguay Round (see, e.g., Martin and Winters 1996).

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broken down into transitory and permanent components. Thus, the specification allows for a rich correlation structure for these factors, both over time as well as with firm characteristics. Further, it is also possible to provide bounds for the role of two specific factors, namely, firmlevel heterogeneity in markups and production technologies.<sup>3</sup> In the body of the paper, we describe these steps in more detail and report key data moments as well as detailed parameter estimates on a country-by-country basis, along with the contributions of each of these forces to observed _arpk_ dispersion.

While the estimates vary by country, there are a number of broad patterns that hold across all the countries in our sample: (i) adjustment costs and informational frictions – although important drivers of investment dynamics – generate only modest _arpk_ dispersion; (ii) correlated, or size-dependent, factors – which implicitly ‘tax’ more productive firms, play a more significant role, especially in developing countries;<sup>4</sup> (iii) a significant portion of the observed _arpk_ dispersion can be attributed to permanent factors, i.e., firm-level fixed effects; (iv) heterogeneity in production technologies (i.e., firm-level differences in output elasticities of capital and other inputs in the production function) can potentially account for between about one-quarter and one-half of dispersion in _arpk_ . This is an important result, as it suggests that a non-negligible portion of observed dispersion may not entail a ‘misallocation’ at all; and (v) markup dispersion is generally modest.<sup>5</sup> Taken together, these latter two factors – markup and technology heterogeneity – can explain as much as 50% of observed _arpk_ dispersion. But this leaves a substantial unexplained component, which seems to stem from other – potentially distortionary – factors. The rest of the paper is organized as follows. Section 2 describes our data sources and reports key moments used in the estimation. Sections 3 and 4 contain our main results. Section 5 concludes.

# **2 The Data**

We use data compiled from a number of different sources. First, we use the Bureau van Dijk Orbis database. Orbis contains firm-level data (including both public and private firms) for a number of countries (although the extent of the coverage varies widely).<sup>6</sup> We focus mainly

> 3The data to compute markup dispersion are only available  in four of the 11 countries, highlighting some of the data hurdles future work along these lines may have to overcome.

> 4‘Correlated’ distortions of this kind have been emphasized in, e.g., Restuccia and Rogerson (2008), Guner et al. (2008), Bartelsman et al. (2013), Buera, Moll, and Shin (2013), Buera and Fattal-Jaef (2016), Hsieh and Klenow (2014) and Bento and Restuccia (2016).

> 5Note that this refers to dispersion in markups within an industry, not the average level of markups or the dispersion across industries.

> 6For a comprehensive description of the Orbis database, see Kalemli-Ozcan et al. (2015). Gopinath et al. (2017) also use the Orbis data in a misallocation context, albeit for a developed country, Spain.

3
# **Figure 4: Skill Intensity of China’s Manufacturing Exports**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Skill Intensity<br>Cumulative Export Share, 1992<br>Cumulative Export Share, 2005<br>Note: Data uses HS 6-digit classifications. The skill intensity is measured as the ratio<br>of non-production workers to total employment from the Indonesian manufacturing census<br>at the 5 digit ISIC level for 1992.<br>100<br>80<br>60<br>40<br>Cumulative Export share 20<br>0<br><!-- End of picture text -->

# **Figure 5: Skill Intensity of China’s Manufacturing Exports Excluding Processing Trade**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Skill Intensity<br>Cumulative Export Share, 1992<br>Cumulative Export Share, 2005<br>Note: Data uses HS 6-digit classifications. The skill intensity is measured as the ratio<br>of non-production workers to total employment from the Indonesian manufacturing census<br>at the 5 digit ISIC level for 1992.<br>100<br>80<br>60<br>40<br>20<br>Cumulative Export share - np<br>0<br><!-- End of picture text --> 22
# **Table 2:** Savings Functions for the Panel Data of Developing Countries, 1960-90

|||Depe<br>(t-|ndent Variab<br>Statistics in|le: Savings ra<br>parenthesis)|te||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|
|GDP per capita growth|4.634<br>(3.88)|5.634<br>(5.87)|4.723<br>(4.01)|5.701<br>(6.01)|5.094<br>(4.04)|5.855<br>(5.74)|4.928<br>(3.81)|5.952<br>(5.79)|
|Lagged GDP growti per capita||2.289<br>(2.03)||2.243<br>(2.00)||2.342<br>(2.00)||1.439<br>(1.31)|
|Dependency ratio|-0.898<br>(-4.16)|-0.735<br>(-3.48)|-0.772<br>(-3.80)|-0.630<br>(-3.18)|-0.903<br>(-3.79)|-0.748<br>(-3.21)|-0.844<br>(-3.18)|-0.701<br>(-2.73)|
|Lagged Savings Rate||0.273<br>(2.84)||0.271<br>(2.77)||0.268<br>(2.58)||0.298<br>(2.84)|
|Overall Education Stock|-1.695<br>(-3.18)|-1.750<br>(-3.38)|||||||
|Lagged Overall Education Stock|1.334<br>(3.00)|1.490<br>(3.40)|||||||
|Primnary Education Stock|||-1.762<br>(-3.37|-1.732<br>)(-3.40)|||||
|Lagged Primary Education Stock|||1.363<br>(3.16)|1.447<br>(3.40)|||||
|Secondary Education Stock|||||-0.965<br>(-2.60)|-0.777<br>(-2.52)|||
|Lagged Secondary Education Stock|||||0.810<br>(2.60)|0.671<br>(2.53)|||
|Tertiary Education Stock|||||||0.054<br>(0.47)|-0.027<br>(-0.26)|
|Lagged Tertiary Education Stock|||||||-0.082<br>(-0.98)|-0.002<br>(-0.02)|
|AdjR<sup>2</sup>|0.157|0.286|0.160|0.285|0.155|0.271|0.124|0.252|
|DW|1.85|2.05|1.86|2.06|1.82|2.05|1.88|2.05|
|Observations|259|259|259|259|259|259|259|259|

All variables are expressed in log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixedeffects have been introduced to account for countries' characteristics.
# **5.2 Market Diversification**

Next, we estimate a market diversification model to see how firm characteristics explain the level of market diversification. Our empirical strategy is to estimate the following Tobit model, regressing the previous set of independent variables used above on the two different measurements of market diversification. The first measurement is simply the number of export markets by region, as used in the bivariate analysis in Section 4. The second measurement is the number of export market regions, each weighted by geographical distance from exporting countries to destination regions in order to capture the extent of geographical dispersion of export markets. For each of the six regions, distance from each country where exporting firms are located is estimated by taking weighted average bilateral distance between the exporting country and all countries in the destination region, weighted by GDP of the latter.<sup>22</sup> In both these measurements, the dependent variable of market diversification is censored below at 0.

The results of these estimations are presented in Table 7. The patterns of influences among the set of regressors are quite similar to those in the case of the export intensity model. There are positive significant effects from size, foreign ownership, capital intensity, new vintage capital, and Internet access. Customs delay negatively affects market diversification.

Interestingly, foreign ownership by Africans appears to have more significant positive effect on market diversification, compared to foreign ownership by non-Africans. For the specification II, foreign African share has a significant positive coefficient, but not foreign non-African share. Considering the presence of the _network effect_ associated with foreign ownership, as discussed above, one may naturally think that non-African foreign owners would have stronger networks in markets outside Africa, hence contributing to market diversification. Also, foreign direct investment (FDI) from countries outside Africa may likely provide invested firms with access to more advanced technologies or higher technology effect, allowing these firms to penetrate into more markets. However, the network effect could also constrain the market diversification at the firm level in the sense that the network externality leads to concentration of exports in certain markets. In fact, Table

> 22 Distance data are from CEPII (Centre d'Etudes Prospectives et d'Informations Internationales).  The author thanks Souleymane Coulibaly for suggesting this measurement.

18
The initial situation for food trade (trade in raw food from agriculture and processed food from industry) is of particular importance for the simulations in this paper. Drawing on data in Tables 2.3-2.5, for the net food exporter, exports, imports, and the trade balance for food (raw agricultural and processed industrial) represent 7.2, 4.4, and 2.8 percent of GDP, respectively; for the food net importer, the same shares are 2.8, 7.7, and -4.9 percent.<sup>15</sup> At a more disaggregated level, exports and imports represent only a few percent of domestic output or domestic demand for food agriculture (unprocessed food) but are more important for processed food for which the correspond shares range between 8 and 27 percent.

# <u>3. SIMULATIONS</u>

Initially we analyze the _base_ and world price shock simulations for each of the two LIC archetypes, starting with the net exporter. Given that the impact of world price shocks on the net exporter LIC are positive or only mildly negative, not warranting major domestic policy responses, we limit our domestic policy simulations to the net importer archetype, for which we address the impact of domestic subsidy and transfer policies aimed at mitigating negative effects of world price shocks.

BASE AND WORLD PRICE SHOCKS: THE CASE OF THE NET EXPORTER The _base_ scenario is designed to represent a plausible projection for an LIC economy for the period 2009-2025; it also serves as a benchmark for comparisons with alternative simulations.

For the _base_ scenario (but not for the other scenarios), growth in GDP at factor cost is exogenous. The growth rate in 2010 is set at 6.3 percent, the observed aggregate rate for all LICs. Starting from 2011, we impose the average annual growth rate for the same group of countries for the period 2000-2010, 5.2 percent.<sup>16</sup> Other key assumptions may be summarized as follows:

- Government spending. Real government education services grow at rates that are adjusted endogenously to ensure that services per student grow at around 2-3 percent per year (close to the growth in GDP per capita). Drawing on cross-country analysis, the growth rate is higher the lower the educational cycle. For other government functions – health, agricultural services, public infrastructure and the rest of the government – it is assumed that spending is a fixed share of absorption (using the base-year share).<sup>17</sup>

> 15 To exemplify these calculations, the food export GDP share of 7.2 percent is the product of a total export GDP share of 22.8 percent and a food share of 31.8 in total exports.

> 16 Given an annual population growth rate of 2.2 percent the per-capita average annual growth rate for 2000-2010 for the aggregate of all LICs was 3.1 percent (World Bank 2011b).

> 17 Although the differences often are small, it is preferable to fix payment flows relative to absorption instead of GDP. Absorption is a better measure than GDP of the capacity of an economy to spend in different areas. The two measures may grow at significantly different rates in the face of changes in net transfers or factor income from

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23 Table 2 Composition of Tax Revenues in **CMte** d'lvoire for Selected Years (percentage)a

||1980|1983|1986|1987|1988|1989|1990|**1991c**|
|---|---|---|---|---|---|---|---|---|
|(% of Total Revenue):|||||||||
|Production taxes|25.6|29.7|27.7|30.7|31.9|33.9|38.5|29.7|
|Import duties|32.0|30.6|35.9|33.6|34.3|37.8|32.7|43.2|
|Export taxes|10.5|8.7|11.9|11.8|8.6|3.7|_1.5_|1.7|
|Income taxes|32.0|31.1|24.5|24.0|25.2|24.7|27.4|25.3|
|Tax Revenue as % of<br>GDP|23.0<br>_<br>_|22.2<br>_<br>_|20.4<br>_<br>_|21.5|20.3|19.1|19.4|19.9|

a: Does not always add to 100 due to rounding e: Estimates Sources: World Bank (1987, 1991); Chamley (1991) Several features of the Ivorian economy and the tax system important for tax incidence analysis are especially worth noting. One is large interhousehold transfers, typically operating between urban households and rural family members. These imply that taxes usually thought of as being borne by one group of households may have part of their burden transmitted to other households through reduced transfers. A second is the narrow base of key taxes, which implies more specificity in tax incidence than in countries with broadly based taxes, since taxes primarily affect more narrowly defined socio-economic groups (government employees, cocoa farmers, etc.), rather than income ranges. A third is the economy's relationship to the exernal sector, and the tax implications that follow; with
outcome variables (scale 1-4) for ease of interpretation. _Politician approval_ , the principal conceptual outcome, is measured for five different leaders: village chairman, village executive officer, ward executive officer, ward councillor, and member of parliament.<sup>12</sup> Between 70 and 80 percent of respondents report that they either approve or strongly approve of these leaders in the 2010-2011 round of the NPS. The key independent variable is whether respondents or anyone in their household slept under a free net in the night before the survey ( _“any free net”_ ). Variable definitions for the various measures of bed net ownership and use are provided in the appendix.

# **Variation in Access to ITNs**

I exploit the variation in possession of a free bed net driven by whether the 2010-11 ITN distribution campaign happened before or after an individual was surveyed by the NPS. Thanks to the randomized roll out of the survey, in conjunction with the phased introduction of the ITN distribution campaign, I can compare citizens who were surveyed just prior to receiving a net with those who were surveyed just after receiving a net.

One data issue is that although we know the month and year of all survey interviews, we do not know the exact _day_ on which the survey was conducted. The month and year is recorded but the survey day is erased for anonymity. Therefore I impute a date of the 15th day of the month to all observations. I define respondents as treated if the survey team arrived within 3 months (90 days) after the mass bed net distribution campaign (such that the _post_ variable = 1), omitting the 15 days before and 15 days after the survey date to account for uncertainty about the exact survey day of the month<sup>13</sup> . Respondents who were surveyed within the 90 days before the ITN campaign arrived in their village are control ( _post_ = 0).<sup>14</sup> Later I relax this assumption to test multiple definitions of

> 12The question is: “Overall, would you say you approve or disapprove of the job your [official] is doing?” (Possible responses are: Strongly approve, approve, disapprove, strongly disapprove, or don’t know). In the main specification I create a binary outcome variable by coding strongly approve and approve as 1, and strongly disapprove and disapprove as 0.

13In later robustness checks I include these 15 days before and after the inputed survey date. 14This bandwidth was chosen to maximize comparability with estimates from the 2008 National 13
place. The law that determines who has control over assets and thus affects the types of economic opportunities that are available is family law. Family law covers issues of marriage, but also of marital property and who can control it – within the marriage and at its end, whether through divorce or inheritance. And it is here that gender gaps can be—and still too often are— explicit.

There are also several country specific studies that find significant impacts of the reforms to family law. In studies that analyze the impact of the Hindu Succession Act Amendment of 2005, equal inheritance rights for girls and women are shown to be correlated with increased educational attainment, higher household investment in girls as well as higher age at marriage (Deininger, Goyal, and Nagarajan, 2010). Equal inheritance rights for women are also associated with higher women’s autonomy (Roy 2008).

Assessments of changes in divorce laws in the Unites States show that women were more likely to initiate a divorce in states where women’s position was strengthened. Also the changes in divorce laws were correlated with a fall in domestic violence in these states. As illustrated by Gray (1998) and Stevenson and Wolfers (2006), part of the effect may come from shifts in intrahousehold bargaining power.

Similarly, changes in the family law in Ethiopia were associated with a substantial shift in women’s economic activities (Hallward-Driemeier and Gajigo, 2013). As of 2000, Ethiopia requires both spouses’ consent in managing marital property, enabled women to work outside the home without needing permission from their spouse and raised the minimum marriage age for women. The reform, which is now applicable across the country, was initially rolled out in select regions and cities. Using two nationally representative household surveys, one in 2000 just before the reform and one five years later, Hallward-Driemeier and Gajigo (2013) find that 5
**_Knowledge spillovers._** More educated countries tend to be richer. More important, countries whose citizens’ education grows faster tend to see faster economic progress. The link between country education and economic growth is quantitatively strong. Krueger and Lindhal (2000) find that each additional year in the average education of a country results in a GDP increase of 5–14 percent, a substantial income gain. Of course, the direction of causality is not always clear. Economists have long recognized that “education is both the seed and the flower of economic development.”<sup>8</sup> For example, countries that improve their education systems could at once change other policies that enhance growth, thus complicating the causal interpretation of the relationship between education growth and economic growth.

The same correlation between education and economic growth across countries exists within countries across regions. Indian states with the highest level of literacy and highest level of college graduation also have the highest incomes. The same is true in countries as diverse as Brazil, Germany, and Indonesia.

Figure 1 shows the relationship between the average salaries of high school graduates in each U.S. metropolitan area and the fraction of workers with a college education in those areas. Tables 1 and 2 hinted at a positive relationship between local human capital and salaries across U.S. metropolitan areas. The figure shows that this link is robust, and it holds true for most U.S. cities. There appears to be a clear positive association, indicating that the more college graduates, the higher the salaries for high school graduates.<sup>9</sup> The economic effect is quite large. The earnings of a worker with a high school education rise about 7 percent as the share of college graduates in his city increases 10 percent.

**Figure 1. Fraction of college-educated workers and salary of high school graduates in the United States, by metropolitan area**

_Source:_ Moretti 2012.

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8 Figure 2: Decomposition of Trade Growth into its Short-Run and Long-Run Components <!-- Start of picture text --> 15%<br>10%<br>5%<br>0%<br>Short‐run component<br>‐5%<br>Long‐run component<br>‐10% Trade growth<br>Predicted Trade growth<br>‐15%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Source:_ IMF World Economic Outlook (October 2015) and authors' calculations. _Notes:_ Trade refers to imports of goods and services. Trade growth calculated as log difference.

Weak global demand explains the cyclical component of the trade slowdown. Historically, the negative effect of a crisis on trade performance has not been limited to the crisis period, but has persisted through the medium term (Freund 2009, Abiad et al. 2014). The weakness in import demand is a symptom of overall weakness in aggregate demand. Indeed, in the aftermath of the Global Financial Crisis, global trade weakness was most pronounced at the epicenter of the crisis – in high-income economies, notably the United States and the Eurozone. More recently, the sharp decline in commodity prices and the transition to a slower growth rate in China are lowering import demand in emerging economies (Constantinescu, Mattoo and Ruta, 2016).