rafmacalaba/datause-encoder-data huggingface dataset:
|Graduate share|0.11|0.16|0.17|0.21|0.22|
|---|---|---|---|---|---|
|N|29031|32541|29513|27198|27306|

_Note:_ Averages for married women age 25–54, calculated using sampling weights. _Source_ : NSS Employment and Unemployment Survey A remaining channel—one not captured in control variables—is through social stigma. It is possible that the stigma associated with low-skilled jobs (including in agriculture, menial jobs in manufacturing and construction, or in domestic services) increases as women attain low and intermediate levels of education. This means education is associated with an increased (fixed) utility cost of engaging in low-skilled work. This channel can also explain the upward sloping part of the education U-curve: once women attain secondary or post-secondary education, the utility cost disappears as they gain access to white-collar jobs that are not subject to social stigma. In all, the education U-curve conditional on income, social status, and the sectoral structure of employment (which we further discuss below) can be explained by social stigma associated with different types of jobs.

In addition, the positive effects of higher education could reflect higher wages or positive selection effects. As we find very similar estimates when controlling for the own wage, we think the own wage channel does not play an important role, despite high returns to education in the labor market. On the other hand, it is quite plausible that the positive higher education effect partly reflects an upward bias due to endogenous selection into higher education of girls with stronger labor market orientation. This would not only bias upwards the effects of higher education, but is also consistent with the large decline in the positive effects of secondary and graduate education over time. If increasing educational attainment has been driven by the growing supply of education, for example, highly educated women in 1987 were more positively selected than those in 2011. We cannot test this directly without modeling educational attainment itself, which is beyond the 20
# **References**

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- **Akerman, Anders, Ingvil Gaarder, and Magne Mogstad.** 2015. “The Skill Complementarity of Broadband Internet.” _The Quarterly Journal of Economics_ , 130(4): 1781.

- **Atkin, David, Amit K Khandelwal, and Adam Osman.** 2017. “Exporting and Firm Performance: Evidence from a Randomized Experiment.” _The Quarterly Journal of Economics_ , 132(2): 551–615.

- **Atkinson, Anthony B.** 1987. “On the Measurement of Poverty.” _Econometrica_ , 55(4): 749– 764.

- **Bartel, Ann, Casey Ichniowski, and Kathryn Shaw.** 2007. “How does Information Technology Affect Productivity? Plant-level Comparisons of Product Innovation, Process Improvement, and Worker Skills.” _The Quarterly Journal of Economics_ , 122(4): 1721– 1758.

- **Bertrand, Marianne, and Sendhil Mullainathan.** 2001. “Do People Mean What They Say? Implications for Subjective Survey Data.” _American Economic Review_ , 91(2): 67–72.

- **Bloom, Nicholas, and John Van Reenen.** 2007. “Measuring and Explaining Management Practices Across Firms and Countries.” _The Quarterly Journal of Economics_ , 122(4): 1351–1408.

- **Bloom, Nicholas, Erik Brynjolfsson, Lucia Foster, Ron Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen.** 2019. “What Drives Differences in Management Practices?” _American Economic Review_ , 109(5): 1648–83.

- **Bresnahan, Timothy F., Erik Brynjolfsson, and Lorin M. Hitt.** 2002. “Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence.” _The Quarterly Journal of Economics_ , 117(1): 339.

- **Brynjolfsson, Erik, and Lorin M. Hitt.** 2000. “Beyond Computation: Information Technology, Organizational Transformation and Business Performance.” _Journal of Economic Perspectives_ , 14(4): 23–48.

- **Caselli, Francesco.** 2005. “Accounting for Cross-country Income Differences.” _Handbook of Economic Growth_ , 1: 679–741.

- **Comin, Diego.** 2000. “An Uncertainty-Driven Theory of the Productivity Slowdown in Manufacturing.” PhD diss. Harvard University.

- **Comin, Diego A, Danial Lashkari, and Mart ́ı Mestieri.** 2020. “Structural Change with Long-run Income and Price Effects.” _Econometrica_ , Forthcoming.

- **Comin, Diego A, Mikhail Dmitriev, and Esteban Rossi-Hansberg.** 2020. “The

43
fail if we believe that workers in, say, Peru, care less about wage shocks (and more about utility shocks) than workers in Costa Rica do. There might be isolated cases where such a scenario can arise, but this is unlikely to be a systematic problem for our hierarchy.

There is still the issue of the implications of normalizing _ν_ . Even if _ν_ is the same across countries, it could be different from 1. How different can it be and if so, can this affect the estimation of _C_ ? In other words, can the ranking change under a different normalization? First, _ν_ ≈ 1 is a reasonable approximation. Artuç (2012) indeed argues that the most plausible estimates of _ν_ using very rich U.S. panel data are actually small, with _ν_ = 1 being in fact a good approximation to the true value. Moreover, there is little variation in _ν_ across specifications. In consequence, our normalization actually provides reasonable estimates for the level of _C_ as well. Second, we can estimate _C_ for alternative values of _ν_ in a small neighborhood of 1, say in the interval [0 _._ 90 _,_ 1 _._ 1]. In Table A1, we report the baseline _C_ (column 1), and the estimated _C_ for _ν_ = 0 _._ 9 (column 2) and for _ν_ = 1 _._ 1 (column 3). As it can be seen, these alternative estimates are typically within 5 to 10 percent of the baseline _C_ . Moreover, the correlation between them, at 0.86 for low _ν_ and 0.99 for high _ν_ , is very high so that the ranking of countries in terms of the mobility costs is robust.

A different concern is whether the normalization of _ν_ affects the results from the simulations. For a given _C_ , if the true _ν_ is higher than 1, then the response of the employment flows to the utility shocks relative to the wage shocks would be underestimated. This means that the response of workers to the wage differential created by a trade shock would be overestimated. In the end, the simulations would produce a more responsive labor market than it really is. If the true _ν_ is instead lower than 1, then the simulations would produce a more sluggish labor market. The evidence in Artuç (2012) based on U.S. data suggests _ν_ could be, if anything, a bit lower than 1. Labor markets would thus be slightly more responsive than what our simulations suggest.

We can assess this directly by re-running the simulations using the estimates of _C_ and _ν_ in the limits of the interval for _ν_ , [0 _._ 90 _,_ 1 _._ 10].<sup>19</sup> We did this for all countries in the sample and results are in an online appendix. Here, we show differences in the dynamics of real wages in Food and Beverages for six countries: the top two countries in terms of _C_ , Peru and Azerbaijan, two countries 19Note that we are re-estimating _C_ for the different values of _ν_ and, as a result, the simulations mix the effects of both changes.

25
affected by underreporting of women’s work but have the disadvantage that women working part time are not considered as active in the labor force if they spend the majority of their time in domestic duties. This is important to keep in mind, but as noted above this affects at most 5 percent of the sample and does not affect time trends.

To provide some verification of participation rates from the NSS Employment and Unemployment Survey using other data, we compare the 2004-05 numbers to the 2004-05 wave of the India Human Development Survey (Desai et al. 2009). There is no major time criterion to be considered as a worker in the IHDS survey, and there is considerably more probing as respondents are asked to specify each household member’s contribution to each family business as well as any other activities earning an income or a wage.<sup>8</sup> For married women in urban India in the age group 25–54, the IHDS data show an employment rate (unemployment is not recorded) of 19.8 percent, which is very close to 19.6 percent based on the NSS data. The participation rate for married women in urban Delhi is also very similar between the NSS 2004–05 wave (19.4 percent) and a survey done for a study on women’s work in 2006 (19 percent; see Sudarshan and Bhattacharya 2009).

Figure 2 shows that the urban FLFP rate has a U-shaped relationship with education and that the stagnation in FLFP hides a combination of rising participation among women with low education and a decline in participation rates of highly educated women. As income, wages, and access to different types of jobs are important candidates for explaining these patterns, we now turn to a brief discussion of those.

# **Figure 2: Urban female labor force participation rate by education level**

> 8 See http://ihds.umd.edu/questionnaires.html for the IHDS household questionnaires.

11
but with an elasticity of substitution that is finite, The modified version adds features of a transitional economy (e.g. treatment of CMEA exports) and various taxes into the model.<sup>3</sup> Second, to highlight revenue uncertainty and introduce risk analysis in the simulations, the framework permits the sampling of random variables pertaining to key parameters or exogenous shocks. The Monte Carlo method (as well as the Latin Hypercube technique) is employed as in Go and Sinko [1994].4 Using this approach, alternative tax instruments and their revenue impact, encompassing many "what-if" scenarios, can be compared regarding the range of tax rates required - What is the most likely rate needed? What is the dispersion or variance? What are the relative welfare effects of different taxes? And what tax policy covers an acceptable risk?

The salient features of the framework are discussed below. To carry out the model, data from any Eastern European country may be used. Mainly because of data availability, a Czechoslovakia-like economy with 1989 as the base year is employed for illustrations (see Table 1). However, the conclusions are drawn and applied to transitional economies in general. A list of equations appears in the appendix. A reference such as 'A2' means equation '2' in the list.

# **3.1. Production and CMEA Exports**

To account for the importance of CMEA trade in the framework, gross output in the economy, GDP, is defined as the sum of exports to the CMEA region, exports to the rest of the world excluding 3Denizer and Gelb (1992) also use a simple general equilibrium framework with dualistic (rural-urban) structure to look at transformnation issues in Mongolia.

_4Go_ and Sinko [1994J implements Monte Carlo sampling method in a simple tax model of an open economy (Sri Lanka). A discussion of the statistical inference and confidence set in the context of CGE models and simulations when parameters are uncertain can be found in Abdelkhalek and Dufour (1993).

10 _Go:_ **_Revenue_** _Uncerwainot_
31. Iyer S. (2002) Demography and Religion in India. Oxford University Press: Delhi.

32. Jacoby, H. G. and Mansuri, G. (2005), ’ _Watta Satta_ : Exchange Marriage and Women’s Welfare in Rural Pakistan’, mimeograph, The World Bank.

33. Jeffery R. and Jeffery P. (1997), Population, Gender and Politics: Demographic Change in Rural North India. Cambridge University Press: Cambridge.

34. Jensen, R. (2003), Equal Treatment, Unequal Outcomes: Generating Sex-Inequality Through Fertility Behavior, mimeograph, Watson Institute.

35. Kishor, S. (1993), ‘”May God Give Sons to All”: Gender and Child Mortality in India’, American Sociological Review, vol. 58, No. 2, April, pp. 247-265.

36. Klasen, S. (1994), ”Missing Women Reconsidered”, World Development, vol. 22, pp. 1061-1071.

37. Landes, D. S., 1998. The Wealth and Poverty of Nations. Little, Brown and Company, London.

38. Minault, G (1998), Secluded Scholars. Delhi: Oxford University Press.

39. Moulasha, K and Rao, G. R. (1999) ‘Religion-Specific Differentials in Fertility and Family Planning’, Economic and Political Weekly, 34 (42), pp. 3047-51.

40. Murthi, M., Guio, A-C, and Dreze, J. (1995) ‘Mortality, Fertility, and Gender Bias in India: A District-Level Analysis’, Population and Development Review, 21(4), pp. 745-82.

41. Niraula, B. B. and S. Philip Morgan (1996), ‘Son and Daughter Preferences in Benighat, Nepal: Implications for Fertility Transition’, Social Biology, Vol. 42, pp. 256-73.

42. Obermeyer C. M. (1992) ‘Islam, Women and Politics: The Demography of Arab Countries’, Population and Development Review 18 (1), pp. 33-60.

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18
Insurance penetration in Tunisia lags behind several countries at a similar level of income and economic development (Table 1). The growth of insurance has been held back by several structural problems.

The Tunisian insurance industry has long been subject to restrictive regulations that have limited its ability to engage in technical risk and financial management. Several of the companies report cumulative deficits that exceed their authorized capital and thus operate with negative equity. In fact, the capital shortfalls of these companies exceed the capital of all the other companies so that the industry as a whole suffers from negative equity. The compulsory personal lines are in chronic deficit, while the returns on financial investments have been suppressed by the low yields on government securities as well as on the equities in which insurance companies have invested their technical reserves. Insurance also suffers from a large tax burden, especially on the premiums paid in some lines of business.

**Tahfe** 1 **International Comparison of Insurance Premiums, 1992 (percent of GDP)**

|Africa||Asia||Latin America||
|---|---|---|---|---|---|
|South Africa|12.8|China|1.0|Brazil|4.3|
|Zimbabwe|4.8|Korea|12.3|Panama|3.3|
|Kenya|2.4|Japan|8.6|Chile|3.0|
|Morocco|2.1|Taiwan|4.8|Venezuela|2.2|
|Tunisia|1.7|Malaysia|3.8|Uruguay|2.0|
|||Singapore|3.7|Colombia|1.8|
|||Philippines|2.3|Argentina|1.6|
|||Thailand|2.0|Mexico|_1.5_|

# Source: Sigma, World Insurance in 1992, Swiss Reinsurance Company, 3/94.

The adverse effects of premium regulation in some lines have been compounded by ill-advised competition for market share in others. Companies that are better managed and are more aware of the costs of different coverages are often undermined by the low premiums offered by companies with weak capital base or less sophisticated management. The industry also suffers from widespread mistrust between insurers and the insured, which is aggravated by the lack of adequate information and by the weakness of controls against moral hazard and inflated loss claims. To contain costs, the industry has often engaged in delaying tactics and disputes with loss claimants. This has contributed to its negative public image and has fueled the mistrust betveen insurers and insured, especially in the compulsory automobile line.

To improve inatters. there is a strong need for a reform of regulation, strengthening of supervision, recapitalization and restructuring of capital-deficient companies, privatization of state-owned companies. modernization of financial management to enhance the investment performance of insurance companies. intensive training programs for both regulators and market practitioners, and a major campaign to improve the public inage of the industrv.

2
the real exchange rate. As inflation picked up during this period, the real exchange rate continued to appreciate.<sup>14</sup>

**Figure 10: Trends in Nominal and Real Exchange Rate**

<!-- Start of picture text --> 70 160<br>Phase 3: RER continued<br>Phase I: nominal<br>to appreciate despite 150<br>60 and real exchange some nominal<br>rate depreciated<br>exchange rate depreciation 140<br>50<br>130<br>40<br>120<br>30<br>Phase 2: nominal and 110<br>real exchange rate appreciated<br>20<br>during the surge 100<br>10 90<br>Nominal Exchange rate Real Exchange Rate (RHS)<br>Rs./$<br>Index=100 in 1990<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Note: Both nominal and real exchange rate are with respect to US $, and are defined such that an increase is depreciation. Source is RBI’s Handbook of Statistics on the Indian Economy

# **3.3 Reserve Management—the level of reserves has increased for the most part, in keeping with increased external liabilities**

Emerging markets hold reserves for a variety of reasons--mercantilism, insurance against shocks to their current and capital accounts, as an indicator of external solvency, and to use it to stabilize the exchange rate. While in the 1980s and 1990s countries held reserves mainly to defend the level of their exchange rate or to insure against shocks to the current account, insuring against capital account shocks has become a more important motive in the last two decades. Thus, it is perhaps unsurprising, that keeping up with the increased volume of capital flows, the average reserve holding in emerging markets increased sharply in the last four decades, from about 5 percent of GDP in the 1980s to 25 percent in 2010s (Ghosh, Ostry and Tsangarides, 2012b; Aizenman et al, 2013).

India too has accumulated reserves over time, Figure 11. During the early 1990s-early 2000s, the stock of reserves increased at a measured pace keeping with moderate volume of capital flows. It was followed by a sharp accretion of reserves during the period of rapid

> 14 Basu et al. (2015), and Joshi (2013) attribute the increase in current account deficit during this period to the loss of exchange rate competitiveness.

14
level in the population census, parameter estimates from this “first stage” model can be used to generate the distribution of _yh_ for any target population in the census conditional on its observed characteristics and, in turn, the conditional distribution of _W_ . Elbers et al (2002, 2003) study the precision of the resulting estimates of _W_ and demonstrate that prediction errors will fall (or at least not rise) with the number of households in the target population, and will also be affected by the properties of the first stage models, in particular the precision of parameter estimates. A general rule of thumb is that welfare estimates obtained on this basis will be estimated fairly precisely as long as the target population comprises at least 1,0005,000 households.<sup>7</sup> The first-stage estimation is carried out using household survey data.<sup>8</sup> The empirical models of household consumption allow for an intra-cluster correlation in the disturbances (see Elbers, Lanjouw and Lanjouw, 2002, 2003 for more details). Failing to take account of spatial correlation in the disturbances would result in underestimated standard errors in the final poverty estimates. Different models are estimated for each region and the specifications include census mean variables and other aggregate level variables in order to capture latent cluster-level effects. All regressions are estimated with household weights and with parsimonious specifications to be cautious about overfitting. Heteroskedasticity is also modeled in the household-specific part of the residual.

Simulation methods are applied to predict per-capita expenditure at the level of each household in the population census. Since predicted household-level per capita consumption in

> 7 The relationship between precision of the poverty map estimates and the size of the community is influenced to a large extent by the explanatory power of the ‘first stage’ regression models that underpins the idiosyncratic error associated with the poverty estimates. Experience in a variety of countries has shown that when R<sup>2</sup> ’s are around 0.6 or higher, that component of the overall error that varies with size of target population is effectively negligible when dealing with communities of 1000 households or more.  With lower explanatory power the minimum size of the target population in the census needs to be larger (see Elbers et al, 2002, 2003). 8 These surveys are stratified at the region or state level, as well as for rural and urban areas.  Within each region there are further levels of stratification, and also clustering.  At the final level, a small number of households (a cluster) are randomly selected from a census enumeration area.

6
|# (age 0-5, female)*|0.29|0.28|0.31|0.29|
|---|---|---|---|---|
||[0.06]***|[0.04]***|[0.06]***|[0.04]***|
|# (age 0-5, male)*|0.31|0.32|0.33|0.33|
||[0.06]***|[0.04]***|[0.06]***|[0.04]***|
|# (age 5-15, female)*|0.15|0.15|0.17|0.16|
||[0.05]**|[0.03]***|[0.05]**|[0.03]***|
|# (age 5-15, male)*|0.23|0.26|0.24|0.26|
||[0.05]***|[0.03]***|[0.05]***|[0.03]***|
|# (age 15-55, male)*|0.16|0.28|0.18|0.29|
||[0.06]**|[0.04]***|[0.06]**|[0.04]***|
|# (age, over 55, female)*|-0.1|-0.04|-0.11|-0.04|
||[0.10]|[0.06]|[0.10]|[0.06]|
|# (age, over 55, male)*|0.3|0.38|0.29|0.37|
||[0.11]**|[0.07]***|[0.12]*|[0.07]***|
|Sindh|3.12|3.1|3.69|3.33|
||[0.29]***|[0.20]***|[0.36]***|[0.23]***|
|NWFP|0.68|2.76|0.69|2.87|
||[0.09]**|[0.19]***|[0.10]*|[0.21]***|
|Balochistan|0.74|2.65|0.91|2.89|
||[0.13]|[0.23]***|[0.16]|[0.26]***|
|school distance|0.36|0.34|0.41|0.37|
||[0.11]**|[0.08]***|[0.13]**|[0.09]***|
|presence of girl school|0.02|0.02|0.02|0.02|
||[0.00]***|[0.01]***|[0.00]***|[0.01]***|
|presence of boy school|0.48|0.96|0.49|0.97|
||[0.16]*|[0.27]|[0.16]*|[0.27]|
|median wage for men*|-0.01|0|-0.01|0|
||[0.00]***|[0.00]***|[0.00]***|[0.00]***|
|% household having piped water|0.45|0.71|0.49|0.74|
||[0.09]***|[0.06]***|[0.10]***|[0.07]***|
|% households have pumped water|1.39|1.2|1.44|1.22|
||[0.18]*|[0.10]*|[0.19]**|[0.10]*|
|time fixed effect, 01/02|0.76|0.85|0.69|0.82|
||[0.07]**|[0.05]**|[0.06]***|[0.05]***|
|time fixed effect, 05/06|0.83|0.81|0.72|0.76|
||[0.08]*|[0.04]***|[0.07]***|[0.04]***|
|N|12308||123|08|

Note: 1. * continuous variables, where coefficients are presented. Otherwise, relative risk ratio (RRR) is reported. 2. capita is adjusted with adult equivalence.

31