rafmacalaba/datause-encoder-data huggingface dataset:
36 24. The first and third rows of Table 3 are taken from the calculations in Table 2, while the second row (unofficial economy) is the difference between the third and first rows.

25. Sources for information utilized: Poland: Research Center for Economic Studies; Timothy E. Heleniak, The World Bank. Czech Republic: Timothy E. Heleniak, The World Bank. Hungary: Janos Arvay and Andras Vertes, "Impact of the Hidden Economy on Growth Rates in Hungary," International Association for Research in Income and Wealth, 23rd General Conference, St. Andrew's, New Brunswick, Canada, 21-27 August 1994. Romania: Romanian Society of Economists (SOREC). Bulgaria: Timothy E. Heleniak, The World Bank. FSU: Berkeley-Duke Studies of the Second Economy; Janine Braithwaite, "From Second Economy to Informal Sector: The Russian Labor Market in Transition," mimeo, World Bank, 1994. Note that for CEE countries the point estimates from available studies are from the early 1990s, thus necessitating extrapolation backward and forward in our macroelectric framework.

26. It ought to be noted, however, that although the Czech Republic's share of the unofficial economy today, at 18 percent, is still low relative to other countries, the share grew significantly during the period--it was extremely low initially, estimated at less than 5 percent.

27. In reality, there is a paradox. It is precisely in those settings with large unofficial economies that there often has been less inclination to liberalize in the first place. This in turn led to the large size of the unofficial economy. Hence, a farsighted team of reformists is often needed, one that is prepared to break from the past in light of the evidence and fully liberalize the official economy.

28. Figures calculated on the basis of data from: Ekonomika Ukrayiny v 1994 rotsu; Annual Report of the President of Ukraine to the Verkhovna Rada of Statistics of Ukraine, March 1995; **Statvstychny Schovichnyk Ukrayiny a 1994 rik,** Ministry of Statistics of Ukraine (Kiev: Tekhnika, 1995); data of the Ministry of Finance of Ukraine.

29. These general points on optimal tax rates would require further empirical elaboration; no precise recommendations on tax rates or tax reform measures are intended here. More generally, however, even the entrepreneurs' responses indicate their willingness to return to the official economy if taxes were moderate and stable (and nonretroactive). But the return to the official economy would be gradual, which would need to be factored into the detailed tax calculations.

30. A clear distinction would need to be made between leniency regarding the past and the strict penalties expected from underreporting in the future. In fact, a clear signal from the time of the announcement in the form of stronger enforcement of tough penalties for concurrent illegal activities, would be warranted.

31. See Johnson, Kaufmann, and Ustenko, "Winner, Losers and Survival Strategies," for a formalized and empirical elaboration on these issues.

32. Ibid.
direction.<sup>14</sup> But the signals are not the same across country types or across sectors. Developing countries and developed countries differ in their commitment to autonomous regulation across sectors. Developed countries seem to be much more committed to autonomous agencies for electricity while developing countries seem to be more committed to creating agencies in telecoms. This partially reflects the fact that many of the regulatory issues in telecoms can be dealt with by competition agencies which are common in developed countries but much less so in developing countries—as well as the fact that there were more telecoms deals than energy deals in LDCs during the period covered here.

Table 2 also reveals major differences across regions within developing countries. Latin America is a strong believer in the creation of agencies in both sectors. Eastern Europe has a strong commitment for electricity but not for telecoms. Africa and South Asia actually lead the regions for telecoms although they lag for electricity. The most unexpected information provided by Table 2 may be the rather modest commitment by East Asian countries to the creation of autonomous agencies. Comparing Table 1 and 2 should make it clear that the creation of autonomous agencies is not a sufficient condition to attract private capital in either sector. There is however some partial evidence that for countries with a weak history of governance, it helps.<sup>15</sup>

|**Table 2: Countries with Aut**|**onomous Regulatory Agen**|**cy as of 2002(% of sample)**|
|---|---|---|
||**Electricity**|**Telecoms**|
||(155 countries)|(204 countries)|
|**_By Income Level:_**|||
|Low Income LDCs|33%|65%|
|Lower-Middle Income LDCs|50%|54%|
|Upper-Middle Income LDCs|63%|67%|
|Developed|88%|55%|
|**_LDCs By Region:_**|||
|Sub-Saharan Africa|30%|75%|
|East Asia and Pacific|36%|19%|
|Europe & Central Asia|70%|59%|
|Latin America & Caribbean|73%|73%|
|Middle East & North Africa|7%|50%|
|South Asia|25%|75%|
|**Total**|**53%**|**60%**|

Source: Author compilation from various sources (ITU for telecoms and consultation with World Bank, IDB, ADB , EBRD and EEC economists for electricity) A second indicator is the extent to which corruption has declined with reforms in the sector. Here the story is not as positive. While there is no long term monitoring of corruption in this sector, there is partial information available from various recent surveys of perception by foreign investors summarized in the World Bank Governance database. The database shows that, between 1999 and 2002, for the richest developing countries, corruption has declined but extremely modestly (no change is rated at 4 on a scale of 1 to 7 and that country group was rated at 4.1). The other two country groups (low middle and low income LDCs) have seen corruption increase (the rating is 3.3 and 3.7 respectively). Of course, these are

> 14 See for example Stern and Holder (1999) and Stern, J. and J. Cubbin (2003)

> 15 See for instance  the recent book by Kessides (2004) for an overview of the debate.

11
Borc<sup>4</sup> ar prices convorted Into rubles at exchange rates of two<sup>and four rubles</sup> to the dollar are also shown In Table I for reference and comparison.<sup>The border</sup> prices for commercially traded products are prices c.l.f. northern<sup>European ports</sup> &djusted to farm gate product definitions.<sup>2</sup> For example, the beef price Is beef c.l.f. northern European ports adjusted to live welght using the standard<sup>Soviet coefficlent</sup> of .55 live welght to carcass weight. The boruer price for mlik<sup>Is equlvalent to $10</sup> per hundredwelght converted to rubles at the stated exchange<sup>rate.3</sup> For little traded products, such as potatoes, the listed border<sup>price Is the</sup> marginal price of the third quarter of domestic producers; 75 percent<sup>of Llthuanlan</sup> producers recelved a price at or below the llsted price,<sup>In choosing this price for</sup> nontraded products, I assumed that the price (for average quality)<sup>after the reform</sup> would be based not on the hlghest current price, but on one that was<sup>In the high end</sup> of the current range.

Relative prices In Llthuanla In 1987 differed from border prices;<sup>grains and mli</sup> were relatively low and meat high. Virtually all dalry producers<sup>received less than</sup> border prices even at two rubles to the dollar and the difference<sup>Is greater at four.</sup> Meat prices were not as low relative to world values as were<sup>milk prices.</sup> Output Prices, Land Quality, and Profitabillty Differentiated prices and meticulous measurement of land quality<sup>were Intended</sup> to capture rents assoclated with preferential access<sup>to land at no cost (Poshkus,</sup> 1P79). If the system worked weii, farF2a would show<sup>a range of profitabillty, but</sup> there would be no systematic link between land quality<sup>and profitabillty, unless some</sup> other factor contributing to profitability were strongly<sup>correlated with land quality.</sup> Table 3 shows palr-wise correlation coefficlents for prices,<sup>land quality, and</sup> profitability In 1987. For example, farms that recelved<sup>high prices for rye tended also</sup> to receive hlgh prices for barley, as Indicated by the<sup>positive, significant correlation</sup> coefficient of .28. Individual product prices tended not to be strongly<sup>correlated wlth</sup>
Newell, R.G., Prest, B.C. and Sexton, S.E., 2021. The GDP-temperature relationship: implications for climate change damages. Journal of Environmental Economics and Management, 108, p.102445.

Patel, D., Sandefur, J. and Subramanian, A., 2018. Everything you know about cross-country convergence is (now) wrong. Peterson Institute for International Economics blog post, October, 15, p.2018.

Patel, Sandefur, Subramanian, 2021, The new era of unconditional convergence, Journal of Development Economics, Volume 152, 2021, 102687, <u>https://doi.org/10.1016/j.jdeveco.2021.102687</u> And https://www.cgdev.org/sites/default/files/new-era-unconditional-convergence.pdf Pritchett, Divergence, Big Time, 1997, Journal of Economic Perspectives, <u>https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.11.3.3</u> Quah, 1993, Empirical cross-section dynamics in economic growth, Eur. Econ. Rev., 37 (2–3) (1993), pp. 426-434 Romer, 1990, Endogenous technological change, J. Polit. Econ., 98 (5) (1990) Roy, S., Kessler, M. and Subramanian, A., 2016. Glimpsing the end of economic history? unconditional convergence and the missing middle income trap. Center for Global Development working paper, (438).

Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 70(1), pages 65-94.

World Bank, 2020, On the Front Lines of the Fight Against Poverty. <u>https://openknowledge.worldbank.org/server/api/core/bitstreams/ebc3ef9e-ca7f-5959-8dfe2a8a1f2a25dc/content</u> World Bank, 2023, Evolving the World Bank Group’s Mission, Operations, and Resources: A Roadmap https://www.worldbank.org/en/news/statement/2023/01/13/world-bank-group- <u>statement-on-evolution-roadmap</u> World Bank, 2023, Africa’s Resource Future.

<u>https://openknowledge.worldbank.org/server/api/core/bitstreams/2cfa1ec3-1318-4415-aa8ec8f3ce5e11ab/content</u>

# Appendix A – Extra Figures and Tables

34
41

|**Sectors**|**Modes**|**Categories**|**Measures**|**K ii**|
|---|---|---|---|---|
||||**Sector specific measures**<br>|**ey restrctons**<br>|
|||Condition on consumer|Demonstration of domestic unavailability of service desired|YES|
|||Condition on services|Sector of the firm using the service|YES|
|||Condition on services|Size of the deposit (loan)|YES|
|||Condition on services|Interest rate (deposit or loan accounts)|YES|
|||Condition on services|Currency deposited|YES|
|||Condition on services|Period or maturity (loan)|YES|
||**MODE 1**|Condition on services|Term of the insurance policy (insurance)|YES|
|||Condition on services|Value of the insurance policy (insurance)|YES|
|**FINANCIAL**||Condition on services|Obliged to cede to a domestic re-insurer|YES|
|**SERVICES**<br>**RETAIL**||Condition on services|Percentage that must be ceded|YES|
|**(**<br>**BANKING,**||Condition on consumer|Approval required|YES|
|**AUTOMOBILE,**<br>**LIFE AND**||Condition on consumer|Registration required|Not covered|
|<br>**REINSURANCE**||Condition on services|Other forms of restrictions|YES|
|**SERVICES)**||Form of Entry|Greenfield branch allowed|YES|
|||Operations|Restrictions on the number of branches|YES|
|||Operations|Restrictions on the number of ATM|YES|
|||Operations|Restrictions on transactions in local currencies|Not covered|
||**MODE 3**|Operations|Restrictions on raising capital domestically|Not covered|
|||Operations|Access to central Bank discount window|Not covered|
|||Operations|Access to main payment system|Not covered|
|||Operations|Access to deposit insurance schemes|Not covered|
|||Operations (reinsurance)|Obliged to cede to a domestic re-insurer|Not covered|
WPS6182 Policy Research Working Paper 6182

# Evidence-Based Implementation Efficiency Analysis of the HIV/AIDS National Response in Colombia

_Antonio Moreno Arturo Álvarez-Rosete Ricardo Luque Nuñez Teresa del Carmen Moreno Chavez Rosalía Rodriguez-García Fernando Montenegro Luis Ángel Moreno Alejandra Suarez Lissi Pedro Magne Concardo Michel Eric Gaillard_ The World Bank Human Development Network Health, Nutrition and Population Unit August 2012
earlier to complement the household-based descriptive analysis of the previous section with a counterfactual exercise based on a model of imperfect competition domestically and internationally. The simulation exercise is particularly useful here as the noise due to vanilla price volatility drowns the faint signal from policy reform. With limited data, counterfactual analysis can help separate the two and by providing orders of magnitude.

Suppose then that the vanilla market can be approximated by modeling three types of players: a supply side with atomistic producers (farmers); a demand side black-boxed with a constant price-elasticity of demand for aggregate (Madagascar and Indonesia) natural vanilla; and a layer of imperfectly competitive curers-stockers-traders engaged in Cournot competition in the middle. In this set-up, reminiscent of the vanilla market in Madagascar, the taxation of exports followed during phase 2 leads to an outcome akin to double marginalization (see annex A2 for a detailed précis of the model and calibration to Madagascar and Indonesian data). Starting from observed mark-ups, we let the model determine the number of traders consistent with imposed behavior under the current policy regime of no taxation and domestic market intervention restricting entry. These model-generated numbers are close to the anecdotal evidence (about five traders).

Then, in a counterfactual experiment we reduce the number of traders to a single one (i.e. we resuscitate the marketing board) and re-impose taxation at the prereform maximum rate of 82% during phase two. Resuscitating the marketing board in this framework generates implied FOB and producer prices consistent with this policy-imposed limited competition. We then use the counterfactual producer prices to recalculate farmer incomes using Madagascar’s 2001 household survey. Finally, we recompute poverty and inequality indices (poverty headcount, poverty gap and Gini coefficient) and compare the actual outcome with the counterfactual outcome. The difference in poverty headcounts is then an estimate of the number of individuals lifted out of poverty by the vanilla sector reform.

The gist of the model, which is laid out formally in Annex A2, boils down to a simple formula relating the share of the world price retained by Madagascar’s producers as a function of demand and supply elasticities and, more importantly, the number of local traders. Namely, letting _PM_ be the selling price of _P_ Madagascar’s vanilla (whose formula is derived in the appendix), _pM_ its farmgate producer price, _σ_ the elasticity of substitution between Madagascar’s and 28
# **Underwriting and Loan Servicing**

Prospective borrowers must have a business or employment to be considered for a loan. Unit personnel visit the work site of each prospective borrower before issuing the loan. At the same time, the loan officer checks on the prospective borrower's character by talking to the local village chief and/or neighbors. Most BKK managers have found that neighbors provide more accurate character references than village chiefs. If the borrower is married, his or her spouse must also sign the promissory note. Loans are disbursed 1 day to 2 weeks after the loan application is received. Borrowers must come to the unit twice to obtain a loan - once to complete the application and once to collect the funds, Collections procedures vary across BKK units. Units serving villages a considerable distance from their facility send out field staff to collect installments. Units serving villages that have relatively easy access to their facility require that borrowers make payments at the unit. However, even these latter units send staff to visit borrowers' villages if payments are more than 2 to 7 days late. There is no penalty for late payments. Nevertheless, previous repayment performance is taken into account when determining whether to issue additional loans. Units give new borrowers small loans and allow subsequent loans to rise in value as the borrower proves repayment reliability.

# **Program Performance**

This section reviews program performance as measured by sustainability and outreach. Sustainability is measured by the program's arrears rate, profitability, and the size of the subsidy required to sustain operations. Outreach is measured by the volume of annual lending and savings activities (scope), and the population it serves (depth of market penetration).

_Sustainability._ According to data supplied by the BPD, for the 34 BKKs in existence before 1993, the volume of loans in arrears by more than 3 months was equal to 26 percent of the volume of outstanding loans in 1995. Because units do not write off loans, it is probably more meaningful to simulate arrears and default rates if the institution wrote off 100 percent of its loans in default

**Table 12 Adjusted Annual Arrears and Default Ratesa for the 34 Original BKKs (Percent)**

||19931|1994|1 19951|
|---|---|---|---|
|Arrears|27.01|6.21|5.91|
|Default|2 3 <sup>.</sup>0 <sup>b1</sup>|3.0|3.5|

a/ The volume of loans in arrears and in default divided by the volume of outstanding loans. Loans in default from previous years that have not yet been written off are subtracted from the annual volume of loans in default and from annual outstanding loans. b/ This figures includes an adjustment to correct for previously unrecorded defaults. However, the actual default rate in 1993 was high compared to that of previous and subsequent years.

each year. If the arrears rate is recalculated in this way and all loans more than 90 days overdue are considered to be in default, then the volume of loans in arrears was 6 percent of net outstanding loans in 1995. Loans in default were equal to 3.5 (Table 12). Arrears and defaults were also relatively low in 1994. For 35
Table 2: **Validation: Internet access**

||Share of m<br>who a|igrants in t<br>re long-sta|he FMP<br>yers|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination in||||
|_FMPs where night lights have_||||
|Low intensity|0.0342|||
||(0.0599)|||
|Medium intensity|-0.0464|||
||(0.0456)|||
|High intensity|-0.1234***|||
||(0.0496)|||
|_FMPs with network coverage_||||
|Not present||-0.0787|-0.0708|
|||(0.1077)|(0.1061)|
|Present (with)||-0.0309*<br>(0.0172)||
|High-speed connection|||-0.0437*|
||||(0.0228)|
|Low-speed connection|||0.0223|
||||(0.0185)|
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

25
_The Cultural Trade Index_ Page | **5** of **14**

# **III. Method**

The CTI would initially be web-based, and the website could be maintained by an international organization or an academic institution. As the first index of its kind, the data could be fed annually or biennially. Nonetheless, improvements in data collection could mean that it might be possible to consider shorter time periods in the future.

Working with statistical agencies, trade ministries, and cultural and media organizations, the web-based system could allow self-reporting data where necessary. Some data may come from UNESCO’s Institute for Statistics, the United Nations Conference on Trade and Development, the World Trade Organization, the International Monetary Fund, the World Integrated Trade Solution, the United Nations Comtrade Database, Fair Trade Organizations, and other sources.

With respect to ranks, in addition to the global rankings, ranking could also be regional and economic group classifications (for example low income, mid income and upper income countries). The CTI could look like this:

## Table 1 **<u>Version 1</u>**<sup>**13**</sup>

|**Country**|Exports of<br>cultural<br>services -<br>contribution<br>to GDP<sup>a</sup>|Exports of<br>cultural<br>goods -<br>contribution<br>to GDP<sup>a</sup>|Percentage<br>of people<br>employed in<br>the cultural<br>sector|Cultural<br>contribution<br>and related<br>sectors to<br>GDP (%)|Government<br>Expenditure<br>on cultural<br>trade<br>promotion<sup>b</sup>|Population<br>Expenditure<br>on imports<br>of cultural<br>services<sup>c</sup>|Population<br>Expenditure<br>on imports<br>of cultural<br>goods<sup>c</sup>|**Rank**|
|---|---|---|---|---|---|---|---|---|
|**A**|E.g. 2 %|E.g. 4%|15%|8%|USD$5 M|USD $10 M|USD$15 M|**#1**|
|**B**|1%|3%|14%|7%|USD$3 M|USD$10|USD$15 M|**#2**|

> 13 This version was first discussed in “Creative Trade for Human Development,” the author’s paper that was commissioned by UNDP for the 2015 Human Development Report, _Work for Human Development_ . It is unlikely that any one country would lead in all the indicators provided. So this version could be the most tasking to construct. a Since GDP is often used to measure economic activity, these figures may shed light on the contribution of the cultural goods and services to a given country’s economy, where data are available.

> b The percentage of the government’s expenditure on promoting cultural trade is likely to help show the administration’s interest in promoting this area.