rafmacalaba/data-use-mentions-extended huggingface dataset:
across all sectors of the economy. The second is a diagonal matrix with elements equal to the compensation of employees’ shares of the sector’s total output. Using these two intermediate matrixes as multipliers of a diagonal matrix with elements equal to either the sector’s domestic production or exports, one can obtain the compensation of employees’ shares of final outputs or exports. Appendix 1 provides more details and formalizes these calculations.

Using the same method, we can isolate the jobs content of exports by replacing the underlying labor value added share in production with the number of jobs contained in production, which can be derived from ILO employment data or country-specific data sources. ILO employment statistics contain data for a number of countries, including South Africa, for 11 macro sectors. In order to get a more refined sectoral disaggregation, we also match the GTAP data with employment statistics from South Africa’s labor force surveys. This allows the examination of a different type of disaggregation across 16 sectors.

With this methodology, we can split the _total_ contribution of labor (either the labor value added or the number of jobs) to final output and exports into its _direct_ and _indirect_ components based on the linkages with the rest of the economy. The direct component measures the wages paid or number of jobs a sector uses to produce its own output or exports directly. The indirect component measures the wages paid or number of jobs a sector contributes to other sectors’ output or exports—if measured by forward linkages— or the wages paid or number of jobs a sector uses from other sectors to produce its output or exports—if measured by backward linkages. _Forward linkages_ are the indirect contribution of a sector when considering the contribution of that particular sector as an input to other sectors’ domestic production or exports. This treats the particular sector as an upstream activity, and measures how much labor a particular sector is used by all other sectors. _Backward linkages_ are the indirect contribution of a sector when considering the contribution of all other sectors to
, 1997, page 166). It was the outcome of the idea born during the Middle Ages and developed thereafter that science should be applied to industry and the practical affairs of humankind. As Azariadis and Stachurski (2005) point out, ―While the scientific achievements of the ancient Mediterranean civilizations and China were remarkable, in general there was little attempt to apply science to the economic problems of the peasants. Scientists and practical people had only limited interaction.‖ Lin (1995) argues that the transition from innovation based on the experiences of artisan/farmers in the pre-industrial revolution period to innovation based on controlled experiments guided by science after the industrial revolution was the key factor. Societal incentives in pre-modern China did not favor the move toward the human capital accumulation needed for the new system of innovation.

The result of this process was that (at least prior to the year 2000) the global economy was dominated by the few industrialized economies that existed in the world, and most of these few economies had become industrialized either as leaders or earlier followers of the nineteenth century industrial revolution. Historical data (Figure 3) dramatically reveal the divergent pattern of growth across country groupings. In the late nineteenth century, the Western European countries and their colonial ―offshoots‖ began to experience an historic take-off in incomes per 5
# **Figure 2 Share of Intellectual property in the capital stock and capital formation in investment**

Intellectual property as % of machinery, transport equipment and intellectual property <!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014<br>Capital Investment<br><!-- End of picture text --> _Source_ : Penn World Tables version 9.0

# _Assumptions for depreciation rates_

The depreciation rate from the BEA for equipment has been modified to allow for both the share of software investment and research and development in China. It is set as a weighted average of the software depreciation rate used in the Penn World Tables, the depreciation rate for R&D estimated by Li and Hall (2017) and the BEA depreciation rate for equipment in the private sector and government sectors. The weights are the shares of software and R&D in the stock of equipment and software as estimated in the Penn World Tables plus an estimate of the stock of R&D capital (see below). To obtain the overall depreciation for each sector, the modified depreciation rate for equipment is weighted with the BEA rate for structures. The weights reflect the average shares for each type of asset for investment in each sector in urban areas in 2010 (Table 6).

**Table 6 Investment share by asset in urban areas**

||Business|Government|Infrastructure|
|---|---|---|---|
|Equipment|0.279|0.110|0.110|
|Structures|0.721|0.890|0.890|

_Source:_ CEIC In 2016, the resulting aggregate depreciation rate for the business sector is estimated at 7.8%, while the rates in the government
. Wage bill and average wage estimate is taken from IMF Report No. SMI/9516 of January 12,1995 and relates to 1993. Manufacturing wage estimate is from IMF Report No. SMI96194 and relates to 1993.

# **Malaysia**

Population and labor force figures are drawn from the World Bank's Social Indicators of Development 1996 and refer to 1994. Unemployment rate is taken from IMF Report No. SM/95/236 of September 15, 1995 and relates to 1995. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993. Unemployment figures are from CIA Factbook 1995 and are for 1993.

Central and Local Government employment estimates are from the Malaysia Embassy's information section and relate to 1994. Education employment is taken from UNESCO. Health employment is taken from the Malaysia Health Development DroLect of November 15, 1993 and relates to 1990. It consists of 3,016 doctors (43% of the registered doctors-- according to the report, 56% are in the private sector), 24,364 nurses, and 12,578 assistant nurses. Health is the responsibility of the Federal Government.

GDP and Consolidated Central Government wages and salaries are from IMF Government Finance Statistics Yearbook, 1995 and are for 1994.

# **Maldives**

Data on paid employment in non-agricultural activities are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1990. Unemployment data are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1990.

Central Govemment, Education and Health employment is taken from IMF Report No. SM 96/154 of June 26, 1994 and relates to 1995. Note that expatriate workers (2,023 people in 1995) were subtracted from central government employees.

# **Marshall Islands**

Labor force and population are taken from IMF Report 95/122 of May 26, 1995 and refers to 1994.
analysis to these two programs due to two key reasons. First, both SGRY and NREGA were “integrated workfare programs” with employment generation and rural infrastructure creation as their dual objectives. Before 2001, these two objectives were addressed by multiple, smaller programs. Hence, no program before 2001 was structurally similar to SGRY and NREGA. See Bahal (2020) for a detailed discussion on India’s major rural welfare programs since 1980. Second, district-level expenditure information is not available for any program before SGRY. District-level program expenditure data is publicly available only for NREGA. The expenditure data for SGRY used in this paper was specially sourced from the Ministry of Rural Development, Government of India. The sub-sections below discuss and compare SGRY and NREGA in detail.

> 5 See also Afridi et al. 2016; Das and Singh 2013; Das 2018; Mani et al. 2020.
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2302 Why Liberalization Alone Has Not<br>Improved Agricultural Productivity<br>in Zambia: The Role of Asset<br>Ownership and Working Capital<br>Constraints|Klaus Deininger<br>Pedro Olinto|March 2000|M. Fernandez<br>33766|
|WPS2303 Malaria and Growth|F. Desmond McCarthy<br>Holger Wolf<br>Yi Wu|March 2000|H. Sladovich<br>37698|
|WPS2304 Disinflation and the Supply Side|Pierre-Richard Agenor<br>Lodovico Pizzati|March 2000|T. Loftus<br>36317|
|WPS2305 The Impact of Banking Crises on<br>Money Demand and Price Stability|Maria Soledad Martinez<br>Peria|March 2000|A. Yaptemco<br>31823|
|WPS2306 International Contagion: Implications<br>for Policy|Roberto Chang<br>Giovanni Majnoni|March 2000|E. Mekhova<br>85984|
|WPS2307 Surveying Surveys and Questioning<br>Questions: Learning from Worid Bank<br>Experience|Francesca Racanatini<br>Scott J. Wallsten<br>Lixin Colin Xu|March 2000|P. Sintim-Aboagye<br>37644|
|WPS2308 How Small Should an Economy's<br>Fiscal Deficit Be? A Monetary<br>Programming Approach|Paul Beckerman|March 2000|H. Vargas<br>38546|
|WPS2309 What Drives Private Saving around<br>the World?|Norman Loayza<br>Klaus Schmidt-Hebbel<br>Luis Serven|March 2000|E. Khine<br>37471|
|WPS2310 How Politics and Institutions Affect<br>Pension Reform in Three<br>Postcommunist Countries|Mitchell
A number of macroeconomic and institutional factors are associated with greater levels of SME financing. Table 5 presents pairwise correlations of the ratio of SME loans to GDP and a number of macroeconomic and institutional factors.<sup>11</sup> Consistent with earlier research on SME and enterprise financing, we find a positive correlation between the overall level of economic development measured by income per capita and financial development measured by the ratio of private credit to GDP with the level of SME financing. Legal frameworks and the overall business environment are also important factors affecting the level of SME financing. For example, the ability to open and close a business is found to be an important factor associated with growth. Using information from the _Doing Business_ database, we find a negative correlation between the number of days it takes to start and close a business and the value of SME financing.

In addition, we consider a number of parameters describing financial institutions operating in a country. We do not find a statistically significant correlation between the share of foreign- or state-owned banks and levels of SME financing, which is consistent with bank level evidence in Beck _et al._ (2008b). Unlike firm level analysis in Beck _et al._ (2005b), we do not find a statistically significant level of correlation between the level of bank concentration and the ratio of SME financing to GDP. However, broader retail outlet networks measured by the number of bank branches per 100,000 adults from the _Financial Access_ database are associated with more SME financing. Countries where banks have less efficient structures measured by a higher ratio of overhead costs to total assets, higher interest rate margin and a greater cost to income ratio, tend to have lower levels of SME financing.

Even after controlling for the overall development using income per capita as a proxy, we find that the framework for starting businesses and the overall level of development of the financial market are significantly correlated with the levels of SME financing (Table 6). We also find that parameters characterizing
,000<br>100,000|100,000|400,000|
|Management Structure|0.05|0.78|0.86<br>0.93<br>1.02|1.11|4.75|
|Project Preparation % of Civil Cost|1<br>2.05|3.35|6.09<br>6.63<br>7.23||25.35|
|Project management % of Civil Cost|2.5|5.12|8.38<br>15.22<br>16.59|18.08|63.38|
|Survey, Household Data, Community||31.39|51.33<br>93.24<br>101.63|110.78|388.37|
|Consultation & Design|||||0.00|
|Civil Works||204.92|335.04<br>608.66<br>663.44|723.15|2535.22|
|**Total**|**2.10**|**245.57**|**401.69**<br>**724.69**<br>**789.91**|**853.12**|**3017.08**|

Source: Authors’ calculations NB. Rs. 1 crore is equal to roughly $ 200,000. Rs. 3000 crore is roughly equal to $600 million 28 The average household size is assumed to be 4.7 persons based on recent sample surveys of slums. Many gamtals have good services, or are in the process of upgradation. We have left them out of this initial estimate. If the city takes on a strategy of providing services for all as advocated in this paper, a more detailed survey and assessment
phone-based interviews, potentially on a random sample of interviews and/or random segments of interviews, can allow survey managers at headquarters to review interviewer skills, explanations, and interactions with respondents, enabling the identification of behaviors that are outside the norm or are not as intended by survey management. These audio audits can also allow cross-referencing of data points in the recorded data and the audio exchange, as a further quality control measure. In the LSMS-supported HFPS surveys, audio audits were employed in Ethiopia and Nigeria and were found to be effective in identifying weakly performing interviewers, to which additional supervisory efforts were directed (Gourlay et al., 2021).

# 5. Cost considerations

Cost considerations are central to every survey operation. Perhaps the most appealing feature of phonebased survey implementation in low-income contexts is the reduced cost relative to traditional in-person data collection. Data for the World Bank’s LSMS-ISA and High-Frequency Phone Surveys on COVID-19 offer a useful cost comparison of in-person and phone survey modes, for the same countries, at about the same time and on overlapping samples (Table 3).

When comparing _costs per completed interview_ , in-person surveys are about 30 times as expensive compared to phone surveys. In this sample of countries, the cost per in-person interview ranges from 230 USD per household in Burkina Faso, to 409 USD in Tanzania.<sup>10</sup> For the same countries, the range of per interview costs for phone surveys is from 8 USD in Tanzania to 12 USD in Malawi. Comparing the _cost per question_ substantially reduces these cost differences, with phone surveys generally appearing to be 3-4

> 10 See Kilic et al. (2017) for a more comprehensive review of the cost of in-person household surveys.

21
_3_ instance, rather than at local rates unless their government's bonds are quoted in US dollars and the government is widely believed to repay all current and future debts.

**The equity risk premium** **_(rm_** - _rf)._ The second standard measure in the estimation relates to the level of additional return that is required to persuade investors to hold equities in preference to the risk free instrument. There is much controversy surrounding the calculation of this element-recent UK regulatory experience has focused on figures between 4% and 6% while some parts of traditional finance theory suggest orders of magnitude of, at most, 2%. An alternative is to measure the historical spread between the yield on a government security and that of a general market index. In the US, this could be the spread between the yield on a 1 year Treasury Bill and the returns on the 500 Standard & Poor index. Evidence has suggested that figures between 8% and 10% are found when this approach is adopted, both in the US and the UK. This may reflect a world-wide premium, to which a country specific premium may have to be added. However, determining an exact figure from this premium is impossible and so range should be employed. For most countries, using between 5% and 8% should establish a credible set of boundaries.

**The equity beta** (fi,). The final, and only company specific, element to be established is that of the equity beta. This measures the relative riskiness of the company's equity compared to the market as a whole. To accurately measure the equity beta it is normal to use at least three years worth of daily share price information and preferably five years worth-especially if monthly data is used.<sup>4</sup> Since many of the projects that will be considered for privatization in less developed countries will be provided either by unquoted companies or international firms, it will not be possible to establish
relative to Brazil may be a weak proxy for tradables-to-nontradables productivity and thus poorly capture the Balassa-Samuelson effect.

To test the robustness of the set of fundamentals identified as driving the peso’s equilibrium exchange rate, we repeat the analysis using annual data for 1980-2014, in which we include alternative measures of our fundamental variables (see detailed description in Appendix Table A.1). In particular, we use Argentina’s terms of trade<sup>39</sup> index (exports-to-imports price ratio available quarterly), improved measures of productivity differentials to better capture the Balassa Samuelson effect, and reserves and trade openness measured as a percent of GDP. We find that our choice of fundamentals is robust to estimation at annual frequency. We obtain a single cointegrating relation in which coefficients are significant and have the predicted signs (Table A.10 columns 1 and 3). The estimated cointegration equation for our new preferred specification can be written as: Terms of trade improvements (wealth effect), increases in labor productivity of agriculture relative to the service sector vis-à-vis main trading partners excluding China (Balassa-Samuelson effect), rising reserves, and decreased trade (e.g., tighter trade restrictions), all appreciate the real exchange rate. The speed of adjustment coefficients (error correction) yield half-lives of approximately 2 and 5 years in the specifications using the REER and inflation-adjusted REER, respectively, in line with the empirical literature.

According to the estimated long-run relationship using the inflation-adjusted REER, the peso was overvalued from the mid-90s until the devaluation in 2002, and undervalued thereafter until 2010, after which an overvaluation has re-emerged. In 2014, the estimated cointegration relations suggest the peso was overvalued by 15% according to inflation-adjusted REER, contrasting the 7% undervaluation measured according to the unadjusted REER. Thus, we find a large discrepancy between the two measures of the REER.

Residual testing shows signs of possible misspecification: we fail to reject zero autocorrelation of residuals but find evidence of non-normality<sup>40</sup>
4 predicated on assumptions that were difficult to test. In contrast to the official estimates, one widely circulated alternative put the actual decline in poverty at only 2.8 percentage points, in effect implying an increase in the absolute number of poor people by about 5 million (Sen and Himanshu, 2004). This particular estimate drew on alternative, abbreviated consumption data from the employment module of the NSS survey.

In an attempt to restore comparability across the Indian surveys via prediction methods, Deaton (2003) exploited the fact that the section of the consumption module that pertained to "30-day" expenditures, had not changed between rounds. He estimated the probability of a household in the 55<sup>th</sup> Round being poor as a function of its per capita 30-day expenditures in that round and the relation observed between 30-day (log) per capita expenditures and total (log) per capita expenditures during the 50<sup>th</sup> Round. The reliability of these new poverty estimates, suggesting a decline of 7 percentage points, depended on the validity of the assumption that there had been no change in the Engel curve relating 30-day type expenditures to total expenditures over time. This assumption rules out substitution effects following relative price shifts or changes in tastes between included and excluded expenditure sub-components. Sen and Himanshu (2004) examined these assumptions in detail, and showed them to be far from innocuous.

Kijima and Lanjouw (2003) considered an alternative poverty prediction method. Drawing on a small-area estimation (SAE) approach introduced by Elbers, Lanjouw and Lanjouw (2002, 2003) for the purpose of developing ―poverty maps‖, they also used a subset of explanatory variables that were strictly comparable between survey rounds, but confined their attention to variables, such as household demographics and stocks of assets, that came from outside the consumption module. The poverty estimates based on
utility type. To compute the exact HUS for each type of energy or utility, we follow the procedure of the Ukrainian government, which first computes the total HUS for a household based on the social norms and the household’s income, and then distributes it among the various utilities in strict proportion to each utility bill’s share out of total utility bills. This is done separately for the heating season (November-March), heating season shoulder months (April and October), and for the summer. We use the shares of the annual total bills to compute the portion of the HUS that applies to each utility. Since the share of the total utility bills accounted for by each of gas, electricity, water, sewer, and district heating is generally different across these the seasons, this is an approximate procedure. Simple calculations based on the examples provided by Ukrainian officials however suggest that this approximation yields figures that are very close to the true ones. For the 2017 and 2018 HBS, we proceed as follows. First, we compute what the total utility bills would have been, had there been no subsidies or benefits. Formally, full utility bills=∑ E + H B B B B , where expj denotes the expenditure (what the household actually paid, after the subsidies or benefits were KK jj=1 jj subtracted from the full utility bill) for utility j, and K is the total number of utilities. Next, focusing for example on EE HH+ those households that use piped natural gas, we recognize that gas bill=gas exp + HUSgas, and that HUSgas ≈( gas bill/full utility bills)∙HUS, which means that gas bill = gas exp/(1-HUS/full utility bills). We do the analogous calculations for district heating bills, water bills, sewer bills, and the bills for other types of fuels. Once the full bills have been computed, we can attribute the HUS to utility _j_ as HUSj ≈ HUS∙(bill for utility
|**Country**|**Baseline**<br>**Survey**|**Simulated**<br>**Years**|**Informality Definition**|
|---|---|---|---|
|Chile|2015|2017 and 2020||
|Colombia|2016|2017 – 2020||
|Costa Rica|2016|2017 – 2020|Wk h d t i kltd i|
|Dominican Republic|2017|2018 – 2020|orers wo o no receve wor-reae penson<br>insurance|
|Ecuador|2016|2017 – 2020|.|
|El Salvador|2016|2017 – 2019||
|Honduras|2016|2017 – 2019||
|Mexico|2016|2018 and 2020|Workers who do not receive work-related health insurance<br>benefits.|
|Panama|2016|2017 – 2019||
|Paraguay|2016|2017 – 2020|Workers who do not receive work-related pension|
|Peru|2016|2017 – 2020|insurance.|
|Uruguay|2016|2017 – 2020||

Source: Own elaboration based on SEDLAC Note: Simulated years correspond only to years with the availability of SEDLAC actual data for comparison of the proposed variations of the model.

Following the methodology presented in the previous section, estimation weights are adjusted using neutral distribution for accounting only for the growth in the total population between the baseline survey and the simulated year. The different components of non-labor income follow the methodology described above. However, to facilitate comparisons, international remittances are modeled in all cases using a neutral distribution of the MPOs growth rate for inflows between the base and the simulated years, as follows: In addition, given that there were several changes in social programs for 2020 due to the COVID-19 pandemic and the lack of macroeconomic projections for these programs, it was necessary to assess the models' goodness of fit using a distribution that excludes those programs