rafmacalaba/datause-encoder-data huggingface dataset:
registration status, since the sampling of the surveys is limited to persons who had permanent registration status.<sup>26</sup> In order to trace the wage changes during the period 2002 and 2004, I construct a sample of the panel individuals interviewed both in the VHLSS 2002 and 2004 who were 15-59 years of age in 2004 and had wage jobs as the most time consuming job in both years.<sup>27</sup> This resulted in a sample of 1,746 individuals. Relative to using the repeated cross section data, there exist advantages in analyzing the panel data component of the VHLSS. First, since the panel data model traces the changes in wages of the same individuals between 2002 and 2004, it controls for any impacts on wages resulting from time-invariant observable and unobservable personal characteristics. Second, the panel data approach adjusts for the “composition effect”, i.e., any impacts resulting from the difference in the composition of wage workers between two years. For instance, there is some evidence that the individuals who moved from a non-wage job in 2002 to a wage job in 2004 were less skilled on average than those who had a wage job in both years.<sup>28</sup> If the unobservable component of the selection process to wage employment and the export variable are correlated, the coefficient for the export variable using cross section data may be biased even controlling for observable personal characteristics.

> 26 For instance, the number of workers employed by foreign firms computed from the VHLSS 2004 (0.7 million) in 2004 is smaller than that taken from the _Enterprise Survey_ data (1.0 million), most likely because the VHLSS data exclude migrants who hold a temporary or no registration status. According to the _Vietnam Migration Survey_ 2004 data, only 10.2 percent of the migrants who work for foreign firms possess permanent registration status (Fukase, 2013b).

> 27 Since there exist some errors in the panel identifiers in the original dataset, I used the revised identifier codes provided by McCaig (2009) to match the individuals between 2002 and 2004. Then, I eliminated some observations whose information on gender, age and education are inconsistent. In order to mitigate further the measurement error, I dropped one percent of individuals who experienced the largest wage changes. However, the results are not sensitive to these adjustments.

> 28 The data on panel individuals reveal that those who had a wage job in both years tend to be more educated (with 9.4 years of education on average) than those who moved from a non-wage job in 2002 to a wage job in 2004 (with 7.3 years of education) (VHLSS 2002, 2004). During the same period, the BTA might have accelerated the rate at which less skilled non-wage workers became wage workers. If that were the case, the wage workers who lived in a province more exposed to exports might have consisted disproportionately of “new” wage workers relative to other provinces.

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**<u>Table 2: Overlap between cultivated and digitized parcels and mode of acquisition by type of document</u>**

|<br>**Total**|<br>**Title**|<br>**Offer**<br>**letter**|<br>**Chief**<br>**document**|<br>**No**<br>**document**|
|---|---|---|---|---|
|**Panel A: Extent of overlap**|||||
|Total area of listed parcels in ha<br>107,699|31,751|13,443|46,830|15,675|
|Share located in registered block<br>0.34|0.60|0.50|0.19|0.25|
|if yes, total area of listed parcels in ha<br>41,015|21,696|8,259|8,218|2,843|
|if yes, total overlap area in ha<br>36,024|18,450|7,732|7,219|2,622|
|if yes, share of area overlapping<br>0.88|0.85|0.94|0.88|0.92|
|**Panel B: Mode of acquisition**|||||
|**1. Parcels overlapping with registered blocks**|||||
|Chiefs<br>0.23|0.17|0.03|0.58|0.09|
|Ministry of Lands<br>0.15|0.23|0.20|0.01|0.01|
|Dept. of Resettlement<br>0.07|0.05|0.19|0.01|0.04|
|Ministry of Agriculture<br>0.14|0.12|0.30|0.03|0.08|
|Local Government<br>0.03|0.04|0.04|0.01|0|
|Inherited<br>0.16|0.13|0.09|0.27|0.13|
|Purchased<br>0.21|0.25|0.12|0.08|0.6|
|Don't know<br>0.01|0.00|0.01|0.01|0.05|
|Area of digitized parcel<br>630.90|494.06|538.80|879.81|747.43|
|<br>Area of cultivated (listed) parcel<br>19.33|27.19|14.88|15.39|12.10|
|Number of farms<br>2,122|798|555|534|235|
|**2. Parcels without overlap**|||||
|Chiefs<br>0.57|0.45|0.06|0.79|0.39|
|Ministry of Lands<br>0.03|0.06|0.11|0.00|0.01|
|Dept. of Resettlement<br>0.10|0.06|0.65|0.00|0.01|
|Ministry of Agriculture<br>0.02|0.04|0.06|0.00|0|
|Local Government<br>0.01|0.01|0.01|0.00|0|
|Inherited<br>0.21|0.22|0.06|0.17|0.45|
|Purchased<br>0.06|0.16|0.03|0.03|0.12|
|Don't know<br>0.01|0.01|0.00|0.00|0.02|
|Number of farms<br>4,037|539|562|2,212|724|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey. Information from owner respondents only. _Note:_ There are 14 registered blocks with a total area of 231,758 ha sub-divided into 2,688 digitized parcels and 35 digitized isolated fragments with a total area of 43,866 ha.

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# Using Lotteries to Incentivize Safer Sexual Behavior: Evidence from a Randomized Controlled Trial on HIV Prevention<sup>∗</sup>

Martina Björkman Nyqvist<sup>†</sup> , Lucia Corno<sup>⋄</sup> , Damien de Walque<sup>#</sup> , Jakob Svensson<sup>∗</sup> March 2015 ∗We are grateful to the Ministry of Health and Social Welfare, Lesotho, the World Bank Office in Lesotho, PSI and Leads Consultancy for excellent collaboration. We are deeply indebted to Dr A.F. Ensejor for contributions at all stages of the project and thank S. Berman, S. Cocciolo, F. Grazioli, E. Maffioli and A. Ornaghi for excellent research assistance. Financial support from the World Bank’s Strategic Impact Evaluation Fund, Bank-Netherlands Partnership Program, Trust Fund for Environmentally & Socially Sustainable Development and Knowledge for Change Program, the Swedish Research Council (421-2009-2209) and the Program for Development Research, SIDA, and from Swedish Foundation for Humanities and Social Sciences are gratefully acknowledged. We are grateful for comments and suggestions by T. Bold, D. Strömberg, I. Rasul as well as participants at seminars at Institute of Fiscal Studies; Bocconi University; London School of Hygiene and Tropical Medicine; Stockholm Univ.; Univ. of Bristol; Heidelberg Univ.; Institute of Tropical Medicine, Antwerp; International AIDS Conference 2014; International AIDS Society Conference 2013; International Health Economics Congress 2013; International Conference on AIDS and STIs 2013; UNAIDS Economics Reference Group.

> †Stockholm School of Economics and CEPR, martina.bjorkman.nyqvist@hhs.se; ⋄Queen Mary University and IFS, l.corno@qmul.ac.uk; #The World Bank, Development Research Group, ddewalque@worldbank.org;<sup>∗</sup> IIES, Stockholm University and CEPR; jakob.svensson@iies.su.se.

1
long-term funds are included (portfolio flows; foreign direct investment, which accounts for 50 percent of the inflows; and private loans). Second, a larger sample of 22 developing countries-all of those for which all relevant data are available-for the period 1986-93, is evaluated. As a result, we examine countries in the LCIR group as well as in the HCIR group.'<sup>2</sup> Third, as in Dooley and others (1994), we try to identify some domestic variables that may affect capital inflows and that, at the same time, can be more easily (at least indirectly) modified by policymakers in developing countries.'<sup>3</sup> Finally, because we use annual data, our results have a longer-term interpretation than those of previous studies. Our results also allow for a dynamic analysis of capital inflows, showing that the change in either domestic or external conditions will cause the inflows to adjust over time (as opposed to the static analysis adopted by the papers discussed above).

# A stock adjustment model

Suppose that a group of foreign agents is interested in investing in a particular developing country. It is assumed that there is a desired stock of foreign liabilities for each developing country i, which depends on internal as well as external conditions. This stock can be represented by: where D* is the desired stock of foreign liabilities and Xis the vector of exogenous variables. In any given period t, the desired value of the stock of foreign liabilities (D*i,) may not be completely realized because of infonnation, physical, or procedural constraints. The adjustment process can be represented as: with 0 **<** 6, y < I . This equation specifies that the change in the stock of foreign liabilities responds to the difference between the desired stock of _D_ and the lagged value of _D,_ and the flow of the previous period, plus a random error term **C.** The partial adjustment mechanism implies that private capital inflows

> 12 The final sample was dictated by real exchange rate data availability. The following countries were included in the panel data regression: Argentina, Bolivia, Brazil, Chile, China, Colombia, Costa Rica, Egypt, India, Indonesia, the Republic of Korea, Malaysia, Philippines, Portugal, Thailand, Turkey, Mexico, Venezuela, Jordan, Morocco, Tunisia, and Nigeria.

> _13_ Dooley and others (1994) uses the secondary market price of debt (rather than capital inflows) as the dependent variable.

10
We use four different metrics of vulnerability throughout this paper to assess occupation-specific exposure to the COVID-19 risks. These metrics address the definition of essential workers, the ability to work at home, the degree of income safety, and the extent of health risk. The first three measures capture the effects of supply-side shocks while the fourth is a directly related to exposure to infection. The measures are defined as follows:

**1- Essential workers** : We define jobs as essential if they are exempt from mobility restrictions even in the presence of government mandated lockdowns. When categorizing jobs as “essential,” we follow Fasani and Mazza (2020a) who use the European Commission’s guidelines concerning the exercise of the free movement of workers during COVID-19 outbreak in the first half of 2020,<sup>9</sup> and supplement it with the Dutch government’s definition of “key workers.” Throughout this uncertain period, governments provided lists of occupations considered essential and thus exempt from mobility restrictions and closures during lockdowns. While the criteria for essential occupations vary somewhat from country to country, there is a significant overlap, such as core occupations delivering critical health care (doctors, nurses and other medical professionals) and providing basic goods and services (food, utilities, transport, security, ICT, or research and science). Based on these criteria, we categorize jobs at the three-digit ISCO-08-classification level (which includes 182 occupations) as either essential or non-essential. According to this definition, about 37.8% of the working-age population in the EU works in occupations that are deemed essential.<sup>10</sup>

**2- The ability to work from home:** Our “amenability to home-based work” metric is based on the premise that a person faces lower employment and income risk if she can work at home, and higher risk if she is required to be physically present in the workplace to carry out the required tasks. Recent literature on the impact of COVID-19 on labor markets highlights the feasibility of home-based work (telework) as a key determinant to overcome governments’ mobility restrictions and associated income and/or unemployment risks. The ability to telework also limits the exposure to health risks among workers that face supply shocks. Dingel and Neiman (2020) use the Occupational Information Network (O*NET) surveys to calculate the amenability of different

> 9 <u>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020XC0330(03)</u>

> 10 This result is slightly higher than Fasani and Mazza (2020a) who estimate that 33% of workers in the EU work in essential sectors. The disparities emerge as we include the occupations related to “Agriculture, Forestry and Fishery Laborers” (ISCO-08 code 921), as well as an approximation of essential occupations at the 2-digit level for Bulgaria, Poland and Slovenia as the EU-LFS does not include 3-digit occupations for those countries.

10
range of variation that would be caused through the normal instruments of economic management.

# **Development impacts**

The HIV/AIDS epidemic is placing a severe burden on all levels of<sup>the</sup> health care system throughout the region. Hospital admissions are increasing rapidly and more than half of the available hospital beds are occupied by AIDS related cases, including tuberculosis. For example see Botswana MFDP (2000b). The epidemic is threatening to overwhelm the health services, and the expansion of home based care facilities for AIDS patients is inevitable.

The HIV epidemic has fueled a parallel epidemic of tuberculosis<sup>(TB),</sup> exacerbating the problems of the relatively low coverage of effective TB immunisation in much of the region. For example, see Botswana MFDP (2000a), and the data presented in Figure 3-1.

The education sector has been severely affected by the HIV/AIDS epidemic. The death rates among teachers and lecturers have been increasing sharply in recent years, for example see Botswana MOE and DFID (2000). Although death rates are low among school students, HIV/AIDS has disrupted their home environments as adults fall ill and need to be cared for. There is speculation about how this situation is affecting the quality of teaching and<sup>the</sup> quality of school work. It is clear that the demographic impacts of HIV/AIDS will create significant planning problems for education in the future.

The rapid spread of HIV/AIDS is exacerbated by a cultural environment<sup>in</sup> which issues of sexuality and sexually transmitted disease are not openly discussed. There are a number of pervasive culturally based myths about HIV/AIDS which reduce the impact of some of the prevention campaigns and encourage a fatalistic attitude. The issues raised by the epidemic impinge upon long standing taboos and result in a high level of stigma being associated with HIV/AIDS, for example see UNDP (2000).

Despite an apparently high level of knowledge about the nature of HIV/AIDS and how it can be prevented, the steadily rising HIV prevalence (as measured in ante-natal clinics) indicates little evidence of a widespread change<sup>in</sup> sexual behavior in the region. There is clearly a need to understand the reasons for this and to understand the possible effects that the introduction of an HIV vaccine might have.

# _1.3. Current responses_

The early response to the HIV/AIDS epidemic in Southern Africa, as elsewher<sup>1</sup> e, was essentially biomedical in nature, focusing on increasing national public awareness of HIV, training health workers in AIDS clinical management, and on establishing monitoring systems in ante-natal clinics. This broadened into efforts to prevent the spread of HIV through media campaigns promoting the use of condoms or abstaining from sexual activity. The epidemic progressed unabated however.

_5_
The reestimated ERR of each project is measured via a standard cost-benefit methodology (Gittinger 1982; Squire and van der Tak 1975)4, about two-to-three years after the completion of World Bank funding. According to this methodology, the discounted stream of project costs and benefits is evaluated at shadow (or border) prices. As such, the rates of return will likely differ from the financial rates of return that a private investor would calculate.

# B. Policy perfornance data

Policy distortions indices were gathered from independent sources.<sup>5</sup> The main indices used in this analysis were:

- (a) black market premia: the average annual mark-up of the parallel market rate for foreign exchange over the official exchange rate;

- (b) fiscal deficit of the central government as a share of GDP,

- (c) index of trade restrictiveness, based upon specific policy criteria such as tariffs and non-tariff

barriers;

- (d) index of pricing distortions in tradable goods, measuring the deviation of the domestic price levels from international price equivalencies for final tradable goods; and

(e) real interest rate.

Quantifying characteristics of macroeconomic, microeconomic and trade regimes can be a delicate and inconclusive exercise (Rodrik 1994, Pritchett 1993). Separately and together, however, these five indicators do capture major policy distortions in each economy. The black market premia reflects distortions in the trade, pricing, and exchange rate regime, as well as macroeconomic instability and capital account restrictions; the fiscal deficit is an indicator of macroeconomic instability. Additional variables incorporated into the investment productivity analysis--including years of education, capital/labor ratio, terms of trade changes, and the degree of project complexity--will be discussed below.

> 4 There are some exceptions to this general use of standard methodology in project evaluation. For example, World Bank and OED staff typically value non-traded output of an agricultural project at economic prices, whereas they typically value non-traded output of a power project at the utility's regulated tariff rates.

> 5 See Appendix 2 for a detailed description of this data.
needed to get to a customer base of around 850 retailers buying two to three times a week to achieve the necessary scale to break-even, but was unable to expand sufficiently to reach this level, and lacking a clear pathway to financial sustainability, Agruppa closed as a business on January 20, 2018.

In drawing lessons from this experience, a number of the issues facing Agruppa were those common to many start-ups in terms of difficulties fine-tuning the logistics of product delivery, pricing strategy, paying attention to customer retention while expanding into new areas, and dealing with external shocks such as a fall in agricultural prices (which given a fixed mark-up percentage, lowered the amount earned on each transaction). But, in our view, a larger problem stems from the very market failure of a lack of coordination among buyers that this intervention aimed to fix. Starting small with a limited product offering, limited volume, and limited number of customers means that it is difficult to form contracts with farmers, hard to attract and retain customers, and difficult to cover costs while charging competitive prices. It is only by operating at scale and coordinating together the buying power of a large number of retailers that it is likely to be possible to support a broad product offering, offer an attractive partner for farmers (including closing forward contracts with them), and operate an efficient delivery network. Once the product logistics have been piloted, a much larger investment in implementation and marketing than was possible for the Agruppa team is likely to be needed to enable an enterprise to quickly reach such a scale. Nevertheless, the underlying business idea of agglomerating orders from many small purchasers still may offer promise, and at the time of writing, a new start-up had launched with the related business idea of agglomerating purchase orders from restaurants in Bogota,<sup>20</sup> while the company Twiga had expanded a similar model to Agruppa’s to reach 13,000 farmers and 6,000 vendors in Kenya.<sup>21</sup> A final lesson is for both start-ups and researchers considering implementing randomized trials during the early stage of business launch. The advantage of working together at the launch was that the early data collected could be informative to the social enterprise in knowing more about its customer base, Agruppa was able to use the fact it was doing an impact evaluation and had short-term results in helping fundraise from social investors, and this was a stage where the planned

> 20 See <u>https://www.frubana.com</u> [accessed 20 March, 2019]

> 21 See <u>https://twiga.ke/2018/11/15/press-release-ifc-and-tlcom-invest-10m-in-twiga-foods-an-m-commerce-start-upsupporting-kenyan-farmers/</u> [accessed 29 July, 2019]

24
2007-2009 period; and (ii) their structure remains roughly the same in the first three years of accession.<sup>14</sup> The estimated size of the own resources for the three-year period is €954 mn.

**Table 10: Contributions to the EU Budget, in current prices and share in GDP**

|**Own Resources Payments,**<br>**€ mn, current prices**|**2007**|**2008**|**2009**|**2007-09**|
|---|---|---|---|---|
|Traditionalown resources|24.0|25.0|27.5|76.5|
|Resource based onVAT|35.5|33.7|39.0|108.2|
|Resource based onGNI|207.0|228.7|244.6|680.3|
|Reserve|1.0|1.1|1.2|3.3|
|GreatBritain Adjustment|24.2|30.6|30.7|85.5|
|**Total Own Resources**|**291.7 **|**319.1 **|**343.0**|**953.8**|

_Source: Ministry of Finance, Agency for Economic Analysis and Forecasting and own calculations_

# **_Payments for the subscribed capital share in the EIB_**

As per the Treaty of Accession of Bulgaria and Romania to the European Union, upon accession Bulgaria should pay EUR 14.8 mn corresponding to the share of the country of the capital paid in the subscribed capital of the European Investment Bank. New members subscribe only 5% of their share in eight equal shares till end-2011. These amounts are based on GDP and population keys for each of the countries. In 2007 and 2008 only one payment per year is envisaged, while in 2009-2011 two disbursements annually should be made.

Bulgaria will also have to make payments to the EIB, based on its obligation to cover EIB reserves. However this amount has not been fixed yet and should not be of important size. It is therefore not included in our calculations.

It should be mentioned that upon entry to the European Monetary Union (EMU), Bulgaria will also have to pay for membership to the European Central Bank (ECB), equal to only 5% of its capital share. The payments will be one-off and will be borne by the Bulgaria National Bank (i.e. it will have no immediate effect on the budget).

# **_Payments to the Research Fund for Coal and Steel_**

The Accession Treaty for Bulgaria and Romania also envisages a payment of certain amounts to the Research Fund for Coal and Steel. Bulgaria’s contribution to this fund in particular amounts to EUR 11.95 mn in current prices. It should be made in four installments starting in 2009 and paid as follows, in each case on the first working day of the first month of each year: 2009: 15%, 2010: 20%, 2011: 30%, 2012: 35%.

# **D. DIRECT NET EFFECT OF EU ACCESSION ON THE NATIONAL BUDGET**

Whereas the calculation of the EU funds’ impact on the Bulgarian balance of payment is straightforward, the estimation of the purely budgetary effect reflects the rules under which different EU funds are allocated.In addition, EU accession has additional direct and indirect effects on the revenue side of the budget due to the alignment of the national legislation with the _acquis communautaire_ and second-round positive impacts of EU accession on trade integration,

> 14 See Footnote 4 for the Agency for Economic Analysis and Forecasting estimations of GNI for 20072009.

14