rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6436

# **Abstract**

Employment and earnings statistics are the key link between the size and structure of economic growth and the welfare of households, which is the ultimate goal of development policy, so it is important to monitor employment outcomes consistently. A cursory review of employment data for low-income Sub-Saharan African countries shows both large gaps and improbable variation within countries over time and among countries, suggesting that low quality data are routinely reported by national statistics offices. Unfortunately, policies are formed and projects developed and implemented on the basis of these statistics. Therefore, errors of measurement could be having profound implications on the strategic priorities and policies of a country.

This paper explains the improbable results observed by using data from Uganda, where the labor module contains variation both within and across surveys, to show the sensitivity of employment outcomes to survey methodology. It finds that estimates of employment outcomes are unreliable if the questionnaire did not use screening questions, as labor force participation will be underestimated. Likewise, surveys that use a seven-day recall period underestimate or potentially misrepresent employment outcomes, owing to seasonality and multiple jobs. Common multivariate analysis applied on household survey data will be affected, as the errors in measurement in the dependent and independent variables will be correlated. Corrections to reduce measurement bias in existing data are tested with the survey data; none are found to be completely satisfactory. The paper concludes that there is a knowledge gap about employment outcomes in Sub-Saharan Africa that will continue unless collection techniques improve.

This paper is a product of the Regional Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at opimhidzai@worldbank.org and lfox@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Currency Substitution in Latin America: Lessons from the 1990s*

## Pere Gomis-Porqueras

Economics Department University of Texas at Austin

## Carlos Serrano

World Bank

## Alejandro Somuano

Economics Department University of Texas at Austin

*The authors would like to thank Bruce Smith, Alex Minicozzi, Li Gan, Subal Kumbhakar, Gil Mehrez, Maria Soledad Martinez-Peria and the participants of the University of Texas Macroeconomic seminar.
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remarkably low monetary value on an extra year of life in very poor countries, and a very high value in rich ones.<sup>6</sup> The HDI’s weight on schooling in poor countries is also far higher than its weight on longevity, for no obvious reason. Recognizing that welfare is ―multi-dimensional,‖ and that income is an incomplete metric, does not mean we can credibly collapse the multiple dimensions into a single, uni-dimensional, space. The essential problem is that we do not know the prices for valuation. (Indeed, if we knew them we would presumably have already formed a measure of full income.) Prevailing practice is essentially to make _ad hoc_ assumptions about the welfare function (and hence the weights).

The main aim of this paper is to review the issues raised by using subjective assessments of welfare (or ―well-being‖—I use the terms interchangeably) in both deciding who is ―poor‖ and calibrating multidimensional welfare measures. This is not a survey of the literature. Rather the aim is to assess the case for and against the use of subjective data for this purpose and to identify key knowledge gaps.

Scientific research on subjective well-being emerged in the 1960s in psychology (where the emphasis was on measuring and explaining ―happiness‖ as a state of mind) and economics (where the emphasis was on calibrating welfare functions, including setting equivalence scales).<sup>7</sup> The core data have long been respondents’ self-assessments in sample surveys. Each respondent is asked to rate her ―economic welfare,‖ or a broader concept such as ―satisfaction with life‖ or ―happiness,‖ on an ordinal scale (sometimes called a ―Cantril ladder,‖ after Cantril, 1965). Or the respondent provides money metrics of points on qualitative welfare scales, such as the minimum income needed to ―make ends meet‖ (Hagenaars and Van Praag, 1985; Kapteyn et al., 1988) or for multiple points on a ladder (Van Praag, 1968).

However, it would be fair to say that the use of such data in poverty and welfare measurement has stayed at the fringes of practice. In the eyes of some observers, such as Bertrand and Mullainathan (2001), the long-standing scepticism amongst economists about

> 6 The implicit value of an extra year of life in the new HDI introduced by UNDP (2010) varies from $0.50 per person year in the poorest country to about $9,000 per person year in the richest (Ravallion, 2012a).

> 7 An early contribution to the quantification of ―happiness‖ in surveys was Cantril’s (1965) idea of the ―ladder of life.‖ Early applications of subjective welfare concepts in economics included Van Praag (1968), Kapteyn and Van Praag (1976) and Easterlin (1974).

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outcome of the matching process, and to offer the firm alternative resumes if the initial set of candidates did not work out.

# _3.2.3 Theory of Change: How might this intervention increase employment?_

The intention of this intervention is to lower search costs for both job candidates and for firms. On the worker side, this may connect job candidates to job opportunities that they would have otherwise been unaware of, and additionally provide more information about the potential match quality of a job since we would be telling them that this is a job that fits their personality and ability traits. This could in principle lead to more youth in the treated group finding jobs, and to the jobs they find being of higher match quality.

On the firm side, the intervention can potentially lower the costs of filling job openings that the firm already had by giving them access to a wider pool of job candidates, and by saving them many of the costs of screening these workers. The candidates chosen may also be better matches for the positions, saving them on rehiring costs. In addition, with access to this service lowering search costs, there may be some positions that firms would not have otherwise filled that they now hire workers for, resulting in an increase in total hiring.

# **3.3 Results**

The matching attempts began in January 2012 with the 1,000 treatment firms from the experimental sample. Initial take-up from these firms was low, and so we also included the experimental control sample of firms and the booster sample of firms as possible candidates for matching, and began matching on this full sample in June 2012, ending in December 2012. We have two main sources of data that allow us to see the results of this process. The first are detailed administrative data on the matching process. The second are two rounds of follow-up surveys with the job candidates: a midline follow-up survey in October 2012 that measures short-term impacts midway through the matching period, and an endline survey conducted in May 2013 that measures impacts five months after the conclusion of the matching services. The attrition rates were 8 and 18 percent, respectively, for these two surveys.

Table 3 provides a summary of the outcomes at different stages of the matching process, while Appendix Figure 1 provides a schematic flowchart of the results. Only 134 of the 2,454 firms we 13
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per cent in 1986. 16/ Overall, the share of ferrous metal imports affected by NTBs also rose by about 59 points although no restrictions are applied to these imports in Japan and Norway, and only minimal barriers are encountered in Switzerland (one per cent of trade is affected). 17/ In order to evaluate the overall implications of the 1966 and 1986 statistics on trade affected by NTBs, Table 6 shows aggregate NTB ratios for individual countries as well as the values of trade involved. To ensure comparability, these 1966 and 1986 statistics were computed using a common group of nontariff restrictions, namely, all Type I an II NTBs listed in Table 1. For comparison, two (1986) measures of trade coverage are also shown. The first coverage ratio employs Type I (hard core) measures in its calculation while the second is based on all Type I an II measures.

For all developed countries combined the share of imports affected by NTBs rose from about 25 per cent in 1966 to 48 per cent in 1986. In value

- 16/ Under the terms of MFA some (or all) developing country textile and clothing exports to developed countries are restricted by quotas, as are some exports from Japan. The statistics in Table 5 assume that all products from all exporters in four-digit SITC groups within which MFA and other restrictions are applied are "affected" by these measures. This example illustrates a potential difficulty witlk the "affected" trade concept in the case of discriminatory barriers since the MFA tends to raise rather than reduce imports from developed countries. Yet these exports are also included in the affected trade base. Due to the nature of the concept, measures of "affected" trade are always larger or, at a minimum, equal to measures of actual trade covered.

- 17/ Laird and Yeats (1988) conducted an analysis of changing patterns of NTB use in developed countries over the 1981-86 period and found that a major shift occurred in the use of VERs on import volumes as opposed to other forms of nontariff barriers. Specifically, the share of imports covered by (non-MFA) VERs almost doubled during this interval (it rose from 2.3 to 4.4 per cent in the EEC and from 6.9 to 11.3 per cent in the United States. The share of trade affected by other types of NTBs (notably antidumping and countervailing duties, voluntary export price restraints, tariff quotas and, in the U.S. variable levies) increased by a greater percentage than VERs, but much less in value terms.
where _Yids_ denotes the outcome of interest for woman _i_ in district _d_ in state _s_ . The key explanatory variable of interest in the second stage is _Fds_ , the fraction of constituencies in a district with female winners in the most recent state election. We instrument for _Fds_ with the variable _FCds_ , the fraction of constituencies in a district where a woman won in a close election against a man in the most recent state election. We control for the fraction of constituencies in the district that had a close male–female election, _TCds_ , and include a third-order polynomial, _G_ ( _Mjds_ ), in the victory margin, _Mjds_ , between the winner and the runner-up for every (close or nonclose) male–female election, _j_ . We also interact these polynomials with _Ijds_ , an indicator for whether there was a male– female election in the district. We include district-level controls, _Zds_ , comprising the shares of the district population that are female, urban, and low caste, as well as male and female literacy rates and individual-level controls, _Xids_ , including the woman’s age, years of schooling, and indicator variables for religion, caste, and rural location.<sup>11</sup> We use robust standard errors clustered at the district level for inference.

Lastly, we estimate the following specification using our retrospective woman–year panel to examine the effects on fertility behavior: where _Yidst_ is the outcome for a woman _i_ in district _d_ in state _s_ and year _t_ . We include a vector of individual-level controls, _Kids_ , comprising the number of children that the woman had at the time of the last election and the individual-level controls specified in equations (1) and (2). We also include year fixed effects, _τt_ , to account for year-specific shocks at the national level. We cluster standard errors at the district level. Similarly to our estimation strategy described in equations (1) and (2), we instrument _Fds_ with _FCds_ and include the same set of controls as in equation (3) in the first stage.

In Appendix Table A.4, we use the same IV approach to examine whether the fraction of female leaders in the district predicts predetermined individual or district characteristics. The estimates in Panel A indicate that women’s characteristics, including age, residence in a rural area, religion, caste, and years of schooling, are not predicted by the proportion of female politicians in a district who won in a close elec-

> 11In the regressions based on men’s data, we include the same set of individual-level controls that are instead measured for the male respondents.

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|GRANJA KCAYRA|-13.55|-71.52|3219|72%|
|---|---|---|---|---|
|IQUITOS|-3.78|-73.30|126|97%|
|JUANJUI|-7.17|-76.72|363|56%|
|JULIACA|-15.48|-70.15|3827|57%|
|LA PAMPILLA|-16.40|-71.52|2400|74%|
|LIMA-CALLAO/AEROP.|-12.00|-77.12|13|75%|
|PISCO|-13.73|-76.22|7|90%|
|PIURA|-5.20|-80.60|55|77%|
|PUCALLPA|-8.37|-74.57|149|83%|
|SAN JUAN|-15.38|-75.17|60|74%|
|TACNA|-18.05|-70.27|458|80%|
|TALARA|-4.57|-81.23|85|75%|
|TARAPOTO|-6.50|-76.37|282|77%|
|TINGO MARIA|-9.28|-76.00|665|60%|
|TRUJILLO|-8.08|-79.10|30|80%|
|TUMBES|-3.55|-80.40|27|59%|
|YURIMAGUAS|-5.88|-76.12|184|63%|

_Sources:_ NCDC’s Monthly Climatic Data for the world, SENAMHI.

Once the temperature and precipitation series had been constructed and checked for unrealistic values (there were 21 unrealistic temperatures and 11 unrealistic precipitation observations, which were eliminated), we proceeded to calculate “normal” temperatures and “normal” rainfall for each station-month for the reference period 1960-1990.

# _4.1. Temperature trends_

Using the “normal” values for each station and each month, we calculated monthly anomalies for each station for the whole period (actual temperature minus normal temperature for that month). Anomalies are easier to analyze than the raw temperature and rainfall data, since the seasonal variation is eliminated through the subtraction of normal monthly temperatures. All 24 temperature anomaly series are plotted in Appendix A.

Once we have the series of temperature anomalies, it is straightforward to test whether there is a significant trend. This is done by regressing the anomaly on a trend-variable which has been scaled so that the coefficient can be directly interpreted as temperature change per decade in degrees Celsius. We use a confidence level of 95% to decide whether the trend is statistically significant, which means that the P-value should be less than 0.05 for the trend to be significant.

Table 5 shows the estimated trends for each of the 24 stations in Peru. Of these, 15 show a significant warming trend, typically of 0.2-0.3oC/decade, 4 show a significant negative trend of -0.1 to -0.2oC/decade, and 5 show no significant trend. La Pampilla shows exceptional warming compared to all other stations, but an inspection of the anomaly series (see Appendix A) shows that no warming has taken place during the last 25 years, and that a strange, abrupt drop in temperatures take place in the beginning of the series.

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# **3.1.1** **_Urban versus Rural Nonfarm Entrepreneurs_**

The differences of business constraints perceived by urban and rural entrepreneurs are quite dramatic according to a number of empirical analyses (World Bank 2006a).Figure 3.1 shows Yemen’s rural and urban business constraints perceived by entrepreneurs. As expected, in the informal settings characteristic of the country’s rural areas, government regulations are considered as less major constraints on rural nonfarm entrepreneurs, whereas those regulations, such as land access, courts, and tax rate, do matter for urban entrepreneurs. Corruption is quite a bit higher for urban entrepreneurs (65%) though it is also relatively high for rural entrepreneurs (about 40%).

**Figure 3.1 Comparisons of Urban Small and Rural Entrepreneurs**

<!-- Start of picture text --> urban small<br>rural<br>Customs<br>Electricity Business permit<br>79<br>Financial access Land access<br>65<br>Informal sector<br>57 Courts<br>competition<br>49<br>Transport 40 Tax rates<br>65<br>Corruption Crime<br>Telecommunication Labor edu<br><!-- End of picture text --> _Source:_ Yemen Investment Climate Survey 2009; Yemen RIC Survey 2010 both weighted.

_Notes:_ Informal sector competition in urban is compared with market related obstacles in rural due to mostly informal in rural: Rural enterprises are all household enterprises and small/micro enterprises, whereas urban small enterprises have 5 to 19 employees.

# **3.1.2** **_Constraints Perceived by Farm versus Nonfarm Entrepreneurs_**

Farm and nonfarm entrepreneursare confronted with similar business constraints with slightly different variations (figure 3.2). The top constraint, which is perceived by rural entrepreneurs, is electricity for nonfarm enterprises and water for farm enterprises. Water for farm enterprises can be understood by the nation-wide water shortage. Other major constraints are similar between 6
Learning from Developing Country Power Market Experiences: The Case of Philippines <!-- Start of picture text --> 2015 USD/MWh<br> 700<br> 600<br> 500<br> 400<br> 300<br> 200<br> 100<br> ‐<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Average spot price (2015 USD/MWh) Peak spot price (2015 USD/MWh)<br><!-- End of picture text --> Figure 5‐3 Evolution of yearly average and peak spot price (i.e. in the hour‐ahead market) in Philippines (in real terms).

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

Table 5‐2 Evolution of yearly average and peak spot real price (i.e. in the hour‐ahead market) in Philippines.

|**Year**|**Average spot**<br>**price (2015**<br>**P/MWh)**|**Average spot**<br>**price (2015**<br>**USD/MWh)**|**Peak spot price**<br>**(2015 USD/MWh)**|
|---|---|---|---|
|**2006**|7,154|116|179|
|**2007**|6,451|116|189|
|**2008**|4,292|84|145|
|**2009**|2,372|45|76|
|**2010**|8,250|169|269|
|**2011**|5,417|117|200|
|**2012**|7,684|173|311|
|**2013**|8,357|190|618|
|**2014**|5,669|126|278|
|**2015**|4,465|98|168|
|**2016**|3,278|69|121|
|**2017**|3,205|65|78|

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

Spot electricity prices are largely driven by supply‐demand conditions. Investment in baseload coal‐fired generation and entry of variable renewable energy resources have also caused prices in the market to go down in recent years. In general terms, spot prices have been driven by:

- Increased wholesale competition (due to both new entry and horizontal unbundling in the generation segment)

- More efficient use of generation and transmission resources

- Increased investment in power infrastructure (generation or transmission)

- Increasing generation by renewable resources with Feed‐in‐Tariffs (FIT) and priority dispatch

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Figure A11: Specification Charts for Deviations in Percentage of Days with Rain <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>3 5<br>2<br>1 0<br>0<br>-1<br>* Weather Product * -2 * Weather Product * -5<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>5 4<br>0 2<br>0<br>-5<br>-2<br>* Weather Product TAMSAT* -10 * Weather Product TAMSAT* -4<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>3 4<br>2 2<br>1 0<br>0 -2<br>* Weather Product * -1 * Weather Product * -4<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

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