rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4. FDI Inward Stock (% GDP) and Real GDP Growth**

<!-- Start of picture text --> Emerging Europe EU10 countries<br>200 200<br>150 150<br>100 100<br>50<br>50<br>0<br>0<br>-50 -30 -10 10 30 50<br>-50 -15 -10 -5 0 5 10 15<br>Real GDP growth rate Real GDP growth rate<br>EU candidate countries EU neighborhood countries<br>200 200<br>150 150<br>100 100<br>50<br>50<br>0<br>0<br>-50.00 -30.00 -10.00 10.00 30.00 50.00<br>-50 -30 -10 10 30 50<br>Real GDP growth rate Real GDP growth rate<br>Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.<br>FDI inward stock (in percent of GDP) FDI inward stock (in percent of GDP)<br>FDI inward stock (in percent of GDP) FDI inward stock (in percent of GDP)<br><!-- End of picture text -->

**Figure 5. Composition of Capital Flows in Emerging Regions (median values within groups)**

<!-- Start of picture text --> 12<br>8<br>4<br>0<br>-4<br>-8<br>-12<br>EU10 EU Candidates EU Neighborhood Developing East Asia LAC<br>FDI Portfolio<br>Banking Official<br>Current Account Balance Foreign Exchange Reserves, change<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.

# **3. Estimation Methodology**

Against the background discussed above, the analytical framework of the growth model estimations in this paper builds on an influential paper by Sala-i-Martin et al. (2004) on the statistical significance of growth determinants. Their methodology is based on a Bayesian averaging of existing estimates and aims at narrowing the gap between the growth theories and the extensive empirical work of the past two decades. They examine the relationship between growth and a list of 67 explanatory variables (identified in the cross-country growth regressions from papers in refereed journals) using data for 88 countries during the period 1960-96. The selection of the variables is based on several criteria, such as: (i) sufficient time length availability of the published or computed variables (from 1960 onwards) and (ii) maximization of the number of countries with observations. The strength of the association between each 7
# **VI. Conclusions and Policy Implications**

Pension funds and other institutional investors in developing countries are constrained in achieving broad international diversification by strict controls on foreign investments. These controls are motivated by the desire to prevent capital flight and preserve scarce foreign exchange reserves. It is also sometimes argued that requiring pension funds and other institutional investors to invest locally would stimulate the development of the local capital markets.

International asset swaps can be used to achieve international diversification while averting capital flight and stimulating local capital market development. As argued by Bodie and Merton (2002), the main benefits of asset swaps would come from a significant improvement in the efficient portfolio frontier facing domestic institutional investors. The global equity markets can be used to achieve better diversification and the global fixed-income markets can be used to achieve better hedging opportunities.

Faced with the challenge of allowing their institutional investors to attain greater international diversification when foreign exchange reserves are at a particularly low level, the South African authorities permitted local institutions to “obtain foreign investments by way of swap arrangements”. These turned out to be rather cumbersome and difficult to enforce, but even so they achieved their basic objective of greater diversification without capital flight.

However, the asset swap mechanism used in South Africa deviated considerably from the optimal features of swap contracts that have been underscored by Bodie and Merton. The main benefits of properly designed swap contracts are the ease of transactions, the avoidance of large foreign exchange transactions, the minimization of brokerage and other transaction costs, and the protection from expropriation and market manipulation risks.

Given the presence of 40 South African firms in the MSCI Emerging Markets Free Index (MSCI EMFI) and the existence of a large number of pension and mutual funds from Europe and North America with a strong interest to invest in South African 19
Table 2: Uzbekistan Enterprises Registered as "Natural Monopolies" at the Republic Level (as of October 1, 1999)

|**Sectors with "Natural Monopolies"**|**Number of Enterprises**|
|---|---|
|Production of oil, gas condensate and natural gas|'7|
|Coal mining|4|
|Pipeline transport of oil|1|
|Pipeline transport of natural gas|2|
|Generation, transmission and distribution of electric power and heat|48|
|Railway transport|I|
|Airport services|12|
|Postal services|16|
|Telecommunications|21|
|Water and sewage|30|
|**Total**|142|

_Source:_ Uzbekistan Anti-Monopoly Committee

**_When Monopolies are_** _No_ **_Longer "Natural"._** International experience shows that policies that stifle the operation of inherently competitive infrastructure sectors, as well as prevent the progression of traditional "natural" monopolies into more competitive regimes, impose costs on society in the form of high consumer prices, poor production efficiency and retarded innovation. Distinguishing between competitive versus noncompetitive infrastructure sectors is thus an important policy issue in designing further transition reform initiatives. In this regard, governments worldwide typically categorize public enterprises into three groups, and this taxonomy provides a useful guide for Uzbekistan policymnakers: (a) in "strategic" industries-the national military defense sector, the currency mint, and the mining of rare metals that have national defense applications-there is, with few exceptions, a compelling rationale for state involvement; (b) in "monopoly or quasi-monopoly" industries-usually local-level utilities in the energy, mass transit and communication sectors-the rationale for government involvement, either through direct ownership or regulatory oversight of nonstate-owned service providers, has historically been strong inasmuch as market forces alone can often produce suboptimal results; however, such sectors can evolve to a point where the competitive provision of such services is most efficient, and this outcome has often entailed the divestiture of utility firmns to private owners; and (c) in "commercial" industriesmost of the manufacturing and services sectors-there is generally little justification for state involvement, as competitive market forces often engender the greatest efficiencies.

The overwhelming majority of industrial sectors in countries around the world today possess underlying organizational structures that are _inherently_ competitive. Thus within the typical industrial sector, maximum social efficiency is realized when numerous firms are producing the product (or service); the output share of each firm is not large enough to control the prevailing price in the market; any attempt by a firm to charge an above-market price will produce a loss of consumers or entry by rival 16
The Economic Census is the primary data source on all non-agricultural enterprises in Indonesia – from micro and small enterprises to medium and large firms.<sup>4</sup> It collects detailed information on various aspects of an enterprise’s operations such as workforce characteristics, assets, cashflows, and income. These two factors – universal coverage of non-agricultural firms and the wide range of data collected – are key to identifying and mapping the distribution of economic activities across the country. For this reason, the Census also functions as the sampling frame for other enterprise surveys in Indonesia (Lestari & Wikaningsih, 2011). But as the Census is only conducted decennially, it is neither designed to monitor business activities regularly nor to study enterprise dynamics.

To fill the gaps left by the Economic Census, Statistics Indonesia conducts other surveys during intercensal years. As mentioned earlier, there are two main ones – the IMK Survey, and the Manufacturing Survey. The IMK Survey, which began in 2009, is conducted annually and covers 1 percent of all micro and small enterprises in Indonesia.<sup>5</sup> It includes a range of questions on production and output, capital, labor, and technology use. The Manufacturing Survey is also conducted every year, but unlike the IMK Survey, covers the entire population of medium and large manufacturing firms.<sup>6</sup> This unique feature enables researchers to construct a panel dataset of all manufacturing firms with 20 or more workers. However, the data also comes with several caveats. As Marquez-Ramos (2020) explained, information on some of the variables in the questionnaire (e.g., capital stock, R&D) may be limited due to non-response. Aside from missing data, there are also issues with sectors needing to be surveyed in certain years, typographical errors and outliers, as well as consistency in variable definitions (Marquez-Ramos, 2020). More

> 4 In Indonesia, firm size is determined in two ways. The first approach, used by Statistics Indonesia, is based on the number of employees. Under this definition, microenterprises are those with 1 to 4 employees; small enterprises with 5 to 19 employees; medium enterprises with 20 to 99 employees; and large enterprises with 100 or more employees. The second definition is based on the National Law 20/2008 (Art. 6), and distinguishes between micro, small, and medium firms based on their net assets or annual revenues.

> 5 The seminal 2009 IMK Survey did not use a nationally representative sample of enterprises. The first nationally representative IMK survey was conducted the following year in 2010 (Rothenburg et al., 2016).

> 6 For this reason, the Manufacturing Survey functions as a census, although the term is not used to avoid confusion with the Economic Census (Marquez-Ramos, 2020).

6
is employed. Unfortunately, the RCRE panel itself does not report disaggregated sales for any non-agricultural product, and accordingly it is not possible to construct a price index for non-agricultural inputs or outputs. The use of an index of factory prices may not be ideal in the analysis of household-level businesses that may produce very different products; this point will be addressed further in the discussion of the results below.

In addition, total income is constructed equal to the sum of agricultural and nonagricultural income. Additional analysis reports a dummy variable for outside labor equal to one if the household reports any days worked outside the household and associated wage income; and a dummy variable for migration equal to one if the household reports days worked outside the township. For borrowing, a dummy variable equal to one if the household reports any access to credit (formal or informal) is utilized, as well as a continuous variable equal to the amount borrowed.

Finally, results are reported for consumption of grain staples, non-grain consumption, and total consumption. Grain consumption is the sum of expenditure on grain and the imputed value of own-grain consumption, while non-staple consumption is the sum of expenditure on all consumption items excluding staple grains (both food and non-food), and the imputed value of consumption of own-farm non-grain products. Consumption expenditure is deflated employing a province- and year-specific consumer price index generated by Brandt and Holz (2006).

# **4.1 Two-stage least squares**

The specification of interest can be written as follows, where _Yivpt_ denotes economic outcomes of interest and the primary independent variable is _Iivp,t−_ 1, lagged income from grain quota sales. Standard errors are estimated employing two-way clustering at the province and year level.
than men to be self-employed or family workers (61% for men and 74% for women), and less likely to be employees or employers (31.6% for men and 20.6% for women) (ILO, 2014). Employment by family members and self-employment is classified by the ILO as “vulnerable employment” due to the informal nature of these occupations. As depicted in Figure 2, a large majority of employed women work in vulnerable employment.

**Figure 2: Vulnerable Employment, Agriculture, and Unpaid Work by women in select countries**

<!-- Start of picture text --> 80<br>100<br>80 60<br>60<br>40<br>40<br>20<br>20<br>0 0<br>Employed by non-family member Percent of Women in Agriculture<br>Self-employed Percent of Women Unpaid within Agricultural Work<br>Employed by family member Percent of Women Unpaid within Non-Agr. Work<br>Kenya Ghana Uganda Ethiopia Cameroon Mozambique Madagascar Nigeria Cote d'Ivoire Tanzania DRC Nigeria Ghana Cote d'Ivoire Cameroon Kenya Ethiopia DRC Mozambique Uganda Tanzania Madagascar<br><!-- End of picture text --> Source: Demographic and Health Survey Data 2008-2014; includes data on working women ages 15 to 49. Note: The most populous countries with available data were selected.

# **_Why Young Women’s Employment Matters for Development_**

In 2013, the ratio of female-to-male labor force participation rates was higher in Sub-Saharan Africa than in any other region at 0.84 (World Bank, 2015). However, these high rates of female labor force participation mask underlying challenges. In the same year, the unemployment rate for women aged 15 to 24 was 1.26 times the rate faced by men. Regional variation is quite large: the ratio of the female-to-male employment rate for young people (ages 15 to 24) varies widely across the region, from .44 in Mauritania to 1.24 in Sierra Leone (See Figure 3). While the following discussion is regional in nature, it is important to note that heterogeneity in this ratio varies with country, ethnicity, culture, and urbanization, indicating that policies that do not work under certain circumstances may prove successful in others.

The differences in the constraints facing young women stem from differential access to key resources which enable employment, such as skills, time, and capital, as well as (and related to) underlying norms and institutions that govern men’s and women’s economic and household roles. Despite the relatively higher barriers faced by women, young men tend to dominate the policy dialogue on youth employment, motivated in part by the perceived threat of instability and socially disruptive behavior that could result from their unemployment. The resulting policies are often blind to young women’s needs and constraints.

3
# Behaviour of Finms

regards location, iust over 50% of tho sample were situated within Moscow, a further 16 firms in Moscow oblast and 3 in other oblasts. All were however in fairly close proximity to Moscow. The bulk of the sample were industrial firms with an average current labour force of over 800. The legal status of the sample was reasonably diverse. If we include state property currently rented by collectives alongside collectives and joint stock entities we can see that 11 or over 25 % of the sample were, loosely defined, private firms.

While the sample size is small and localized, the survey results at th.. least provide some crosscheck on the information given by more aggregate data. Comparing the salient 1991 information for the industrial firms in our sample with a Goskomstat survey of over 26,000 Russian industrial enterprises, we find that our sample is, however, strongly biassed toward larger firms. While we find fair correspondence with respect to nominal wages and employment levels, for sales revenues and profits, the World Bank averages are significantly different from the Goskomstat firms. Revenues were over 80% higher for the smallest fims with less than 200 employees but were 30% lower for the other two categories. For gross profits, the World Bank survey reported consistently higher average levels across all size classes. This obviously suggests the dangers of over-generalizing from the sample. Simply put, the universe we have surveyed appears likely to be charctrized by larger firms with higher average revenues and gross profits.

Nevertheless, the survey allows us to get a richer handle on emerging changes to important decision rules with respect to wages, employment and benefits that the aggregate data does not capture. Finallyi we should note that from aggregate data it is clear that the Moscow region has been subject to higher than average employment contraction over 1992; the sample may then reflect some of the upward bias in separations that appears characteristic of the region as a whole.

# 2: Output Changes

Aggregate data for Russia indicate that industrial output declined by around 20% in 1992. The distribution of those losses by branch and region is far from uniform but the evidence broadly points to a significant and generalized negative. shock to output dominated primarily by an aggregate rather than a sectoral process. Nevertheless, there are some signs that sectoral or reallocation shocks have been present. Retating relative producer price changes to relative output changes we ooserve a reasonably robust and positive correlation over 1992.

The heterogeneity of the firms covered in the survey and difficulties in secuing accurate estimates of either value or vol'ume indicators resulted in the output questions being couched in terms of direction of change and broad physical magnitude. As Tables 1 and 2 indicate the picture is fairly mixed across
|IQ-TF EMERG. BAGHDAD WATER SUPPLY|Caroline van den Berg|
|---|---|
|IQ-TF EMERG. BAGHDAD WATER SUPPLY|Caroline van den Berg|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Bolivia Land for Agricultural Development Project.|Enrique Pantoja|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Cambodia Public Financial Management and Accountability|Leah April|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
|Irrigation & Drainage Rehabilitation Project(Serbia)|Nikola Ille|
Figure 4: <!-- Start of picture text --> General Government Revenue (4)<br>in Transition Countries<br>1993-94<br>| Actual E Predict<br>60<br>50 5 0 -. ... .. ..... .. .. ... . .. . ..... .. . .. ... ;E :: : -7 : ; C::::;E : : g; -- ::; :;: : : :; g : ;<br>.S-S.;.\ . ..f. ..; :-. .. ''X i0i .. .' ......... i\T'''' , '.' . :'....0i,A 'f 'j X \. .0 ?7X0<br>.... i.., . . . :. ... .R E# : .. i ... .... iE :i . :: -: :. . .: : . i R : : . . . . :. .EE . E .. . . :<br>: ::" :. 0 .. £ C . 0 : 0 .:. :.;: .:';: i: C C * ..: ~~~~. .,. . . .... ;' ... .2 ..f. . a......aa .: '': . :. ....... - . ., ............. a a<br>... . . . . 2.. 10 - . . Wll W0 A A , W 0 Sl:0000:!000000~~. a .. 0. , a .a .. ......... . . . ..... . ......... . ... ..... . . . . . .. .. .. . ..... .. i . , 2 .. .. ..... 0 ?00 00000 ... . ; :. .. . .. . ... .. .. .. W!l,<br>I 2 0 -I t:, X,~~~~~~.. ra-: - ,., : .. ..... . . .., ... .. F.fl , .t. ..... ' [t. !0"':.;'! .t. '" t ' . '':--0- S.0. '.t. , i .... . .. ..;. ....'t . . . ..<br>3 0. 40 ~ ~ .... E :. .. -F.ii ,::. .. S i --- :-:- ~ EE.E.E ~ ~ ~ ~ oV h .....<br>o ... ... . ..... .. .. L .. L ............. k L L I L -<br>..e .. . . ...... . .. . .. . e e . e . z , . e . e *<br><!-- End of picture text -->

The predicted revenue/GDP ratios based on the regressions discussed above show interesting patterns among the four groups (Table 3 and 4). The countries of _Group 1_ and _Group 2,_ on average, display the highest tax effort compared to model predictions. There is some variation among individual countries, but the message is rather uniform.

In Group 1, Tajikcistan and Uzbekistan raised double the amount of revenue than predicted; Ukraine and Belarus surpassed their predicted revenue level by one-half. Only Armenia and Georgia have theoretically a greater potential to raise revenue than the amount they actually have collected.

_25_
However, many of the same sources asserting the plausibility of the relationship between poverty and HIV are puzzled to report evidence to the contrary. For example, Shelton, Cassell, and Adetunji (2005) report a positive correlation between household possessions and HIV prevalence in Tanzania. Examining the determinants of HIV in five countries with DHS data in Sub-Saharan Africa, De Walque (2006) finds that wealth (measured by an asset index) is positively correlated with HIV status in three of the five countries, especially for females.<sup>6</sup> Finally, using prime-age adult mortality as a proxy measure for HIV/AIDS affected households; several studies find that higher income households are more likely to suffer an adult death (Yamano and Jayne, 2004; World Bank, 2006, among others).

HIV/AIDS is an important problem in Sub-Saharan Africa, especially among young women. CCT programs are now starting to be seriously considered for implementation in the region, with a number of countries piloting or implementing such schemes. There are good reasons to think that such programs may play a role in affecting the incidence of HIV infection among young people through behavioral change, as sexual behavior might reasonably be linked to both schooling and income. However, credible empirical evidence is lacking to establish such a causal relationship. This paper, which reports the findings from a prospective evaluation of a randomized CCT program, is wellsuited to attempt to fill some of this knowledge gap.

# **3. Study Setting**

Malawi, the setting for this research project, is an impoverished small country in southern Africa. Its population of almost 14 million in 2007 is overwhelmingly rural,

> 6 De Walque and Corno (2007) report a similar positive conditional correlation in Lesotho.

7