rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text -->
-<br>Figure 9 b. Log per capita GDP and Services Export Sophistication, 2000<br>IRL<br>CHE<br>GBR<br>IND ISR<br>JPN<br>USA<br>FRA<br>PAK LKA ISL<br>CHN<br>NPL<br>BGD<br>6 7 8 9 10 11<br>log real GDP per capita (2000, PPP)<br>Source: IMF Balance of Payments, 2008 and authors’ calculations.<br>Note: EXPY is calculated as described in the paper<br>-<br>Figure 9 c. Log per capita GDP and Services Export Sophistication, 2006<br>GBRCHEIRL<br>IND<br>ISR<br>JPN USA<br>LKACHN FRAISL<br>PAK<br>NPLBGD<br>6 7 8 9 10 11<br>log real GDP per capita (2006, PPP)<br>9.8<br>9.6<br>9.4<br>9.2<br>log Service EXPY (2000)<br>9<br>8.8<br>10<br>9.5<br>9<br>log Service EXPY (2006)<br>8.5<br><!-- End of picture text -->

_Source: IMF Balance of Payments, 2008 and authors’ calculations. Note: EXPY is calculated as described in the paper_ 19
Figure 2.13. Population shares (age, location, dependency) <!-- Start of picture text --> 90 4.5<br>85<br>80 4.3<br>75<br>70<br>4.1<br>65<br>60<br>55 3.9<br>50<br>45<br>3.7<br>40<br>35<br>30 3.5<br>Share age 0-14 Share age 15-64<br>Dependency ratio Urban share<br>Share age 65+<br>having been higher for the more educated.<br>Figure 2.14 Unemployment (total, by education, youth)<br>14<br>12<br>10<br>8<br>%<br>6<br>4<br>2<br>0<br>Total Primary educated<br>Secondary educated Tertiary educated<br>Age 15-24<br>Share age 65+ (%)<br>Shares 0-14 & 15-64 - ratios (%)<br><!-- End of picture text --> Unemployment data are of dubious quality in countries like Pakistan with substantial employment in subsistence agriculture and other informal sectors. Nevertheless, available data, summarized in Figure 2.14, suggest that the total unemployment rate increased in most years during the period 1990-2005, after which it declined up to 2008, ending up with a net increase for the full period. Throughout the full period, the youth (15-24) unemployment rate was considerably higher than the total rate. A more limited time series for unemployment rates by level of education suggests that, between 2005 and 2008, these rates converged after initially having been higher for the more educated.

Between 1990 and 2008, Pakistan raised the share of the total population that was employed by 15.3 percent, from 27.7 to 32.0 percent. This suggests that the country managed to benefit from the above-noted demographic dividend (from a higher population share in labor force age). A simple decomposition (see Table A2.1) attributes this increase to the following factors: 84.0 percent to the change in the population age structure; 32.3 percent to an increased labor force participation rate; and -16.3 percent to the change in the (un)employment rate. In sum, 11
**Table 7: Including Indicators for Federalism and Ideology, with DIVIDED**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|Divided|-0.033<br>(0.019)*|-0.018<br>(0.012)|0.006<br>(0.007)|
|Turnout|0.126<br>(0.045)***|0.078<br>(0.032)**|0.023<br>(0.019)|
|Federal|-0.038<br>(0.02)*|-0.039<br>(0.016)**|0.004<br>(0.006)|
|Right-wing government|-0.026<br>(0.015)*|-0.016<br>(0.01)|-0.012<br>(0.008)|
|Majoritarian System|-0.01|-0.01|0.001|
|(=1, 0 if Proportional)|(0.023)|(0.015)|(0.007)|
|Presidential System|-0.021|-0.031|0.012|
|(=1, 0 if Parliamentary)|(0.017)|(0.014)**|(0.006)**|
|Regular|0.007|0.02|-0.006|
|Elections<br>(=1, 0 otherwise)|(0.013)|(0.01)**|(0.006)|
|Observations|129|129|147|
|R-squared|0.67|0.78|0.42|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample: Argentina, Australia, Austria, Belgium, Belize, Bolivia, Botswana, Brazil, Canada, Chile, Colombia, Costa Rica, Cyprus, Denmark, Ecuador, El Salvador, Finland, France, Germany, Greece, Guatemala, Honduras, Iceland, India, Ireland, Italy, Japan, Mexico, Nepal, Netherlands, New Zealand, Nicaragua, Norway, Paraguay, Peru, Philippines, Portugal, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Turkey, United Kingdom, United States, Uruguay, Venezuela

33
<!-- Start of picture text -->
-.||I|<br>||<br>||<br>||<br>o I |<br>aS | |<br>©<br>a<br>LO ~<br>3° ~b-L<br>ry i<br>D ~<br>Cc ~ —_—<br>‘<<br>Oo =<br>” ~ eo<br>5co ATS|<br>[o) ~<br>3 ~<br>r=oO \<br>Ww | ] ‘<br>||<br>||<br>| |<br>& |<br>6-5 -4 -3 -2 -1012 3 4 5 6 7 8 9 10 11 12 13 14<br>Fortnight<br><!-- End of picture text -->
**Table 4. Summary statistics on telecom expenditures and users**

|Decile|Telecom<br>Consumers<br>(%)|Annual<br>consumption<br>of telecom<br>services|Annual<br>cons. of<br>telecom<br>among<br>consumers|Consumption<br>per capita|Consumption<br>per capita<br>among<br>telecom<br>consumers|Telecom<br>expenditure<br>as a % of<br>total<br>expenditure|Telecom<br>expenditure<br>as a % of<br>total<br>expenditure<br>of<br>consumers|
|---|---|---|---|---|---|---|---|
|**1**<sup>**+**</sup>|4.1%|166|4,009|40,491|50,457|0.0%|1.2%|
|**2**<sup>**+**</sup>|13.1%|1,004|7,668|71,982|74,627|0.2%|1.3%|
|**3**|16.6%|1,570|9,468|97,836|99,662|0.2%|1.1%|
|**4**|22.2%|2,401|10,800|119,223|120,107|0.2%|1.1%|
|**5**|22.9%|6,946|30,367|143,328|143,115|0.4%|1.9%|
|**6**|22.8%|4,557|19,967|170,249|169,540|0.4%|1.6%|
|**7**|27.2%|5,034|18,504|203,287|202,935|0.4%|1.4%|
|**8**|25.3%|7,525|29,757|245,532|247,397|0.4%|1.7%|
|**9**|29.2%|17,411|59,569|318,732|324,248|0.7%|2.4%|
|**10**|34.6%|21,704|62,762|672,770|699,677|0.6%|1.8%|
|**Overall**|21.8%|6,822|31,299|208,224|266,367|0.4%|1.6%|

Source: Calculations based on EDAM 4. Note:<sup>+</sup> Deciles based on the distribution of consumption per capita. The coefficient of variation of telecom usage was 20 percent or higher in deciles 1 and 2 (greyed out) and thus may be interpreted with caution.

To conduct the simulation, we apply the World Bank-developed tool on Welfare and Competition (WELCOM). The tool allows simulation of the distributional effects of changes in market competition through prices. In short, the tool: i) estimates the expected change in prices resulting from the increased competition by assuming that after new entrants come into the sector the market structure behaves as in perfect competition with the price dropping to marginal cost; ii) identifies all users of telecom services; and iii) applies the estimated price decrease to households that are currently users of telecom services as an estimate of their gain in welfare.<sup>16</sup> Given the lack of guidance in the literature on an appropriate elasticity for telecom services, we run the simulations assuming a price elasticity of -1.5 and -2.5 to provide a range of results. Of note is that in both cases the simulations assume that the demand for communications services is elastic, since in monopolistic markets the firm optimizes its output in the elastic section of the demand curve. Thus, we present the results for the simulation where the opening of the sector leads to a drop in the market share of the incumbent firm to half of the market. That is, we simulate the expected effects in the short- or medium-term after the opening of the sector.

Table 5 presents the main results from the simulations. Results from the poorest quintile are not shown due to small sample sizes. As expected, both simulations show a decrease in price after a new entrant establishes itself in the telecom sector. In the scenario where individuals are assumed to be less responsive to price changes, the price of telecoms experiences a drop of 22 percent,

> 16 For a more detailed description of the methodology, see Araar _et al._ (2018).

12
**Table 1: Labor law enforcement measures**

|_(per 100,000 employees)_|_Mean_|_Minimum_|_25_<sup>_th_</sup><br>_percentile_|_Median_|_75_<sup>_th_</sup><br>_percentile_|_Maximum_|
|---|---|---|---|---|---|---|
|_Inspectors_|11.5|5.6|8.6|9.8|11.55|50.6|
|_Inspections_|7.9|2.9|5.5|6.9|8.9|26|
|_Identified violations_|56.5|21|41|54|66|126|
|_All labor dispute cases filed_|23.6|3.1|12.35|18.2|24.3|289.2|
|_Pay cases filed_|18.8|1.7|9.7|14.6|20.1|264.7|
|_Dismissal cases filed_|1.1|0.4|0.7|0.9|1.3|4.8|

**Table 2: Correlations between regional labor law enforcement measures**

||_Inspectors_|_Inspections_|_Identified_<br>_violations_|_All_<br>_labor_<br>_dispute_<br>_cases_|_Pay_<br>_filed_|_cases_|
|---|---|---|---|---|---|---|
|_Inspections_|0.8454|1|||||
|_Identified violations_|0.4507|0.615|1||||
|_All labor dispute cases_|0.4923|0.6296|0.3702|1|||
|_Pay cases filed_|0.4734|0.6148|0.3786|0.9949|1||
|_Dismissal cases filed_|0.8423|0.7567|0.3307|0.534|0.4925||

**Table 3: Correlation between labor law enforcement (inspector density) and other regional characteristics**

|_Variable_|_Correlation_<br>_coefficient_|
|---|---|
|_Unemployment Rate_|0.6|
|_No. of Firms_|‐0.16|
|_Nominal Wage_|0.07|
|_Urbanization Rate_|‐0.28|
|_Gross Regional Product_|‐0.18|
|_Gross Regional Product per Capita_|‐0.04|
|_Total Industrial Output_|‐0.2|
|_Industrial Output from State Enterprises_|‐0.2|
|_Share of Manufacturing Sector in Output_|‐0.46|
|_Share of Services Sector in Output_|0.34|

20
# **I. Introduction**

Income distribution as a topic of analysis and measurement has mushroomed in recent decades, since Anthony Atkinson argued in his presidential address to the Royal Economic Society in 1997 that the subject should be ‘brought back in from the cold’ (Atkinson 1997). There has been a veritable explosion of interest in the subject among researchers, policy makers, and commentators alike. A virtual industry has emerged focusing on the documentation and analysis of inequality trends at the national and global levels. The interest has been spurred in both the developed and the developing world.

While there has been much progress in the production of inequality statistics around the world, offering expanding opportunities to track trends over time and to make comparisons across countries, the underlying processes that characterize changes in inequality merit further and continued investigation. Notably, it is still rarely the case that the patterns of relative income mobility that underpin changes in inequality are documented, let alone well understood, particularly for developing countries. And yet the normative view one takes of a rise in inequality may differ depending on whether one is observing a simple stretching out of the income distribution—leaving individuals in the same position within the distribution—or the increased inequality is accompanied by individuals or households moving up and down in relative terms within the income distribution. As noted by Paul Krugman, ‘if income mobility were very high, the degree of inequality in any given year would be unimportant, because the distribution of lifetime income would be very even’ (Krugman 1992 00).

Assessing the degree of income mobility poses significant data and measurement challenges. In particular, it is necessary to work with panel data, as only such data permit the tracking of households over time. Collecting such data, however, can be very costly, and can also pose logistical and capacity-related challenges. These constraints are particularly acute in developing countries. The scarcity of panel data has thus rendered the analysis of welfare dynamics difficult, if not impossible, in many developing country settings.

2
76 it constitutes a saving account. Both in the event of death and in the event of living the beneficiary is entitled to a provision.

- _Variable Annuity_ : In addition to the mixed Life–Assurance, there is an explicit participation in the development of financial markets. The corresponding funds are chosen according to the risk profile and the investment horizon of the policy holder.

- _Saving Accounts_

All products can be financed either by regular premiums/contributions (annually, quarterly or monthly) or by a lump–sum transfer. Apart from their main purpose as a specific investment instrument (mainly 3b products), third pillar assets are also widely accepted as a collateral for credits.

# **6.3 Volume of the Third Pillar (Life Insurance Companies)**

Table 33 shows the evolution of capital accumulated in the third pillar by insurance companies. In 2003, 244 billion CHF were invested in the form of capital insurance, i.e., a product that yields a capital payment (CP) at a later date (around the statutory retirement age). Thereof, 35 percent is conditional precautionary capital. The remaining 65 percent are invested in pillar 3b and are therefore unconstrained in their purpose. The total investments in annuity products (A) amount to only 2 billion CHF. Compared to the investments resulting in a lump sum, the total of 2 billion does not even account for 1 percent of this amount. Both types of products have shifted from pillar 3b to pillar 3a over the last years (see Figure 26): annuities, especially during the 1990s, and capital payments, during the last years. As mentioned above, the low degree of annuitization in the third pillar reflects the fact that annuities are still the dominant pay-out option of the first two pillars.

|**Table 33 Evolution of Capital Invested inthe Third Pillar **|
|---|

|Balances|199|6|199|7|199|8|199|9|
|---|---|---|---|---|---|---|---|---|
||CHF mn.|%|CHF mn|%|CHF mn|%|CHF mn|%|
|Capital-|||||||||
|Payments|||||||||
|Pillar 3a (CP)|52,680|29.89%|60,829|32.07%|64,503|31.27%|72,694|31.05%|
|Pillar 3b (CP)|123,549|70.11%|128,858|67.93%|141,806|68.73%|161,459|68.95%|
|<br>Total(CP)|176,229|100%|189,687|100%|206,309|100%|234,153|100%|
|Annuities|||||||||
|Pillar 3a (A)|109|9.40%|135|10.49%|200|14.01%|229|13.78%|
|Pillar 3b (A)|1,050|90.60%|1,152|89.51%|1,228|85.99%|1,433|86.22%|
|Total(A)|1,159|100%|1,287|100%|1,428|100%|1,662|100%|
||200|0|200|1|200|2|200|3|
||CHF mn|%|CHF mn|%|CHF mn|%|CHF mn|%|
|Capital-|||||||||
|Payments|||||||||
|<br>Pillar 3a (CP)|77,954|32.84%|77,489|31.99%|79,448|32.50%|85,429|35.08%|
|Pillar 3b (CP)|159,388|67.16%|164,772|68.01%|164,970|67.50%|158,100|64.92%|
|Total(CP)|237,342|100%|242,261|100%|244,418|100%|243,529|100%|
|Annuities|||||||||
|Pillar 3a (A)|240|13.70%|217|11.79%|212|11.16%|210|10.79%|
|Pillar 3b (A)|1,512|86.30%|1,624|88.21%|1,687|88.84%|1,737|89.21%|
|Total(A)|1,752|100%|1,841|100%|1,899|100%|1,947|100%|

Source: Bundesamt für Privatversicherungen (2003), p.66 sqq.
||1994<br>Actual<br>Amount|Share a|
|---|---|---|
|Educaticn, Culture, and Related Expenses|83858|0.276|
|Health, Family Planning, and Sports|27467|0.090|
|Social Welfare|9416|0.031|
|Government Administration|66539|0.219|
|Police and Law Enforcement|22000|0.072|
|Infrastructure Maintenance|23416|0.077|
|Other services (including subsidies)|71211|0.234|
|Subtotal|303907|1|

Note: capital expenditures, except for urban maintenance, are excluded. Infrastructure maintenance is named "urban maintenance" in China Almanac of Finance 1995. Data Souirce: China Almanac of Finance 1995.

Trhe total fiscal need of 30 provinces in category k (k = education, health, etc.) is the weight **(ak)** multiplied by the total fiscal need of all categories. Denoting total local need of all categories by TN, the total fiscal need in category k is

## rNk = ak*TN

Step 2: determining the fiscal need of each province in category k. For education (k=E), the fiscal need of province i is calculated using the following formula:

# NiE = TNE(PiEi/XjPjEj) = aE*TN(PiEi/£jPjEj)

(4) where NiE is province i's fiscal need for education, aE=0.<sup>27 6</sup> is the weight assigned to education, TNE is the 30 provinces' total fiscal need for education, Pi is the population of province i, Ei is the ratio of the national average **niumber** of years of education to that in province i, and **P,E/jPjFj** is the share of province i's fiscal need in tie 30 provinces' total need for education.

For health (k=H), the fiscal need of province i is calculated using the following formula:

## **NiH** = TNH(PiL,/EjPjLj) = alH*TN(PiLi/EjPjLj)

where NiH is province i's fiscal need for health, cti=O.090 is the weight assigned to health, TNH is the 30 provinces' total fiscal need for health, L, is the ratio of the national average life expectancy to that in province i, P,Li£jPjl; is the share of province i's fiscal need in the 30 provinces' total need for health.

53
# a

competition from foreign substitutes, it also allow endogenous adjustments in the length of production runs.

Given these qualifications, it is clearly an empirical question whether trade liberalization will: (1) increase the average scale of production; (2) shift market shares toward large producers; and (3) bring with it productivity improvement. The remainder of this paper is devoted to ar econometric examinination of these issues.

## III. EMPIRICAL METHODOLOGY

## A. **The Data and** Country Backgrounds

In this section we examine cross-country and inter-temporal contrasts in trade exposure, plant size distributions, and labor productivity distributions for evidence on the empirical relevance of the theoretical effects reviewed in Section II. To do this we utilize annual census data covaring all manufacturing plants with at least 10 workers in Colombia and Chile.<sup>9</sup> But before turning to the empirical models these data support, it is useful to review the cross-country differences and within-country time series fluctuations in trade policies and industrial performance that allow us to identify parameters.

### Chilei°

The Chilean data used in this paper cover the period 1979-35; we begin our overview with the years immediately preceding. like much of Latin America, Chile pursued an inward-oriented development strategy in the 1960s. The system of incentives -- including tariffs, quotas, e:cchange rate policy, and domestic market regulations -- favored manufacturing at the expense of
and Rodrik (2002)) or coordination issues) or government failures. Government failures can be macroeconomic risks, which are identified by firms as “macroeconomic instability.” But government failures also play out in microeconomic risks, from either government agents themselves or from other private agents with unreliable contracting. Our interpretation is that firm complaints of “policy uncertainty” reflect concerns that policy implementation uncertainty creates risks to the appropriability of the gains from firm level investment or innovation<sup>28</sup> .

We argue that a number of the firm reported obstacles in Figure 1 are related to appropriability risks created by policy implementation uncertainty. In addition to “economic and regulatory policy uncertainty” firms identify not just “policy” variables like the tax _rate_ but also tax _administration_ . Firms commonly identify customs _clearance_ as an obstacle, which is arguably more an implementation than a policy issue. Firms’ expressed concerns with “licenses” and “access to land” combine both the policy (is it _de jure_ difficult) but also the _de facto_ concerns about implementation. We believe the broader lens of policy implementation uncertainty also provides a lens into complaints about corruption, both in that it can be disordered and it can create obstacles to firms when corruption is “closed” and is used to close off market competition.

> 28 Bigsten and Soderbom (2005) review the research based on a first round of firm surveys in Africa and concluded the uncertainty, paired with irreversibility, explains low investment in African firms not low productivity.

42
When instead only 50 percent of oil revenue is transferred and the rest invested, for example, then extra disposable income of retirees is lower which reduced the incentive of workers to save less. At the same time, workers also receive a smaller transfer from which they save a relatively larger fraction. These dynamics lead to the conclusion that the higher the share of oil revenue that is invested, the less private saving is crowded out, and the larger total saving will be.

In Figure 5, two scenarios for the share of oil revenue share invested are illustrated. If by default, the government does not invest a single dollar of the oil revenue but rather transfer is all to workers and retirees, then private saving will be identical to the economy’s aggregate saving because it is only workers who save — retirees and the government do not. However, if the government always invests 10 percent of its oil revenue then there will be less transfer to workers who then have less income to save from; as a result their saving falls slightly. On the other hand, aggregate saving in the economy now increases because it picks up the higher government saving.

**Figure 7. To Avoid Crowding Out of the Capital Stock, Merely About 1 Percent of** **<u>Oil Revenue Should Be Invested</u>**

<!-- Start of picture text -->
1.1 0.99 1.005 1.02 1.03<br>1 0.95 0.97<br>0.92<br>0.9 0.85 0.89<br>0.79<br>0.8<br>0.71<br>0.7<br>0.59<br>0.6<br>0.5<br>0.39<br>0.4<br>0.3<br>0.2<br>0.1<br>0 Oil revenue as a share of output<br>0<br>2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>Investment share to avoid resource curse<br><!-- End of picture text -->

Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

This crowding out effect of private saving can also be illustrated by Figure 6 where a scenario of 10 percent investment of oil revenue is compared to a scenario where there is zero investment of oil revenue. It is seen that public investment of oil revenue outweighs the drop in private saving —yielding an overall increase in total saving and, thus, capital accumulation, where the discrepancy between the trajectories for aggregate and private saving is the increasing public saving.

Private saving will consequently be crowded out by the Pre-Salt oil revenue; both in the case where the revenue is fully (publicly) invested, because the increased public investment will tend to crowd out private investment, as well as in the case where the revenue is fully consumed, because if retirees receive any fraction of the revenue they are likely to save a very little share and workers will expect a similar “old-age transfer” in their old age which incites them to save less in their working years. The 19
coffee farmers have benefited (relatively) more from the boom than the rich. Yet, as land tends to be a more important determinant of income and hence poverty in the rural context, it is likely that the better-off farmers have been in a better position to grasp the opportunities of the liberalized coffee market through entry.

Figure 7: Land size and coffee farming, 1992-1999 <!-- Start of picture text --> 1<br>0.8<br>0.6<br>1992<br>1995<br>0.4<br>1999<br>0.2<br>0<br>0 2 4 6 8 10<br>Land size in acres<br>p<br><!-- End of picture text --> Source: Authors’ calculations.

Note: Predicted probabilities of being a coffee farmer conditional on land size (evaluated at the mean of all other right-handside variables).

Figure 8: Education and coffee farming, 1992-1999 <!-- Start of picture text --> 0.5<br>0.4<br>0.3 1992<br>1995<br>0.2 1999<br>0.1<br>0<br>0 2 4 6 8 10<br>Years of schooling<br>p<br><!-- End of picture text --> Source: Authors’ calculations.

Note: Predicted probabilities of being a coffee farmer conditional on educational level (evaluated at the mean of all other right-hand-side variables).

Supply responses of the coffee sector Beside raising the number of coffee farmers, the coffee price boom and the concomitant coffee price liberalization could have provided an incentive to increase coffee production. A supply response to changes in prices can come about via changing yields for a given set of 19