rafmacalaba/datause-encoder-data huggingface dataset:
These ‘insider’ politics can be reinforced if the described positive effects of workrelated interactions interact with existing segregation processes: Psychological rewards and social capital associated with work-place interactions may only benefit those who already have better economic opportunities. This not only increases differences between the employed and unemployed but also potentially undermines successful collective action across groups with uneven access to the labor market.

Micro data provides some indicative support for these processes. The aforementioned correlation analysis by Wietzke and McLeod (2012) indicates that people who derive positive experiences and self-esteem from their work are more likely to engage constructively with public institutions. The authors document that those in work are more likely to participate in demonstrations or to sign petitions. These results hold across a wide sample of nations, even though the estimates are less robust in lower income countries. People in higher quality jobs also tend to be more politically active in all sub-samples, except low income countries.

Other contributions suggest that lower levels of political activism among the unemployed also go along with political attitudes that can undermine support for political institutions. Cross-country correlation analysis of World Value Survey data by Altindag and Mocan (2010) documents that the unemployed tend to hold more negative views about democracy and stronger preferences for rogue leaders.<sup>24</sup> The authors also find that high and sustained rates of unemployment undermine collective trust in democracy. The results are based on simple cross-sectional correlations only. However, the findings are broadly consistent with earlier research on electoral behavior, which suggests that support for incumbent representatives declines with deteriorating personal economic conditions (Markus 1988, Nannestad and Paldam 1997) and weaker macro-economic outlooks in a constituency (Kramer 1971, see also Stigler 1973).

There are indications that these uneven levels of political engagement also go along with the sort of insider-outsider differences over labor market policies often discussed in the political economy literature. Contrary to conventional political economy models, which tend to assume that labor and social democratic movements share a common concern with unemployment, workers are often deeply divided over labor market polices. Survey responses by labor market ‘outsiders’, who are defined by insecurity of their work contracts, indicate a

> 24 However, Wietzke and McLeod (2012) find no systematic associations between employment status and selfexpressed confidence in the government and democratic institutions.

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share of the population coming from the change in the share of the middle and the change in the share of the metropoles respectively. Consistent with the findings in Table 5 (columns (1) and (2)), only rural diversification and migration to secondary towns is statistically contributing to poverty reduction, while migration to the metropoles is not. These results are also robust against the use of different definitions of poverty (Table 6, columns (3) and (4)), the level of initial poverty (Table 6, columns (5) and (6)), non-linear specifications of changes in the shares, and the inclusion of population growth, the polity indicators and the sources of growth.<sup>28</sup> Nonetheless, while not statistically significant, further inspection also shows that the size of the coefficient on the (share weighted) change rate of the metropolitan share increased, approaching (or for the $2-day poverty headcount even exceeding) the size of the (share weighted) coefficient of the change rate of the middle. Moreover, when another definition of metropolis is used, such as the population in cities with population of 750,000 or more in 2007 instead of 1 million or more at the time of the spell<sup>29</sup> (Table 6, columns (7) and (8)), migration to the metropoles contributes as much to poverty reduction as transition into the middle (the coefficients are not statistically different).

Together these findings resonate with those of the Tanzania case study. They are seen as indications that it is especially the size effect which is important in understanding the advantage of the middle, i.e. the greater ability of poorer households to exit poverty by diversifying in the rural economy and connecting with secondary towns, and maybe less the larger ability to find a job as such, once in these locations. While unemployment was higher among the migrants in Dar es Salaam, the premium needed to compensate for this and equalize expected consumption was well below the difference in average consumption levels observed (Table 2). When looking again at the $1-day poverty gap results (Table 6, columns (3) and (4)), there is a larger difference between the two coefficients, suggesting that other

> 28 To save space and maintain focus, the latter robustness tests have been reported. They are available upon request from the authors.

> 29 This avoids discontinuous jumps as cities grow beyond one million during the period of the sample. A disadvantage of this definition is that even if a city has a population of more than 750,000 in 2007, it may not have been large ten years ago.

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Home economies of investors by region and income level <!-- Start of picture text --> Developed, 73.2 Developing, 26.8<br>Latin America &<br>Caribbean, 9.2<br>East Asia &<br>Pacific, 9.8<br>Europe & Sub-<br>Central Saharan<br>Asia, 6.9 Africa, 6.1<br>North America,<br>88<br>Europe & Central Asia, 54.2 % South24 Asia, | csctPacific, asia2.0<br><!-- End of picture text -->

@ Developed @TM Developing
shown in tables 1 through 11. Complete documentation of the scoring and data are provided in Excel spreadsheets online.<sup>18</sup> Although tables 1-11 in this paper should be self-explanatory, we discuss a few examples to clarify the mapping procedure. First consider the mapping for regulation of banking services. The first category in the McGuire and Schuele matrix for banking is licensing of banks. The category has a weight in the McGuire and Schuele matrix of 0.1, and there are five possible scores for the category. If no new license is allowed, the score is 1, for a weighted score of 0.1. If licenses up to three licenses are issued through a complicated, discriminatory and costly procedure, the McGuire and Schuele score is 0.75, for a weighted score of .075. If up to six licenses are issue with an application fee and several requirements, the McGuire and Schuele score is 0.2, for a weighted score of .02. If up to ten licenses are issued with only prudential requirements and an application fee, the McGuire and Schuele score is 0.1, for a weighted score of .01. If licenses are issued with only prudential requirements and an application fee, the McGuire and Schuele score is 0, for a weighted score of 0. If the World Bank database indicates that “market entry is not allowed,” we score this category in the McGuire and Schuele matrix at 1. The World Bank database notes three possible restrictions on the issuance of licenses: (i) there is a limit on the licenses available; (ii) the license is issued at the discretion of the regulatory body; and (iii) the license may be denied despite fulfillment of all publicly available criteria. If the World Bank database indicates all three categories of restrictions apply, we score this category of the McGuire and Schuele matrix at 0.75. If two of these restrictions apply, we score this category at 0.2. If one of these restrictions apply, we score the category at 0.1. If none apply in the World Bank database, we score this McGuire and Schuele category at zero.

There is a category “equity participation permitted,” which has a weight of 0.2 in the McGuire and Schuele matrix for banking. The score in the McGuire and Schuele matrix is 1-s, where s is the maximum equity share in a bank that is permitted for foreign firms. The World Bank database produces this information precisely, so the mapping from the World Bank database to McGuire and Schuele is one to one and very clear in this case.

To give examples where the mapping is more problematical, consider accounting services, where the list of regulatory categories is adapted from Nguyen-Hong. (We call this list

> 18 See the folders: “STRI mapping of data,” “STRI scoring 103 countries,” and “Intra-EU STRI scoring and Calculations” at the link: https://drive.google.com/?pli=1&authuser=0#folders/0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2

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The utility of relative income has not, however, been the main argument made for relative poverty lines. Rather, the case has been seen to rest on the view that there are certain demands on income that are socially determined and that a person is absolutely deprived if those demands cannot be met in a specific social context.

Atkinson and Bourguignon (2001) proposed a neat way of implementing this idea for the purpose of measuring global poverty. They postulated two key capabilities: physical survival and social inclusion. The former is the capability of being adequately nourished and clothed for meeting the physical needs of survival and normal activities. On top of this, a person must also satisfy certain social inclusion needs, which are assumed to be directly proportional to mean consumption in the country of residence. Each capability has a corresponding poverty line, giving the absolute and relative lines. The AB proposal is that one should only be deemed “not poor” if one is neither absolutely poor nor relatively poor. Letting _Z_ * be the minimum expenditure needed to assure that basic consumption needs are met, measured at purchasing power parity (PPP), the AB poverty line for country _i_ is: There are two unknown parameters in (3), _Z_ * and _k_ . AB proposed that _Z_ * should be set at the World Bank’s “$1-a-day” line, on the grounds that this can be considered a reasonably lower bound, since it is anchored to the poverty lines found in the poorest countries (Ravallion et al., 1991). AB then argued that the value of _k_ could also be based on national poverty lines above those found in the poorest countries, by studying how those lines vary with man consumption in the original data base of poverty lines used by Ravallion et al. (1991) to set the “$1-a-day” line. By visual inspection of the Ravallion et al. (1991) data set on national poverty lines at 1985 PPP, Atkinson and Bourguignon set _k_ =0.37. Subsequently, Chen and Ravallion (2001) found that _k_ =1/3 gave a better fit with the Ravallion et al. (1991) data set at 1993 PPP.

However, the AB line fails the WRA in that it has an elasticity of unity for all countries with _M i_ > _Z_ * / _k_ . This is surely implausible. The idea that distribution-neutral growth has no impact on the extent of poverty in new middle-income countries such as China would surely be very hard to accept (not least, we would conjecture, in China). The violation of the WEA stems from the seemingly implausible assumption that the cost of social inclusion is directly 8
more aggregate levels is underpowered and faces greater identification concerns. We address these challenges by combining quasirandom variation from program rules with administrative census data georeferenced to the village level.

We obtain causal identification from the guidelines by which villages were prioritized to receive new roads. As previously described, new roads were targeted first to villages with population greater than 1,000, then those greater than 500, and finally greater than 250. While selection into road treatment may have been partly determined by political or economic factors, these factors do not change discontinuously at these population thresholds. As long as these rules were followed to any degree, the likelihood of treatment will discontinuously increase at these population thresholds, making it possible to estimate the effect of new roads using a fuzzy regression discontinuity design.

We pool villages according to the population thresholds that were applied in each state, so the running variable is village population minus the treatment threshold. Very few villages around the 250-person threshold received roads by 2012, so we limit the sample to villages with populations close to 500 and 1,000. Further, only certain states followed the population threshold prioritization rules as given by the national guidelines of the PMGSY. We worked closely with the National Rural Roads Development Agency to identify the state-specific thresholds that were followed and we define our sample accordingly. Our sample is comprised of villages from the following states, with the population thresholds used in parentheses: Chhattisgarh (500, 1,000), Gujarat (500), Madhya Pradesh (500, 1,000), Maharashtra (500), Orissa (500), and Rajasthan (500).<sup>10</sup> Under the assumption of continuity of all other village characteristics other than road treatment at the treatment threshold, the fuzzy RD estimator calculates the local average treatment effect (LATE) of receiving a new road for a village with population equal to the 10These states are concentrated in north India. Southern states generally have far superior infrastructure and thus had few unconnected villages to prioritize. Other states such as Bihar had many unconnected villages but did not comply with program guidelines.

12
# **3 Data**

The main dataset used in this report is the Jordan Labor Market Panel Survey (JLMPS). This is a longitudinal household survey that was conducted in 2010 and 2016. It contains a host of rich variables on labor market outcomes, gender attitudes and use of digital technologies.<sup>2</sup> Given the large inflow of Syrian refugees experienced by Jordan during this period, the sample includes Jordanian nationals only, aged 15 or more in 2010 and up to 64 in 2016. We restrict the sample to non-forced displaced people who are non-permanently disabled. The sample includes 2,843 women who were present in the 2010 and 2016 waves of the survey, and 2,758 men.

In Table 1 we present descriptive statistics on labor market and technology access variables, and individual and household level characteristics that we use as controls in our econometric analysis. Female labor force participation is low in Jordan. Less than one-fifth of women in our sample were part of the labor force in 2010, and while the percentage increased by 2016, it barely surpassed a quarter of the sample. For the sample of men, the labor force participation rate was high both in 2010 and 2016, averaging 75 percent and 79 percent respectively.

Mobile phone ownership is almost universal and the percentage of women and men living in households that own a laptop increased over time from 10 percent to 28 percent. Access to internet at home more than doubled between 2010 and 2016, increasing from 16 percent to 35 percent.

Figure 1 shows the female labor force participation rate disaggregated by subdistricts. In 2010, 11 of the 84 subdistricts with available data had no women in the labor force. In 2016, the number fell to only 3 subdistricts. On the other end of the scale, in 2010 the female labor force participation rate was below 50 percent in all subdistricts, while in 2016 12 subdistricts had a rate above 50 percent.

> 2Please see Krafft & Assaad (2018) for more information on these data.

5
imports stimulating internal demand by serving as intermediates for production. To test our hypothesis, we present a two-stage methodology in which we first estimate the potential impacts of newly signed trade agreements on trade.

**Figure 1. Imports and exports of goods and services, constant 2010 $ US Panel B.**

<!-- Start of picture text --> $120<br>Imports<br>$100<br>Exports<br>$80<br>$60<br>$40<br>$20<br>$0<br>Billions<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br><!-- End of picture text --> Source: Authors’ elaboration using UN COMTRADE data

**Figure 2. Average weighted applied tariff rates for all products, 1995-2016**

<mark>Source: World Bank</mark>

**Figure 3. Egypt Imports, Industry share.** (WITS, 2022)

**Figure 4. Employment share of top importing industries, 2009-2017, the secondary axis for agricultural products, manufacturing, and services industries.**<sup>**4**</sup>

<!-- Start of picture text --> 25 2.5% 60%<br>50%<br>2.0%<br>20<br>40%<br>1.5%<br>15<br>30%<br>1.0%<br>10 20%<br>0.5%<br>10%<br>5<br>Vegetable Products Mineral<br>Chemical Textiles 0.0% 0%<br>Metals Machines<br>0 Transportation Textiles and Apparel Chemicals Metals<br>Machinery Transportation Minerals<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Vegetables Manufactures Services<br><!-- End of picture text --> 4 Minerals include mining of coal and lignite, extraction of crude oil and natural gas, other mining and quarrying, and the manufacture of coke and refined petroleum products. Transportation includes manufacture of motor vehicles or other transport equipment. Vegetables include growing of cereals, vegetables, fruits, sugar cane, fiber crops, spices, crops, plant propagation, supporting activities for crop production, seed processing and post-harvest crop activities. Chemicals include manufacturing of chemicals, chemical products, and pharmaceutical products. Metals include mining of metal ores, basic metals, metal products and mining support activities. Machinery includes manufacturing of computer, electronic, optical products, electrical equipment, and other machinery.

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left side). Figure 6, on the right side, plots the investment ratio to total population versus real per capita GDP growth for the period 2000‐2008. Interestingly, one can observe an almost total lack of correlation between the investment ratio to population and per capita GDP growth, i.e. a higher level of investment per capita is not associated with the growth rate of income.<sup>20</sup> On the other hand, investment _growth_ and GDP _growth_ are strongly correlated.<sup>21</sup>

**Most of the investment in Togo during the 2000s has been undertaken by the private sector, as the government has not been able to obtain resources ‐ neither from abroad (ODA) nor from domestic sources as a result of a low fiscal revenue base.** Grants (current transfers to government) averaged just over 1% of GDP between 2000 and 2008. More recently, as Togo has been able to re‐engage with the international financial community, official aid has increased to 3.9% of GDP in 2009 and an expected 4.5% in 2010. Fiscal revenues (excluding grants) from central government remained under 17% of GDP during the same period, but are at least expected to remain at the same level during these two years. As a consequence, the public contribution to capital formation has and is expected to increase compared to historical levels (Figure 7).

**Figure 7: Public and Private Gross Capital Formation and National Savings, Togo, 2001‐2010.**

<!-- Start of picture text --> 18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>GFCF, Private GFCF, Public Gross National Savings<br>% of GDP<br><!-- End of picture text --> _Source:_ International Financial Statistics, IMF.

**Togo has a substantially lower than expected gross national saving rate given its level of per capita income (** Figure 8 **).** This fact translates into a persistent current account deficit (Figure 9) that needs to be financed by external sources.

> 21 Real GDP growth vs Real Gross Fixed Capital Formation Growth (USD of 2000), using averages 2000‐2005, gives y = 1.461x – 0.005 with R<sup>2</sup> = 0.424.

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**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

PCs and motherboards, with the latest stage being notebook computers in the period 2001–06, when notebook PC ODMs moved nearly all of their manufacturing from Taiwan to mainland China. As sales of notebook PCs expanded rapidly, surpassing desktop units in the early 2000s, production in Taiwan soared from 2.3 million units in 1995 to a peak of 14.3 million in 2002. However, after 2002, notebook PC production in Taiwan dropped just as rapidly, even as Taiwanese firms produced a larger share of the world’s output, reaching 92 percent in 2008 (see figure 4). This migration contributed to the dramatic expansion of two industry clusters for electronics manufacturing, one in the Pearl River Delta near Hong Kong focused on the assembly of desktop PCs, PC main boards, and peripheral products, and the second in the Yangze River Delta near Shanghai, focused on notebook PC assembly. Smaller Taiwanese contract manufacturers and component suppliers were not able to make this move, leading to a dramatic consolidation among firms specializing in notebook PC production: the number of Taiwanese notebook PC producers fell from 45 in 1993 to only 21 in 2006, with market share shifting dramatically in favor of the largest five producers (Kawakami forthcoming).

**Figure 4** Taiwan, China, Production of Desktop and Notebook PCs, 1986–2006

<!-- Start of picture text --> 16,000<br>14,000<br>production of notebook PCs<br>12,000<br>10,000<br>8,000<br>6,000<br> production of desktop PCs<br>4,000<br>2,000<br>0<br>1986 87 1988 89 1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06<br><!-- End of picture text --> _Source:_ Kawakami (forthcoming) from the Internet Information Search System, Department of Statistics, Ministry of Economic Affairs, China. _Note:_ PCs = personal computers.

The co-evolution of lead firms, suppliers, and platform leaders outlined here reveals a recursive dynamic of outsourcing, upgrading, and further outsourcing; the enabling role of open standards and modular product architecture in the PC sector; the intense competition and rapid product life cycles that drove lead firms to seek to spread risk and lower costs through 29