rafmacalaba/datause-encoder-data huggingface dataset:
# Introduction

This paper reexamines a classic question in international economics: What is the current account response to a transitory income shock such as a temporary improvement in the terms of trade, a transfer from abroad or unusually high production? To answer this question, we construct a world equilibrium model in which productivity varies across countries and international borrowing and lending takes place to exploit good investment opportunities. Despite its conventional ingredients, the model generates the novel prediction that favourable income shocks lead to current account deficits in debtor countries and current account surpluses in creditor countries. Evidence from thirteen OECD countries broadly supports this prediction of the theory.

A simple thought experiment reveals how natural our result is as a benchmark case. Consider a country that receives a favourable transitory income shock. Suppose further that this country saves this shock and has two investment choices, domestic capital and foreign loans. To the extent that the shock does not affect the expected profitability of future investments at home and abroad, a reasonable guess is that investors allocate the marginal unit of wealth (the income shock) among assets in the same proportions as the average unit of wealth. Since by definition the share of a debtor country's wealth invested in domestic capital exceeds one, an increase in wealth (savings) results in a greater increase in domestic capital (investment), leading to a deficit on the current account (savings minus investment). Conversely, in creditor countries the increase in wealth exceeds investment at home, as a portion of this wealth increase is invested abroad. This produces a current account surplus in creditor countries.

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<!-- Start of picture text --> Lowess smoother Lowess smoother<br>BDI BDI<br>SOM SOM<br>ZMB ZMB<br>DRC DRC<br>ETH ETH<br>MOZ MOZ<br>TZARWA KEN RWATZA KEN<br>UGA UGA<br>DJI DJI<br>MWI MWI<br>0 .1 .2 .3 .4 .1 .2 .3 .4 .5 .6<br>bandwidth = .8 Domestic Impediment bandwidth = .8 Cross-border Impediment<br>Note: The line is a semi-parametric estimation of the relationship between the prevalence of undernourishment (respectively<br>the prevalence of food insufficiency) and our indicator of domestic impediments to market integration.<br>80 80<br>70 70<br>60 60<br>50 50<br>Prevalence of food inadequacy 40 Prevalence of food inadequacy 40<br>30 30<br><!-- End of picture text --> <u>Figure 3: Logistic Performance Index and border effects.</u> <!-- Start of picture text --> UGA<br>DRC<br>TZA KEN<br>MWI ETH DJI<br>BDI MOZ ZMB<br>SDN<br>RWA<br>SOM<br>0 .1 .2 .3 .4 .5<br>average border effect<br>LPI Fitted values<br>3<br>2.5<br>2<br>1.5<br>1<br><!-- End of picture text --> Note: LPI values are for 2010 (except for Malawi and Burundi for which LPI values are from 2007 given that data for 2010 is not available).

Finally, we examine the relationship between our estimates of border effect and the Logistics Performance Index (LPI), which reflects perceptions of a country's logistics based on efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The LPI is constructed by the World Bank and ranges from 1 to 5, with a higher score representing better performance. Figure 3 confronts the LPI with our estimates of the 22
<!-- Start of picture text --> 2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040<br>2015 2020 2025 2030 2035 2040<br>Education Gov. cons.<br>Investment Openness<br>Oil rents Non-oil rents<br>Rule of law Corruption<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>Change in contribution Change in contribution<br>0 0<br>-1<br>-1<br>4<br>3<br>2<br>1<br>Change in contribution<br>0<br>-1<br><!-- End of picture text -->

**Figure 6** – Change in the contributions of covariates to predicted GDP per capita growth rates: Scenario 0 vs. 1 (top left), Scenario 1 vs. 2 (top right), and Scenario 2 vs. 3 (bottom left)

Projected growth rates of GDP per capita based on this scenario are moderately higher than in Scenario 0, and by 2040-44, they are expected to be close to 4%. The better part of the difference between Scenarios 0 and 1 is materialized not before the end of the prediction period, however. The top left graph in Figure 6 shows that inclining oil rents in GDP drive the difference to the baseline projections. The expected recovery of the oil prices gradually raises the growth prospects of the country, assuming that other factors remain constant. Most importantly, this improvement can only be materialized, if institutional quality and corruption are not worsening simultaneously. This scenario certainly suggests that recovering oil prices alone are not sufficient for noteworthy and sustainable improvements of GDP per capita growth rates.

**Scenario 2:** Growth predictions based on Scenario 2 are more optimistic, and the underlying assumptions can be found in Table 5. The share of upper secondary and tertiary educated individuals in the working age population follows the medium scenario of the IIASA/VID Education

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Chile’s _empresa modelo_ (model firm) approach to cost-reflective tariff estimation and uses utility-level data from the World Bank’s IBNET database.

The paper is structured as follows: section 2 describes various approaches from the literature to estimating the cost of WSS services, section 3 details our methodology; section 4 presents the results of our analysis; section 5 describes the limitations to our approach; and the final section concludes with a summary of our findings and their policy implications.

# **2. Approaches to estimate the economic cost of water and sanitation**

Since subsidies are the difference between (i) the costs of service provision and (ii) the amount paid by users, defining and estimating the first element of the equation is fundamental to any analysis. When computing the costs of service provision, total economic costs (and, eventually, inefficiencies, or slack<sup>4</sup> ) are taken into account (see Figure 1).<sup>5</sup> In accounting terms, these costs include operating costs, debt service, return on equity, depreciation, taxes, and environmental costs. Beyond these, all service providers present some inefficiencies that result in additional, hidden, costs that should be taken into account.<sup>6</sup> An efficient level of regulation is assumed, which means that tariffs cannot be higher than the economic costs of service.

## **Figure 1 Costs of Water and Sanitation Service Provision**

<!-- Start of picture text --> E C Common Costs<br>F A E<br>Dams, aqueducts, networks, plants, etc.<br>F P F<br>I E Specific Costs F<br>C X Meters, connections, etc. E<br>I Common Costs C<br>E T<br>O Management, IT, etc.<br>N I<br>T P Inputs V<br>E Energy, chemicals, labor, etc. E<br>X<br>C Specific Costs<br>O Meter reading, variable costs C<br>S O<br>T Env. Environmental Costs S<br>Costs<br>S Resource costs and externalities T<br>S<br>Inefficiencies<br><!-- End of picture text --> _Source:_ Authors’ compilation. _Note:_ CAPEX = capital expenditure; IT = information technology; OPEX = operating expenditure.

> 4 This is a controversial issue since the mere existence of a subsidy may create perverse incentives for the utility to relax its productive efficiency, hence augmenting the need for the subsidy. Subsidies to the supplier (i.e., those given directly to the utility like payment for labor, power, or chemicals) disincentivize production efficiency gains. Furthermore, the monopolistic nature of networked service, in combination with poor regulation, allows utilities to pass the costs of their inefficiency on to users (and, eventually, the government).

> 5 Different service types typically entail different costs. The same utility may choose to provide services of varying quality levels to appeal to users with differing consumption needs or abilities to pay.

> 6 See background paper 2 (listed in appendix A) for an analysis and quantification of the impact of inefficiencies on costs and subsidies.

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in outcomes between the two groups can be attributed to the state to which they were assigned to serve.<sup>16</sup> The randomization procedure is as follows: Each university submits a list of its graduating class to NYSC. This list includes the name, gender, marital status, date of birth, course of study, state of origin, and date of graduation. NYSC identifies those who are above 30 years old at graduation and issues them a certificate of exemption, as they are not eligible to serve. Married women and those with chronic illnesses are required to submit documentation in support of their status and are manually assigned to their family’s home state.

All other graduates are randomly assigned to the different states of the country using a computer algorithm. According to NYSC staff, the algorithm takes into consideration the following factors: meeting the manpower needs in different parts of the country, ensuring that no student is posted to his or her home state, and producing a cohort of corps members in each state that reflects the diversity of the overall graduating cohort in the country. NYSC sends back the list of students and their state postings to universities that then distribute to students.

There are two stages in which state assignments may depart from the randomization protocol. First, after students receive their assignment, they may request to be redeployed to a different state given the provisions for health or family reasons (genuine or otherwise), or they may wait until the next batch of postings to be remobilized. As such, the state in which an individual serves may be different from the assigned state. Matching the survey data and administrative data reveals that 87 percent of participants serve in their assigned state. Of the 13 percent that do not, only one individual redeploys from a Yoruba state

> 16Without the exogenous variation from NYSC assignments, it would be misleading to simply compare people living in their ethnic homeland to those who are not since different unobservable characteristics such as openness to other cultures, tolerance, ambition, may affect the migration decision.

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Employment statistics show that in Rwanda, like in many countries in the region, youth face a challenging school-to-work transition: 28% of youth are unemployed, with women facing unemployment rates that are 7 percentage points higher than those of men (Rwandan National Institute of Statistics, 2022). Youth who are able to secure an income generating activity overwhelmingly do so in the informal sector, which accounts for 94% of the income generating activities. The data present a mixed picture of the labor market for tertiary-educated Rwandans, who face a higher unemployment rate of almost 34% but are also far more likely to eventually find formal-sector jobs: more than half of working, tertiary-educated Rwandan youth hold formal jobs.<sup>5</sup> In this context, it is reasonable to expect that a soft skills training, if effective, could improve not only informal income generating activities, but also access to formal jobs. Moreover, improved soft skills may also help participants adapt to changes in work requirements and retain their jobs (Deming, 2017; Heinrich and Housemann, 2021).

The Rwanda Development Board advertised the program on several online job boards and invited youth who graduated from university or technical/vocational education programs in the previous two years to apply. Around 2,400 participants applied to the program, and close to 1,800 completed an online baseline survey, many more than the 450 available vacancies. Of these, 500 women and 500 men were randomly selected and invited to attend information sessions where public lotteries to assign the vacancies of the training were conducted. The lotteries selected 450 participants who were assigned to the training (225 women and 225 men) and 450 participants who were assigned to the control group. Approximately 74% of participants from all around Rwanda attended the bootcamp, even though for logistical reasons it was held in the South of the country following strict COVID-19 guidelines.

Our results indicate that the program facilitated an accelerated entry into the labor market in a period characterized by COVID-19-related disruptions. Using data from three online followup surveys—administered 4, 10 and 15 months after the training, each with very high response rates—we see increases in the probability of earning income, hours worked, and main job earnings. There is evidence of significant increases in job search in the short run and persistent increases in job networks, increasing interactions with other program participants even 15 months after the training. However, there is no evidence that the training facilitated youth transitioning into formal employment, with no effects on changes in access to formal jobs even in the short run. We validated the lack of effect on formal jobs with a snapshot from administrative data on Social Security records, which indicates no differences between treated and control youth in ever being registered with a formal employer 17 months after the training. The short-run labor

> 5All the statistics presented in this paragraph were obtained from authors’ calculations using the microdata of the Rwanda Labor Force Survey.

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<!-- Start of picture text --> 2<br>AGR<br>+<br>SSR<br>eetSBP<br>SRR<br>a MNG<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> AGR<br>©<br>MNG<br>CRF<br>oO* HOPFePRF<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> Nn AGR<br>N<br>N<br>st<br>TCH<br>MARE<br>- Br<br>© ELM<br>RF<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>+<br>~ SSR<br>fee<br>TCH<br>~ oe<br>PRE<br>2<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> ~~<br>- AGR<br>2<br>ea CLRaPRF<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> q<br>Nn<br>«©<br>2<br>+<br>- MNG<br>CLR<br>- ioeELM<br>«<br>2<br>1995 2005 2015<br><!-- End of picture text -->
Morgan, S (2005): Past Themes and Future Prospects for Research on Economic and Social Mobility, in Fields, Gursky, and Morgan (eds) (2005).

Mulligan, C. B. (1999), “Galton vs. the Human Capital Approach to Inheritance,” _Journal of Political Economy_ , vol. 107 (6 Part 2), S184‐S224.

Plomin, R, DeFries, J, P. McGu¢ n, G. McClearn, and (2001): _Behavioral Genetics_ , Worth Publishers, New York, 4th Edition.

Rigobon, R (2003), “Identification through Heteroskedasticity,” _Review of Economics and Statistics_ , 85.

Sato, H and Shi Li (2007a), Class origin, family culture, and intergenerational correlation of education in rural China. IZA Discussion Paper series No. 2642, Institute for the Study of Labor (IZA), Bonn, Germany Sato, H and Shi Li (2007b), Revolution and Family in Rural China: influence of family background on current family wealth, IZA Discussion Paper Series No. 3233, Institute for the Study of Labor (IZA), Bonn, Germany.

Schroeder. E. (2010), “The Impact of Microcredit Borrowing on Household Consumption in Bangladesh", Working Paper, Georgetown University.

Solon, G (2002): “Cross‐Country Differences in Intergenerational Earnings Mobility”, _Journal of Economic Perspectives_ , Summer 2002.

Solon, G (2004), “A Model of Intergenerational Mobility Variation over Time and Place,’ in Miles Croak (ed.), Generational Income Mobility in North America and Europe, pp. 38‐47, Cambridge: Cambridge University Press, 2004 Solon, G (1999), “Intergenerational Mobility in the Labor Market,” in O. Ashenfelter and D. Card eds Handbook of Labor Economics, Vol 3A, Elsevier Science, North‐Holland, Amsterdam.

Tsang, M. C. (1994), “Cost of Education in China: Issues of Resource Mobilization, Equality, Equity, and Efficiency”, _Education Economics_ , Vol.2, Issue 3, 1994.

Tsang, M. C. (2002), “Intergovernmental Grants and Financing of Compulsory Education in China”, Harvard China Review, 3 (2).

Wei, X., M. Tsang, W. Xu and L. Chen (1999), “Education and Earnings in Rural China”, Education Economics, Vol. 7, No.2, pp 167‐187.

World Development Report (WDR) (2005), Equity and Development, The World bank. Yang, D. (2004), “Education and Allocative Efficiency: Household Income Growth during Rural Reforms in China”, Journal of Development Economics, 74, pp 137‐162.

Yang, D. (2004), “Education and Allocative Efficiency: Household Income Growth during 43
and Zambia, trials have been conducted to test how practices such as optimal plant spacing and minimal tillage affect yields and resilience of cereals (barley and sorghum) and local tubers (cassava).

These initiatives and pilots of regenerative agriculture in Sub-Saharan Africa are still at a small scale. Engaging millions of smallholder farmers in Africa and scaling up regenerative agriculture remains hard. Challenges for large-scale adoption include setting up local technical support to customize regenerative agricultural practices that incorporate farmers’ demands, involving farmers to gain confidence and trust in the success of regenerative agriculture, coping with potential loss in yields during the transition to regenerative agriculture, and overcoming costs of adoption.<sup>7</sup>

# **3. Evidence of Impact on Global Agriculture**

This section provides a review of scientific literature on the empirical impacts of regenerative agriculture and other related practices. We focus on impacts on three aspects — agricultural productivity, resilience to climate shocks, and carbon mitigation.<sup>8</sup> In summary, there is insufficient empirical, especially causal, evidence demonstrating that regenerative agriculture can simultaneously achieve productivity gains, resilience, and carbon mitigation. Additionally, the impacts of regenerative agriculture vary depending on underlying climate and economic conditions. Further research is needed to assess the cost-effectiveness of regenerative agriculture under various situations.

|Practice|Productivity|Resilience|Mitigation|
|---|---|---|---|
|No-till practices through<br>meta-analysis of 74<br>published studies in the<br>U.S. and southern<br>Canada (Ogle, et al.<br>2012)|Crop productivity can<br>decrease in cooler and/or<br>wetter climatic<br>conditions, while<br>increasing in some other<br>regions, such as cotton<br>in southeastern U.S.||The overall carbon<br>impact depends both<br>on soil organic carbon<br>(SOC) stocks and<br>carbon input.|

> 7 <u>Farmer Led Regenerative Agriculture for Africa, 2020, Institute for Global Prosperity.</u>

> 8 Only papers published in peer-reviewed journals are included in this section.

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administering regulatory policies aside from the policy itself; (ii) access to information on the regulatory policy (i.e. transparency), and (iii) governance structures for the regulators that set policies affecting trade in services. Consequently, their analysis focuses on the role of governance structures, institutions, and transparency in shaping the environment for trade in services.

**Table 3: TFiS as Percentage Ad Valorem Equivalents**

|**Country**|**STRI**|**DTRI**|**WGI**|**Total TFiS**<br>**([DTRI + WGI]/STRI)**|
|---|---|---|---|---|
|AUS|7.773|10.872|20.735|4.066|
|AUT|8.203|7.103|27.419|4.209|
|BEL|8.464|10.872|29.717|4.796|
|BRA|13.683|0.000|85.021|6.213|
|CAN|9.444|22.859|18.999|4.432|
|CHE|13.327|7.103|15.518|1.697|
|CHL|8.295|0.000|34.169|4.119|
|CHN|17.88|31.586|74.827|5.951|
|COL|11.343|7.103|68.717|6.684|
|CRI|12.013|0.000|59.843|4.981|
|CZE|6.17|7.103|38.632|7.412|
|DEU|5.613|14.711|19.269|6.053|
|DNK|4.885|14.711|17.608|6.615|
|ESP|6.92|7.103|41.254|6.988|
|EST|6.457|7.103|29.557|5.678|
|FIN|7.248|10.872|16.711|3.805|
|FRA|6.803|7.103|32.109|5.764|
|GBR|5.821|10.872|17.515|4.877|
|GRC|7.808|10.872|64.214|9.616|
|HUN|7.281|7.103|53.355|8.304|
|IDN|19.829|18.748|86.753|5.321|
|IND|21.228|14.711|83.426|4.623|
|IRL|5.341|7.103|20.752|5.215|
|ISL|17.842|7.103|29.13|2.031|
|ISR|14.547|7.103|30.697|2.598|
|ITA|7.644|10.842|54.884|8.599|
|JPN|8.714|0.000|25.682|2.947|
|KOR|11.841|14.711|38.944|4.531|
|LTU|5.635|7.103|33.699|7.241|
|LUX|7.159|7.103|20.336|3.833|
|LVA|4.131|7.103|38.112|10.946|
|MEX|14.983|7.103|66.073|4.884|
|NLD|5.054|10.872|16.949|5.505|
|NOR|12.297|7.103|19.113|2.132|
|NZL|8.023|7.103|14.649|2.711|
|POL|7.895|14.711|44.24|7.467|
|PRT|6.419|7.103|38.128|7.047|
|RUS|17.634|31.586|92.548|7.040|
|SVK|7.175|7.103|47.116|7.556|
|SVN|6.797|7.103|47.581|8.046|
|SWE|6.803|10.872|16.957|4.091|
|TUR|13.895|22.859|67.475|6.501|
|USA|9.825|3.519|29.813|3.393|
|ZAF|10.676|7.103|66.575|6.901|

Source: Marel and Shepherd (2019) Note: Total TFIS column shows ad valorem equivalent trade costs linked to TFIS variables. The STRI column is presented for comparison only.

Starting with a simple gravity model without policy variables, then adding each explanatory variable individually, Marel and Shepherd (2019) assess the importance of policy restrictions and TFiS variables 9
41 decided to exclude them. In some cases, we felt that the proposed indicator was not actually a health service intervention: examples include improved water source and adequate sanitation, fulfilment of family planning requirements, and non-use of tobacco. These reflect decisions made by individuals and agencies outside the health system, and health is only one of the factors considered when choices are made. More relevant to UHC would be indicators that capture health prevention and promotion efforts, and efforts by the health system to improve access to relevant goods and services, e.g. family planning; such indicators are hard to come by. More often though, we decided not to use the proposed indicators in Boerma et al. (2014a) because we were unable to find sufficient data to allow comparisons across countries and over time: examples include antiretroviral therapy, tuberculosis case detection and treatment success, hypertension treatment, and diabetes treatment.<sup>22</sup> Data ought to become less of a constraint as household surveys become richer, and as the necessary data for quality adjustments become more available, thereby enabling ‘coverage’ to move closer to ‘effective coverage’. In many countries, in fact, the necessary data are already available, and a comparative analysis of trends in a subset of data-rich countries would be possible immediately. There will, however, be indicators that may not be feasible (or may be prohibitively expensive) to collect through household surveys, raising the question: Where will the data on household living standards come from that are required for the computation of the

> 22 The WHS contains some relevant information. A better source, however, is WHO’s STEPS survey. This has been fielded in only three Latin American countries (Colombia, Paraguay and Uruguay) and WHO is unable to make the microdata publicly available.

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# **<u>Table 3: Household characteristics</u>**

||All||Expe|nditure quin|tiles||
|---|---|---|---|---|---|---|
||Uganda|1|2|3|4|5|
|Female head (%)|14.99|19.57|13.46|14.70|13.08|14.12|
|Age of head (years)|36.68|37.77|35.01|35.28|35.65|39.69|
|Household size|5.70|4.56|4.95|5.50|5.99|7.51|
|**Education of head (%)**|||||||
|None|9.04|17.28|8.78|7.52|6.64|4.93|
|Primary 1-4|12.09|17.86|14.82|11.79|9.59|6.36|
|Primary 5-7|30.03|34.98|36.00|32.57|26.98|19.58|
|Secondary|36.29|25.19|32.51|36.14|42.76|44.91|
|Tertiary|11.78|3.71|7.39|11.05|13.57|23.23|
|**Occupation of head (%)**|||||||
|Farmer|32.14|49.00|37.32|29.16|24.15|20.98|
|Trader|21.15|14.39|18.10|21.48|26.04|25.78|
|Professional|15.33|6.11|11.08|14.91|18.04|26.56|
|Craftsperson|16.78|17.73|20.57|18.81|15.79|11.00|
|Student/other|4.18|4.16|3.78|4.23|4.31|4.43|
|**Ownership of assets (%)**|||||||
|Bicycle/cycle/car|49.24|37.49|45.65|48.42|54.78|59.93|
|Permanent house|38.11|21.31|30.09|38.15|44.32|56.81|
|Rural household|58.84|72.94|66.30|57.54|52.93|44.42|

Source: Authors’ own calculations based on the Second National Integrity Survey 2002 for Uganda.

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