rafmacalaba/datause-encoder-data huggingface dataset:
is to undertake syatematic sensitivity analyses of our major results with respect to plausible bounds<sup>on these</sup> elasticities. Even if we are unable to specify a point estimate with any precision, our priors over the likely bounds that these elasticities could take are quite strong. To the extent that our major conclusions are robust to<sup>perturbations</sup> over these bounds, we do not see our uncertainty over specific values of these elasticities as a<sup>weakness of the</sup> model.<sup>12</sup> We report the results of these sensitivity analyses, which involve 1000 simulations for each counterfactual policy, in Section, _5._ They allow us to conclude that our main results are robust, at least with respect to plausible uncertainty over elasticities.

In the present version of the model we only have one private household in Turkey. It is important to note, however, that there are several powerful theorems in international trade theory to show that one can effect Paretoefficient reforns for multiple households providing there are aggregate (real) income gains and one accepts some weak conditions on pattems of demand and ownership.<sup>13</sup> These results do not rely on the availability of lump-sum redistributive taxes, nor do they address the issue of an optimal reform package. What they do show is that one can focus initially on aggregate gains in income and welfare, knowing that the redistributive aspects of the problem<sup>do</sup> have a solution that leaves each household at least as well off as before the reform. This is not a complete substitute for actually solving for the equity effects of a reform package, but it is a partial substitute.<sup>14</sup> The SOE model is generated with the GAMS software developed by Brooke, Kendrick and<sup>M.araus ( 19881</sup> and solved with the MPS/GE software developed by Rutherford [1989].'s 12 These remuark should not be interpreted as denying the value of any new erprirical work on generating such elasticities. On the contrary, any effon that could generate better bounds on these point estimates<sup>**is**useful in generating poficy conclusions that carny greater</sup> credibility, even if those conclusions will still be probabilistic<sup>in nature.</sup> 13 See Dixit and Nonnan (1980; pp. 79/801 (19861. The conditions on demand and factor ownership<sup>pattems are primnarily to rule out</sup> 'pure exchange' economies. These conditions are trivially met in our mnodel.

14 It would be a relatively straightforward matter to extend our model to acconmmodate multiple households if coresponding data were available.

15 The systenatic sensitivity analyses reported in an appendix are undertaken with the MPSS software implementing the procedures developed by Harrison and Vinod [19921.

-9-
Table 4: Inequality measures for earnings, based on actual and simulated earnings within racial/ethnic group and within gender

|||**Simula**<br> <br>|**ting returns on**<br>|**ly**<br> <br>|**Simulating ret**<br> <br>|**urns and chara**<br>|**cteristics**<br>|
|---|---|---|---|---|---|---|---|
||Observed|equal treatment<br>|Equal treatment|all<br>|equal treatment<br>|equal treatment|all|
|||by<br>race/ethnicity<br>within gender|by gender<br>within<br>race/ethnicity|segments<br>treated as<br>white men|by<br>race/ethnicity<br>within gender|by gender<br>within<br>race/ethnicity|segments<br>treated as<br>white men|
||(a)|(b)|(c)|(d)|(e)|(f)|(g)|
|**Theil(1)**||||||||
|Bolivia|0.60|0.53|0.56|0.51|0.57|0.58|0.55|
|Brazil|0.65|0.61|0.64|0.63|0.64|0.66|0.65|
|Guatemala|0.78|0.75|0.79|0.76|0.75|0.76|0.79|
|Guyana|0.32|0.35|0.32|0.34|0.36|0.31|0.33|
|**Theil(0)**||||||||
|Bolivia|0.73|0.63|0.68|0.59|0.66|0.72|0.65|
|Brazil|0.58|0.57|0.58|0.57|0.58|0.59|0.59|
|Guatemala|0.86|0.85|0.89|0.84|0.81|0.77|0.86|
|Guyana|0.29|0.31|0.27|0.29|0.31|0.28|0.30|
|**Gini**||||||||
|Bolivia|0.56|0.54|0.55|0.53|0.55|0.56|0.55|
|Brazil|0.57|0.56|0.57|0.56|0.56|0.59|0.57|
|Guatemala|0.61|0.61|0.62|0.61|0.59|0.60|0.63|
|Guyana|0.39|0.41|0.38|0.40|0.40|0.39|0.40|
|**90th percent**<br>|**ile wages /10**<br>|**th percentile wa**<br>|**ges**<br>|||||
|Bolivia|35.4|24.3|31.3|22.2|26.9|34.0|26.6|
|Brazil|10.4|11.8|12.2|12.1|14.2|15.5|16.7|
|Guatemala|36.8|32.5|27.2|23.9|30.3|28.3|21.4|
|Guyana|5.5|5.4|4.7|4.9|5.3|4.7|4.7|
|**50thpercent**|**ile wages /10**|**thpercentile wa**|**ges**|||||
|Bolivia|9.8|7.5|9.7|7.1|7.6|9.9|7.7|
|Brazil|2.6|2.9|2.9|2.9|3.5|3.3|4.2|
|Guatemala|10.3|9.3|7.9|7.2|8.7|7.5|5.8|
|Guyana|2.6|2.4|2.2|2.3|2.5|2.2|2.3|

30
Figure 12 shows the GDP per capita by country group over time. AA countries have the highest GDP per capita, as expected, and the gap separating them from other countries has increased more recently. BAs’ GDP per capita increased in the second period; the ABs collapsed; and the BBs remained flat all the way, only showing some modest improvement in the more recent past. Because AB and BA groups accounted for less than 6 percent of the region’s GDP in the 2000s, and for about 12 percent of the population, it is reasonable to think that AA and BB countries alone guide the regional economy.

The CV of GDP per capita of AA countries increased from 0.027 in 1975–90 to 0.073 in 1991–2005; the BBs remained almost unchanged, increasing only from 0.038 to 0.041. Thus, the increasing income inequality identified in figure 7 is mainly driven by the large and rising income dispersion among AA countries.

We examined whether there is income convergence within country groups. For each group, we ran unconditional growth regressions against GDP per capita in 1975. The results showed no convergence between AA countries nor between the more homogeneous BB countries.

<!-- Start of picture text --> Figure 12: GDP per capita by country-group<br>5000<br>4500<br>4000<br>3500<br>AA<br>3000<br>2500<br>2000 AB BA<br>Median<br>1500<br>1000<br>BB<br>500<br>0<br>1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> 25
Tables 1 and 2 report the 2002 UNODC figures on opiates and cocaine for major nations and regions, along with my judgment of recent trends. There is minimal use of cocaine in Asia or Africa.

The use of opiates is very broadly distributed both geographically and in terms of relative wealth. The bulk of opiate users<sup>5</sup> are in developing nations. Even though China has a very low estimated prevalence rate, which may reflect the low investment in data collection, it has more opiate addicts than all but three or four other nations, simply because of its population.<sup>6</sup> India, with a moderate estimated prevalence, has by far the largest number of opiate addicts, for the same reason. In most of Western Europe and the United States there has been little growth in opiate addicts.<sup>7</sup> Asia and Eastern Europe have seen sharp increases in recent years, with Central Asia being most affected (Ponce, 2002; Roston, 2002).

The bulk of cocaine users reside in a few rich countries. The United States dominates that market but there has been substantial growth in Western Europe since about the mid-1990s.

Retail expenditures are dominated by rich country consumers, simply because retail prices are so much higher in those nations.<sup>8</sup> However the prices received by growers and traffickers are not dependent on the final destination. A shift of consumption from Western Europe to China has no significance to Afghanistan producers in terms of revenue; the export price from Afghanistan is the same, regardless of the final consumption destination. Hence it is approximately true that consumers in the developing world account for most of the earnings of opium producers, as opposed to the revenues of traffickers and retailers in developed countries.

> 5 Opiates are products of the opium poppy; they include opium, which is usually smoked, morphine (rarely used), analgesics such as Oxy-Contin and heroin.

> 6 An unpublished estimate based on a 2004 survey by Chinese researchers (Chen Xiaobo, Xie Hua and Zhou Tie) has generated an estimate of about 2 million heroin addicts.

> 7 A series of estimates sponsored by the U.S. Office of National Drug Control Policy have been inconsistent with respect to trends.  The most recent study, published in 2001 and reporting estimates for the period 1988-20000, showed a decline of about one third in the number of frequent users of cocaine and of heroin over that period.

> 8 For example, it was estimated that in Thailand, a relatively successful developing nation, the annual expenditure for a heroin addict in the mid-1990s was approximately $1150, compared to $30,000 in Italy.  UNDCP (1997)

3
International Finance Statistics databases. Choi and Hashimoto (2018) proxy for data transparency policy reforms using subscriptions to the IMF’s Data Standards Initiatives and find that such reforms lead to falls in sovereign bond spreads. The study finds that the larger quantity of data produced, as measured by the index, is positively correlated to the quality of bureaucracy, investment and financial sector development. More recently, Hodelin (2022) find the that the quantity of data produced in the World Bank’s World Development Indicators is positively related to economic growth, especially for middle income countries.

We build on these studies by using a comprehensive and rigorous measure of data transparency, known as the Statistical Capacity Indicator (SCI). The SCI measures both the production of micro and macro data as well as the frequency and quality of such data through adherence to international standards. The SCI has been used to assess the statistical capacity of economies and regions (Devarajan, 2013; Angrist et al., 2021) Kubota and Zeufack (2020) use the SCI to explore how data transparency can reduce the external cost of borrowing, as proxied by sovereign bond spreads, in Sub-Saharan Africa. Furthermore, we explore the effect of data transparency on overall economic growth over a period of considerable uncertainty – during and after the global financial crisis. Transparency is a good antidote for uncertainty. Shambaugh and Shen 2018) find that government transparency reduces the duration of inflation and currency crisis by reducing information asymmetry, improving effectiveness of monetary policy and bolstering public confidence. As far as we are aware at the time of writing, our study is the first to empirically explore the effect of data transparency, using a comprehensive measure, on overall economic growth for developing economies during and after the global financial crisis.

# **III. Estimation Strategy**

The identification strategy involves two strategies. The first is dynamic panel estimation methods to account for endogeneity concerns. The second is cross-sectional data where we lag the predictors to the initial time period of the data set (2005) and the level of income per capita at the end period of the data set (2018). This 12
# **1. Cotton Reforms and Poverty Trends in Zambia**

Zambia is a landlocked country located in southern central Africa. Clockwise, neighbors are Congo, Tanzania, Malawi, Mozambique, Zimbabwe, Botswana, Namibia, and Angola.<sup>4</sup> In 2000, the total population was 10.7 million inhabitants. With a per capita GDP of only 302 US dollars, Zambia is one of the poorest countries in the world and is considered a least developed country.

The Republic of Zambia achieved Independence in 1964. A key characteristic of the country is its abundance in natural resources, particularly mineral deposits (like copper) and land. Due to high copper prices, the new Republic did quite well in the initial stages of development. Poverty and inequality, however, were widespread and this raised concerns among the people and the policymakers. Soon, the government began to adopt interventionist policies, with a much larger participation of the state in national development. Interventions included import substitution, price controls of all major agricultural products (like maize), nationalization of manufacturing, agricultural marketing and mining.

In the 1970s and 1980s, the decline in copper prices and the negative external conditions led to stagnation and high levels of external debt. A crisis emerged and a structural adjustment program was implemented between 1983 and 1985. Riots in 1986 forced the government to later abandon the reforms in 1987. A second IMF program failed in 1989, when the removal of controls in maize led to significant price increases.

In 1991, the Movement for Multi-Party Democracy (MMD) was elected. Faced with a sustained, severe recession and with a meager future, the new government began economy-wide

> 4This section relies heavily on Balat and Porto (2004).

3
- Besley, Timothy, and Robin Burgess, “Can Labor Regulation Hinder Economic Performance? Evidence from India”, Quarterly Journal of Economics (2004), 91-134.

- Bollard, Albert, Peter Klenow, and Gunjan Sharma, “India’s Mysterious Manufacturing Miracle”, Working Paper (2011).

- Bönte, Werner, Oliver Falck and Stephan Heblich, “The Impact of Regional Age Structure on Entrepreneurship”, Economic Geography 85 (2009), 269-287.

- Bruhn, Miriam, “License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico”, Review of Economics and Statistics (2011).

- Chakrabarti, Saumya, and Anirban Kundu, “Formal-Informal Sectors’ Conflict: A Structuralist Framework for India”, Journal of Economic Development 34:2 (2009), 27-66.

- Chen, Martha, “Women in the Informal Sector: A Global Picture, The Global Movement”, SAIS Review 21:1 (2001), 71-82.

- Chen, Martha, Jennifer Sebstad, and Lesley O’Connell, “Counting the Invisible Workforce: The Case of Homebased Workers”, World Development 27:3 (1999), 603-610.

- Ciccone, Antonio, and Robert Hall, “Productivity and the Density of Economic Activity”, American Economic Review 86:1 (1996), 54-70.

- Davis, Steve, John Haltiwanger, and Scott Schuh, Job Creation and Job Destruction (Cambridge, MA: MIT Press, 1996).

- De Soto, Hernando, The Other Path: The Invisible Revolution in the Third World (New York, NY: Harper & Row, 1989).

- Dehejia, Rajeev, and Arvind Panagariya, “Services Growth in India: A Look Inside the Black Box”, Working Paper (2010).

- Deichmann, Uwe, Somik Lall, Stephen Redding, and Anthony Venables, “Industrial Location in Developing Countries”, World Bank Research Observer 23:2 (2008), 219-246.

- Delgado, Mercedes, Michael Porter, and Scott Stern, “Clusters, Convergence, and Economic Performance”, Working Paper (2010).

- Duranton, Gilles, and Diego Puga, “Micro-Foundations of Urban Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4 (Amsterdam: North-Holland, 2004), 2063–2117.

- Fernandes, Ana, and Ariel Pakes, “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data”, Working Paper (2010).

- Fernandes, Ana, and Gunjan Sharma, “Together We Stand? Agglomeration in Indian Manufacturing”, Working Paper (2011).

- Foster, Lucia, John Haltiwanger, and C.J. Krizan, “Aggregate Productivity Growth: Lessons from Microeconomic Evidence”, in Dean, Edward, Michael Harper, and Charles Hulten (eds.) New Developments in Productivity Analysis (Chicago, IL: University of Chicago Press, 2001), 303372.

19
The suggested refinements of the existing DAU offer significant improvements of the program. These include simpler and more meaningful and easily understood indicators. The deteremination of allocation by group leads to equal treatment of equals. Factor weights are objective and would be stable as these are determined by taking moving average of aggregate spending by the group as a whole. There is more clear and transparent need equalization. Both pool and allocation are determined by formula. Total pool , however, can be constrained by affordability. Nevertheless, there is one major drawback of the proposed design – unconditonal fiscal gap compensation strengths autonomy but without accountability to local residents. A second alternative retains this drawback but moves the current program from gap filling to an equalization program.

# **_Alternative 2: Moving from a Gap Filling To a Comprehensive Fiscal Equalization Approach_**

Calculation of fiscal capacity and expenditure needs would follow the same approach as under the gap filling approach described above. However surplus or deficiency of per capita fiscal capacity and per capita expenditure needs with reference to average or other explicit standard of equalization will be calculated. The jurisdictions in net deficiency positions will receive equalization payments from the center equivalent to the net deficiencies calculated after taking into account net positions with respect to capacity and needs. This alternative has the clear advantage of having an explicit standard of equalization determine the total pool as well as distributions. It has the disadvantage of having complex and controversial determination of expenditure need equalization as has been the experience in Australia (see Shah 2004).

# **_Alternative 3 – An Almost Ideal Approach:  Fiscal Capacity Equalization supplemented by Output-based Operating Grants for Merit Services and Capital Grants or Acess to Capital Market Finance to Overcome Infrastructure Deficiencies_**

Under this option, fiscal capacity equalization follows the same approaach as under alternative #2. However expenditure need compensation is done through output-based operating transfers for merit public services where allocation is based upon the share of service population and there is no conditionality on spending but instead customized conditions on service delivery performance in terms of service access and quality for individual jurisdictions and providers for continuation of the grant program. The design of such transfers were spelled out in an earlier section. In addition to these operating transfers, there would also be a need to have a capital grant and capital market access program to deal with infrastructure deficiencies as discussed in the 23
The second most extreme stress test is represented by the country specific alternative scenario, whereby the public debt ratio is projected to increase to 110 percent of GDP by 2011. The results of the sensitivity analysis of public debt dynamics highlight the central role of credible policies that deepen fiscal adjustment in building confidence. As results from these tests indicate (see Table 3), the base case scenario is rather optimistic and there are a number of risks to the debt dynamics.

_Table 3 Debt Sustainability in Standard Bound Tests (% of GDP)_

||2006|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|
|**Baseline**|**54**|**49**|**44**|**40**|**34**|**29**|
|||**Alte**|**rnative**|**scena**|**rios**||
|A1. Key variables are at their|||||||
|historical averages in 2005-09|**60**|**59**|**59**|**58**|**57**|**55**|
|A2. No policy change (constant<br>primary balance) in 2005-09|**54**|**67**|**63**|**59**|**55**|**51**|
|A3. Country-specific shock in<br>2005, with reduction in GDP<br>growth (relative to baseline) of<br>one standard deviation|**96**|**105**|**108**<br>**Bound**|**113**<br>**tests**|**121**|**130**|
|B1. Real interest rate is at<br>historical average plus two<br>standard deviations in 2005 and<br>2006|**76**|**97**|**94**|**92**|**89**|**85**|
|B2. Real GDP growth is at<br>historical average minus two<br>standard deviations in 2005 and<br>2006|**71**|**94**|**108**|**122**|**137**|**153**|
|B3. Primary balance is at|||||||
|historical average minus two<br>standard deviations in 2005 and<br>2006|**60**|**61**|**57**|**53**|**48**|**43**|
|B4. Combination of 1-3 using<br>one standard deviation shocks|**77**|**97**|**96**|**95**|**94**|**92**|
|B5. One time 30 percent real<br>depreciation in 2005|**63**|**59**|**55**|**50**|**46**|**41**|
|B6. 10 percent of GDP increase<br>in other debt-creating flows in<br>2005|**64**|**59**|**55**|**51**|**46**|**41**|

The no-policy change scenario, (A2 in table 3), which assumes that primary balance to GDP ratio would remain at its actual 2005, will be identical with the base case, which already assumes a constant primary balance to GDP ratio of 6.5 percent.

21
Having calculated _y_<sup>_CHE_</sup> for 1970-1975, in **step 2** we update the growth rates for all of the other countries in SSA for 1970-1975. This is necessary because these countries are now, either directly or indirectly, connected to Switzerland, instead of the Central African Republic, through **W3** . **Step 3** then involves iterating steps 1 and 2 until convergence between successive iterations in each element of the vector of SSA country growth rates, including Switzerland in the place of the Central African Republic, is achieved.<sup>40</sup> In **step 4** we calculate _log(GDP per capita)1975_ for both Switzerland and all other countries in SSA. This is required because the value of this control variable in one sub-period is endogenous to growth in the previous period. Finally, in **step 5** , we repeat steps 1 – 4 for all subsequent time periods (i.e. for 1975-1980, ..., 1995-2000).

Figure 4 shows the results. In 1970, Switzerland's real GDP per capita in the counterfactual simulation is the same as its actual observed level. However, as time progresses, an ever-widening shortfall of simulated GDP per capita below its observed level emerges as a result of the weaker spillover effects. By 2000, Switzerland's real GDP per capita is 9.3 % lower under the counterfactual. Cumulating the losses over 19702000 gives an aggregate real GDP loss of $334 billion (2000 international dollars), which was the equivalent of 162 % of Switzerland's real GDP in 2000.

> 40 Convergence is assumed to have occurred when the absolute difference between each country's growth rate in successive iterations is less than 0.001 %.

36
# **Contents**

|**1**<br>**Int**|**roduction**|**1**|
|---|---|---|
|**2**<br>**Lit**|**erature Review**|**2**|
|2.1|Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>2|
|2.2|Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>5|
|2.3|Survival and Exit . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>6|
|**3**<br>**Da**|**ta and Methodology**|**7**|
|3.1|Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>7|
|3.2|Methodology<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>8|
||3.2.1<br>Probit Model . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>9|
||3.2.2<br>Multinominal Logit Model . . . . . . . . . . . . . . . . . .|. . . .<br>9|
||3.2.3<br>Heckman Selection Model<br>. . . . . . . . . . . . . . . . . .|. . . .<br>10|
||3.2.4<br>Panel Data Analysis<br>. . . . . . . . . . . . . . . . . . . . .|. . . .<br>11|
|**4**<br>**Em**|**pirical Findings**|**12**|
|4.1|Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>12|
||4.1.1<br>Determinants of Enterprise Operation. . . . . . . . . . . .|. . . .<br>12|
||4.1.2<br>Determinants of Type of Business Activity . . . . . . . . .|. . . .<br>16|
|4.2|Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>19|
||4.2.1<br>Productivity Measure . . . . . . . . . . . . . . . . . . . . .|. . . .<br>19|
||4.2.2<br>Dispersal. . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>19|
||4.2.3<br>Determinants of Non-Farm Enterprise Productivity . . . .|. . . .<br>21|
|4.3|Survival and Exit . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>25|
|**5**<br>**Su**|**mmary and Concluding Remarks**|**28**|

i