rafmacalaba/datause-encoder-data huggingface dataset:
costs. Direct costs are the sum of out-of-pocket costs for resources used in the education or training program. Indirect costs, on the other hand, represent the market value of resources that require no direct outlay of funds, for example, the value of a trainee's time spent in skills development or that of a facility owned by an education or training institution. The inclusion of indirect with direct costs provides a means for comparing programs that use different proportions of these resources.

Labor market signals that help managers to improve the match between education and training and the skill needs of enterprises, like those introduced above, provide information on the balance of skills demand. By regularly updating these sign1als, manpower planners can offer managers of education and training programs timely information for determining which programs shouid be expanded and which contracted. Other signals for this purpose can be obtained thiough the use of employer surveys, tracking help wanted ads in newspapers and professions.' journals, and monitoring the introduction of new industries and production technologies. Employer surveys in local markets surrounding education and training institutions can be used by planners and managers of training programs to collect information on employer assessments of the quality of skills training, the introduction of new production technoiogies, and projected skiil needs. Trends in help wanted ads for specific skills are another source for guiding curriculum and staffing decisions along with careful monitoring of sector employment trends.

Labor Market Efficiencv. The monitoring of wages and employment as labor market signals for schooling and training can also be used as a guide to how well labor markets are performing their allocation function. Labor market analysis of these measures and others can identify bottlenecks that inhibit wage flexibility and labor mobility and impede the adjustment of labor markets to macroeconomic shocks and distort the signals given to education and training. Competitive labor markets are expected to link wages with productivity. Observing the movement in time of wages and value added per worker in an industry sector, for example, provides a signal regarding the flexibility of wages. Mazumdar (1990) cites real wage growth outpacing productivity growth as a factor raising unit labor costs and reducing international competitiveness in manufacturing during the early 1980s in 14
small largely because the countries are poor, and not because these countries have received less attention from the research community once we control for income.

Of some interest is the fact that statistically, the US is _not_ an outlier in the volume of research that is produced on it. We examined two possibilities. First, we checked whether the US lay outside the predicted confidence interval (accounting for both the variance of the error and the estimates themselves) from a regression that excluded the US from the estimating equation. Second, we checked for the leverage that the US exerted on the estimated coefficients, by statistically comparing the coefficient vector in regressions with and without the US. Both tests showed that US was no different from other countries once our set of observable characteristics was controlled for; the volume of research for the US lies well within the predicted confidence interval and excluding the US leads to the same coefficient estimates as its inclusion. In other words, a lot more is produced on the US because it is rich and it is big; the natural comparator for the US is all of Europe and here, the volume of research is very similar.

Given that the inclusion of data availability/quality reduces the estimated coefficient on GDP, is it likely that better measures of data would drive the coefficient even lower? A priori this could certainly be true: data collection might not be a priority for poorer countries and might also be difficult to carry out given local conditions (infrastructure, local capacity, etc.) To the extent that data alone, instead of GDP, drive publications, we should find some evidence that the release of _new high quality_ data in low-income countries has some impact on the volume of research.

We look at the correlation between publications and the release of two highly influential and high quality household surveys—the Living Standards Measurement Survey (LSMS) and the Demographic and Health Survey (DHS)—in countries that reported one such survey sometime between 1985 and 2004. Figure 4 plots average publications against the date of release for the first such survey in the country. Taking an event-studies approach, we construct a variable called “Time to Data Release” that is country and time specific, and defined as the difference between calendar year and the year of data release (the event). The variable takes negative values for dates prior to release, is 0 in the year the data were released and takes positive values thereafter. On the vertical axis we plot the average number of publications, against “Time to Event”. As is clear, publications 11
_28_ or the number of accounts serviced, yielding an estimate of the fixed fee to be charged to have revenues cover fixed expenses. The end result is a set of prices which comprise the two-part pricing schedule of Table 4.

**Table** 5: **Central bank revenues and payment volumes** (United States, 1996)

|_Paymentservice_|_Revenues_|_Revenue_<br>_composition:r_|_Volume;_|
|---|---|---|---|
||_(US$ millions)_|_(%)_|_(%)_|
|Check|603|74|-.4_")_|
|ACH|79|10|16.1|
|Funds transfer|97|12|8.3|
|Book entry securities<br>transfer|17|2|9.7|
|Non-cash<br>collection|7|1|23.2|
|Special<br>cash services<br>(2)|7|1|n.a.|

_Source: Board of Governors of the Federal Reserve System (1997), various tables._

- _(1) Unsorted check volume increased by 1.6 %, presorted checks fell by 9.1%, and returned checks rose by 2.9% (giving the -.4% weighted average shown)._

- _(2) The provision of currency and coin to the general public is deemed a public service and therefore is provided at no charge to banks. Special cash services account for only about 2% of the total cost of the (priced and nonpriced) cash service._

# 10. Example of a pricing system - S.W.I.F.T.

The principles discussed above can be well illustrated by reviewing the structure of the Membership Pricing and Cost Recovery philosophy of S.W.I.F.T. The following information is based on the content of the November, 1996 S.W.I.F.T. User Handbook and is referenced with the approval of S.W.I.F.T.

**_Membership pricing:_** The S.W.I.F.T. Membership Pricing Structure consists of the following elements:

- + Joining fees;

- Annual support charges; and

- Annual charges for additional services.

**_Joining fees,_** which are payable by all new customers joining the S.W.I.F.T. network, consist of the entry fee and one-time charges related to connection.

All financial institutions joining S.W.I.F.T. pay an entry fee. The entry fee, to some extent, is designed and levied to ensure that new members are not being disproportionately
To circumvent the problems that arise from applying a threshold without regard to a household’s income, the alternative is to use a financial margin (FM) threshold. The FM is defined as where _DI_ and _BLC_ are, respectively, disposable income and the basic living costs of household _j_ , and _DSE_ is their debt service expenditure. A household is considered at risk if the financial margin is negative.<sup>11</sup> But how is the _BLC_ calculated? Two options are possible. The first option is to use the data provided by statistical offices on subsistence consumption levels and apply this to each household using the composition characteristics of individual households. Specifically, subsistence consumption is adapted to each household using an equivalized household size, which is the sum of the first adult plus 0.5 times the number of other adults in the household plus 0.3 times the number of children, where adults are defined as people aged 14 years old and over and children are those aged 13 years old and younger. The _BLC_ is calculated on a annual basis and per person, and is converted into annual household _j_ expenditures by and where _EQ_ is the equivalized household size in household _j_ . The other option is to estimate _BLC_ for all households in the HBS using an equation given by and where _BLC_ * is drawn from the HBS questionnaire (i.e., households are asked what is the minimum income they need), _DI_ is household disposable income, _A_ and _C_ are the number of adults and children in each household, and _R_ is an indicator variable that distinguishes between urban and non-urban regions; this is to control for regional differences in price levels. The third term on the right-hand-side is defined as _β_ 2 ( _A_ -1) and captures household adults other than the head of household; the constant in the estimated equation captures the head of household effect. While this econometric estimation was pursued, it did not materially change the paper’s conclusions; thus, to ease the exposition, this methodology is not discussed in this paper.

# **Stress-Testing Households**

In assessing household financial distress, the first step is to assess a household’s vulnerability before exposing it to economic shocks. Figure 4 shows the households that have debt in each income quintile and tags households with debt as vulnerable (the dark blue columns) using the two definitions of risk described earlier: an arbitrary threshold and a financial margin threshold.

Three findings are worth noting. First, only 1 out of 3 households have debt; i.e., 34 percent of all households have some type of debt, the last non-blue colored columns in each panel of Figure 4. In other words, debt is not as widespread as in advanced economies. Second, debt is more common among upper income quintiles; i.e., 14 percent of households in the lowest quintile have

> 11 Households can be at risk (i.e., vulnerable) even though they do not have debt (i.e., disposable income below basic living costs). But since the interest of this paper is to examine indebtedness, only households with debt can be tagged as vulnerable.

7
|||Share of<br>revenue (%)|0.1<br>10.5<br>17.3<br>3.5<br>6.3<br>7.6<br>4.2<br>2.9<br>29.7<br>5.2<br>12.9|not lower) bound|cause averages are||
|---|---|---|---|---|---|---|
|, 2009_†_|**lected tarif**|Share of<br>imports (%)|2.4<br>46.0<br>23.4<br>3.9<br>5.0<br>4.9<br>1.8<br>0.8<br>8.2<br>1.1<br>2.5|n with upper (but|ms authorities be|ven tarif.|
|ands, Syria|**Col**|Share of<br>total (%)|1.3<br>37.3<br>22.1<br>2.4<br>8.7<br>5.0<br>4.7<br>4.9<br>2.5<br>10.8<br>0.1|anges are give|y Syrian custo|mpt from a gi|
|s by b||Lines|39<br>1,080<br>641<br>70<br>252<br>146<br>136<br>142<br>72<br>314<br>3|ds, and r|defned b|ntries exe|
|of tarif line||Share of<br>revenue (%)|0.0<br>10.5<br>17.3<br>3.5<br>6.2<br>7.5<br>4.3<br>2.9<br>29.5<br>5.4<br>12.9|l Syrian tarif ban|om actual bands|may include cou|
|Distribution|**tutory tarif**|Share of<br>imports (%)|0.0<br>47.8<br>23.6<br>3.9<br>5.0<br>4.9<br>1.9<br>0.9<br>8.4<br>1.2<br>2.5|fective of 10 actua|rates can difer fr|ner groups, which|
|e 1:|**Sta**|of<br> (%)||ly re|lected|part|
|Tabl||Share<br>total|0.1<br>37.5<br>22.4<br>2.3<br>8.7<br>5.1<br>4.8<br>5.1<br>2.5<br>11.3<br>0.1|be broad|and col|trading|
|||Lines|2<br>1,087<br>649<br>68<br>252<br>147<br>138<br>149<br>73<br>327<br>3|hosen to|Statutory|across all|
|||nds|0.5<br>5–2<br>5<br>8<br>5–12.5<br>.5–17.5<br>.5–25<br>–35<br>–45<br>–55<br>–60|Intervals c|inclusive.|computed|
|||Ba|0–<br>0.<br>2–<br>5–<br>8.<br>12<br>17<br>25<br>35<br>45<br>55|_†_|||

11
directly for steel and chemicals in specification (3), a one standard deviation change to the elasticities increases the predicted probability of a new tariff by only 22% to 0.39. Nevertheless, even in this specification the result is economically important and statistically different from zero.

To conclude this section, a large literature has explored the politicaleconomic determinants of US antidumping and safeguard tariff policy. Our paper is the first to develop an empirical model of US tariff formation in which antidumping and safeguard policies are treated as time-varying cooperative tariffs in a self-enforcing trade agreement. Our evidence is consistent with the Bagwell and Staiger (1990) theory, as we find that US antidumping and safeguard tariffs are more likely the larger is lagged import growth, the greater the increase in the exporter’s share of the US market, the lower the variance of imports, and the less elastic are US import demand and foreign export supply.

# **4 Conclusion**

Our paper generates supportive evidence for the Bagwell and Staiger (1990) model of self-enforcing trade agreements. More generally, we show that the theory of cooperative trade agreements provides an empirically useful framework for understanding important trade policies like antidumping and safeguard tariffs. Using data from 1997-2006, we find that these new US tariffs are consistent with an increase in the incentive to raise “cooperative” tariffs as in the Bagwell and Staiger (1990) model of self-enforcing trade agreements. This paper presents three pieces of evidence supportive of this theory: the likelihood of these new import tariffs is increasing in the size of import surges, decreasing in the elasticities of import demand and export supply, and decreasing in the standard deviation of import growth. A one standard deviation increase in each of these variables is economically important, changing the probability that these tariffs will be imposed by 35%, by 88%, and by -76%, respectively. Our results are robust to restricting our analysis to the US’s top ten trading partners and to analyzing the imposition of antidumping and safeguard tariffs. The results provide empirical support for models of trade agreements that emphasize the importance of the terms-of-trade motive in tariff setting, and they complement other empirical research (Broda, Lim ̃ao, and Weinstein 2008; Bagwell and Staiger, 2011) on trade policy formation.

This empirical investigation of US trade policy raises additional questions for future research. The use of antidumping and safeguard policies 19
Table 3 shows country-specific access to improved water and sanitation infrastructure following the classification in Table 2. On average, about 63% of the children in our sample do not have access to any form of sanitation (including improved and flush toilets) and about 37% lack access to safe drinking water; figures that are very much in line with estimates by the WHO/UNICEF<sup>11</sup> for Sub-Saharan Africa (68% without access to sanitation and 40% without access to safe drinking water).

# **3. Methodology**

As discussed in the introduction, the analytical work of this paper is divided into three parts. In the first part, we estimate the empirical relation between water and sanitation infrastructure and child mortality. In the second part, we use the model parameters empirically fitted in the first part to predict country-specific aggregate improvements in child mortality that could be achieved by investments in water and sanitation infrastructure. In the last part, we combine our mortality improvement estimates with cost estimates for the required infrastructure upgrades to compute country specific investment needs as well as the implied costs per lifeyear saved.

The estimation of the mortality effects of water and sanitation infrastructure is based on a logistic model. The dependent variable is the probability of a child under the age of 5 dying during the 12 months preceding the survey. The model we estimate is given by: where _pijk_ is a binary mortality indicator for child _i_ in household _j_ in the DHS survey _k_ . _W_ 1, _W_ 2 are the two indicators marking improved and privately piped water access of the household respectively, and _S_ 1, _S_ 2 are indicators representing improved latrines and flush toilets, respectively. _Cij_ and _H j_ are vectors of child and household characteristics, and _Surveyk_<sup>are survey fixed effects. Standard errors are adjusted for clustering at the primary</sup> sampling unit (cluster) level. The estimated coefficients of equation (1) are reported as odds

> 11 http://www.wssinfo.org/

11
**Figure 1**

**Higher Education Entrants, Graduates, and Enrollments for the China, 1949-1990**

<!-- Start of picture text --> 2400000<br>2100000<br>1800000<br>1500000<br>1200000<br>900000<br>600000<br>300000<br>0<br>1948 1953 1958 1963 1968 1973 1978 1983 1988<br>Entrants Graduates Enrollment<br><!-- End of picture text --> 27
17 on the contrary, under a military-conservative regime, Chile suffered a significant decline in non-wage benefits during the 19708. Responding to pressures from unions and other influential coalitions, governments have at times expanded severance payments and other benefits -- and, in the process, increased acceptance for these policies by casting them as a concern for worker welfare. From the side of union's objectives, non-wage benefits are also considered a key variable in wage bargaining. The emphasis of unions on non-wage benefits derives in part from the non-taxable status that these benefits normally enjoy.-i/ The validity of using nonwage benefits as a proxy for labor market distortions is supported by a detailed study of twenty developing countries that found nonwage benefits to be positively related to other qualitative indicators of labor market distortions (Riveros 1989). Countries in which nonwage costs were a small part of total labor costs (in 1985) were also generally regarded as having little government or union intervention, such as Chile (252), Hong Kong (20Z), Korea (202), and Singapore (352). By contrast, the share of nonwage costs were high in interventionistslunionized countries such as Argentina (46Z), Mexico (45Z), and Brazil (38Z).

A possible objection to using labor market distortions as an explanatory variable is that this is a structural feature of an economy unlikely to change significantly. Fortunately, this is not the case for the countries under consideration, mainly due to the frequent institutional and political changes

> 11/ Non-wage benefits are nontaxable in all four countries considered, and this tax status did not change throughout the period. Therefore, the observed variability in 9 through time cannot be attributed to changes in the tax laws.
# **1. INTRODUCTION**

Cognitive and noncognitive skills are often considered key to understand economic decisionmaking. Empirical work with data from the US and Europe has made important advances in understanding both the causes and the consequences of skill formation (Heckman, 2007). Increasingly, cognitive, noncognitive and technical skills have also become the focus of analysis in development economics, with recent work on the determinants of skill formation (Attanasio et al, 2015), and the importance of skills for later life outcomes (Gertler et al, 2014). Development economists have also long worried about the role of many hard-to-observe skills as potential confounders in empirical analyses.

The low level of skills among farmers in developing countries is thought to be one of the main drivers of productivity differences between the agricultural and other sectors in the economy (Lagakos and Waugh, 2013). Young (2013) argues that sorting on skills explains the urban-rural productivity gaps observed in most developing countries and Gollin, Lagakos, and Waugh (2014) show that productivity differences become smaller when accounting for observed human capital differences. Understanding such potential selection at the micro-level arguably requires measures that go beyond years of schooling attained or literacy and more complex measures of skills or abilities are sometimes included in household surveys in developing countries. Yet the measurement of skills in developing country field conditions poses substantial challenges, and the related measurement error and its potential implications for empirical work have received little attention.

This paper contributes with the results of a skill measurement experiment among farmers in rural Kenya, to document the measurement challenges and discuss potential solutions and implications. We designed and implemented a relatively large survey focused on measuring different types of skills, and use the data to shed light on the reliability and validity of a wide set of commonly used skill measures and on the predictive power of the measured skills. We refer to cognitive skills as those that capture hard skills such as abstract reasoning power, language and math skills; noncognitive skills capture soft or socio-emotional skills, including a wide set of personality traits, such as self-esteem, tenacity, conscientiousness, locus-of-control, and attitudes-to-change. The measure of technical skills focuses on agricultural knowledge and know-how, given that the data come from poor rural individuals whose main occupation is in agriculture.<sup>2</sup>

> 2 Hence we broadly follow the distinction of Heckman and Kautz (2012) who distinguish between cognitive abilities, personality traits, and other acquired skills.

2
<u>Table2:Characteristics of beneficiaries by welfare quintiles</u>

|VariableName|Quintile1|Quintile2|Quintile 3|Quintile4|Quintile 5|
|---|---|---|---|---|---|
|Age (%)||||||
|18-30|15.6|17.3|19.1|20.2|20.9|
|30_40|14.0|14.9|15.6|16.2|16.2|
|40_50|14.4|15.1|16.7|18.0|19.2|
|50_60|12.0|13.0|14.0|15.5|16.8|
|60_70|18.5|17.7|16.4|14.9|13.8|
|70-80|23.8|20.2|16.3|13.1|11.0|
|Female (%)|57.1|57.0|56.4|56.4|56.3|
|Education (%)||||||
|no formal education|3.2|1.7|1.3|1.0|0.8|
|Primary|25.6|22.9|19.5|16.4|13.7|
|Secondary|61.1|63.5|65.0|65.4|63.6|
|high school or equivalent|2.1|2.5|3.2|3.9|5.0|
|above high school|8.0|9.4|11.0|13.3|16.9|
|Education of Head||||||
|no formal education|2.3|1.5|1.4|1.2|1.3|
|Primary|29.3|27.8|24.7|22.0|19.2|
|Secondary|60.4|61.8|64.0|65.0|64.6|
|high school or equivalent|0.6|0.6|0.7|0.8|0.9|
|above high school|7.3|8.3|9.3|11.0|14.0|
|Employment Status|29.6|46.7|53.1|56.6|58.7|
|Household size (SD)|3.6(2.1)|3.8(2.0)|4.0(2.0)|4.1(1.9)|4.2(1.8)|
|<br>Relationship to head||||||
|head self|45.3|42.5|39.4|37.4|35.9|
|Spouse|20.3|20.9|20.7|20.1|19.7|
|Children|27.8|30.1|33.1|35.5|37.0|
|Other|6.6|6.5|6.8|7.1|7.4|
|Urban|41.8|29.0|27.5|29.5|34.1|
|Region||||||
|Tbilisi|8.2|9.2|9.3|9.6|12.5|
|Guria|4.8|5.4|4.7|4.4|3.5|
|Racha-Lechkhumi, qvemo||||||
|svaneTi|4.1|3.4|2.4|1.9|1.5|
|Kakheti|11.3|12.4|13.3|12.6|12.2|
|Imereti|20.6|19.6|18.4|18.8|17.9|
|Mckheta- Mtianeti|4.6|4.0|4.1|4.0|3.7|
|Samegrelo|21.1|14.6|11.7|10.2|8.2|
|<br>samtckhe-Javakheti|2.4|3.6|4.8|6.0|8.1|
|Qvemo Qartli|5.5|7.7|10.9|12.6|13.8|
|Sida Qartli|8.9|9.1|8.8|9.0|8.8|
|Achara|8.1|10.8|11.6|10.9|9.8|

Note1: limited to ages between 18 to 80; 2. total number of beneficiaries is 738,342; 3. F-test shows significant difference between groups, with p<0.001 for all variables listed.

23