rafmacalaba/datause-encoder-data huggingface dataset:
14 titanium used in weapon systems for strength. Such a noncompetitive consideration results in a toll that overfinances provision as the rising factor-supply curve diminishes economies of scale of cost thus leading the ratio of average to marginal cost to be less than one. If the toll per unit covers marginal cost, it will then cover average cost.

In the fourth row of cells in Table 1, four examples of club IPGs are listed. Transnational parks include the Great Barrier Reef off the coast of Queensland, Australia, as well as tracts of pristine rain forests worldwide. Even national parks qualify as transnational because of their international visitors. Toll schemes are used for these parks as a means for financing land acquisition, park infrastructure, and park maintenance - i.e., park provision and maintenance. INTELSAT, a private consortium of nations and firms as members, operates as a club to share a communication satellite network in geostationary orbit that carries most international phone calls - and television transmissions. Data from remote-sensing satellites e.g., LANDSAT surveying - are sold to users in a club-like arrangement based on individual demands for surveys. Canals - - and waterways the Suez Canal and the St. Lawrence Seaway permit exclusion and monitoring, thus representing club IPGs.

# _Joint products_

The final category of IPGs, listed in the bottom row of Table 1, consists _of joint products_ for which activities simultaneously yield two or more outputs that may vary in their degree of publicness. Joint products may be purely public, impurely public, or private. As nation-specific private and club good benefits become a greater share of the joint products, market and club arrangements can be applied to finance the activity, thus eliminating the need for any push or coaxing from some governmental body. Suppose that only nation-specific benefits characterize
the first “pilot” examples of integration and automation of data management systems are given by the Asycuda platform for trade taxes, which then laid out the foundations for introducing automated systems at the level of domestic tax administration as well.

Technology can significantly boost all core functions of custom tax administration – especially if through an integrated custom management system as Asycuda that processes all necessary customs documentation such as cargo manifests, customs declaration, payments, and other regulatory requirements in a timely fashion (UNCTAD, 2020). First, technology could help to more clearly identify the taxable base, such as the value and quantity of im– ported goods for instance, cargo information can be captured by scanning barcodes on shipments at the port of departure and communicating the information in real time to Asycuda. Likewise, integrated systems requiring a unique taxpayer identification number from traders would significantly help the tax administration identify the tax base and seamlessly connect traders with other tax databases (World Customs Organization, 2014).

Second, in relation to the above, technology could greatly improve the enforcement of tax collection and monitoring of performance. Using the information on tax liabilities and taxable entities, a sophisticated IT system could embed independent valuation reports from third parties and introduce explicit monitoring tags informing inspectors of high-risk declarations. In the most advanced versions of Asycuda, statistical tools and algorithms are included to detect fraud. Artificial Intelligence systems such as pattern recognition can be of great help in supporting risk assessment and profiling policy (World Customs Organization, 2014).

Lastly, technology could, more broadly, speed up the customs processes of importing and exporting of goods, incentivizing foreign traders through a smoother and less burdensome cross-country trading experience. In this sense, Single Windows are one-stop-shop electronic systems, usually integrated with Asycuda, that allows traders to submit information at a single-entry point to fulfill all import and export regulatory requirements. By reducing interactions with inspectors, Single Windows streamline and standardize traders’ 16
# **V. Estimating Program Impacts Using the ICS-ENIA Panel**

We now turn our attention from the cross-section to the linked ICS-ENIA panel to more fully exploit the availability of annual data on a range of final outcomes. Our objective here is to estimate the longer-term impacts of program participation controlling for the effects of observed and unobservable productivity attributes, and to test for differences in the treatment effects of four broad types of support programs – business development services (BDS), cluster programs, technology development, and credit programs. Also of interest is investigating the time effects of program participation, and how quickly or slowly program impacts are realized over time. Finally, we are interested in testing the sensitivity of our program impact estimates to the possibility that program participation inhibits firm exit from our panel data.

As noted in the previous section, traditional PSM methods are not well set up to accommodate the specific structure of our data. In this section, we adopt a more flexible regression approach that allows us to estimate treatment effects taking into account differing entry points into programs, use of multiple types of programs, widely varying time since program participation, and year specific shocks. We rely on fixed-effects models to eliminate the effects of unobserved firm heterogeneity as a source of bias in estimates of program impacts. In the spirit of the PSM approach, we continue to focus on the matched sample of treatment and control group firms in the region of common support identified by their propensity scores.

Consider an expanded equation (1) in levels: which includes the program indicator _D_ , an interaction term between _D_ and a variable _YRS_ measuring years-since-first-participated in the program, and the time-invariant error term<sup>_v_</sup> _i_<sup>. Estimating equation (8) in levels is likely to lead to biased estimates of</sup> because of the omitted variable<sup>_v_</sup> _i_<sup>, with the direction of bias being determined by the</sup> correlation between<sup>_v_</sup> _i_<sup>and Di. When the correlation is negative, as in the case illustrated</sup> in Figure 1 where less productive firms are attracted to government support programs, estimates of  are likely to be biased downwards. The fixed effects estimator addresses this possibility by taking deviations from variable means so that equation (8) can be rewritten as: where firm variable means are denoted by a single subscript _i_ . Like first differencing, the fixed effects transformation eliminates the potentially confounding effects of<sup>_v_</sup> _i_<sup>.</sup> This framework allows us to address several issues. First, are estimates of program impacts potentially biased by unobserved firm heterogeneity? We compare treatment effects estimated from a levels model (equation 8) and a fixed-effects model (equation 9) 20
_Mexico: The 1997 Pension Reform_ _39_ overall strategy, will have a limited contribution in the near term because of the relatively young age structure of the population.

# **Navigating in a Sea of Skepticism**

Prior to the initiation of the reform, there was concern regarding the lack of public confidence and the low level of financial sophistication across a broad spectrum of the population as key factors hindering the growth of institutional savings. This stems from a public perception that the government management of funds or provision of services has been both inefficient and often corrupt. The public is skeptical that contributions to government and private savings schemes will result in later benefits to them. Instead the public fears that contributions will be used for excessive public expenditures rather than for investment in economically viable and desirable public projects. Moreover, the proposed investment regime for the unique SIEFORE will not help to dissipate this perception, since most of the pension money will initially be invested in Government debt securities. In addition, government or private savings schemes have often not defined individual accounts clearly, and the quality of information disclosed to individual savers has been poor, further reducing confidence.

To address these concerns, the Government has initiated a public awareness campaign to bolster support for the reform and to encourage participation in the new pension system. It should be also highlighted that there has been an extremely positive initial response to the new system - as demonstrated by the affiliation of over 95% of potential workers by December 1997.

<!-- Start of picture text --> Worker Affiliation<br>(Cumulative from February - December 1997)<br>12,000,000 100.0<br>- 90.0<br>10,000,000 - 80.0<br>*a 8,000,000 - 700 T<br>0 I ~~~~~~~~~~~~~~~~~60.0 0<br>co 6,000,000 I 50.0 ¢<br>_ ~~~~~~~~~~~~~~~~~~40.0 m<br>o 4,000,000 I3.0 S<br>Z ~~~~~~~~~~~~~~~~~~~~~~20.0<br>2,000,000<br>10.0<br>0 ~~~~~~~~~~~~~~~~~~~~0.0<br>co < 5 ' E .2 E E<br>LL 0<br>Month C°<br>|TotalAffiliated in Month % S Workers in t<br><!-- End of picture text -->
33

# **6. CONCLUSION**

The importance of better access to policy information on global services trade was the impetus for our attempt to put together this database on services trade restrictiveness. We hope that the information available through the database can be of use to researchers, policymakers and business. Similarly, we hope that this product could act as a medium for further discussions on the topic of services trade, and that such discussions and feedback would assist in continually improving the database in the future.

# **REFERENCES:**

- Borchert, Ingo, Batshur Gootiiz and Aaditya Mattoo (2012), “ _Policy Barriers to International Trade in Services: Evidence from a New Database_ ”, World Bank Policy Research Working Paper, forthcoming.

- Deardorff, Alan V. and Robert M. Stern (2008), “ _Empirical Analysis of Barriers to International Services Transactions and the Consequences of Liberalization_ ”, Chapter 5 in: Aaditya Mattoo, Robert M. Stern and Gianni Zanini (eds.), _A Handbook of International Trade in Services_ , Oxford University Press, pp. 169-220.

- Hoekman, Bernard (1996), “ _Assessing the General Agreement on Trade in Services”_ , in: Will Martin and L. Alan Winters (eds.), _The Uruguay Round and the developing countries_ , Cambridge: Cambridge University Press, pp. 88-124.

- OECD (2011), “ _Services Trade Restrictiveness Index: Opening the Way for Services Trade Liberalization”_ , downloadable at http://www.oecd.org/trade/stri.

- OECD (2009), “ _Methodology for Deriving the STRI_ ”, paper presented at the OECD Experts Meeting on the Services Trade Restrictiveness Index (STRI), 2-3 July 2009, Paris.
<!-- Start of picture text --> i<br>=<br>¢ Bt<br>fh - i Ea OP 4 y a bald ed<br>* & i aN ) \ 4<br>SF- At g a NS = SF<br><!-- End of picture text --> <!-- Start of picture text --> ' ‘| as t+ 4) pvoody + - ; 4 - " ‘<br>. ; z<br>&, ' Aion 91. JPa) 4 Cy) |\ : ee vef, = ats P -<br>WL " ap! i &, - ; ° owe "3 ¥ r<br>- = - ) .<br>TRO 4250s SEPTEMBER 2016 ’ ;<br>Thiseresmap was by the Map , aed =4<br>on this mop donot bly onthe = 8% [aS 23% r<br>part of The World Bank Group, any ‘ us :<br>judgment on the legal status of any “< my - ‘<br>territory, or any endorsement or 3 .<br>acceptance of such boundaries. 2<br><!-- End of picture text -->
Figure 13: The incidence of involuntary temporary employment is higher in Sweden than in other Nordic countries across age groups <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>EU15 Sweden Nordic EU15 Sweden Nordic EU15 Sweden Nordic<br>average average average average average average<br>Ages 15- Ages 25- Age 65+<br>64<br>Could not find a permanent job Did not want a permanent job<br>temporary employment, percent, 2013<br>Temporary employees by main reason for<br><!-- End of picture text -->

Source: Eurostat, Labor Force Survey database, 2014 edition. Note: The Nordic average excludes Sweden.

The percentage of adult temporary workers (ages 25–64) in involuntary temporary employment is also higher in Sweden than in other Nordic countries (78 percent of adult temporary workers in Sweden are on involuntary temporary contracts, compared with an average of 69 percent for other Nordic countries). Across all EU15 and Nordic countries, involuntary temporary employment is very low among workers age 65 or above. The fact that most temporary employment for older workers is voluntary mitigates the concern about duality of the labor market for this age group. But achieving some convergence in employment protection legislation between temporary and permanent employment is an important step to facilitate the integration of other fragile groups into the Swedish labor market.

# **_Part-time employment_**

Another form of nonstandard employment commonly used in EU and OECD countries is part-time employment. Just as with temporary employment, part-time employment can also be the result of a choice made by workers—often with the aim of reconciling work and other commitments, such as education or family life—or the result of a decision made by companies. For example, companies might reduce working hours to cope with changing business conditions or to deal with strict labor regulations and high labor costs. An indicator that can help distinguish whether part-time 27
**Table 1. Population Aging in China**

||1995|2000|2030|2050|2100|
|---|---|---|---|---|---|
|Proportion<br>of population<br>over<br>age 60|9|10|22|26|30|
|Demographic<br>dependency<br>ratea|17|18|43|53|63|
|System dependency<br>rateb|21|24|42|55|71|
|Reformed system dependency<br>ratec|21|15|25|33|46|
|Average age of population|30|31|38|40|42|
|Expected duration of retirement<br>with current retirement age|19|20|23|25|26V|
|Expected duration retirement-if<br>retirement<br>age increases|19|19|19|19|_19C_|
|% Workers who are urban-if no<br>urban in-migrationd|26|26|25|25|24|
|**i**Workers who are urban-with<br>urban in-migration'|26|28|39|46|49|
|% Workers who are urban+TVE<br>(rural industry)|47|54|82|84|85|

Source: World Bank Population projections and simulations by authors.

- a Population over age 60/ population aged 20-59

- b Pensioners/covered workers, based on simulations with unchanged retirement age, rural/urban division and coverage

- Pensioners/covered workers, based on simulations with increased retirement age, rural/urban migration and expanded coverage. See Table 2 for rate of increase in retirement age, rate of urban in-migration and coverage expansion assumed.

- d By "urban" is meant existing cities. As population and industry grows in "rural" areas they too will become "urbanized", but they are not considered "urban" in this table.

- e These numbers apply to 2065, not 2100

37
**Figure 8: Nationality of Formal Enterprise Owners**

<!-- Start of picture text --> Timorese-<br>Foreign Joint<br>Ownership<br>Foreign 8%<br>21%<br>Timorese Timorese<br>living in Timor- living in Timor-<br>Leste after Leste before<br>1999 1999<br>6% 65%<br><!-- End of picture text --> <!-- Start of picture text --> Source: UES, 2004.<br><!-- End of picture text -->

- iii. _Age:_ An analysis of ownership of the enterprises by age shows that owners are on average below 40 years of age.  The mean age for owners of formal enterprises is 39 years, the range is 18-82 and their median age is 38.5. In the case of informal enterprises, the average and median ages of owners too are only slightly different at 37 and 35 years respectively (see Figure 10).

**Figure 9: Ownership of enterprises by sex**

<!-- Start of picture text --> 100 83.85<br>80<br>57.2<br>60 42.7<br>40<br>16.15<br>20<br>0<br>Formal Informal<br>Male Female<br>Percent<br><!-- End of picture text --> Source: UES, 2004.

28. _Location:_ Where do these enterprises operate from and does this give us an indication of their size and scale of operation ? The results are presented in Figure 11. Only one-fourth of the informal and about 38 percent of formal enterprises have a “formal” place of business such as a factory, shop or kiosk. Conversely, almost 61 percent of the informal and about 43 percent of the formal enterprises are home-based. While the former is no surprise, the latter points to the fact that even formal enterprises are small and have characteristics of informality.

29. Overall, the snapshot that emerges is not of formal establishments that have manufacturing businesses, but that of enterprises that are small, operating from the home or from construction sites (perhaps as construction contractors rather than large construction companies). Informal businesses clearly appear to be very small family owned enterprises that operate from the home, or streets and market stalls. This is consistent with the sectors in which these enterprises operate (trading and petty services dominating).

15
Using these andu; = ;, we ha e: <!-- Start of picture text --> MOLS — Mer 0<br>0 2o,,<br>yols _ pF (yi — aj Paya} —(a% — (yi — 27 8)?)(yi — @F 8) xy<br>—(a% — (yi — 278)?)(yi — 27 B) at (a0 — (yi — a7 B)?)?<br>O27 ee 01x<br>0 x1 uu o2<br>MMIB — _B F(1—F) —(F+¥i- L)(L= 2F) po FLYi-1 wat<br>m F2(1—F) F(1—F) a<br>— _p, ;F( i)(l—fP( i)FC i)) vat = _yMLB<br>rary ry vit? aya; ry Li<br>7<br>punem _ 7 ff" f?— (fy aaiar “oea? Lff 0.al oeQu; i" 0 x 0 1x4<br>ia. 2<br>Ors By E ( F(a) 2+<br>_1 (5)i(ai))?| Be [nse]T ys _ _ye<br>Oy 0 7 (Zan)? om<br><!-- End of picture text -->

Also, note the following: 9°" (at 0) = gC (at, 0) =1— FC i) = 9"? (a7, 0).

Therefore, we ha e: OLS _= YM pe/o M, fa/@u Upp /Cu My? = Mfe MLC Lpe/Ou M, = .

0 29
_Figure 26: Real change in services consumption during crisis (2014‐2016) for major socio‐economic groups depending on initial level of food consumption, %_ <!-- Start of picture text --> 2 expenditure on services per capita in 2014, in BYN in prices of 2014<br>0 hh_single_parent<br>15 20 25 30 35 40 45 50 55<br>-2 retiree<br>hh_3more_kids nonemployed<br>-4 hh_2kids child<br>rural large_cities<br>other_edu<br>-6<br>average<br>small_cities<br>-8 hh_no_kidsworking_age hh_1child<br>employed<br>-10 sec_specialized_edu<br>minsk<br>high_edu<br>-12<br>partly_employed<br>-14<br><!-- End of picture text --> Note. Services exclude utilities, education and healthcare. Source: Authors’ estimates based on HSS data.

# 5.3. Savings

Precautionary savings could be an important consumption smoothing mechanism for households, and as such, the evolution of saving (and dissaving) during the crisis is of interest to this inquiry. Unfortunately, the HBS data do not have data on the savings stock or loans taken, but do have data on saving, dissaving and loans paid. In 2014, 42 percent of the population reported positive net savings, while 19 percent of the population dissaved (negative net savings). In 2016, at the height of the crisis, only 35 percent of the population saved a portion of their incomes, while the number of those who dissaved increased to 24 percent. Those with relatively higher incomes also had the highest dissaving rates in 2016 (32 and 38 percent for 8‐9<sup>th</sup> and 10<sup>th</sup> decile correspondingly).

In other words, while savings are an important insurance mechanism, they are available mostly for the well‐ off households that were able to accumulate significant funds in the pre‐crisis period. Indeed, in the pre‐crisis year 2014 only 23 percent of the poorest decile reported savings, and they amounted only to 2.5 percent of their disposable income, while 49 percent of the top decile had savings amounting to 4.3 percent of their disposable income on average. While many households engaged in dissaving before the onset of the crisis, and even more so during the economic recession, average net savings rates and shares of saving in disposable income remained positive for almost all socio‐economic groups.

23
Policy Research Working Paper 9184

# **Abstract**

Agglomeration boosts economic growth. A vast literature has empirically assessed the effects of agglomeration by estimating the city population elasticity on wages. This conventional approach is not necessarily suitable for analyzing urbanization at the early stage in developing countries, where a majority of urban workers engage in self-employment and/or informal jobs. Focusing on one of the poorest and largest among those countries, this paper sheds light on an aspect of urbanization and agglomeration: the transition in the mode of labor from self-employment/informal jobs to wage employment/formal jobs. Applying the instrumental variable approach to national labor force survey data sets, the analysis underscores several labor market transitions across space in urban Ethiopia. First, the town population size and the share of workers with wage employment are strongly correlated. The probability of engaging in wage work increases by 4.5 percentage points with a log increase in population size. Second, this relationship is particularly strong among disadvantaged workers, such as the female, young, and/or less educated population. Finally, the study documents higher labor force participation and lower underemployment in larger towns.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at snakamura2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development 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 names 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team