rafmacalaba/datause-encoder-data huggingface dataset:
|National Water Development Project(02)|Midori Makino|
|---|---|
|National Water Development Project(02)|Midori Makino|
|National Water Development Project(02)|Midori Makino|
|National Water Development Project(02)|Midori Makino|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Pilot Fisheries Developent Project|Douglas W. Lister|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Rural PovertyReduction Project - Minas Gerais|Edward William Bresnyan|
|Pilot Social Fund Project|Evelyn Awittor|
|Pilot Social Fund Project|Evelyn Awittor|
|Pilot Social Fund Project|Evelyn Awittor|
|Pilot Social Fund Project|Evelyn Awittor|
|Pilot Social Fund Project|Evelyn Awittor|
|Infrastructure Asset Management|William D. O. Paterson|
|Infrastructure Asset Management|William D. O. Paterson|
|Infrastructure Asset Management|William D. O. Paterson|
|Infrastructure Asset Management|William D. O. Paterson|
|Infrastructure Asset Management|William D. O. Paterson|
|Infrastructure Asset Management|William D. O. Paterson|
|Training& Employment Support Project|Bassam Ramadan|
|Training& Employment Support Project|Bassam Ramadan|
|Training& Employment Support Project|Bassam Ramadan|
|Training& Employment Support Project|Bassam Ramadan|
|Training& Employment Support Project|Bassam Ramadan|
|Training& Employment Support Project|Bassam Ramadan|
|African Virtual University|Robert J. Hawkins|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Conservation and Sustainable Use of Medicinal Plants Project|Berhane Manna|
|Maloti-DrakensbergTransfrontier Conservation and Developme|nt PFrauke Jungbluth|
changing nature of local job opportunities as India urbanizes accounts for much of the observed decline in female LFPR.

Figure 5: The Census and the NSS tell opposite stories on rural female unemployment <!-- Start of picture text --> Female Employment (percent of Female Unemployment (percent of<br>working-age females) working-age females)<br>50<br>15<br>2001 2001<br>40<br>2011 2011<br>10<br>30<br>20<br>5<br>10<br>0 0<br>Rural Urban Rural Urban Rural Urban Rural Urban<br>Census NSS Census NSS<br><!-- End of picture text --> _Notes:_ Estimates from the NSS are from 1999-2000 and 2011-12 survey rounds and based on the Usual Status definition. Estimates from the Census based on the All Workers definition, which combines both main and marginal workers.

# 2. Location effects

As in other parts of the world, the rapid urbanization process of India has been accompanied by a massive decline in the number of agricultural jobs. What is perhaps more unusual about India is that this structural transformation has not been associated with a substantial increase in manufacturing jobs, or in wage employment. Instead, employment in the services sector has surged. And most of the expansion of employment out of agriculture has been in casual jobs or informal activities.

The central hypothesis of this paper is that the change in job opportunities, and especially the way this change has materialized along the rural-urban gradation, are fundamental to understanding why female LFPR is declining in India. Assessing this hypotheses requires considering labor demand and the rural-urban gradation, in addition to standard individual and household factors, as determinants of female labor force participation.

9
It is not only the quantity of skilled labor that is important for a well-functioning innovation system. Education has to be relevant for industrial needs and provide skills that enhance technological change. Evidence suggests that employers increasingly value higher order skills such as finding, analyzing, and applying knowledge, instead of passive subject knowledge (World Bank 2002a). Therefore the development of new pedagogical approaches such as problem-based learning and reflective practice are becoming increasingly important.

Rapid expansion and diversification of the higher education sector has caused significant variation in the quality of higher education services in the region. Many universities struggle with outdated teaching materials and lack of laboratory equipment. In the hard sciences it is often difficult for students to engage in experiments and practice in real-life circumstances. The ability of universities to supply society with high quality advanced education is also challenged by low qualifications of university teaching staff in Latin America. Only a small proportion of academic staff in the region holds doctoral degrees and is actively involved in research. For example, less than 4 percent of faculty in Mexico and Colombia has a PhD degree. The only exception to the generally low level in the region is Brazil, where 30 percent of the staff in federal universities has doctoral level qualifications (Figure 2).

**Figure 2. Proportion of professors with doctoral degrees, 2001**

<!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>UK Brazil Chile Argentina LA average Venezuela Mexico Colombia<br>Source: World Bank (2002b)<br>Percentage of teaching faculty<br><!-- End of picture text --> While coverage has increased in undergraduate university education, expansion of graduate education has been less pronounced in Latin America. Master and Doctoral level programs constitute roughly 3 percent of tertiary enrollment in the region (Holm-Nielsen et al. 2005). As a consequence, the number of completed PhD degrees is quite small in Latin America and the number is only slowly increasing (RICyT 2006). This challenge is also reflected in relatively low stock of researchers. Brazil – a regional leader in science & technology – has about one full-time equivalent researcher per thousand people in the work force which should be compared to 6 and 7 in Spain and the Republic of Korea, respectively (RICyT 2006). The low number of researchers has negative implications for the region’s ability to build critical mass, provide advanced skills to knowledge-intensive companies, and provide training for the next generation of researchers.

8
Table 2: Effects of gender quota on outcomes at the concentration level

||Acce|ss to IHEs|||Score related|outcomes|||
|---|---|---|---|---|---|---|---|---|
||Share<br>females|Share females<br>low-SES|Total avg.<br>score|Female avg.<br>score|High-SES fe-<br>male avg. score|Low-SES<br>female<br>avg. score|Male avg.<br>score|Female to male<br>avg. score ratio|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Estimators|||||||||
|OLS|0.09***|0.03***|-3.21*|-10.92***|-14.01***|-4.98**|-0.52|-0.04***|
||(0.02)|(0.01)|(1.66)|(2.11)|(2.48)|(2.40)|(1.79)|(0.01)|
|Borusyak et al. (2021)<sup>_ζ_</sup>|0.09***|0.04***|-1.15|-11.00***|-10.26***|-7.36***|1.88|-0.05***|
||(0.02)|(0.01)|(1.96)|(1.98)|(2.10)|(2.01)|(2.05)|(0.01)|
|Sun-Abraham (2020)|0.08***|0.025***|-2.32|-9.43***|-12.53***|-3.98|0.10|-0.04***|
||(0.018)|(0.006)|(1.81)|(2.19)|(2.39)|(2.87)|(1.99)|(0.007)|
|Concentration FE|✓|✓|✓|✓|✓|✓|✓|✓|
|University*year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Academic pro*year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|_Control group mean_|0.28|0.10|225|224|228|221|225|0.99|
|N|3119|3119|3119|3119|2723|2699|3119|3119|

Note: Each coefficient is from a separate regression. The dependent variables in columns 1 and 2 are the share of females and share of females from low socioeconomic backgrounds, respectively, which can be interpreted as average percentage point increase in the share of female in treated concentrations due to the quota introduction. In columns 3–7, the dependent variables are overall average score, average score for females, average score for high- and low-SES females, and male students average score at concentration level, respectively; it can be interpreted as points decrease/increase. Column 8 denotes female to male average score ratio at concentration level. All the models include stated fixed effects. Standard errors are clustered at concentration level and reported in parentheses. Significance level: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 _ζ_ Owing to the imputing nature of the estimator by Borusyak et al. (2021), the actual number of observations used to estimate the result is slightly lower than that with the OLS and Sun-Abraham (2020). The _N_ represents the number of observations used to compute the OLS and Sun-Abraham (2020) estimates.
Table 3.3. Incidence of Public Spending on Education is South Africa in 1994

||Share of Primar|y Subsidy|Share of Educati|on Subsidy|
|---|---|---|---|---|
|Quintile|Disaggregated Unit|Average Unit|Disaggregated Unit|Average Unit|
||Cost|Cost|Cost|Cost|
|1|18.9|25.8|14.1|19.9|
|2|17.7|23.3|15.4|20.7|
|3|16.5|19.7|16.0|19.7|
|4|19.1|17.8|19.6|19.1|
|5|27.8|13.5|34.9|20.3|

Source: Castro-Leal (1996) as cited by Demery (2000) The demand for public services is also sensitive to the _quality_ of those services. The quality of health services depends on such things as drug availability, staff skills and the general upkeep of the facilities. Castro-Leal et al. (1999) suggest that unit cost variations among socioeconomic groups can be considered a proxy for variations in the quality of the provided service. They cite the case of South Africa under the apartheid regime where the pupil-teacher ratio in black schools was more than twice that in white schools. These unit cost variations reveal indeed a greater degree of inequality both in the distribution of public resources and that of welfare outcomes than would an incidence analysis based on national average cost.

This point is well illustrated, again with data from South Africa. Table 3.3 presents results from an analysis of public expenditure on education in South Africa based on two different estimates of unit cost. One is race-specific while the other is an average across races. The results based on the mean unit subsidy are misleading. They would lead one to believe that in 1994, the poorest quintile got about 26 percent of the total subsidy for primary education versus 14 percent for the richest. For the overall education subsidy, the use of the mean unit subsidy implies that each quintile received more or less it proportionate share of public spending on education. Race-specific unit subsidies tell a different story. For primary education, the results based on this measure reveal that the poorest quintile got 19 percent of the subsidy compared to 28 percent going to the richest. For overall spending on public education, calculations based on 31
Table 3: Evolution of productivity

||**Labor productivity (sales**|**per worker in 2009 US$)**<br>|**Total factor**|**productivity**<br>|
|---|---|---|---|---|
||Mean|Median|Mean|Median|
|**2006**|40,834.38<br>[37,144.52; 44,524.23]|30,115.93<br>[27,224.56; 32,333.8]|2.34<br>[2.22; 2.46]|2.22<br>[2.04; 2.33]|
|**2010**|61,021.07<br>[40,803.56; 81,238.57]|40,045.05<br>[35,577.81; 45,986.21]|2.76<br>[2.59; 2.93]|2.54<br>[2.38; 2.72]|
|**2017**|50,029.06<br>[42,930.46; 57,127.66]|41,843.06<br>[38,414.61; 46,530.6]|2.43<br>[2.26; 2.59]|2.31<br>[2.13; 2.43]|

Notes: Survey weights are applied in the computation of the data presented above. Lower and upper bound 95 percent confidence intervals are presented in brackets.

Turning next to establishments’ management practices, the data from the 2017 Enterprise Surveys show that, based on the average management score, establishments in Colombia perform best, while management practices appear to be, on average, worst in Bolivia; this is displayed in Figure 2.

_Figure 2: Average management scores presented from best to worst_ Note: Survey weights are applied in the computation of the data presented above The pattern displayed in Figure 2 (Colombia best and Bolivia worst) appears to hold across the distribution of establishments’ management, and not just on average, as illustrated in Figure 3 below. The distribution of management practices displayed in Figure 3, confirms a result noted by Bloom and Van Reenen (2007, 2010) that low average management scores at the country level can be driven by poorly performing firms in the left tail of the distribution. The thicker left tail in the distribution for Bolivia is clear in the graph below.

12
Using this unique dataset, we find that the largest and most creditworthy buyers receive contracts with the longest maturities, as measured by net days, from smaller, investment grade suppliers. In comparison, early payment discounts seem to be used as a risk management tool to limit the potential nonpayment risk of trade credit. In particular, early payment discounts are generally offered to smaller and non-investment grade buyers, where nonpayment risk tends to be greater. Our results suggest that contract terms are jointly determined by supplier and buyer characteristics.

The paper continues as follows. Section II presents summary statistics of our unique data on trade credit contracts. Section III reviews theories of trade credit. Section IV presents the empirical results. Section V concludes.

# **II. Data and Summary Statistics**

We use a novel database of trade credit contracts for close to the universe of suppliers of 56 large buyers.<sup>1</sup> The data are provided by PrimeRevenue, an online network that links large, global companies, their suppliers, and third-party financial institutions, via the Internet. PrimeRevenue provides software and an IT platform for buyers to post their invoices directly; and suppliers to choose whether to be paid at maturity of the contract or to factor the contract and be paid immediately at a discount.

Our data are a snapshot of outstanding receivables as of December 1, 2005. Importantly, this snapshot is before PrimeRevenue started factoring the receivables. Buyers generally post invoices for all ‘important’ suppliers, which is estimated by PrimeRevenue to capture over 90% of total inputs to the buyer. Our database includes information for 29,019 contracts, which includes 56 large buyers and 24,140 suppliers. This includes multiple supplier contracts within and across buyers.

The data include complete information on contract terms: spend (contract amount), net days (days within which the buyer has to pay the amount owed), discount days (days within

> 1Because purchasing history is proprietary information, we do not know the identity of buyers in our sample. However, as discussed in this section, PrimeRevenue provided us with buyer characteristics (such as size, sector, and location) and the fact that almost all buyers in our sample are Fortune 500 .

4
|Health 4: Immunization for Diphtheria, Pertussis and Tetanus . . . . . . 63<br>Health 5: Immunization against Poliomyelitis.<br>.<br>.<br>.<br>.<br>.<br>. . . . . . . . 67|
|---|
|Health .<br>Imunization against<br>Measles ................<br>. 71|
|Education 1: Net Enrollment Rates ..................<br>. 75|
|Education 2: Gross Enrollment Rates .................<br>. 79|
|Education 3: Frimary School Completion Rates . . . . . . . . . . . . . . 83|
|Education 4: Primary Students in Private Schools . . . . . . . . . . . . 87|
|Education 5: Student/School Ratios ..................<br>. 91|
|Education 6: Student Teacher Ratios .................<br>. 95|
|Undernutrition 1: Index of Undernutrition . . . . . .<br>. . . . . . . . 99|
|Undernutrition 2: Index of Undernutrition Summary Table . . . . . . . . 103|
|Consumption 1: Growth in Per Capita Private Consumption.<br>. . . . . . . 104|
|Consumption 2: Growth in Per Capita Private Consumption<br>Summary Table ..... . . . . . . . . . . . . . . . . . . . . . . .<br>. 108|
|Consumption 3: Index of Per Capita Private Consumption . . . . . . . . . 109|
|Consumption 4: Index of Per Capita Private Consumption<br>Summary Table ..... . . . . . . . . . . . . . . . . . . . . . . .<br>. 113|
|Expenditure 1: Education Expenditure as Percent of Total<br>Government Expenditure .......................<br>. 114|
|'Expenditure 2: Educatlon Expenditure as Percent of Total<br>Government Expenditure SummAry Table . . . . . . . . . . . . . . . . . 118|
|Expenditure 3: Health Expenditure as Percent of Total<br>Government Expenditure .......................<br>. 119|
|Expenditure 4: Health Expenditure as Percent of Total<br>Government Expendlture Summary Table ................<br>. 123|
|Expenditure 5: Other Welfare Expenditure as Percent of Total<br>Government Expenditure .......................<br>. 124|
|Expendlture 6: Other Welfare Expenditure as Percent of Total<br>Government Expenditure Summary Table ................<br>. 128|
|Expenditure 7: Interest Payments as Percent of Total<br>Government Expendlture Summary Table . . . . . . . . . . . . . . . . . 129|
# **1 Introduction**

Since the 1940s, a five-day workweek followed by a two-day weekend has dictated the rhythm of economic activity in the US.<sup>1</sup> According to the American Time Use Survey, in 2013, 83% of employed persons worked on any given weekday, whereas only 34% of employed persons worked on a given weekend.<sup>2</sup> This suggests that the structure of the week has real economic consequences that are beyond the accounting of time. Yet, little is known about its normative properties: does it maximize welfare, or can we improve welfare by switching to a different labor-leisure cycle? For example, it has recently been proposed to transition to a four-day workweek, followed by a three-day weekend.<sup>3</sup> If adopted, how will this policy affect output and welfare?

The starting point of this paper is the observation that given the need for wide-scale coordination in both production and leisure, observing an equilibrium outcome in which the current workweek is practiced is uninformative of its optimality.<sup>4</sup> Given the multiple-equilibria nature of the calendar, assessing the optimality of the current workweek requires a more structural approach.

To this end, this paper develops a simple representative agent framework in which both labor productivity and the marginal utility of leisure evolve

> 1For an excellent survey on the history of the week, see Zerubavel [1989].

> 2See the American Time Use Survey Summary 2013, released on Wednesday, June 18, 2014.

> 3See, for example, “Should Thursday Be the New Friday? The Environmental and Economic Pluses of the 4-Day Workweek”, _Scientific American_ , July 24, 2009, by Lynne Peeples; or, “Where the Five-Day Workweek Came From: It’s a relatively new invention - is it time to shave another day off?”, _The Atlantic_ , August 21, 2014, by Philip Sopher.

> 4For example, a factory worker will not be able to work on a weekend since his factory will be closed. Similarly, on a workday, a worker on vacation would not derive the same utility from leisure since his family and friends will be working. For a recent study in psychology on the benefits of coordinated leisure, see Hartig et al. [2013]. For further reading on the coordination aspects of production and leisure, see Alesina et al. [2006], Jenkins and Osberg [2004] and Pande and Pantano [2011].

2
surveys. We do nonetheless examine these simulations in part as a point of comparison with existing literature and, also as a point of contrast with our main simulation.

For the second simulation, we assume that the HHS - NAS gap is mainly a result of the incomes and consumption of better-off households being inadequately captured in survey data (primarily in terms of item nonresponse and underreporting). This simulation draws on the empirical evidence discussed in Section 3, that measurement error in surveys disproportionately comes from underestimating the top tail of the distribution. Our approach is informed by the work of Lakner and Milanovic (2016) and closely follows Chandy and Seidel (2017a, 2017b) who adjust the top tail of each survey distribution, in proportion to the HHS - NAS gap. Both fit a Pareto distribution to allocate the HHS - NAS gap to the richest household of the survey distribution. Their methods build on an approach suggested by Atkinson (2007) who uses a Pareto imputation to “elongate” the upper part of the distribution. Lakner and Milanovic add the full HHS - NAS gap to the top decile of the distribution in their data (though with an upper bound). We follow the approach of Chandy and Seidel (2017b), who add a top segment to the Lorenz curve from the survey distribution, with income corresponding to _half_ the HHS - NAS gap, fitted with the pareto distribution from the top survey decile. In this method, the elongation of the distribution is a function of both the size of the gap and the observed inequality of the top survey decile.

# **_Poverty_**

Several researchers have proposed uniformly scaling up survey data to match national accounts. Bhalla (2002) scales up survey means to match HFCE from national accounts to estimate global poverty and inequality. Bourguignon and Morrisson (2002), Sala-i-Martin (2006), Sala-i-Martin and Pinkovskiy take a similar approach but scale up to match per capita GDP. Chen and Ravallion (2010) offer a more measured approach to scaling up survey distributions by allowing the scaling factor to be informed by both national accounts and survey means.<sup>25</sup> Since per capita income and consumption in national accounts are generally higher than in surveys, scaling up survey data in proportion to the gap leads to lower estimated poverty rates,

> 25 More specifically, they scale up survey means to correspond with the average of the observed survey mean and a predicted survey mean based on a regression with national accounts estimates as the explanatory variable.

21