rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

We factor-analyze the Knowledge Assessment Methodology (KAM) data. The KAM data was developed at the World Bank to assess countries’ readiness for the knowledge economy. The data potentially draw the attention of policymakers to specific areas deserving of more attention and future investments. We factor-analyze KAM data in order to reduce those variables to their essential dimensions or factors. Our main objective in undertaking the factor analysis is to quantify the factors for each country, that is, compute factor scores on each factor. To this end, the paper details these issues in the factor analysis of the KAM data – in detail whether the KAM data should be factor-analyzed, the optimal dimensionality of the data, and giving political-economic meaning to the factors. We find that the KAM data are not just amenable to factor analysis but they greatly benefit from it. There are enough inter-correlations among the variables that the real information in the data can be distilled down to a smaller number of dimensions.

We use two factor analytic methods – Principal Components (PC) analysis and “true” factor analysis which we estimate using maximum likelihood (ML). While PC analysis focuses on explaining the variance in the data, the ML method seeks to explain the intercorrelations in the data. We should therefore expect the two methods to produce different results. While the results are different (PC analysis requires many more dimensions to rationalize the data than ML analysis), there are common themes.

A contribution of the paper is identifying the political-economic dimensions in the KAM data and measuring them for (ordinal) comparisons over time. We embrace the idea of a simple structure of the dimensions and allow these dimensions to be correlated with each other. The output from the factor analysis is used to graphically analyze how countries have changed their rankings on the underlying dimensions over the 1995-2002 period.

34
<!-- Start of picture text --> Figure 1: SWIFTNet year on year growth in trade<br>finance messages<br>2.0% —SJ<br>0.0% ~—<br>co Cc foe) oe co ioe) oo ioe) ise) Cc co iv) Q a a fon) con a fon) fon) a a<br>§<br>2.0% 2 2 9 ONS oe—TMFF 8 FF FFF Fe ee ee gs<br>Te S 85fs a¢ —@FS 5S =7 ZOOPeg cgesa8 SekgsSesser5 $ § = eas23-3<br>4.0% -4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>-10.6%<br>-12.0% —¢— year on year growth (in %) | +<br>16.0% 9 ——————_____ 1S. 15.2%<br>Source: www.swift.com<br><!-- End of picture text -->
2017).<sup>6</sup> Specifically, it pays attention to the role of a reference network, which refers to the group of people whose actions and beliefs individuals care about when they act. Depending on the context and the sphere of life that gender norms refer to, this group can be given by neighborhoods, villages, or people on the street. Hence, as people move from one place to another, they move across reference groups and may knowingly change their behavior to comply with the norms in place in the new setting (Choe et al., 2014).

The analysis focuses on behaviors or actions and attitudes or empirical expectations (Alesina et al., 2013).<sup>7</sup> These two components are measured and analyzed separately, rather than combined in an index. Following previous studies, survey clusters are used as a proxy for reference networks (Storey & Kaggwa, 2009; Uthman et al., 2011; Vyas & Heise, 2016). The prevalence of behaviors and attitudes at the reference network level is inferred by aggregating (non-self) reported values across individuals in the same cluster, as it is reasonable to think that people residing in the same cluster might have direct contact with each other. This approach is consistent with the feminist literature which has theorized gender norms as having blurry boundaries, rather than focusing on a particular group with similar demographic characteristics (Oakley, 2015). Also, following gender norms theory, which focuses on the alignment between the norm and personal attitudes, the analysis in this paper assumes that gender norms become less (more) traditional when both attitudes and behaviors become less (more) rigid.

The analysis examines 17 items (10 attitudes and 7 behaviors) classified into five domains. _Reproductive health,_ is measured using one indicator for attitudes and one indicator for behaviors, as shown in Table 1. The question on attitudes refers to women’s approval of contraception to prevent pregnancy. The proxy for behavior measures the ability of women to decide upon the use of contraception. The norms that limit _women’s access to economic opportunities_ , are measured by one indicator for attitudes and two indicators for behaviors. These items deal with the intersection of family and work as well as the ability of women to decide on their earnings. The norms that limit _women’s mobility,_ are proxied by one indicator for attitudes and one indicator for

> 6 As articulated by Cislaghi and Heise (2020), operationalizing this definition using only quantitative measures also requires recognizing that they may fall short in capturing institutional aspects.

> 7 According to Harper et al. (2020), although gender norms are invisible, they are reflected in behaviors and attitudes.

11
<!-- Start of picture text --> Correlation between sectoral productivity and change in employment share<br>a India<br>=3 1970-1560 1980-1990<br>a=]aa= ee.. ow gue °“ (ieee low be eyten<br>a =n en nn n<br>= 7 ee ft ~ egOpn<br>=o ote ° ‘Ont<br>= 7<br>3o 06 04 02-02C7) (=)ed 02<br>= 1990-2000 2000-2010<br>1g 7 122 fre Lr 25 .<br>3a ow Fie on din _ “ Fie ow drain fire<br>3 -= ae |(Dee Crs On -. = —Ons(ahTC O-<br>£ re ee a en a ee ee ee ee<br>a .<br>z Change in employment share<br><!-- End of picture text -->

<!-- Start of picture text --> Correlation between sectoral productivity and change in employment share<br>> Thailand<br>=3 1970-1980 1980-1990<br>osg= 77 ceMy De ow" Tatex bth<br>= = - cae<br>2 os 08 S _ fae<br>~ . 7 -<br>Ss 4 4 Z<br>2:3 15 A 05 0 ~ 05 2 15GPA “05 0 5<br>a 1990-2000 2000-2010<br>rol Wh y min<br>3S °] ae win | RRoe i,<br>o em7 tiOr 3 a -o (DraBe<br>3 oe; | °7 eeSoa<br>5 > A 05 0 05 WT OS 0 05<br>w -<br>z Change in employment share<br><!-- End of picture text -->
**Table 16: Control of Corruption 2008** (Governance score: from – 2.5 to + 2.5)

|NewEUcountries|Selected other EUcountries|
|---|---|
|Slovenia(+0.95)|Finland(+ 2.34)|
|Estonia(+ 0.94)|Denmark(+2.32)|
|Hungary (+0.55)|NewZealand(+ 2.32)|
|Slovakia(+ 0.43)|Sweden(+2.24)|
|Poland(+0.38)|Germany (+ 1.77)|
|Czech Republic(+ 0.37)|United Kingdom(+ 1.77)|
|Latvia(+0.29)|UnitedStates(+ 1.55)|
|Lithuania(+0.18)|Spain(+ 1.18)|
|Croatia(+ 0.12)|Portugal(+ 1.08)|
|Romania(-0.06)|Italy (+0.13)|
|Bulgaria(- 0.17)|Greece(+ 0.10)|

_Source:_ Governance Matters, Worldwide governance Indicators 1996-2008, The World Bank Group, 2009.

# **14. Bringing It All Together: Are There Best Practices to Follow?**

103. In the previous sections we have discussed the estimated size of undeclared work in the NM-EU countries, its potential causes, and policies to tackle it. We have argued that undeclared work may have numerous sources that may interact, in particular a high overall tax burden, a high labor tax wedge, notably on low earnings, other features of the tax system which provide incentives to under-declare earnings such as unfavorable family taxation, large gaps between tax burdens of different income sources (notably between capital income and wage income and between dependent employment and self-employment), and ease of tax compliance. The divide between formal and informal work is also affected by a number of non-tax factors, such as general framework conditions for formal sector activity, the extent of corruption, and the skill level of the workforce. Based on the discussion above, Table 17 provides a higly tentative qualitative rating of these factors for the various countries. It is clear that there is a high level of uncertainty with such a rating system as it is only based on some hypotheses about the causes of undeclared work and some assessments, which may be debatable, rather than on a model-based econometric exercise.

104. The latter approach would be more meaningful for assessing the effect on the various factors on undeclared work, but would require a much larger sample of countries. Given these caveats, our tentative conclusion is that among the NM-EU countries Estonia has probably the best conditions for containing undeclared work while in Bulgaria conditions are the worst. Comparing the level of undeclared work with tax and non-tax factors, it also appears that the NM-EU countries are in different equilibriums with respect to undeclared work and the impact of taxation. This suggests that in some countries tax reductions may have little effect if other framework conditions are not improved at the same time. Combating undeclared work thus requires a broad approach, which not only strengthens tax enforcement, but also makes it easier to comply with the tax system and with other regulations and which increases the benefits of compliance. This involves improving tax policies and tax administration and other framework conditions for regular economic activities and improving government institutions more generally. As progress in these areas is uneven among the NMEU countries the design of policies needs to consider country-specific conditions: 45
<!-- Start of picture text --> Log productivity<br>12 Mean<br>10 —_ x=<br>| © | i<br>8 «| Oo qe<br>6 1970s 2010s|1970s]] 2010s|1970s 2010s'1970s 2010s<br>AEs<br>EMDEs EMDEs EMDEs<br>Club 1 Club 2 Club 3-5<br><!-- End of picture text -->

<!-- Start of picture text --> 2010 US$<br>=—Club 1 =—Club 2 =—Club 3<br>128,000 Club4 —Club5<br>32,000 _<br>8,000 ee<br>2,000 =<br>500<br>26 eee 8880 we<br>2 O22 82 22 99 8 §8<br><!-- End of picture text --> <!-- Start of picture text --> Log productivity 2010 US$<br>12 Mean =—Club 1 =—Club 2 =—Club 3<br>10 —_ x= 128,000 Club4 —Club5<br>| © | i 32,000 _<br>8 «| Oo qe 8,000 ee<br>6 1970s 2010s|1970s]] 2010s|1970s 2010s'1970s 2010s 2,000 =<br>AEs 500<br>EMDEs EMDEs EMDEs 26 eee 8880 we<br>Club 1 Club 2 Club 3-5 2 O22 82 22 99 8 §8<br>Percent Percent =Growth ¢ Count (RHS) Count<br>4 4 25<br>3 ¢ o 20<br>2 15<br>2<br>Sd 10<br>0 || = ,<br>-2 Transition Remainin Transition Low<br>AEs EMDEs|/EMDEs EMDEs EMDEs |EMDEs toClub1 Club2 to Club2_ productivity<br>Club 1 Club 2 | Club Club 3 | Club 4 | Club 4 | 4 | | Club5 (3-5)<br><!-- End of picture text -->

<!-- Start of picture text --> Percent<br>4<br>2<br>-2<br>AEs EMDEs|/EMDEs EMDEs EMDEs |EMDEs<br>Club 1 Club 2 | Club Club 3 | Club 4 | Club 4 | 4 | | Club5<br><!-- End of picture text -->
instance, by the sheer number and density of offices, shops, bars and restaurants they contain. Inside and immediately surrounding the Shinjuku station, for example, are 1,319 retail shops, with annual sales of 10.8 billion yen, and 1,288 bars and restaurants. The Shibuya station houses 906 retail stores, with annual sales of 4.3 billion yen, and 579 additional bars and restaurants are available just outside (TMG 2002b: 112).

Antenna districts can achieve this scale because they cluster around transportation stations where multiple subway and train lines intersect and interconnect the cities of greater Tokyo with one another and with the central core. Millions of people flow into central Tokyo every day from greater Tokyo and the national capital region. More than four million passengers traverse Shinjuku station everyday and nearly three and a half million commuters daily pass through the turnstiles at Ikebukuro Station, the hub for eight rail and subway lines connecting the northern precincts of the city with the western suburbs (Andoh 2004; TMG 2002b: 114). These terminals draw even more people as final destinations for commerce and entertainment. Colleges are usually found in antenna districts, as well, giving young consumers yet another reason to spend time there.

Antenna districts are the trendiest locales in Tokyo. Here consumers are exposed to the widest possible range of luxury items, the highest quality goods, and the latest fashion. Clothing outlets, for example, spread all over Tokyo, but the highest concentration of boutiques is in Ginza and Shibuya, where the sheer number and density of clothes and international luxury brand shops attract young, fashion-oriented people. These districts have the highest fashion sales figures in Tokyo and the highest concentration of designer boutiques for women and children (MLIT 2003a: 53). The New York Times has called Tokyo "the real international capital of fashion" [<sup>27</sup> ] and Japanese consumers are said to account for nearly 50% of worldwide spending on luxury goods (Ishibashi 2002). Young working women pay the most attention to international luxury goods. No doubt they buy luxury items partly for status but they also prize craftsmanship and they are willing to save to purchase their favorite brands and even spend months or years on waiting lists to secure the items they desire.[<sup>28</sup> ] Japan has one of the more egalitarian income distributions in the world and Tokyo does not suffer the extreme income polarization found in Western world cities, like New York, London and Paris. Nor does the average household income in central Tokyo differ much from the surrounding communities of greater Tokyo, a stark contrast to the pronounced city-suburban 26
Table 4 contains daily amounts of per capita gasoline expenditure and the monthly household level of consumption among the population in the Islamic Republic of Iran. A strong gradient in expenditure and consumption of gasoline is clearly apparent, consistent with transportation ownership. The richer population is more likely to own cars and tends to spend and consume more gasoline compared to the less wealthy population who either do not own transportation or own motorcycles only. Thus, the richest 10 percent consume about 116 liters per month on average compared to 20 liters consumed by the poorest 10 percent. Another way to look at this is to check the distribution of population by gasoline consumption (Figure A1 in the annex). The largest share of population without gasoline consumption is from the poorest decile – 61 percent, compared to 22 percent for the richest decile.

Table 4. Expenditures and consumption of gasoline in the Islamic Republic of Iran by deciles

|deciles|Dailyexpenditure ongasoline,IRRper capita|Quantity,litersper monthper HH|
|---|---|---|
|poorest|1,476|20|
|2|2,493|31|
|3|3,482|41|
|4|4,098|47|
|5|5,270|57|
|6|6,190|65|
|7|7,349|74|
|8|8,935|84|
|9|10,982|98|
|richest|15,487|116|
|Total|6,576|68|

Source: Authors’ calculation using forecasted HEIS 2018/2019. Note: Overall daily consumption of gasoline is estimated to be around 53.3 mln liters. This is quite close to gasoline consumption numbers reported by Iranian authorities.

As a result of higher consumption of gasoline, richer people are the main beneficiaries of fuel subsidies as shown in Figure 4. The richest decile gets a 10 times higher direct benefit per capita through subsidized gasoline compared to the bottom decile. This is consistent with previous studies (Mostafavi-Dehzooei and Salehi-Isfahani 2017, Salehi-Isfahani et al. 2015).

Figure 4. The per capita daily benefit through gasoline subsidies across deciles, Iranian rial <!-- Start of picture text --> 100,000<br>90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>Source: Authors’ calculation using forecasted HEIS 2018/2019.<br>Note: Benefit is calculated as the difference between costs recovery and the price multiplied by quantities consumed.<br>Iranian rial per capita, daily<br><!-- End of picture text --> 6
|||Produ|ctivity|Table 1: M<br>Invest|oments o<br>ment|f Investment D<br>Inve|ynamics<br>stment Growth||Avg. Pro|d. of K|
|---|---|---|---|---|---|---|---|---|---|---|
||Num. Obs.|_ρ_|_μ_<br>_σ_<br>2|_ρ_(_i, i−_1)|_σ_<br>2 (_i_)|_ρ_(∆_i,_∆_a−_1)|_ρ_(∆_i,_∆_i−_1)|_σ_<br>2 (∆_i_)|_ρ_(_arpk, a_)|_σ_<br>2 (_arpk_)|
|ARG|993|0_._89|0_._05|0_._19|0_._04|0_._31|_−_0_._36|0_._06|0_._58|0_._54|
|BRA|1389|0_._90|0_._08|0_._13|0_._05|0_._35|_−_0_._39|0_._09|0_._60|0_._65|
|CHN|797047|0_._91|0_._14|0_._04|0_._08|0_._25|_−_0_._36|0_._14|0_._68|0_._92|
|COL|44909|0_._95|0_._09|0_._13|0_._04|0_._28|_−_0_._35|0_._07|0_._61|0_._98|
|MEX|3208|0_._93|0_._07|0_._17|0_._01|0_._17|_−_0_._39|0_._02|0_._69|0_._79|
|MYS|548|0_._95|0_._06|0_._31|0_._02|0_._18|_−_0_._29|0_._03|0_._86|0_._73|
|TWN|2076|0_._96|0_._04|0_._34|0_._03|0_._21|_−_0_._36|0_._04|0_._66|0_._57|
|THA|4025|0_._95|0_._07|0_._26|0_._06|0_._27|_−_0_._32|0_._08|0_._57|0_._88|
|TUR|830|0_._89|0_._08|0_._11|0_._05|0_._37|_−_0_._38|0_._09|0_._57|0_._56|
|JPN|60720|0_._98|0_._03|0_._13|0_._02|0_._17|_−_0_._40|0_._03|0_._48|0_._43|
|USA|34260|0_._93|0_._08|0_._25|0_._04|0_._13|_−_0_._30|0_._06|0_._55|0_._45|

_Notes_ : This table reports moments in firm-level investment dynamics across eleven countries. The data sources are described in Section 2 of the main text. The first panel reports the persistence and variance of the innovations in the firm-level productivity process, _ρ_ and _σ_<sup>2</sup> , respectively. The second panel _μ_ reports the serial correlation and volatility of investment rates, _ρ_ ( _i, i−_ 1) and _σ_<sup>2</sup> ( _i_ ) . The third panel reports the correlation of investment growth rates with lagged innovations in productivity, _ρ_ (∆ _i,_ ∆ _a−_ 1) , and the serial correlation and volatility of investment growth, _ρ_ (∆ _i,_ ∆ _i−_ 1) and _σ_<sup>2</sup> (∆ _i_ ) . The last panel reports the correlation of the average revenue product of capital with productivity, _ρ_ ( _arpk, a_ ) and the variance of the average revenue product of capital, _σ_<sup>2</sup> ( _arpk_ ) . All series are regressed on industry-by-year fixed effects to extract the firm-level variation within an industry, where industries are defined at the four-digit SIC code level.
# **Introduction**

There is keen awareness across developed and middle income countries of the developing world that increased longevity and aging populations will place significant and growing burdens on working age adults in the relatively near future. In the US and other economies with pay-as-yougo social security systems, these burdens will be transmitted through fiscal systems. Even where public transfer mechanisms are not as important, working age adults may nonetheless face increasing burdens associated with supporting the elderly through both financial and in-kind transfers.<sup>1</sup> Increasing the retirement age is frequently viewed as one feasible means of easing burdens on working age populations, and yet it is likely that exits from productive activity are shaped by household wealth and individual preferences as well as institutions and policy. Alternatively, continued participation in the workforce may reflect the ability of workers to learn new skills and to remain productive into older age. With an eye toward providing insight into the retirement decision in East Asia, this paper presents descriptive evidence on retirement and labor supply patterns in China, Indonesia and Korea.

While China’s rapid demographic transition is frequently highlighted in news accounts because of the sheer size of its aging population, Korea and Indonesia are also confronting rapidly aging populations.<sup>2</sup> In contrast to most developed countries, however, rural and urban populations face significantly different retirement systems. Differences across rural and urban areas in both retirement patterns and access to financial support is most extreme in China, where most longterm residents in urban areas have had formal wage employment, retire at a relatively young age and receive substantial support from pensions. Rural residents, by contrast, have lacked pension support and may expect to work in farming or other agriculture-related activities until relatively late in their lives.<sup>3</sup> In this sense, urban residents of China with formal sector employment face

> 1Lee, Mason et al (2011) highlight the implications of population aging for sustainability of public and private transfer systems across Africa, Asia, Europe, Latin America and the United States.

> 2Recent research by demographers at the US Census Bureau suggests that the old age dependency rates in 2020 will reach 22, 19 and 13 percent, for Korea, China and Indonesia, respectively, and by 2040 these rates will rise to 53, 40 and 25 percent (Kinsella and He, 2009). A preliminary release from China’s 2010 census informs us that 13.3 percent of China’s population is now over 60 as opposed to 10.3 percent in 2000, while the size of the future workforce has dwindled, with individuals under 14 accounting for 16.6 percent of the population, down from 23 percent in 2000 (National Bureau of Statistics, 2011).

> 3New initiatives are currently underway in rural China. A government-subsidized contributory rural pension piloted in 2009 will be rolled out to cover all rural counties over the next three years. In cities, a new pension scheme,

2
shown that military expenditure is the main source of volatility in U.S. government expenditure, this is not the case in all developing countries. In fact, for some developing countries, military expenditure is a negligible portion of budgetary expenditure. And in those developing countries whose expenditures are driven by large swings in military spending, it is hard to argue that these war buildups have little direct impact on GDP. Moreover, there is a growing literature arguing that either business cycle conditions or swings in natural resource prices are major drivers of public expenditure in developing countries’. This makes it difficult to find a general econometric instrument that drives public spending in developing countries, but which has no direct effect on economic activity. On the tax side, there is to date no comprehensive documentation of tax changes as in Romer and Romer (2010) for countries other than the U.S. and the U.K. Second, the SVAR approach may be more effective at identifying unanticipated fiscal shocks in developing countries than in high-income countries. while Ramey (2010) has presented evidence that SVAR shocks were forecasted by the private sector, this is less likely to be the case in developing countries. As IMV discuss, government expenditure is very volatile on a quarter-to-quarter basis in developing countries. Fiscal planning is poor and not credible. Quarterly fiscal data is subject to large revisions even a year or two after the fact. It is therefore unlikely that the public will have foreseen many of the fiscal shocks identified by an SVAR, and even less likely that their exact timing at a quarterly frequency will have been known in advance. Moreover, if the volatility of public expenditures is driven largely by political pressures to increase expenditures when resources are abundant, a VAR should be able to pick up on this relationship, as long as it is relatively sys- <u>~ 58ee for example KaminskReinharty,</u> and Végh (2004), Izetzki and Végh (2008) for stylized facts; Talvi and Végh (2005), Alesina, Campante, and Tabellini (2008) and Ilzetzki (forthcoming) for political economy theories of this phenomenon. 14