rafmacalaba/datause-encoder-data huggingface dataset:
modeling framework that is required, and locates the various models discussed in earlier sections as specializations within that framework.

Time use data are essential for estimating the models presented in Section 4. Section 5 discusses some of the more serious limitations of currently available time use surveys. Of primary importance for policy analysis is the collection of data on all economically active members of the randomly selected households included in the survey. Omitting active members severely limits the purposes for which the data can be used. The section also discusses the advantages of conducting a time use survey as a module of a household income and expenditure survey. Because economists have tended to regard data on domestic time use as irrelevant for their purposes, many surveys have been designed in response to the interests and modeling approaches of other social science disciplines. As a consequence, they frequently omit data on economic variables that are required for modeling household production and demand systems. This makes the data difficult to use for policy evaluation. These problems are illustrated using as case studies the Statistics South Africa 2000 Time Use Survey and the time use module included in the Nicaraguan 1998 Living Standards Measurements Survey. A concluding comment is contained in Section 6.

# **2 Basic modeling approaches**

The appropriate approach to modeling the behavior of the household and the effects of changes in policy variables depends ultimately on the set of questions being asked. A clear distinction should be made between two stages of the modeling process: first, the stage in which a theoretical model is developed to address the questions at hand, independently of the issues of estimation and data availability; and secondly, the stage in which, in the light of the available data, a possibly specialized and simplified version of the model is specified for empirical estimation. At least one advantage of this process is the awareness of the implications of data limitations for the interpretation of the resulting empirical estimates. A second advantage is that, since it makes clear the costs imposed by 3
Marriage is almost universal in India, with average age at marriage around 19 in urban India in 2004–05 (Desai et al. 2010). According to the NSS survey data, the share of ever-married women among urban women age 25–54 was 95.3 percent in 2011 and 96.5 percent in 1987. Because in the age group 20–24 the marriage rate declined over time as more women pursue higher education and postpone marriage, we focus our analysis on women age 25 and older. We also exclude the 2 percent of all married women in this age group who report being head of their household.<sup>6</sup> Female participation rates are calculated using women’s reported usual status, which refers to a reference period of 1 year in which the principal activity is the activity in which the respondent spent the majority of time. Subsidiary activity status is recorded as well but is not taken into account in our analysis, as it affects less than 5 percent of the adult urban female population and its definition is not consistent over time.<sup>7</sup> Nonetheless, the pattern of female labor force participation is similar across different age groups, when including unmarried women, and when including labor force participation in both principal and subsidiary activities (see Table A1.1 in the appendix).

One might worry that even though unpaid family workers and own account workers are considered part of the labor force, women’s work is underreported. Survey respondents may be reluctant to report women’s contributions to family businesses or may not consider a woman’s work to be different from her general domestic duties. This type of underreporting will mainly affect participation rates in rural areas, where women spent much more time on farm activities that are less likely to be considered as work, and will affect subsidiary status activities more than principal status activities because the former includes work done for only a few hours per day or during peak season only, etc. Principal status participation rates in urban India are arguably least

> 6 We exclude them because we are interested in the role of women’s own versus household head education, among others. Participation rates of female household heads are higher, and declined from 39 percent in 1987 to 30 percent in 2011.

> 7 Before the 2004 wave, there was no lower bound on the number of hours spent on a particular activity to be considered as subsidiary activity, but in 2004 the minimum was set at 30 days of the reference year.

10
27 Table 4. Population and Percentage Distribution by World Bank Region and Coamtry Department, 1985-2100

|Region and||Pop|ulation|(million|s)||Perc|entage|<br>of w|orld|populati|on|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>department|1985|1990|20 .|2025|2050|2100|1985|1990|2000|2025|2050|2100|
|World total|4,843|5,2N:|6,au5|8,262|9,639|10,560|100.0|100.0|100.0|100.0|100.0|100.0|
|Borrowersandnon-borrowers|||||||||||||
|Sub-Saharan Africa|457|535|724|1,287|1,774|2,168|9.4|10.1|11.7|15.6|18.4|20.5|
|AsiaandOceania|2,573|2,803|3,272|4,185|4,713|4,997|53.1|53.1|52.7|50.7|48.9|47.3|
|Europe. Middle East,<br>and North Africa|1,147|1,224|1,383|1,750|2,003|2,190|23.7|23.2|22.3|21.2|20.8|20.7|
|Amierica|667|721|825|1,040|1,150|1,205|13.8|13.7|13.3|12.6|11.9|11.4|
|Borrowers only|||||||||||||
|Africa<br>Region|415|487|664|1,195|1,658|2,035|8.6|9.2|10.7|14.5|17.2|19.3|
|1Occidental and Central|40|47|64|113|155|<br>187|0.8|0.9|1.0|1.4|1.6|1.8|
|2Eastern|106|124|169|303|422|520|2.2|2.3|2.7|3.7|4.4|4.9|
|3South-Central<br>and|||||||||||||
|Irdian ocean|52|61|83|149|205|251|1.1|1.2|1.3|1.8|2.1|2.4|
|4Western|119|141|194|351|486|<br>593|2.5|2.7|3.1|4.2|5.0|5.6|
|S Sahetian|36|41|55|98|138|<br>173|0.7|0.8|0.9|1.2|1.4|1.6|
|6Southern|62|73'|<br>100|181|253|<br>310|1.3|1.4|1.6|2.2|2.6|2.9|
|Asia Region|2,382|2.603|3,056|3,948|4,473|4,756|49.2|49.3|49.3|47.8|46.4|45.0|
|1 other South|134|151|190|291|367|<br>423|2.8|2.9|3.1|3.5|3.8|4.0|
|2 Southeast|274|304|367|500|581|<br>626|5.7|5.8|5.9|6.1|6.0|5.9|
|3China|1,040|1,117|1,279|1.544|1,648|1,695|21.5|21.1|20.6|18.7|17.1|16.1|
|4 India|765|844|1,002|1,323|1,542|1,655|15.8|16.0|16.1|16.0|16.0|15.7|
|5 Indonesia and Pacific|168|184|218|290|335|358|3.5|3.5|3.5|3.5|3.5|3.4|
|_EWA_Region|476|536|667|997|1,249|1,425|9.8|10.1|10.7|12.1|13.0|13.5|
|1 Pakistan and Turkey|147|169|217|343|443|<br>519|3.0|3.2|3.5|4.2|4.6|4.9|
|2 North Africa|55|<br>63|81|123|<br>154|<br>172|1.1|1.2|1.3|1.5|1.6|1.6|
|3 Middle East|170|197|258|411|530|<br>609|<br>3.5|3.7|4.2|5.0|5.5|5.8|
|4 Europe|105|107|112|120|<br>122|<br>125|2.2|2.0|1.8|1.5|1.3|1.2|
|LAC Region|388|430|517|706|821|<br>879|8.0|8.1|8.3|8.5|8.5|8.3|
|1 Brazil|136|150|180|242|<br>278|<br>287|2.8|2.8|2.9|2.9|2.9|2.7|
|2 Central America|104|116|143|205|244|<br>263|<br>2.1|2.2|2.3|2.5|2.5|2.5|
|3 Caribbean ard Other|70|78|95|130|151|<br>159|1.4|1.5|1.5|1.6|1.6|1.5|
|4 Tenperate and Tropical|78|85|100|130|<br>148|<br>158|1.6|1.6|1.6|1.6|1.5|1.5|

Note: World Bark regions and coumtry departments are defined in Table 14.
Direct taxes and most contributions are progressive and equalizing, as indicated by the positive value of the Kakwani index (figure 6). The personal income tax is the most progressive of all, while at the same time equalizing and very slightly poverty-increasing, as assessed by its marginal contribution to the poverty rate and the Gini index (figure 7). Contributory social security schemes such as the Provident Fund and NPPF are also progressive, though the magnitude is smaller than direct taxes. Among social insurance contributions, the rural insurance scheme has a large regressive impact (figure 6). Most social contributions are equalizing yet slightly poverty-increasing, with the NPPF having the largest effect on increasing poverty.

Figure 6. Progressivity of Direct Taxes and Figure 7. Marginal Contribution of Taxes and Contributions (Kakwani Index) Contributions to Change in Poverty and Gini <!-- Start of picture text --> 0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

<!-- Start of picture text --> GIS<br>Rural Insurance<br>GISL<br>Provident fund<br>NPPF<br>Personal Income Tax<br>-0.5 0 0.5 1<br>Change in Poverty Headcount Change in Gini<br><!-- End of picture text --> <!-- Start of picture text --> Source : World Bank staff estimation using BLSS 2017.<br>Note: Positive marginal contribution to Gini implies that<br>the tax/contribution is equalizing.<br><!-- End of picture text --> Indirect taxes are uniformly progressive which could be attributed to at least two reasons (figure 8). First, the tax base under the existing sales tax system is small. Second, consumption patterns vary distinctively across the income distribution such that the tax burden falls disproportionately on households that are better off: for example, the green tax is levied on vehicles that are only afforded by richer households. All indirect taxes are poverty-increasing yet inequality-reducing, with the exception of excise taxes which do not exert a noticeable impact on the Gini (figure 9). Most taxes and contributions are pro-poor as they are disproportionately concentrated among the better-off. Unsurprisingly, this is not the case with rural insurance given that the rural (urban) population to whom this is applicable is more likely to fall in the lower (upper) deciles.

or transfer compares the poverty/inequality indicator with and without the intervention. Concentration of taxes/transfers can also be shown using the share of taxes and social spending paid/received by each income decile.

20
Indian economy are ambiguous, weakening the extent of its claimed contributions to development.

There are several reasons why the contributions of Mode 1 services trade to Indian development are not well understood. The period 2001‐2006 was one of rapid increases in FDI for India, obfuscating the impact of the significant services exports and imports growth in that period. Theoretical models that have attempted to predict and explain the effects of increased outsourcing have also failed to provide solutions that hold up when subject to generalization. Analytical solutions from models that accurately depict the real world are difficult to obtain since the problem would require too many dimensions to be able to solve with any ease.

This paper provides insight into this problem by using a simulation model built on a specific‐factors framework and using real world data to obtain an upper‐bound numerical solution of how sensitive factor prices in different sectors may have been to the increase to the Indian services exports and imports observed between 2001 and 2006. To focus on the contributions of just the changes in the services trade, the model ignored FDI flows and was calibrated such that the services trade changes led to the historically observed changes in skilled wages.

As a result of this partial, historical shock, the wages of all workers increased, although unskilled workers’ wages rose by only 6 percent, compared to the historical 39 percent increase for skilled workers. Owners of capital in the services sector benefited from the Mode1 trade enhancing technological improvement as the rental rates of capital in that sector rose. At the same time, owners of capital in other industries became worse off as capital prices fell. Driven by the wage increases and the higher rental rates for service sector capital, the total factor income in India increased. However, the sharp losses in factor income for capital specific to the agriculture and manufacturing industries imply a worsening of the income gap between the owners of such capital.

While most of these factor price changes were attributable to expanding demand for Indian Mode 1 exports, increasing supplies of Mode 1 imports also had significant influence on prices. Specifically, they proved improved skilled labor wages and services‐sector capital rates to the benefit of skilled workers and capital owners in the services sector. The impact of the increase in Mode1 imports supply on capital rates in non‐services industry was extremely small compared to the impact of expanding Mode 1 exports.

24
western economic development program. For example, the transfer payment earmarked for minority areas was established in 2000 in order to support the minority areas’ economic development. Similar grants are now being earmarked for re-development of China’s rustbelt region in the Northeast.

Table 3 summarizes the overall transfer payment structure in China in recent years.

- Table 3: 1995 2004 Transfer payment from the central to sub national level

|Fiscal year|1995|1996|1997|1998|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|---|---|---|---|
|Total|2449|2655|2784|3228|3931|4588|5893|7348|8656|10177|
|Tax rebating and<br>fixed subsidy amounts|1982|2060|2124|2196|2234|2326|2431|3328|4196|4335|
|Transfer payments for<br>fiscal capacity|91|107|143|155|336|506|1108|1586|1884|2618|
|Of which::<br>Standardized fiscal transfer|20|35|50|61|75|85|138|279|380|745|
|Earmarked for minority areas.||||||25|35|39|55|76|
|Income distribution adjustment.|||||108|217|631|817|901|993|
|Grant for “fees and<br>tax reform” in rural<br>area|||||||80|245|305|307|
|Grant for abolition<br>of agriculture tax||||||||||217|
|Special transfer payments|375|489|518|878|1360|1756|2355|2434|2577|3223|

Source:Calculations using “Finance Yearbook of China” from 1996 to 2005. Units = (100 million) Note: sub-items do not add to category total because of exclusion of minor items.

19
has been accompanied by some liberalization of accounting services. As Hegarty explains, under the agreement reached by the EU countries, a French company listed on the Frankfurt Stock Exchange can file its accounts according to French standards and is not under an obligation to indicate how the statements would be different if done under German standards- showing a conscious acceptance of differences in regulatory design.

A recent debate in the EU about tax policy demonstrates how, in the absence of a clear economic rationale for continuous harmonization, decisions eventually become very politicized. EU finance ministers are interested in harmonizing the base on which company taxes are calculated. However opinion is divided on this issue. Countries such as Britain and Ireland and some of the newer members from Eastern Europe are reportedly not interested in tax base harmonization while France and Germany are. The former group believe in the value of tax competition and institutional diversity while the latter believe in stopping “unfair” competition.<sup>6</sup> Another debate demonstrates how different economic and social values, that could easily remain “different” come under attack once the harmonization bandwagon has taken off. An EU directive limits the number of hours an employee can work to 48 in a week. The UK has managed to opt-out of this directive but it seems that a strengthening of the directive could deprive the UK of this right.<sup>7</sup> Time and political bargaining will tell which side wins out.

The laws (and regulations ) of different countries in a given area may vary both in the substance and also in the degree to which there are detailed prescriptions or guidelines. Furthermore, there may be one or several laws/regulations that determine the conduct of any entity in a single transaction type. For example, in Spain the legal sources

> 6 Financial Times, Monday, September 13th, 2004. Pg 4.

> 7 Financial Times, Tuesday September 14th, 2004. Pg 4.

17
- **Chinn, Menzie D., and Hiro Ito.** 2006. “What matters for financial development? Capital controls, institutions, and interactions.” _Journal of Development Economics_ , 81(1): 163 – 192.

- **Choi, In.** 2012. “Efficient Estimation of Factor Models.” _Econometric Theory_ , 28(2): 274–308.

- **Choi, In, and Hanbat Jeong.** 2018. “Model selection for factor analysis: Some new criteria and performance comparisons.” _Econometric Reviews_ , 0(0): 1–20.

- **Choi, In, Dukpa Kim, Yun Jung Kim, and Noh-Sun Kwark.** 2018. “A multilevel factor model: Identification, asymptotic theory and applications.” _Journal of Applied Econometrics_ , 33(3): 355–377.

- **Cowan, Kevin, José De Gregorio, Alejandro Micco, and Christopher Neilson.** 2008. “Financial diversification, sudden stops and sudden starts.” In _Current Account and External Finance_ . eds. by Kevin Cowan, Sebastian Edwards, and Rodrigo Valdés, Chap. 5 159–194.

- **Eichengreen, Barry, and Poonam Gupta.** 2015. “Tapering talk: The impact of expectations of reduced Federal Reserve security purchases on emerging markets.” _Emerging Markets Review_ , 25 1 – 15.

- **Forbes, Kristin.** 2014. “Financial deglobalization?: Capital flows, banks and the Beatles.” 11, Speech given at Queen Mary University.

- **Forbes, Kristin J., and Francis E. Warnock.** 2012. “Capital flow waves: Surges, stops, flight, and retrenchment.” _Journal of International Economics_ , 88(2): 235 – 251.

- **Forbes, Kristin, Dennis Reinhardt, and Tomasz Wieladek.** 2017. “The spillovers, interactions, and (un)intended consequences of monetary and regulatory policies.” _Journal of Monetary Economics_ , 85 1 – 22.

- **Ghosh, Atish R., Jonathan D. Ostry, and Mahvash S. Qureshi.** 2018. _Taming the Tide of Capital Flows A Policy Guide_ .: MIT Press.

- **Gilchrist, Simon, and Egon Zakraj ̃sek.** 2012. “Credit Spreads and Business Cycle Fluctuations.” _American Economic Review_ , 102(4): 1692–1720.

73
to re-state here that using 5-year, non-overlapping panel data has the disadvantage of reducing the number of observations, and thus statistical power, substantially. In addition, the short-run relationship between the RRA and GDP growth is of substantial interest, too. By construction, 5- year panel data eliminates much of this short-run relationship. Nevertheless, we realize that it has been common practice in the growth literature to report estimates that are based on 5-year, nonoverlapping panels. These estimates should be viewed as complimentary to the distributed lag estimates that we reported in Table 11.

Thus, with the above in mind, it is gratifying that the estimates in Table 12 produce very similar results to the distributed lag estimates that are based on annual panel data. Controlling for country fixed effects as well as time (i.e. 5-year) fixed effects, Table 12 shows that increases in the absolute relative rate of assistance had a significant negative effect on GDP and consumption growth. Quantitatively, the estimates in columns (1) and (2) imply that, on average, a ten percentage point increase in the absolute relative rate of assistance reduced GDP growth over a 5-year period by around one percentage point; and consumption growth by around half a percentage point. Columns (3) and (4) document that this significant negative effect of increases in the absolute RRA on GDP and consumption growth continues to hold when excluding outliers. These fixed effects estimates, that are based on 5-year non-overlapping panel data, therefore reconfirm the main finding of our baseline analysis: distortions to the relative price between agricultural and non-agricultural products have had a significant negative GDP and consumption growth effect in Sub-Saharan African countries.

# **5.3 Effects on Sector Size, Agricultural Production, and Investment**

We now examine the effects that changes in the relative rate of assistance to agriculture had on the size of the agricultural sector, real agricultural output growth, and aggregate investment. To start off the discussion on these channels through which variations in the RRA have real effects on the 24
forces. US full-time workers are on average some three years older than their Brazilian counterparts, and earn much higher wages: the nominal exchange rate in September 2007 was 1.90 BRL to the USD, so average wages in 2007 in this sample were approximately 6.6 times higher in the US than in Brazil. US workers are also much more educated, and the female share of the labor force is higher there. Over the twelve years between 1995 and 2007, both labor forces became a little older, and more educated. Educational attainment rose in both countries, but more markedly in Brazil, which started from a much lower level. Completion of high school in Brazil almost doubled over the period, and the college-educated share also rose from 6.4% to 9.4%. The female share of the labor force was essentially stable at 47% in the US, but rose from 37% to 41% in Brazil, driven primarily by a higher rate of female labor force participation (Ferreira, Firpo, and Messina (2016)).

# **4.2 Results**

Before we present results for the _GIC_ , we compute standard inequality measures for hourly real wages for both countries. Table 3 summarizes some of the main changes in the wage distributions in the US and Brazil over this period. The first panel presents five common measures of relative wage inequality for the two countries in 1995 and 2007, as well as for the counterfactual wage distribution _FY_ (1) _|T_ ( _·|_ 0). The inequality measures are the Gini coefficient, the Theil-T index (that is, the Generalized Entropy measure with parameter = 1), the mean log deviation (also known as Theil-L, or _GE_ (0)), the relative mean deviation, and the standard deviation of logarithms. The second panel presents the growth rate in mean hourly wages ( _γAGR_ ) and the average of quantile-specific growth rates, across quantiles, denoted Mean _GIC_ .

– Table 3: Inequality measures hourly real wages (HRW) US and Brazil

||Fac|tual|US<br>Counter Factual|Fac|B<br>tual|ra<br>C|zil<br>ounter Factual|
|---|---|---|---|---|---|---|---|
||1995|2007||1995|2007|||
|Gini|0.355|0.383|0.381|0.539|0.490||0.473|
|Theil Entropy|0.205|0.260|0.258|0.536|0.457||0.444|
|Theil mean log deviation|0.218|0.254|0.250|0.511|0.411||0.383|
|Relative mean deviation|0.257|0.274|0.272|0.404|0.366||0.348|
|Standard deviation of logs|0.682|0.710|0.703|0.963|0.852||0.814|
|Growth of mean wage (_γAGR_)|0.1|73|0.101|0.0|17||-0.150|
|Mean _GIC_|0.1|27|0.063|0.1|38||-0.016|

26
# **Does Tougher Import Competition Foster Product Quality Upgrading?**

Ana M. Fernandes<sup>a</sup> Caroline Paunov<sup>b</sup> The World Bank OECD Development Centre and Queen Mary, University of London Keywords: import competition, transport costs, product quality, incremental innovation, output unit values, plant-level data, Chile.

## **JEL Classification codes** : O31, F14, L6.

a Ana Margarida Fernandes. The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: afernandes@worldbank.org.

> b Caroline Paunov, OECD Development Centre, 2, rue André Pascal, 75 775 Paris Cedex 16, France. Email: caroline.paunov@oecd.org and caroline.paunov@gmail.com.

We thank Jonathan Haskel for comments. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or the OECD.