rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 In-person Survey Methodology and Results**

## **3.1 Methodology**

The in-person survey was conducted by the Zambia Statistical Agency from February 2021 to April 2021 in collaboration with the World Bank and several UN agencies. Data was collected from over 10,000 households with respondents answering questions on household welfare and COVID-19 socio-economic impact at the individual and household level. The household level questionnaire was answered primarily by the household head, whereas the individual survey consisted of all people 10 years of age or older in households available at the time of the interview. The average proportion of eligible members in a household who participated in the survey (excluding children under 10 years of age) was 71.5 percent. Similar to what takes place for Demographic and Health Surveys, the individual survey modules were conducted in a way that meant people did not have to provide their answers in front of other household members. The in-person survey employed a two-stage stratified cluster sample design. Firstly, 419 Enumeration Areas (EAs) were selected with Probability Proportional to Size (PPS) of the stratum. Secondly, systematic sampling was employed to select 25 households from each EA (ZamStats, 2021). The sampling frame was based on the 2010 Census of Population and Housing. The sampling frame covered 10,490 households and produced results that were representative at the national, provincial, and urban/rural levels. The survey yielded a high response rate as 97 percent (10,213 households) of the planned households were reached.

Sample weights in the survey were constructed using the 2015 LCMS (CSO, 2016) methodology. To construct weights, the inverse of the probability of an EA being selected was multiplied by the inverse of the probability of a particular household in an EA being selected by 1 (i.e., the probability that an individual in that household is selected). These weights were then post-stratified to match population projections based on the 2010 Zambian census.

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We also have information about condom use: the survey investigates condom usage at last intercourse for Swaziland and Zimbabwe, while in Lesotho it elicits information on the last contraceptive method used in the males sample and on condom usage during the last intercourse in the females sample. Looking at the pooled samples of Lesotho and Swaziland almost 33 percent of men report using condoms during their last sexual intercourse compared to 26 percent of women. Condom use is much lower in Lesotho than in the other countries, except for women in Zimbabwe, where the share of respondents using condoms is very low and equal to 8 percent. Another great advantage of the DHS is that it investigates whether the last intercourse occurred with a spouse or with another partner. Hence, we can compare condom use inside and outside the marriage. As expected, the fraction of currently married respondents reporting condom with a spouse is low, ranging from 8 percent in Lesotho to 28 percent in Swaziland for men and from 3.3 percent in Zimbabwe to 22.7 percent in Swaziland for women. A much larger share of people report using condoms outside marriage, with the highest averages recorded in Swaziland.

Turning to the analysis of the independent variables, the share of miners in the sample is equal to 3 percent and the fraction of women with a miner as partner is almost eight times larger. The average age of individuals is 28 years while the average age of the partner in the women sample is much higher and equal to 38 years. Except for Lesotho, where women have the highest educational level of achievement, the average years of education are similar across genders in all the three countries. The share of the urban population is lower in Lesotho compared to Swaziland and Zimbabwe. The analysis uses a set of indicators for durable goods held by the household (television, radio, refrigerator, motorbike, car, bicycle, electricity) as a proxy for wealth. Indeed, the wealth quintiles provided in the DHS can be misleading as a measure of wealth, particularly for African
8|0.043|7|2.97|8|0.150|6|0.063|8|0.137<br>5|
|Uganda (2002)|0.50|1|0.179|2|0.295|3|34.01|1|1.094|4|0.200|3|0.318<br>4|
|South-Africa (2000)|0.25|6|0.090|6|0.030|8|4.88|6|0.046|8|0.067|7|0.047<br>8|

_Source: our elaboration on Global Income Distribution Database (GIDD). ‘r’ indicates rank._ As a final exercise, one may want to ask the question of whether the IL indexes proposed proxy any of the criteria currently used to allocate aid. In Table 5, we show the average net official development assistance (ODA) in percentage of the gross national income received by the same African countries considered before. The net ODA comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Among the selected countries, Mauritania received the most generous amount of aid, followed by Ethiopia. Note that Mauritania is the least poor country (see estimates in Table 4) and Ethiopia is the fourth poorest country out of the eight considered). More generally none of the criteria summarized in Table 4 could justify the distribution of aid listed in Table 5.

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It is not clear that the same holds for some of the mature programs in India, Indonesia, Egypt, Pakistan and Bangladesh. In fact, it should be mentioned that sector work regarding mature programs hai been extremely limited.

At the aggregate level, there seems to be a clear correspondence between the volume of sector work and the number of projects: compared to 71 PHN related sector and economic reports, there are 60 projects. However, we find that sector work has not always been a precursor of project development. Our tentative analysis indicates that there are 22 projects in 17 countries where no sector work was done before the project was appraised (Table A-2). There are 23 sector reports for 22 countries where the sector work has been completed, but where no 18
|settlements|process CCEs and publically<br>fund claims on an ex-post basis|Early non-repayable ‘on account’ part-payment up<br>to a value of 25% of ‘likely claims’ to be made in<br>cases where weather indices and other local<br>information suggest that a large loss has been<br>incurred.|
|---|---|---|
|Risk<br>classification|Both premium rates and<br>threshold yields determined<br>based on simple formulae.<br>Wide variation in the actuarial<br>value of NAIS products within<br>a state.|Actuarial design and ratemaking based on a<br>statistically robust Experience-Based Approach.<br>Probable yields are based on 7 year moving<br>average of actual yield, with Bühlmann credibility<br>smoothing between nearby insurance units.<br>Indemnity levels and commercial premium rates<br>are determined at the district level for each crop<br>using ten years of actual yield data. Farmer<br>premium rates are increasing in the commercial<br>premium rate (Figure 5).|
|Data quality|A lack of standardization,<br>trained personnel, and<br>monitoring for CCEs exposes<br>the NAIS to significant delays,<br>basis risk and the risk of<br>manipulation.|Use of technology, standardization and<br>monitoring to improve the quality of CCEs. CCEs<br>to be video recorded with GPS-tagged footage.<br>Data to be provided to the insurer by SMS at the<br>time of the CCE to allow real time monitoring.<br>Remote sensing data to be used to target the<br>number of CCEs to be conducted at harvest in<br>insurance units, and to monitor CCE reports.|
|Involvement<br>of private<br>sector|No
their excess reserves while ministries of finance are actively managing their foreign currency risk without having counterparty risk and by minimizing the operational cost and risk. In fact, the analysis indicates that the ALM approach allows sovereigns first to benefit from natural hedges without a specific need for modelling, then to optimize the reserve portfolio ensuring that higher level objectives of reserve management have been met.

It is important to note with caution that even though ALM could address several concerns and reduce FX risk, the amount of external debt is influenced by the degree of domestic debt market development. As almost all the government revenues are denominated in domestic currency, access to international markets is limited and exchange rate fluctuations have been an important source of concern, it might be a natural hedging mechanism for many developing and emerging countries to issue in local currency denominated debt. However, as indicated by the joint The World Bank and the IMF Government Bond Markets Handbook, developing a deeper and more liquid local currency capital market is a long and dynamic process which requires a stable and credible macroeconomic policy framework; sound fiscal and monetary policies; effective legal, tax, and regulatory infrastructure; smooth and secure settlement arrangements; and a liberalized financial system with competing intermediaries. Therefore, for almost all developing and

> 18 Several countries in the sample have received advisory services from the World Bank Treasury in debt and reserves management, potentially contributing to a greater awareness of asset and liability matching already.

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**y** _i_<sup>_t_:</sup><sup>**y**</sup> _i_<sup>1=(</sup><sup>_y_</sup> _i_<sup>1</sup> 1<sup>_, y_</sup> _i_<sup>1</sup> 2<sup>_, . . . , y_</sup> _iD_<sup>1)</sup><sup>_,i_=1</sup><sup>_,_2</sup><sup>_, . . . n_1and</sup> **y** _j_<sup>2=</sup> � _yj_<sup>2</sup> 1<sup>_, y_</sup> _j_<sup>2</sup> 2<sup>_, . . . , y_</sup> _jD_<sup>2</sup> � _, j_ = 1 _,_ 2 _, . . . , n_<sup>2</sup> _._ . Additionally, we observe a set of demographic variables **z**<sup>1</sup> _i_<sup>_,_</sup><sup>**z**2</sup> _j_<sup>.In the case of panel data, individuals can be tracked across</sup> time, in which case variables are indexed by the same index, _i_ , at both periods and _n_<sup>1</sup> = _n_<sup>2</sup> . Otherwise, the datasets refer to repeated cross-sections. The _change in the_ 2The do-file code that implement this decomposition could be provided upon request 8
0.091|0.010|||||
|R-squ.(within)|||||||0.058|0.152|0.241|0.050|

_Notes:_ * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Estimated via OLS. Standard errors are clustered at the country level. _Source:_ Authors’ analysis based on the variables and data sources described in Section III, Table 1.

> 19 Increases in revenues may stem from changes in tax policy and tax administration as well as from other factors (e.g., conflict, natural disasters, or commodity prices) which are not directly captured by the indicators included in the DEA input index.

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Pedersen et al. 2008). From the perspective of sending regions, local amenities (e.g., security and public services) play an important role in shaping the intentions to emigrate (Dustmann and Okatenko 2014).

The remainder of the paper is organized as follows. Section 2 describes the dataset assembled for this study. Section 3 presents the empirical results. Section 4 concludes.

# **2 Data**

We have built a novel dataset on corruption levels and bilateral migration flows for EU28 and EFTA countries between 2008 and 2018. Exploiting both a cross-section and a time dimension is the main contribution of this study. First, bilateral migration flow data (instead of net migration rates) allow us to disentangle the overall effect of corruption on migration into two distinct parts: push (encouraging emigration) and pull (discouraging immigration) factors. We also exploit the time dimension to include fixed effects absorbing the impact of unobserved and time-invariant factors.

> 4International bilateral migration stocks come from the World Bank Global Migration Database.

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( _Instituto de Pesquisa Econômica Aplicada_ [IPEA]), and city spending on education and culture (National Treasury Secretariat). All these variables are available at the city level in IPEADATA. All nominal monetary variables are deflated by the IPCA<sup>44</sup> consumer price index.

Our final sample includes only schools that have information for the main variables of interest, listed in table A1 over the period 2007–11. Thus, our study uses a subsample of the schools that are actually participating.<sup>45</sup> Table A3 summarizes the characteristics of schools that joined _Mais Educação_ in 2008 and remained until 2011 but have missing information for selected variables and were thus removed from the analysis. The table compares them to treated schools used in our analysis. In general, excluded schools had similar school infrastructure (library, basic sanitation, sports court, etc.) but presented worse educational outcomes, that is, lower test scores and higher dropout rates. If schools with initially higher performance students benefit more from the extension of include the ninth grade of rural public schools. Hence, in our sample we exclude rural schools because it is not possible to follow the evolution of their student test scores between 2005 and 2011.

> 41 School flow is the progression of students in school grades at any given level of education. In IDEB it is measured by pass rates.

> 42 For schools and municipalities, IDEB uses _Prova Brasil_ , but at the state level and nationwide SAEB is used instead.

> 43 Reported number of enrollments in schools that joined _Mais Educação_ considers the school census of the previous year.

> 44 _Índice Nacional de Preços ao Consumidor Amplo_ (IPCA).

> 45 For the sample schools that joined the program in 2010 and remained until 2011, we exploit the sample of participating schools that have information for all variables of interest in the period 2009–11.

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_There is an obvious difference between rural and urban postings. Working in rural areas involves helping the poor... in urban areas, one can learn, have more income, have good schools for one’s children._

--Health worker in Ethiopia

# **1. Introduction**

Health services depend critically on the size, skills, and commitment of the health workforce. Human resources have been a long-standing policy concern in both developed and developing countries. A number of recent reports have pointed out that human resources comprise a fundamental constraint to improving health outcomes and reaching the Millennium Development Goals (Joint Learning Initiative, 2004; USAID, 2003; 2002; World Bank, 2004). While human resource challenges take many dimensions— shortages, geographic imbalances, international migration, low skills and inappropriate skills mix, poor performance and low motivation, etc.—this paper focuses on the specific issue of geographical imbalances. Geographic imbalances in the health workforce have been a persistent feature of nearly all health systems. Given the obvious relationship between the number of health workers and the capacity to deliver services—both in terms of volume and quality, the distribution of health professionals has implications for equity in access to health services. But the distribution of health professionals is also an efficiency issue: the under-provision of cost-effective interventions in some areas implies that overall health outcomes could be improved through a reallocation of resources.

Our analysis is based on a survey of final year nursing and medical students in Ethiopia. The survey included detailed questions on student background and characteristics, the use of contingent valuation methods to elicit monetary valuations of different types jobs in the health sector, and survey-based and experimental approaches to measuring intrinsic motivation. Together, these data shed new light on the factors that influence the willingness to work in rural areas among a new cohort of health workers.

Ultimately, the difficulties in attracting and retaining staff in rural facilities are rooted in the preferences and choices of health professionals. Evidence from various countries suggests that, while financial
high levels of informality where the majority of land transactions remains off the register, in line with what is found by recent studies (World Bank 2006).

In many instances of post-communist transition, a key reason for land holdings to be fragmented is that households received different types of land (cropland, pastures, orchards, vineyards) or land with different levels of quality or associated infrastructure (Tanaka 2007). Data on land use suggests that this may be the case in Albania as well; land holdings are made up of irrigated and rainfed fields for annuals (close to 0.3 ha each), rainfed and irrigated orchards (520 and 180 m<sup>2</sup> , respectively), and pastures (900 m<sup>2</sup> on average). Not all the available area is cultivated or transferred through markets; indeed some 18% of households, most of them in the country‟s coastal and central parts, leave land fallow for reasons unrelated to productivity.<sup>6</sup> With some 12% of the total (from 0.16 ha in coastal to 0.04 ha in mountain areas), the area affected is not negligible. Some two thirds of this has been fallow for 5 years or more, pointing towards potentially far-reaching implications for agricultural incomes overall. Most notably, the amount of land left idle is about three times what has been transferred through land rental markets in 2005.

In line with what was discussed earlier, the value of agricultural output, from crops and livestock together, amounts to approximately $1,000 per household, slightly less than half (48%) of which comes from livestock production. Magnitude and composition of agricultural income vary across regions, with the highest levels ($ 1,318, about two-thirds of which from crop agriculture) in the coast and the lowest ($ 850, some two-thirds of which from livestock) in the mountains. About 21% of producers have access
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- Roubini, Nouriel and Sala-i-Martin, Xavier. "A Growth Model of Inflation, Tax Evasion, and Financial Repression," Journal of Monetar= Economics, April 1995, 35(2), pp. 275-301.

- Roubini, Nouriel and Sala-i-Martin, Xavier. "Financial Repression and Economic Growth," Journal of Development Economics, July 1992, 3(1), pp. 5-30.

- Saint-Paul, Gilles. "Technological Choice, Financial Markets and Economic Development," European Economic Review. May 1992, 36(4), pp. 763-81.

- Scharfstein, David. "The Disciplinary Role of Takeovers," Review of Economic Studies. April 1988, 55(2), pp. 185-99.

- Schiantarelli, Fabio. "Financial Constraints and Investment: A Critical Review of Methodological Issues and International Evidence," in Is Bank Lending Important for the Transmission of Monetary Policy?, eds. J. Peek and E.S. Rosengren, Boston, MA: Federal Reserve Bank of Boston Conference Series No. 39, June 1995, pp. 177-214.

- Schiantarelli, Fabio and Sembenelli, Alessandro. "Form of Ownership and Financial Constraints: Panel Data Evidence From Leverage and Investment Equations," World Bank Policy Research Working Paper No. 1629, 1996.

- Schumpeter, Joseph A. Theorie der Wirtschaftlichen Entwicklung. Leipzig: Dunker & Humblot, 1912. [The Theory of Economic Development, 1912, translated by Redvers Opie. Cambridge, MA: Harvard University Press, 1934.]

- Sirri, Erik R. and Tufano, Peter. "The Economics of Pooling," in The Global Financial System: A Functional Approach. eds. Dwight B. Crane, et al., Boston, MA: Harvard Business School Press, 1995, pp. 81-128.

- Sharpe, Steven A. "Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationships," Journal of Finance, September 1990, 45(4), pp.
# I. Introduction

There is a long-standing debate among policy makers and scholars on whether increasing financial resources can improve health and education outcomes. The question has been and remains equally relevant both in developed and developing countries. While a recent strand of literature (Lafortune, Rothstein, and Schanzenbach 2018; Jackson, Johnson, and Persico 2015) provides very rigorous evidence of a strong positive causal relationship between school spending and student outcomes for the United States, there is no clear sense of whether this relationship can be generalized to other countries and to other important dimensions of human capital beyond learning.

The Human Capital Index (HCI), as a cross-country metric that benchmarks human capital outcomes for the next generation, one of the key pillars of the Human Capital Project (HCP) (World Bank Group 2018a), has highlighted how differences in human capital, as proxied by a combination of education and health indicators, are associated with large productivity differences across countries. Countries in the bottom 10 percent of the HCI ranking are losing at least 60 percent of the productivity that they could have achieved if they had full health and full education. For countries in the top 10 percent, the lost potential is equivalent to 20 percent of what they could have obtained by achieving their full human capital potential. Such large differences in outcomes naturally raise the question of whether countries at the bottom of the HCI ranking are investing enough resources to close the human capital gap. Answering this question is complex for a variety of reasons.

First, the human capital outcomes that we observe today are the result of investments that countries made in the past and there is no clear evidence on how long it takes for these investments to materialize into better outcomes. Second, since the HCI puts emphasis on the _quality_ as well as on the _quantity_ of human capital (Kraay 2018), the nature of spending might be as relevant as the amount of spending. For instance, there is well-established evidence that high-quality pre-school programs that target the most disadvantaged children are an effective
_highly concentrated in a few producer countries or the main world supplier is China were more vulnerable to the pandemic._ The COVID-19 shock may have adversely affected productivity due to reduced mobility and the disruption in in-person production activities but less so for more complex and knowledgeintensive products. This is because, for such products, productivity relies more on knowledge collaborations that were effectively shifted to virtual exchanges. The exponential growth of virtual online interactions for research and innovation collaborations - critical for productivity - and the wider use of virtual platforms have proven to be highly effective as is illustrated by the surprisingly quick responses to the pandemic, namely the rapid development of vaccines (Paunov and PlanesSatorra, 2021). Our third hypothesis to test in the empirical work is as follows: _H3: The productivity of complex and knowledge-intensive production processes may increase relative to that of less complex and knowledge-intensive processes since the former benefited more from remote work interactions. Hence less complex and knowledge- intensive products were more vulnerable to the pandemic._

# **2. Data**

# **High-frequency bilateral product level trade data**

Our analysis relies on several sources of data. For trade outcomes, we use monthly data on import flows at the product level by the major markets of the 27 EU countries, Japan, and the US from January 2018 to December 2020. The specific sources of data are, respectively, Eurostat monthly trade flows for EU countries, the Ministry of Finance for Japan, and the United States International Trade Commission for the US. These data are exploited to examine the evolution of export flows 8