rafmacalaba/data-use-mentions-extended huggingface dataset:
income gradient in the _SAH_ of adults, although he did find that _SAH_ tends to be higher for men, to fall with age, to rise with education and to be higher for the employed.

We use data from two rounds, 2002 and 2000, of the Russian Longitudinal Monitoring Survey (RLMS).<sup>10</sup> This is a comprehensive socio-economic survey for a nationally representative sample. The 2002 round of the RLMS included an unusually detailed module on health. The sampled households included 9,100 adults (3,900 men and 5,200 women). For aspects of the analysis we also exploit the less complete data available for the 6,000 adults (2,600 men and 3,600 women) in the 2000-02 panel that can be formed from the RLMS. The RLMS sample is re-designed at each survey round to assure that it remains representative.

The survey included the following _SAH_ question, asked of each adult in the sampled household: “ _How would you evaluate your health? Very good, good, average (not good, not ” bad), bad, very bad, don’t know/refuse_ . Figure 1 gives distributions of the answers for 2002. We find that 16.5% of adults rated their health as “very bad” or “bad;” this was true of 20.0% of women and 11.7% for men. Men rate their overall health higher than women. However, this is not reflected in some other health indicators. Women tend to live much longer: in 2001, male life expectancy was 59 years, versus 72 for women (Heleniak, 2002). And Russian men tend to have worse “health lifestyles” (higher alcohol consumption, smoking more, fattier diets).<sup>11</sup> The gender difference in _SAH_ appears to reflect a psychological difference.

The 2002 survey included questions on functional abilities, specific ailments and recent disease histories. The questions covered the
0_._302<sup>_∗∗∗_</sup>||
||||(0_._009)||||(0_._009)||
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Adjusted R2<br>|0_._548<br>|0_._663<br>|0_._673<br>|0_._626<br>|0_._553<br>|0_._665<br>|0_._674<br>|0_._630<br>|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh<br>1|34548<br>1|34548<br>1|34548|75505<br>1|34548<br>1|34548<br>1|34548|75505|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DOU method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Urban center” or “Urban cluster”) are included in specifications (4) and (8). The baseline category is “Rural” in all specifications, except (4) and (8), where it is “Urban cluster”. Baseline categories for the control variables are the following: “Married” for marital status of household head, “No education” for education of household head, and “Employed in Agriculture” for employment of household head. Robust standard errors are in parentheses. Observations are weighted using population weights.

43
Learning from Developing Country Power Market Experiences: The Case of Colombia

# 6 Power market performance

## 6.1 Wholesale and Retail Prices and Market Efficiency

The most relevant power submarkets in Colombia are the following (see Figure 6-1 also):

1. Financial Bilateral Contracts (mostly long-term supply contracts), with 67 TWh (78%) and $11,305 BCOP during 2017.

2. Spot market (pool), with 19 TWh (22%) and $ 2,032 BCOP during 2017.

3. Capacity market: reliability payments and firm energy auctions.

<!-- Start of picture text --> Liberalized<br>medium-term<br>Pool market<br>contracts market<br>15% (GWh)<br>24%<br>Regulated long-<br>term contracts<br>market (GWh)<br>0%<br>Liberalized long-<br>Regulated term contracts<br>medium-term market<br>contracts market 20%<br>41%<br><!-- End of picture text --> Figure 6-1 Relative size of power submarkets in Colombia during 2016, as percentage of total traded energy.

Source: Own elaboration based on data collected by the World Bank.

Incomes for Colombian Gencos include supply contracts, the spot market (including transactions at the spot price, reconciliations due to system constraints and Automatic Generation Control services), and the firm energy market (reliability market). The revenue structure varies across power plants (see next table). For example, firm energy payments account for about 15% of total revenues for major Gencos. However, firm energy payments account for up to 50% of revenues of fully thermal Gencos such as Termocandelaria and Termovalle (see Table 6-1).

34
our modi...cations to standard TFP even more important if one wants to use TFP data to capture welfare. As is the case with taxes, welfare rises with increases in output holding inputs constant, even if there is no change in actual technology.

Note that we would get a qualitatively similar result if, instead of labor market rationing, we assumed that the household has monopoly power over the supply of labor, as in many New Keynesian DSGE models. As in the example above, we would need to construct the true labor share by using the household’s marginal disutility of work, which would be less than the real wage. In this environment, we would obtain the welfare-relevant labor share by dividing the observed labor share by an assumed value for the average markup of the wage over the household’s marginal rate of substitution between consumption and leisure.

The second example shows that there are situations where our previous results in sections 2 and 3 are exactly right and need no modi...cation, even with multiple wages and labor market rationing. Consider a household that comprises a continuum of individuals with mass Nt. Suppose that each individual can either not work, or work and supply a ...xed number of hours L<sup>b</sup> . In this environment, the household can make all its members better o¤ by introducing lotteries that convexify their choice sets. Suppose that the household can choose the probability qt for an individual to work. The representative household maximizes intertemporal utility: where qtU (Ct<sup>0;</sup> L � L<sup>b</sup> ) + (1 � qt)U (Ct<sup>1;</sup> L) denotes expected utility prior to the lottery draw. Ct<sup>0</sup> and Ct<sup>1denoterespectivelyper-capitaconsumptionoftheemployedandunemployedindividuals,</sup> while average per-capita consumption, Ct is given by: Assume that the individuals that work face
an extensive compliance module in the follow-up survey of all treatment and control households. Table A4 of the <u>Online Appendix summarizes the</u> treatment compliance results, as reported in the survey. We find that the program was successful in delivering its components according to randomized assignment. At follow-up, 97% of treatment households report being aware of the TUP program, compared to 51% in the control group. Among the aware households, the majority indicate having received any of the program inputs in the treatment group (99.5%), compared to only 3% in the control group. Respondents reported receiving livestock assets in 96% of treatment and 1.5% of control households. All other intervention components had similar levels of compliance. Independent monitoring data from CHA/MISFA report similar levels of compliance, with 98% of treatment households represented on their official record of households receiving support, compared to 0.1% of control households. The data thus suggest high compliance 17
Smith 1998; Huybens and Smith 19991.5 According to the financial services view, it is not bankbased or market-based systems per se, but rather the overall level of financial developmen **t** hat stimulates the flow of external funds to worthy firms.

The _legal-based view_ ILaPorta, Lopez-de-Silanes, Shleifer, and Vishny 1999b] rejects the analytical relevance of the bank-based versus market-base **d** ebate altogether. This view instead argues that countries with legal codes that protect outside investors and legal systems that enforce those codes will have financial systems that facilitate extemal finance. Thus, the legalbased view predicts that the component of overall financial development defined by the legal system critically influences the expansion of existing firms and the formation of new ones.

To evaluate the competing predictions of the bank-based, market-based, financial services, and legal-based views, we use a panel of 42 countries and 36 industries over the 1980s. As in Rajan and Zingales (1998) and Cetorelli and Gamberra (2000), we focus on industries and decompose industrial growth into that part due to the creation of new firms and that part due to the expansion of existing firms. Rajan and Zingales (1998, henceforth RZ) show that industries that are externally dependent - _industries that are naturally heavy users of external finance -_ grow relatively faster in economies with higher levels of financial development. Thus, as a first step, we examine whether externally dependent industries grow faster in market- or bank-based financial systems, or whether it is the overall level of financial developmen **t** hat enhances the growth of externally dependent industries. We also assess whether the legal system plays a critical role in producing a financial system that fosters the growth of externally dependent firms. In a second step, we decompose industry growth into the growth of the number of establishments

> 5 Also, see Stulz (2000) who notes that stock markets offer an exit option for entrepreneurs and therefore decreases banks' market power. Stock markets also enable an entrepreneur who has obtained
platform use constructed from _Google Trends_ data. Using the two proxies (separately) helps us check on the robustness of the two approaches.

<!-- Start of picture text --> Figure 6: Google Trends Digital Platform Use Proxy<br>Fig. 6(a)<br>Online Travel Platform Usage, 2004-2018<br>2004 2006 2008 2010 2012 2014 2016 2018<br>USA<br>India Mexico Korea<br>Ukraine Jordan Nigeria<br>Source: Author's calculation using Google Trends information.<br>Fig. 6(b)<br>100<br>80<br>60<br>40<br>Google Search Index 20<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Online Travel Platform Usage, 2004-2018<br>2004 2006 2008 2010 2012 2014 2016 2018<br>USA<br>India Mexico Korea<br>Ukraine Jordan Nigeria<br>Source: Author's calculation using Google Trends information.<br>Fig. 6(b)<br>100<br>80<br>60<br>40<br>Google Search Index 20<br>0<br><!-- End of picture text --> Last, we complement our data set with several sources that provided the required control variables. Standard gravity model variables came from the U.S. International Trade Commission’s Dynamic Gravity Dataset.<sup>12</sup> The World Bank’s World Development Indicators and the World Economic Forum (2018) were used to supplement the USITC data set and in exploring some of the determinants of the demand for tourism services.

> 12  See Gurevich and Herman (2018).

Page **16** of **34**
Given that export participation has a positive effect on the provision of technical upgrading within firms, an important complementary question is the extent to which the corresponding trainees benefit from these additional skills in the form of higher labor earnings. To examine this question, we exploit detailed information on training records and earnings trajectories at the worker-firm level. Because entry in export markets may affect wages via several forms of rent sharing (Egger and Kreickemeier, 2009; Egger, Egger and Kreickemeier, 2012; Davis and Harrigan, 2009; Helpman, Itskhoki and Redding, 2010), we restrict our attention to firms that were always exporters over the period of analysis. Using models with worker-firm fixed effects to account for unobserved heterogeneity of workers and firms, we document a robust positive relationship between completion of technical upgrading courses and hourly wages within exporting firms. This relationship holds within small, medium and large exporting firms.

In addition to the work cited above, this paper complements and extends other strands of existing research. In particular, it adds to a growing empirical literature using workerfirm data to examine the link between exporting, skills and wages (Fr ́ıas, Kaplan and Verhoogen, 2012; Helpman, Itskhoki, Muendler and Redding, 2012). An important question in this literature has been the extent to which higher wages among exporting firms reflect sorting on individual ability or wage premia due to rent sharing. By suggesting that export participation leads to skill upgrading of firms’ _existing workforce_ via job training, and that this training leads in turn to higher wages, we uncover a related but still distinctly different channel by which exporting influences skill utilization and labor earnings. In doing so, our paper adds to a small literature on how other aspects of globalization influence job training. In particular, Hogrefe and Wrona (2015) develop a theoretical model examining the impact of offshoring on individual skill upgrading through on-the-job training. The model predicts that offshoring, by scaling up workers’ wages, creates previously unexploited skill upgrading possibilities and, thus, leads to more
CDR data to produce key statistics, prepared a first draft of the data section, and approved the final version of the manuscript. EK and MM contributed to the analysis, revised the first draft and approved the final version.

**Supplementary materials.** No supplementary material intended for publication has been provided with the submission.

**Abbreviations:** COVID-19, Coronavirus disease 2019; CDR, Call Detail Records; FTP, file transfer protocol; GBoS, Gambia Bureau of Statistics; GNI, Gross National Income; IMEI, International Mobile Equipment Identity, ISMI, International Mobile Subscriber Identity, IT, Information Technology; LGA, Local Government Area; MNO, mobile network operator; OD, Origin-Destination, PURA, Public Utilities Regulatory Authority; SIM, subscriber identification module; SMS, short message service; VPN, virtual private network;

**20** | P a g e
s average value is close to zero. Hence, in the set up of Equation 3.1, the parameter β shows the correlation between public investment and sovereign risk for the country with an average level of investment quality. We also center the public investment variable by subtracting its sample mean (0.07). Thus, the parameter δ indicates the correlation between

> 24 The list of control variables includes: general government gross debt (percent of GDP), total reserves (percent of GDP), log of exchange rate (LCU per USD, period average), CPI inflation, real GDP growth, real GDP per capita (constant 2015 USD, log), current account balance (percent of GDP), Laeven-Valencia financial crisis dummy variables (systemic banking crisis, currency crisis, debt crisis, and debt restructuring), private credit as a share of GDP, natural resource rents (percent of GDP), log of real net ODA received, Chinn-Ito financial openness index, foreign trade (percent of GDP), net FDI inflows (percent of GDP). As the sample for which we have CDS data is smaller than the sample for which we have credit ratings data, we try to preserve observations by reducing the number of controls in baseline parsimonious specifications and show that the results are robust to using the full set of controls. We also check that collinearity is not an issue (the correlation matrix is reported in Table A5). The reverse causality from sovereign risk to public investment is also not a concern: the correlation between sovereign risk measures and public investment ratio is low, and regressions of public investment ratio on sovereign risk, controlling for other variables in the specification, also does not yield a statistically significant effect of sovereign risk on public investment.

17
refused to participate; (iii) firms that could not be located; and (iv) firms that had closed (see Table 2). We follow Ramalho et al. (2010) and classify firms as no longer operating if they fell into categories (iii) or (iv), or if they were in category (i) but reported that they had filed for insolvency or bankruptcy.

Although effort was made to collect reliable contact information to allow for the collection of panel data in BEEPS, it is possible that some firms could not be located because

> 18 See World Bank (2009) for a description of the sample.  In Turkey, only the manufacturing subsample was targeted for follow-up in the financial crisis survey (Ramalho et al., 2010).  Non-targeted firms (i.e., firms for which no information was collected on their operating status at the time of the financial crisis survey) are excluded from the sample by necessity.

13
|0.293|0.005|0.130|0.001|0.105|
|Observations|1,606|1,606|1,606|1,606|1,606|1,606|1,606|1,606|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of probit models. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws and their matched controls. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

23
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _22_ lic of Yemen.<sup>30</sup> This highlighted that it is possible to track dollar-denominated prices, broadly for the five food categories that comprise the FAO index, and draw comparisons with price events in global markets when the exchange rate is also monitored. In this paper, it is simply noted that figures A2 to A4 provide some visual guidance on how global price change events propagate at the national level, as nearly all the country graphs display inflation events in 2020. The figures indicate that there may be substantial cross-country heterogeneity in the timing and amplitude of the price surges, which likely relates to differences in factors such as whether a country is a net importer or exporter of food and how the local currency is managed during times of crisis. Finally, since not all products matter the same for household well-being, future analysis may use the estimates developed by this paper to explore food-specific price dynamics or produce inflation estimates based on food baskets that incorporate information on expenditure shares.

Deploying statistical methods to enhance data gathering may help improve price estimates more widely. For instance, additional investments could be redirected to broaden the scope of data gathering to include additional sampling locations and food items rather than be used to strengthen data coverage of existing narrow monitoring operations. This could give a more complete view on subnational inflation than can be achieved by pure data gathering alone. Moreover, gathering data on additional food items can help produce more accurate predictors for missing observations. The imputations primarily utilize contemporaneous relations between the prices of different items, and so priority in data gathering should be given to ensuring that in each month prices of at least some items at some markets are observed. Ensuring that some data is available most of the time periods is more important than ensuring that most of the data is available in some of the time periods. In addition, predictions are more reliable in high inflation episodes and less reliable in high volatility episodes, so
Surveys**

All the surveys in our LSMS research sample contain some transport questions, which are synthesized by modules or sections in the household and community questionnaires separately. For the household questionnaires, Table 3 summarizes the availability of the common modules of expenditure, durable goods such as vehicle ownership, and access to facilities, and Table 4 lists the numbers of available transport questions by module in the household questionnaires. For the community questionnaires, Table 5 summarizes the availability of community questionnaire and the questions regarding roads, public transport, and relevant changes of transportation in the community. Some uncommon questions in the household and community questionnaires customized by individual country are listed in Annex 5. Annexes 1 to 4 contain the details of the common transport questions in household and community questionnaires for the 12 countries in LSMS collection and 18 countries in LSMS-tagged collection accordingly.

# **2.2.1. Transport Questions in Household Questionnaires**

The typical LSMS household survey contains the following three types of transport questions: 7
savings: Table 3 presents the same information from a different angle: it considers the impact on the headcount index, poverty gap, and P2 measures of spending the 2.2 billion sol reference budget without geographic targeting, and estimates the budget that would be necessary to achieve this same reduction in poverty when geographic targeting is conducted by the infant mortality rate, imputed poverty, or FONCODES index, respectively. The results show that the '° These expenditures included programs in housing (Banco de Materiales, ENACE), potable water, sanitation, and electricity (UTE-FONAVI), educational infrastructure (INFES), food and nutrition (PRONAA), social services (INABIF), and numerous multi-sectoral programs (FONCODES, COOPOP, INADE) (World Bank 1996).

**'** The curves for the headcount index tend to be much more choppy. The reason for this is that transfers will not reduce the headcount index unless at least one person in the country (or, in this case, one person in the survey) is bumped over the poverty line. This need not happen, for example, if resources are spent on those who are well below the poverty line. An increase in expenditures on the poor will always reduce poverty, however, if poverty is measured by the poverty gap or P2 measures.

12 Once again, bootstrapped standard errors suggest that the differences between, on the one hand, the no targeting case and, on the other hand, the measures of imputed poverty, infant mortality, and FONCODES index are statistically significant at the 1% level or better. The differences between the infant mortality rate, FONCODES index and imputed poverty are not significant (estimates available from the author upon request).

12
**Figure 3. Management practices by area (Manufacturing)**

<!-- Start of picture text --> Management score<br>1.50 2.00 2.50 3.00 3.50 4.00<br>2.02<br>1.54<br>Mozambique 2.00<br>2.01<br>2.20<br>2.22<br>2.24<br>Ethiopia 2.40<br>2.25<br>2.05<br>2.23<br>2.05<br>Ghana 2.24<br>2.11<br>2.37<br>2.54<br>2.45<br>Kenya 2.66<br>2.43<br>2.57<br>2.49<br>2.23<br>Nigeria 2.50<br>2.39<br>2.64<br>2.25<br>1.93<br>Tanzania 2.34<br>2.10<br>2.41<br>2.32<br>2.12<br>Zambia 2.39<br>2.21<br>2.41<br>3.28<br>3.25<br>United States 3.52<br>3.19<br>3.18<br>Management (overall) Operations Monitoring Target-setting People-management<br><!-- End of picture text --> Source: World Management Survey. Adapted from Lemos & Scur (2014).

The weaker management practices implemented by Mozambique’s firms include lack of lean manufacturing, limited development of a talent mindset, lack of planning and targeting, and the limited use of documentation to capture lessons. The variability in relation to the mean in