rafmacalaba/datause-encoder-data huggingface dataset:
could result in a reallocation of production activities by facilitating trade in producer services (Francois, 1990b). Furthermore, sectors that are particularly affected by this provision are those without an inherent requirement of proximity in the provision of the services, such as business and financial services, which arguably are particularly important in the context of offshoring.<sup>41</sup>

# _Robustness checks_

The findings discussed above are robust to alternative specifications.<sup>42</sup> First, including an additional dummy variable for the European Union (EU) does not alter the main findings. Controlling for the EU may be important since the nature and implications of the EU agreement are clearly different from all other RTAs. Table A.3 shows that becoming part of the EU is associated with larger trade flows and higher GVC participation, and the magnitude of the effect is larger than an average RTA. However, the main findings that services agreements increases GVC participation of developing countries and that the non-establishment provision plays a key role in promoting GVCs are not driven by the EU agreement. Second, the results are robust to controlling for the age and different waves of RTAs. Considering that services agreements are always signed either together with or later than RTAs in goods, some may worry that the services agreement dummy is capturing an “aging effect” of RTAs. If the trade-promoting effect of RTAs tends to grow over time, this could bias the effect of services agreements upwards. However, including interaction terms between RTA and age or year dummies does not alter the main findings.

Third, even with the rich set of fixed effects included in the econometric analysis, the issue of endogeneity is not fully tackled if there are other timevarying changes in bilateral trade costs that are specific to country pairs. To partially address this concern, I include interaction terms between distance and year dummies which would capture the changing effects of distance over time, as in Bergstrand et al. (2015).<sup>43</sup> If distance is an important element in bilateral trade costs that is changing over time, this approach would control for some changes in bilateral trade costs, other than trade agreements, that could affect trade flows. The findings are robust to this specification.

Finally, the results are also robust to including all countries available

> 41The data on services agreements in DESTA unfortunately do not allow sectoral analysis for different services sectors. Some other databases do provide more details on commitments at the sectoral level, but are limited to only a subset of agreements.

> 42See Appendix A.3 for results.

43I am not able to include the full set of controls suggested by Bergstrand et al. (2015) because there is no clear equivalent of intra-national trade flows for GVC-exports.

24
# Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS920|Economic Incentives and Point<br>Source Emissions: Choice of<br>Modeling Platform|Raymond J. Kopp|June 1992|C. Jones<br>37754|
|WPS921|Road Infrastructure and Economic<br>Development: Some Diagnostic<br>Indicators|Cesar Queiroz<br>Surhid Gautam|June 1992|M. Laygo<br>31261|
|WPS922|Central America at a Crossroads|Sylvia Saborio<br>Constantine Michalopoulo|June 1992<br>s|M. Colinet<br>37044|
|WPS923|Listening to Firms: How to Use Firm-<br>Level Surveys to Assess Constraint<br>on Private Sector Development|Andrew H. W. Stone<br>s|June 1992|P. Infante<br>37642|
|WPS924|How Reduced Demand for Children<br>and Access to Family Planning<br>Accelerated the Fertility Decline in<br>Colombia|Rafael Rofman|June 1992|0. Nadora<br>31091|
|WPS925|A General-Equilibrium-Based<br>Social<br>Policy Model for Cote d'lvoire|Ngee-Choon Chia<br>Sadek Wahba<br>John Whalley|June 1992|A. Cox<br>34778|
|WPS926|Options for Reshaping the Railway|Neil E. Moyer<br>Louis S. Thompson|June 1992|B. Gregory<br>33744|
|WPS927|General Equilibrium Effects of<br>Investment Incentives in Mexico|Andrew Feltenstein<br>Anwar Shah|June 1991|C. Jones<br>37699|
|WPS928|Pesticide Externalities, Comparative<br>Advantage, and Commodity Trade:<br>Cotton in Andhra Pradesh, India|Nalin M. Kishor|July 1992|D. Ballantyne<br>38004|
|WPS929|Managing Pollution Control in Brazil:<br>The Potential Use of Taxes and<br>Fines by Federal and State Governm|Antonio Estache<br>Kangbin Zheng<br>ents|July 1992|A. Estache<br>81442|
|WPS930|Participatory Development: Myths<br>and Dilemmas|Robert Picciotto|July 1992|A. Muhtasib<br>84573|
(indirect) impact on firm performance in terms of employment and sales growth (Fisman and Svensson, 2007; Aterido, Hallward-Driemeier and Pages, 2011) as well as investment (O’ Toole and Tarp, 2012), but not on the duration of policy implementation associated with the provision of particular government services. A notable exception is the study by Kaufmann and Wei (1999), which demonstrated that firms that pay bribes tend to spend more, not less, time with government officials overall. While suggestive, their paper neither examines actual implementation times nor controls for firm-fixed effects. Moreover, it does not systematically assess heterogeneity, which is an important but often overlooked corollary of the “grease the wheels” hypothesis.

Policy implementation times directly affect firm performance and are a very suitable metric for assessing government effectiveness both within and across countries (which perhaps explains why they feature prominently in cross-country comparisons of regulations such as the World Bank’s _Doing Business_ database). In addition, there is ample evidence that the impact of regulation on economic outcomes is contingent on its implementation, and that protracted policy implementation impedes trade (Freund et al., 2011), job creation (Freund and Rijkers, 2012), firm entry (Klapper, Laeven and Rajan, 2006), productivity (Nicoletti and Scarpetta, 2003) and growth (Djankov et al., 2005).

The World Bank Enterprise Surveys are well suited for analyzing the association between bribing and policy implementation duration. In addition to containing detailed information on firm characteristics, the surveys include questions on a set of government services or permits that might have been sought, whether extra payments or gifts were requested, and the time it took to complete the process. Having data on multiple transactions for the same firms not only allows for the testing of the robustness of results across services, it also allows for controlling for firm fixed effects, such that identification is based on variation in the incidence of bribe requests across different types of permit applications made by the same firm. The data also enable us to assess heterogeneity by means 3
<!-- Start of picture text --> FIGURE 1: WHEAT YIELDS IN ARGENTINA AND INDEA CORRELATING TO<br>THE SCIENTIFIC REVOLUTION (Average Annual Yields in 1,000 Kilograms/Hectare)<br>- Argentina - India<br>3 Horsepower Mechanical Power Science<br>2.5<br>2<br>1.5<br>0.5<br>1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995<br><!-- End of picture text -->

> Adapted from Pardey, Chang-Kang and Alton 2001

_Health:_ Ill-health is both a cause and a consequence of poverty. Over the past century, science and technology provided the basis for the largest ever aggregate improvements in human health. Life expectancy is up sharply worldwide [See Figure 2 on the following page]. Certain scourge diseases have been eliminated, (e.g., smallpox) while the morbidity and mortality associated with everyday health-related events like childbirth and routine infectious disease have declined sharply. In health, the cumulative effects of scientific advances are very evident: while health indicators have always varied with income, during the 20th Century, the same real increase in income led to progressively greater health improvements. As knowledge accumulates, the same money buys increasingly better health. Still, progress is good but uneven. The developing world still accounts for a disproportionate amount of the global burden of disease, and research spending on health is severely skewed away from the concerns of developing countries. Far too many countries are trapped in cycles of "high-fertility, high mortality" that the world's better-off countries have broken. Indoor air pollution, dysentery, water-borne disease (e.g., cholera), vector-borne disease (e.g., malaria, dengue, etc.) and AIDS account for millions of deaths annually and are hitting hardest the countries that are least prepared and can least afford to deal with them. Some of these issues can be addressed using current knowledge, (e.g., dysentery) while still others require scientific breakthroughs in S&T (e.g., AIDS and malaria).

> Figure 2 illustrates changes in life expectancy at birth in the developing world, presented by region, as compared to the developed world. As the figure shows, from 1960 to 1998

> many regions have begun to catch up to the high income countries' average life

> expectancy. However, the grim improvements in some regions, such as Sub-Saharan Africa, illustrate the need for continued attention to the inequities in health attainment.

6
3.44 <!-- Start of picture text --> woN 249<br>Boa 196<br>2<br>2<br>8<br>sz o7<br>° European Union Balkans<br><!-- End of picture text --> <!-- Start of picture text --> woN 249 245<br>Boa<br>£<br>2<br>8<br>sz o7<br>©<br>° European Union Balkans<br><!-- End of picture text -->
**Figure 2.  Livestock losses in Mongolia, 1971-2004**

<!-- Start of picture text --> 20.0%<br>18.0% % 2004 GDP<br>16.0% % livestock value<br>14.0%<br>12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> Source: National Statistics Office of Mongolia.

An analysis of hazard frequencies and intensities is critical to assessing the country’s exposure to livestock losses. Risk assessment models provide a set of metrics, i.e., quantitative measurements of potential losses with respect to the frequency of the events. As shown in Table 2 and Figure 3, the value of the Mongolian livestock in 2004 is estimated at US$971 million. The average annual loss is US$ 46 million, i.e., 5 percent of the total value of the livestock and 3.3 percent of 2004 GDP. However, the annual loss is highly variable, as a direct consequence of infrequent natural disasters, with a standard deviation estimated at US$ 36 million. Once every five years, the annual livestock loss would be US$ 53 million and this estimation goes up to US$ 143 million for catastrophic events occurring once every 30 years.

**Table 2. Livestock risk profile in Mongolia**

||US$, million|Percentage of<br>assets at risk|Percentage of<br>2004 GDP|
|---|---|---|---|
|Assets at risk (2004)|971|-|61.1%|
|Average annual loss|46|6.0%|3.7%|
|Standard deviation|36|4.6%|2.8%|
|PML (1 in 30 year event)|143|18.5%|11.3%|
|PML (1 in 20 year event)|107|13.9%|8.5%|
|PML (1 in 10 year event)|67|8.7%|5.3%|
|PML(1 in 5year event)|53|6.8%|4.2%|

Source: authors.

11
# **V.2 The Effects of Exogenous Economic Growth on Capital Flows**

After obtaining the instrumented per capita output growth, we use it as the main explanatory variable to examine the impact of output growth on different types of capital inflows. Note that we also control for growth of trade partners and commodity index. In addition, country fixed effects, time fixed effects and country-specific time trend are also controlled. The standard errors are clustered as the country level.

Table 2 shows the results of the regressions. The dependent variables _are the log of real ODA, FDI and public debt inflows per capita_ . The main explanatory variable is the instrumented change in log of real output per capita. This variable is arguably exogenous to capital inflows.

Table 2 shows that exogenous domestic output growth has no impact on different types of capital inflows. The inflows do not seem to be driven by commodity index or growth of trade partners either.

|VARIABLES|log(ODA inflows)|log(FDI inflows)|log(public debt inflows)|
|---|---|---|---|
|Instrumented output growth|-0.413|-9.476|4.997|
||[1.914]|[9.119]|[4.758]|
|Growth of trade partners|9.007|4.053|4.417|
||[6.105]|[18.276]|[22.639]|
|Commodity Index|-0.395|-0.820|1.711|
||[0.593]|[1.971]|[1.176]|
|Time fixed effects|Y|Y|Y|
|Country fixed effects|Y|Y|Y|
|Country time trend|Y|Y|Y|
|F-test|1.13|0.45|1.62|
|No of countries|34|34|33|
|Observations|1,065|931|898|

Clustered-Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Table 2: Impacts of output growth on capital inflows The results show that from 1979 to 2012, on average, exogenous output growth within a country in SubSaharan Africa does not attract capital inflows. This stands in contrast with the capital flows literature, who tends to show that within a country, capital inflows are pro-cyclical (e.g. Broner et al, 2013 and 19
panel data (e.g., Andrews, Bellmann, Schank and Upward, 2009; Martins, 2004, 2011; Heyman, Sjöholm and Tingvall, 2007) and many of them find much reduced foreign wage premiums. However, few studies trace the same individuals in developing countries, perhaps due to data constraints. Exploiting the panel component of the VHLSS 2002 and 2004, this study investigates how the wage levels of identical individuals changed if they “switched” between domestic and foreign sectors.

While Vietnam’s “formal” enterprise employment has been growing rapidly (Ramstetter and Ngoc, 2007b), Vietnam’s “formal”<sup>5</sup> sector in terms of employment represents still only a fraction of its economy (e.g., about 17 percent of total employment in 2004 (VHLSS, 2004)) and the remaining workers belong to large agricultural and other informal sectors. One of the roles of foreign firms in Vietnam is to create “formal” job opportunities, drawing workers from the informal sectors. Such an employment effect of FDI has been documented in other developing countries, especially in the context of the expansion of export-oriented manufacturing production.<sup>6</sup> However, previous studies based on firm-level data as well as those using matched employer-employee data ignore the wage effects on workers who are moving from informal sectors. Another advantage in using the VHLSS is that the data enable to extend the wage comparison analyses to the informal sector, filling the gap in the literature.<sup>7</sup>

> 5 Throughout the paper, jobs in foreign, private and state enterprises as well as those in government are referred to as “formal” employment. “Informal” employment is defined to include “informal wage” workers and “self-employed”. The impacts of foreign ownership on income for self-employed are not considered in this paper due to the lack of wage data.

> 6 See Anderson and Dimon (1999) for Mexico, Fu and Balasubramanyam (2005) for China, Glick and Roubaud (2006) for Madagascar. For instance, applying the Smith-Myint model of “vent for surplus” to China, Fu and Balasubramanyam (2005) find that the expansion of exports from labor-intensive manufacturing, assisted by FDI, accelerated the transfer of surplus labor from the agricultural to the export sector. For Mexico, Anderson and Dimon (1999) report evidence that export oriented production in _maquiladoras_ (across-border export processing plants) created formal sector job opportunities for single women.

> 7 The VHLSSs include data for a category of workers who reported as “work for other households,” which are used as a proxy for the “informal” wage workers.

4
<!-- Start of picture text --> 50<br>@ Preschool Primary (Secondary @ Tertiary<br>S 40<br>3 L<br>230 | | | | fey pS LU<br>= T EGO WM MH NM YS ®<br><!-- End of picture text -->

<!-- Start of picture text --> = 20 ~ ~~ ww NN ~ iN ~~ Ss<br>= TM YN WN WN ON ON ON<br>: tof hn hn ke =.<br>fe)<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market Income + Pensions decile<br><!-- End of picture text --> <!-- Start of picture text --> 16<br>@ Clinic (2 Hospital © Primary TM Secondary M Tertiary<br>14<br>& 12 *<br>s> ,<br>& 10 —_—- _—<br>3s3 —_ = ————————————— _——<br>—<br>= 8<br>=<br>s<br>s 6<br>Ss<br>2<br>=sc 4<br>2na<br>& 2<br>oO<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market Income + Pensions decile<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->
# **1 Introduction**

Over one 100 years ago, Corrado Gini (1912) published his seminal book _Variabilit`a e Mutabilit`a_ where he presented for the first time the distributional index that today is associated with his name. The Gini index remains one of the most popular statistical indices of all times and continues to be the subject of studies across the social sciences. A search on JSTOR finds 265 articles with the name ‘Gini’ in the title, 366 with the word gini in the abstract and 16,594 with the word gini in the text. A search on econpapers finds 423 journal articles with the word gini in the title or keywords. The majority of articles from these two sources concerns inference, decompositions, the relation with the Lorenz curve, various extensions or formulations of the Gini, and relations with other measures such as deprivation.

It is known that the Gini index can be expressed in many different forms. A recent paper on the origins of the Gini index by Ceriani and Verme (2012) reports 13 different forms that emerged in the literature since its first formulation by Corrado Gini in 1912. With some basic manipulation of these indexes, it is also possible to see that eight of these forms can be expressed as sums of individual values across a given population. This is a simple but interesting observation because it implies that these eight formulations of the Gini can potentially be used to decompose the Gini into population subgroups. For example, one could estimate the individual contribution to the Gini for all members of a given population, sum these contributions by males and females and then sum results for the two groups to obtain the overall Gini. This simple procedure would overcome the century-long issue of the Gini decomposition into population subgroups.

The problem with this approach is that the individual contribution to the Gini _per se_ has no meaning. We cannot really talk of individual inequality. By definition, inequality is measured across a set of individuals. However, if we were able to provide a meaning and a set of desirable properties to the individual contribution to the Gini, then these individual values would become meaningful in their own right, could be added across groups and used for the Gini decomposition into population subgroup.

The paper follows this approach. We define the individual contribution to the Gini as a 2
# **Appendix A1: Riots, Conflict Events and Political Instability in North Africa**

To assess whether the ICRG political risk indicator is a good indicator of political instability, we explore its correlations with arguably more objective proxies for political turmoil, notably indicators of political protests and conflict events, which we obtained from the Armed Conflict Location and Event Dataset database. The latter dataset covers only Northern African countries for the entirety of the period and is thus not available for our entire sample. Nonetheless, for the Northern African sub-sample we find strong and very significant correlations between the ICRG risk indicators and measures of political protests, conflict events, and fatalities associated with political violence – as demonstrated in Table A2 below. The ICRG index thus correlates strongly with objective proxies for political instability.

**Table A1: Correlations between ICRG measure and objective proxies for political risk**

|**Correlation**|**s between ICRG**|**measure and objective proxies for**|**political risk**|
|---|---|---|---|
|**ρ (N=210)**<br>**p-value**|**Political Ins**<br>**(ICRG)**|**tability**<br>**Riots (ln)**|**Battles (ln)**|
|**Riots (ln)**||0.498||
|||_P=0.000_||
|**Battles (ln)**||0.488<br>0.653||
|||_P=0.000_<br>_P=0.000_||
|**Fatalities (ln)**||0.490<br>0.686|0.886|
|||_P=0.000_<br>_P=0.000_|_P=0.000_|

For illustrative purposes, we also plot the evolution of riots and conflict events by country over time. Perhaps the strongest commonality between the graphs is the increase in political turmoil associated with the Arab Spring. The graphs also show that while the political risk measure mimics political violence, this is by no means its only determinant.

33
# **1. Introduction**

One of the initial definitions of food security from the 1970s was based on the narrow concept of whether there was sufficient production of food in a nation to support the population.<sup>1</sup> Influenced in part by Sen (1981), and in recognition of the obvious inadequacies of this view, the concept was expanded over the next two decades to include considering the distribution of the aggregate food supply, in particular whether individuals have resources to gain adequate access to food (FAO, 1983). The concept of food security was further broadened to account for whether individuals could properly utilize the nutrients in food (e.g. due to factors such as proper hygiene)<sup>2</sup> and, also whether the availability, access and utilization of food was stable over time (FAO, 1996).

As the conceptualization of food security broadened, so too did the measures. Much of the empirical analysis of food security has focused on measures of food supply (e.g. food balance sheets)<sup>3</sup> and food access (e.g. calories and dietary diversity) both at a given point in time and over time.<sup>4</sup> Analysis based on change over time in the measures of food availability and access have been interpreted as measures of stability of food security.<sup>5</sup> Stability though,

> 1 First expressed at the 1974 World Food Summit as summarized by Ram et al. (1975).

> 2 The concept of food utilization includes concerns of intra-household distribution of food, nutritional value of food, and nutrient absorption (Jones et al. 2013; Barrett 2010). Expanding the definition to include utilization acknowledges that two households, consuming similar calories per capita, can look substantially different in terms of food security status.

> 3 For an overview, including discussion of food availability measures, see Jones et al. (2013).

> 4 World Bank (1986) was one of the earlier reports to measure the stability of availability and access to food (as measured by food supply and caloric intake) over time.

> 5 First differences in these measures have also been used to separate chronic from transitory food insecurity.

2