rafmacalaba/datause-encoder-data huggingface dataset:
industrial composition in Georgia. For example, education, health, and culture are mainly statefinanced whereas manufacturing, trade, and finance are privately-financed. Therefore, industrybased analysis can shed further light on the findings of the public/private sector analysis. When the index is defined in terms of 13 industrial categories, as expected, we obtain a higher value, indicating that 47.47 percent of workers have to be moved in order to achieve gender balance in employment.

Next, we consider the Duncan index of occupational segregation. As we move from the least detailed breakdown (4 skill categories) to the most detailed (299 four-digit categories), the Duncan index rises from 0.3463 to 0.7033. The latter index is the closest to the commonly reported index out of the three occupation-based indices in Table 2. It reveals the degree of occupational segregation much higher than what is observed in industrialized countries. For example, the recent estimates of the Duncan index for the US based on the three-digit 1990 Census occupational classifications indicate the value of about 0.50 in 2011 (Hegewisch and Hartmann, 2014).

During the 2004 – 2007 expansion the degree of industrial and occupational segregation increased (Figure 3). After 2009 however, a downward trend is discernable in the index calculated using all, but the most detailed, categories (Figure 3). This would indicate a decrease in the degree of industrial and occupational segregation. With that said, these changes are not large, which is not unusual. For example, Anker (1998) reports that between 1973 and 1990, the Duncan index in the Netherlands decreased from 0.671 to 0.588 when measured in threedigit SIC categories. On the other hand, in Japan between 1970 and 1990 it hardly changed, if anything increasing from 0.526 to 0.529. In the United States, after declining from about 0.70 to 0.50 between 1970 and mid-1990s, the index stalled at about 0.50 in 2011 (Hegewisch and Hartmann, 2014).

Figure 3. Duncan index trends in Georgia.

11
output linkages. The trade shock they refer to is the rising import competition from China. They find that inter-industry linkages do in fact magnify the employment effects of trade shocks.

Our paper is related to literature that uses the Bartik instrument to isolate exogenous changes in labor demand. Blanchard and Katz (1992) construct exogenous employment shocks at the state level from 1970 to 1989, and estimate the effects of the shocks on unemployment and wages. Since their Bartik instrument includes all industries in all sectors, they do not estimate the spillover effects of job losses. Moretti (2010) uses three years of data (1980, 1990 and 2000) and a Bartik-typed instrument to estimate the _long-term_ employment multiplier across U.S. cities. Although we use a similar approach to Moretti (2010), our focus is different. While Moretti’s focus is on the long-run “geography of jobs” (i.e. how jobs are allocated across the country), we are interested in identifying the input-output linkage channel and estimating the short-run job loss spillover from tradable industries to supporting industries.

Our paper is also related to a growing amount of literature on labor market dynamics during the Great Recession. Ohanian (2011) argues that the decline in economic output and income during the Great Recession was due to severe distortions in the labor market, and not due to declines in capital or productivity. Hoffman and Lemieux (2016) show that the collapse in construction employment was a major factor contributing to the fall in overall employment. <mark>Kroft et al. (2016) show that the massive 2008 employment collapse, combined with the slow recovery in demand prevented employment from picking up quickly. Similarly, Foster et al. (2016) find that job destruction in the 2008–2009 period was accompanied by a very large drop in the job creation rate, which limited the scope for re-allocation of jobs and pushed many laidoff workers into long-term unemployment.</mark> 6
# **3  PATTERNS OF SERVICES TRADE POLICY**

We begin by describing the pattern of policy across economies, sectors and modes in 2016. We then assess how policy has changed in the decade after the financial crisis.

# **3.1  Services trade policy in 2016 (68 economies)**

In 2016, openness to services trade differed substantially across the 68 economies currently included in the STPD.<sup>13</sup> In principle, the STRI can range from 0 (completely open) to 100 (completely closed). At the aggregate level, i.e. looking across all sectors and modes, the median STRI value is about 41, and the STRI scores lie strictly below 53 for 90 percent of the economies. The less restricted economies have an overall STRI below 30, while the most restrictive economies show an overall STRI of approximately 65.

In our sample, 39 economies are classified as high income in 2016, while 16 are upper-middle income and 13 lower-middle income. Most economies included in this sample are generally open to services trade (i.e. STRI value below 50), and none is highly restrictive at the overall economy level. While poorer economies have on average more restrictive policies (STRI value of around 46), most higher income economies are clustered together around lower levels of restrictiveness, around an STRI value of 40 (Figure 3).

Policies in middle income economies display a wide range, from an STRI of about 21 to about 65. A few economies such as Colombia, Dominican Republic, Ecuador, Kenya, Peru, South Africa or Pakistan are relatively open. Interestingly, some of the most restrictive policies today are visible in the fast-growing economies of Asia (China, India, Indonesia, Malaysia, Philippines and Thailand), as well as in the Middle East and Northern Africa, including the Arab Republic of Egypt, Oman and Tunisia. This pattern is consistent with the recent observation that East Asian economies attract high inflows of investment into services sectors (Mode 3) in spite of high policy restrictiveness, including ownership limitations (Barattieri et al., 2016).

## **Figure 3: Overall STRI by economy and level of income68 economies, 2016**<sup>**14**</sup>

<!-- Start of picture text --> IND IDN<br>MMR CHN<br>PAN<br>PHL EGY THA MYS OMN<br>VNM TUN LUX<br>KEN BGD PAK NGA UKR LKAPERZAFCOLBRADOMCRI MEXARGURY CHLKAZTURLVAGRCRUSHUNPOLPRTLTUESTSVKSVNCZEISRESPITAKORNZLFRAGBRFINBELCANDEUAUTAUSISLDNKSWENLDCHT USAHKGCHEIRLNOR SGP<br>JPN<br>ECU<br>8 9 10 11<br>Log GDP/capita 2016<br>80<br>60<br>40<br>STRI 2016<br>20<br>0<br><!-- End of picture text -->

> 13 The analysis is based on data for 68 economies.  Data for additional economies (Jordan, CEFTA Parties and other African economies) are currently being collected and processed for future inclusion in the STPD.

> 14 It encompasses 1,534 economy-subsector observations from across 68 economies and 23 subsectors.

12
SSB could issue recommendations on how the institution could best fulfill its social role as well as promote Islamic finance.<sup>14</sup> In addition to internal corporate arrangements, national regulators and international standard setters implement guidelines for SSBs. These often refer to SSBs’ general duty to ensure _Shariah_ compliance of transactions and, less frequently, indicate areas of competence, composition and decision-making. Table I provides an overview of practices in selected countries that have introduced guidelines or legislative references on the functioning of SSBs.<sup>15</sup> Table I - Regulations on Internal _Shariah_ Advisory**

|Country|SSB terms<br>of reference|SSB<br>composition|SSB<br>decision-<br>making|SSB<br>appointment<br>& dismissal|SSB Fit and<br>Proper<br>Criteria|
|---|---|---|---|---|---|
|**_Bahrain_**|�|�|unspecified|�|�|
|**_DIFC*_**|�|�|unspecified|�|�|
|**_Indonesia_**|�|unspecified|unspecified|�|�|
|**_Jordan_**|�|�|�|�|unspecified|
|**_Kuwait_**|�|�|�|unspecified|unspecified|
|**_Lebanon_**|�|�|unspecified|�|unspecified|
|**_Malaysia_**|�|unspecified|unspecified|unspecified|�|
|**_Pakistan_**|�|�|unspecified|�|�|
|**_Philippines_**|�|�|unspecified|unspecified|�|
|**_Thailand_**|�|�|unspecified|�|�|
|**_U.A.E._**<br>∗Dubai Internat<br>** See Annex I f|�<br>ional Financial Cen<br>or details of the lega|�<br>tre<br>l bases and theprovi|unspecified<br>sions of these regul|�<br>ations|unspecified|

∗ Dubai International Financial Centre ** See Annex I for details of the legal bases and the provisions of these regulations _Source: Official Government websites and Central banks’ Annual Reports._ The functioning of SSBs raises five main issues of corporate governance: independence, confidentiality, competence, consistency, and disclosure. The first

> 14 The 2002 Annual SSB report of Islamic Bank Bangladesh Ltd contains suggestions on investment strategies, such as housing schemes, to help the poor and on research policy, such as the publication and translation of Islamic banking books. (www.islamibankbd.com, last visited April 18, 2005). 15 Only those countries are included where authorities have implemented laws, acts or issued circulars and regulations on internal _Shariah_ Supervisory Boards. Annex I details the legal bases and the provisions of these regulations.
**Kornai, Janos.** 1990. _The Road to a Free Economy. Shifting from a Socialist System: The Example of Hungary_ . New York: W. W. Norton.

**Kornai, Janos.** 2001. “Ten Years after the Road to a Free Economy.” In Boris Pleskovic and Nicholas Stern, eds. Annual World Bank Conference on Development Economics 2000, pp.46-66, Washington DC, World Bank, 2001.

**Kornai, Janos, Eric Maskin, and Gerard Roland.** 2003. “Understanding the Soft Budget Constraint.” _Journal of Economic Literature_ , 41(4): 1095-136.

**Laffont, Jean-Jacques, and Jean Tirole** . 1993. A theory of Incentives in Procurement and Regulation. Cambridge, MA: The MIT Press.

**Lane, Julia, Harry Broadman and Inderjit Singh.** 1998.  “Labor Flexibility, Ownership and Firm Performance in China.” _Review of Industrial Organization_ , 13: 621635.

**LaPorta, Rafael, and Florencio Lopez-de-Silanes.** 1999. “The Benefits of Privatization: Evidence from Mexico.” _The Quarterly Journal of Economics,_ 114: 11931242.

**Lau, Lawrence J., Yingyi Qian, and Gerard Roland** . 2000. “Reform without Losers: An Interpretation of China's Dual-Track Approach to Transition.” _Journal of Political Economy_ , 108(1): 120-43.

**Lehmann, Hartmut, Jonathan Wadsworth, and Alessandro Acquisti.** 1999. “Grime and Punishment: Job Insecurity and Wage Arrears in the Russian Federation.” _Journal of Comparative Economics_ , 27(4): 595-617.

**Lipton, David, and Jeffrey Sachs.** 1990. “Creating a Market Economy in Eastern Europe: The Case of Poland.” _Brookings Papers on Economic Activity_ , 1990(1): 75-147.

**Lízal, Lubomír, and Jan Svejnar.** 2002. “Investment, Credit Rationing and the Soft Budget Constraint: Evidence from Czech Panel Data.” _The Review of Economics and Statistics_ , 84(2): 353-70.

**Maurel, Mathilde.** 2001. “Investment, Efficiency, and Credit Rationing: Evidence from Hungarian Panel Data.” William Davidson Institute Working Paper 403.

**Megginson, William L.** 2005. _The Financial Economics of Privatization_ . New York: Oxford University Press.

**Megginson, William L., and Jeffrey Netter** . 2001. “From State to Market: A Survey of Empirical Studies on Privatization.” _Journal of Economic Literature_ , 39(2): 321-389.

37
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POLICY RESEARCH WORK[NC, PAPER 1 715

# Summary findings

Canagarajah, Ngwafon, and Thomas present a poverty profile of Nigeria for the years 1985 and 1992 and show how poverty changed between those years. They show the nature of poverty in both years for different deciles of the population, different levels of education, different ages for household heads, and different parts of the country, including urban and rural areas There is no official poverty line in Nigeria, so they selected one based on two-thirds of average per capita spending in 1985 (N 395 a year per capita in 1985 prices). N198 (one third of mean per capita household spending) is used to indicate extreme poverty. The bottom 17 to 18 percent of income distribution had a lower standard of living in 1992 than in 1985 by any measure, but all other income groups had a higher standard of living.

An increase in mean per capita household spending reduced the proportion of the population in poverty from 43 percent to 34 percent.

But the benefits of growth were not shared equally by different parts of the country. Growth of household spending was faster in the southern and middle parts of the country and slower in the north. Poverty in Nigeria is overwhelmingly rural and regional, but is also greatly influenced by age, education, and the nature of employment. Most of the poor, especially the extremely poor, are uneducated.

The 8.9 percent decline in poverty was the net result of a + 13.6 percent growth factor and a -4.7 percent income distribution factor.

Improving the quality of data collection and analysis and establishing systems for monitoring poverty are important for policymaking. Canagarajah, Ngwafon, and Thomas recommend an integrated living standards survey to provide baseline data and to permit analysis of household behavior.

This paper - a joint product of Human Development 3 and Institutional and Social Policy, Africa Technical Families - is part of a larger effort in the region to analyze welfare and poverty issues. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Betty Casely-Hayford, room J8-270, telephone 202-473-4672, fax 202-473-8065, Internet address bcaselyhayford@q)worldbank.org. January 1997. (84 pages)

**_Thbe Policy Research Working Paper Series disseminates the findings_** _of_ **_work in progress to encourage the exchange of ideas about_** _developmtent issues. An_ **_objective_** _of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the n.-'nes of the authors and should be cited accordingly. The findings,_ **_interpretations, and conclusions expressed in this_** _paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._

Produced by the Policy Research Dissemination Center
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE than previously used (such as stand posts, boreholes, and improved latrines) for meeting the MDGs, thereby saving $165 million annually.

**Table 20. Funding gaps, by sector**

|$ millions|||||||
|---|---|---|---|---|---|---|
||ICT|Irrigation|Power|Transport|WSS|Total|
|Needs|(288)|(18)|(785)|(423)|(574)|(2,088)|
|Spending*|266|7|457|423|322|1,474|
|Within sector reallocation|22|0|118|0|20|160|
|Potential efficiency gains|78|0|709|401|128|1,317|
|**Funding gap**|**—**|**(11)**|**—**|**—**|**(104)**|**(115)**|
|Across sectors reallocation potential|62|0|0|2,558|0|2,620|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology. * traced to needs. — = Not available.

# What else can be done?

While Angola’s infrastructure reconstruction needs are large in absolute terms, they look manageable relative to the size of the country’s fast-growing economy. Moreover, Angola has already amply demonstrated its commitment to channeling significant volumes of petroleum rents toward infrastructure development. A significant expansion in power generation capacity has taken place, and a huge road investment program is currently under way. Consequently, Angola is one of a very few African countries that does not really face any significant infrastructure funding gap.

But this conclusion is contingent on Angola capturing the sizeable $1.3 billion of resources that are each year being lost to inefficiency, equivalent to almost 5 percent of GDP. Two policy measures alone would suffice to capture the bulk of these resources, and deserve priority attention. The first is the increase of power tariffs toward cost-recovery levels. The second is an easing of the pace of the roadsector investment program to reduce the problem of underexecution of budgetary resources.

Given the urgent pressure to reconstruct Angola’s infrastructure platforms, there is some evidence that decisions have not always been optimal. For example, the expansion of generation capacity has not been matched by reinforcements in transmission and distribution that would allow the power to flow through to end users. And the scale of Angola’s road-investment program seems to have outstripped the implementation capacity of the key sector institutions. There is also evidence that the water and sanitation sector may not yet have received as much attention as it deserves.

In addition, Angola’s considerable achievements in infrastructure expansion over the past few years have been almost entirely funded from public investment, with significant support from non-OECD financiers. Angola has only captured about 0.4 percent of GDP in private investment for infrastructure, compared with numerous African peers that have managed to capture 1–3 percent of GDP in private investment (figure 27). Given the size and vibrancy of Angola’s economy it ought to be feasible in the 42
**Table 6. Regressions of Changes in Self-Reported Food Insecurity against Economic Growth**

|Regression No.|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Means of deflating|GDP|Total|Food|GDP|Total|Food|
|economic growth|deflator|CPI|CPI|deflator|CPI|CPI|
|Outliers removed?|No|No|No|Yes|Yes|Yes|
||Robust|Robust|Robust|Fixed|Fixed|Fixed|
|Regressor|regressor|regressor|regressor|effects|effects|effects|
|Economic growth Coefficients|−0.24***|−0.14***|−0.12***|−0.21***|−0.22***|−0.23***|
||(0.06)|(0.04)|(0.04)|(0.08)|(0.06)|(0.07)|
|Elasticities|−0.56**|−0.55***|−0.47**|−1.25**|−0.93***|−0.82***|
||(0.27)|(0.20)|(0.19)|(0.48)|(0.29)|(0.30)|
|No. of observations|291|275|271|271|256|252|
|No. of countries|120|112|111|113|106|105|
|R-squared|0.05|0.04|0.03|0.06|0.05|0.05|

_Source_ : Dependent variables are from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and food and total CPI data are from the ILO (ILO 2011). _Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. The robust regressions are estimated using the _rreg_ command in stata, with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. Outliers are identified based on _dfbetas_ greater than 0.20. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

In table 7, we run the same regressions with the addition of separate price change indicators to determine whether certain types of inflation have additional explanatory power over real economic growth rates. Specifically, we add inflation in the total CPI and food CPI relative to the nonfood CPI. The first represents an aggregate price effect, and the second represents a relative food price effect. Table 7 shows that overall inflation has a significant positive effect on 22