rafmacalaba/datause-encoder-data huggingface dataset:
analysis based on Figure 3.IU suggests the following conclusions: (i) parents believe that the daughters have more academic ability, and (ii) the daughters enjoy a more favorable school environment. When we combine these with the evidence above that _θ_<sup>ˆ</sup> 1<sup>_d>θ_ˆ</sup> 1<sup>_s_,weget</sup> the conclusion that the parents must hold pure daughter preference. All these conclusions seem implausible given the accumulated evidence on gender bias against girls in India in the economics and sociology literature.

The discussion above suggests that the evidence on the functional form of the mobility equation, returns to education, and the investment equation taken together cannot reject the following joint null hypothesis: (i) complementarity between financial investment and father’s education is not important in urban India, i.e., _δ_ 5 = 0, (ii) parents systematically underestimate the ability of daughters, and (iii) the daughters face unfavorable school environment. In what follows, we confront the estimates from the mobility equation to check whether this set of null hypotheses is sufficient to explain the pattern of the inequalities in (15) above.

# **Learning about Gender Bias: The Intergenerational Mobility Equation**

We begin with the estimates of the quadratic coefficients. When interpreted in terms of the theoretical analysis of section (2.2) above, _ψ_<sup>ˆ</sup> 2<sup>_d_=</sup><sup>_ψ_ˆ</sup> 2<sup>_s_impliesthefollowing:</sup> From the estimates of the investment equation, we know that _θ_<sup>ˆ</sup> 1<sup>_d<θ_ˆ</sup> 1<sup>_s_,whichimplies</sup> that equation (19) cannot be satisfied without some form of bias against daughters that works through either _δ_ 2 and/or _δ_ 4. It is important to recognize that _δ_ 2 and _δ_ 4 are the parameters of the “true” production function, and thus do not depend on parental bias, but are affected by bias in the school. However, unfavorable school environment found earlier does not help satisfy equation (19). This follows from the observation that, starting at no bias, i.e., � _q_<sup>_d_</sup> = _q_<sup>_s_</sup> = _q_ �, a lower school quality for the girls � _q_<sup>_d_</sup> _< q_ � reduces the value ˆ 2 ˆ 2 of _δ_ 2<sup>_d_</sup> _θ_ 1<sup>_d_</sup> , while a higher school quality for the boys ( _q_<sup>_s_</sup> _> q_ ) increases _δ_ 2<sup>_s_</sup> _θ_ 1<sup>_s_</sup> . The � � � � upshot of the above discussion is that, to satisfy equation (19), we need gender bias in the form of _δ_ 4<sup>_d>δ_</sup> 4<sup>_s_,implyingthatthediminishingreturnstothedirecteffectoffather’s</sup> 22
- Jensen, Jesper, Thomas Rutherford and David Tarr (forthcoming), “The Impact of Liberalizing Barriers Against Foreign Direct Investment: the case of Russian accession to the WTO,” Review of Development Economics. Available at: <u>http://www.worldbank.org/russia-wto.</u>

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Maritime and Air Transportation Service Sectors in Russia.” Available at <u>http://www.worldbank.org/trade/russia-wto.</u>

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004b), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in Financial Services Sectors in Russia.” Available at <u>http://www.worldbank.org/trade/russia-wto.</u>

- Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs," _American Economic Review,_ 79:85-95.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), “Foreign Direct Investment in Services and the Domestic Market for Expertise,” _Canadian Journal of Economics._

- Marshall, J.N. (1988), _Services and Uneven Development_ , London: Oxford University Press.

- Matusz, Steven J, and David G. Tarr (2001), “Adjusting to Trade Policy Reform,” in A. Krueger (ed.), _Economic Policy Reform_ , Chicago: University of Chicago Press.

- McKee, D.L. (1988), _Growth, Development, and the Service Economy in the Third World_ , New York: Praeger Publishers.

- Romer, Paul. (1994),  “New Goods, Old Theory and the Welfare Costs of Trade Restrictions,” _Journal of Development Economics_ ,” 43(1), February, 5-38.

- Rutherford, Thomas F. and David Tarr (2003), "Regional Trading Arrangements for Chile: Do the Results Differ with a Dynamic Model?" _Integration and Trade,_ Vol. 7, Number 18, 117-139.

- Rutherford, Thomas F. and David Tarr (2002), "Trade Liberalization and Endogenous Growth in a Small Open Economy," _Journal of International Economics_ .

- Rutherford, Thomas F. (1999), "Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem:  An Overview of the Modeling Framework and Syntax", _Computational Economics_ .

- United Nations Conference on Trade and Development and World Bank (1994), _Liberalizing Trade in Services: A Handbook_ , New York and Geneva: United Nations.

- United Nations Conference on Trade and Development, Division on Transnational Corporations and Investment (1995 and 1996), World Investment Report 1995 and 1996, New York and Geneva: United Nations.

Vernon, R. (1960), _Metropolis 1985_ , Cambridge: Harvard University Press.

- Warren, Tony (2000), “The Impact on Output of Impediments  to Trade and Investment in Telecommunications Services,” in Findlay, Christopher and Tony Warren (eds), _Impediments to Trade in Services: Measurement and Policy Implications_ , (London: Routledge).

18
# **References**

<mark>Albagli, Pinjas and Rau, Tomás, (2019), The Effects of a Maternity Leave Reform on Children's Abilities and Maternal Outcomes in Chile,</mark> _<mark>Economic Journal</mark>_ <mark>, 129, issue 619, p. 1015-1047.</mark> Acevedo, P., Cruces, G., Gertler, P., and Martinez, S. (2017). ‘Living up to expectations: How job training made women better off and men worse off’. NBER Working Paper 23264. Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w23264 <mark>Amin, Mohammad, Islam, Asif and Sakhonchik, Alena, (2016), Does paternity leave matter for female employment in developing economies? Evidence from firm-level data,</mark> _<mark>Applied Economics Letters</mark>_ <mark>, 23, issue 16, p. 1145-1148.</mark> <mark>Aslim, Erkmen, Panovska, Irina and Taş, M. Anıl, (2021), Macroeconomic effects of maternity leave legislation in emerging economies,</mark> _<mark>Economic Modelling</mark>_ <mark>, 100 (C): 1-21.</mark> Baker, M. and K. Milligan (2008a). How Does Job-Protected Maternity Leave Affect Mothers’ Employment? Journal of Labor Economics 26 (4), 655–691.

<mark>Baum, Charles L. and Ruhm, Christopher, (2016), The Effects of Paid Family Leave in California on Labor Market Outcomes,</mark> _<mark>Journal of Policy Analysis and Management</mark>_ <mark>, 35, issue 2, p. 333-356</mark> <mark>Bennett, Benjamin, Erel, Isil, Stern, Léa H. and Wang, Zexi, (2020), Paid Leave Pays Off: The Effects of Paid Family Leave on Firm Performance, No 27788, NBER Working Papers, National Bureau of Economic Research.</mark> <mark>Berger, Lawrence M. and Waldfogel, Jane, (2004), Maternity leave and the employment of new mothers in the United States,</mark> _<mark>Journal of Population Economics</mark>_ <mark>, 17, issue 2, p. 331-349.</mark> <mark>Bertrand, Marianne, Sandra E Black, Sissel Jensen, Adriana Lleras-Muney (2019), Breaking the Glass Ceiling? The Effect of Board Quotas on Female Labour Market Outcomes in Norway,</mark> _<mark>The Review of Economic Studies</mark>_ <mark>, Volume 86 (1): 191–239. https://doi.org/10.1093/restud/rdy032</mark> <mark>Besamusca, Janna, Tijdens, Kea, Keune, Maarten and Steinmetz, Stephanie, (2015), Working Women Worldwide. Age Effects in Female Labor Force Participation in 117 Countries,</mark> _<mark>World Development</mark>_ <mark>, 74, issue C, p. 123-141.</mark> <mark>Bloom, D.E., Canning, D., Fink, G.</mark> _<mark>et al.</mark>_ <mark>Fertility, female labor force participation, and the demographic dividend.</mark> _<mark>J Econ Growth</mark>_ **<mark>14,</mark>** <mark>79–101 (2009).</mark> <u><mark>https://doi.org/10.1007/s10887-0099039-9</mark></u> Borrowman, Mary & Stephan Klasen (2020) Drivers of Gendered Sectoral and Occupational Segregation in Developing Countries, Feminist Economics, 26:2, 62-94, DOI: 10.1080/13545701.2019.1649708 Bossler, M., Mosthaf, A. and Schank, T. (2020). Are female managers more likely to hire more female managers? Evidence from Germany. Industrial and Labor Relations Review, 73, 190–222.

42
discretionary commuting trips are complementary to the generation of income through work. Discretionary travel is modeled as _derived demand_ , determined by the consumer’s disposable income, and by the full opportunity cost of a trip which is the monetary cost of the trip plus the value of the time it takes to make the trip. This approach contrasts with formulations in which the consumer is treated as if the miles themselves are the objects of consumption (see, for example, Parry and Timilsina, 2008). Our formulation allows a consumer to respond to an increase in a trip’s cost by making more trips to closer destinations and fewer trips to more remote ones. This substitution of destinations of different proximities (see Anas and Rhee (2006)), is not captured in the current application because the highly aggregated nature of the Beijing data we are using does not distinguish among spatially different destinations. Still, the microeconomic structure of our model allows capturing a rich list of substitution responses by the consumers facing the toll or tax. More precisely, there are five margins that are active in the model:

- (1) Switching one’s commute from the car to public transit or to the non-motorized modes of bicycling and walking;

- (2) Similarly switching the mode of one’s discretionary (i.e. shopping) trips away from the car;

- (3) Reducing the number of discretionary trips by all modes, since the toll or the tax reduces the disposable income and increases the cost of travel per trip;

- (4) Giving up one’s car to save the cost of operating a car, and also the annualized cost of car ownership;

- (5) Renting more housing if the substitution effect of the toll or tax (which raises the delivered cost of non-housing goods) dominates over the income effect.

The fifth marginal effect is reinforced by the general equilibrium pecuniary effect that the increase in travel cost per trip caused by the congestion toll or the tax on gasoline reduces disposable income and thus housing demand. Given a fixed housing stock and fixed population in the short run, the lowered demand lowers the rent per square meter of housing. This rent reduction causes more substitution in favor of housing, compounding what was already caused by the substitution effect of the gas tax or the congestion toll. Under both taxes, the drop in the rent on housing causes welfare gains for the two lowest income quintiles. The reason for this is that relatively few poor consumers travel by car to begin with. Hence, they get little grief from the increase in after-tax monetary travel costs but benefit from the pecuniary externality of the 5
**Figure 3. Share of direct, indirect, and foreign services value added in world gross exports, million USD**

<!-- Start of picture text --> 10,000<br>9,000<br>2,155<br>8,000<br>7,000<br>6,000<br>3,946<br>5,000<br>4,000<br>496<br>3,000<br>2,000<br>1,337 3,292<br>1,000<br>1,072<br>0<br>1995 2000 2005 2008 2009 2010 2011<br>Domestic direct Domestic Indirect Foreign<br><!-- End of picture text --> Source: Miroudot (2016) based on OECD-WTO TiVA 2015.

Figures 2 and 3 suggest that an increasing part of manufacturing exports corresponds to services value added and that a growing share of these “additional” services is being sourced abroad. Note, however, that since these measures are based on cross-border trade, value added from foreign services traded through commercial presence is counted in the “domestic” category, as previously mentioned. The apparent shift toward foreign services in the decomposition of services value added probably understates what actually happened.

# **_The share of services value added in exports varies significantly across countries_**

Decomposition of services value added in exports by country in 2011 supports the general observations that the share of services value added is high and that embodied services explain a large share of the total for all countries (figure 4). Countries differ, however, in their share of services value added in exports, ranging from 35% in Chile to close to 90% in Luxembourg. These differences reflect some specialization patterns: countries on the bottom of the figure specialize in exports of commodities (Chile, Norway) or manufactured goods (Republic of Korea, Mexico), while economies on the top are services exporters. Countries specialized in services also have more indirect (domestic and foreign) services value added in exports because services are mainly produced with other services.

10
Despite this, the SACs formed lacked basic governance rules. A presidential decree establishes that five members of any SAC should be selected during an open meeting with community organizations. Any resident in the community can become a member, except elected civil servants or those working directly in the activities executed by the public institution hosting the SAC. In addition, the creation and composition of the SACs should be notified to the Department for Prevention of Administrative Corruption (DPCA), thus preventing the establishment of long-lasting commissions which could lead to conflict of interest and capture. The implementation evaluation report reveals that the creation of SACs in some municipalities did not follow these rules. Many of the SAC members in the municipalities of the intervention are civil servants or CBO members with limited skills to monitor infrastructure projects or to analyze municipal budgets, hence showing low independence and little capacity within the SACs.

While there were oversight institutions, the GGM and the CJM, supervising the actions of SACs and working together with the CBOs _probably_ played a major role in ensuring that local governments provided information. As soon as the ATI project finished in November 2011, SACs in at least three out of seven municipalities faced severe problems to finance their operations, since participants could not afford paying for transportation. As a consequence, meetings and requests for information stopped after the completion of the ATI pilot project.

These challenges encountered during the pre-program and implementation phases of the program provide useful explanations for the observed impacts (or absence of impacts) of the ATI pilot program.

# _a. Indicators of impact_

Quantitative data were collected to investigate whether the ATI project had impacts on knowledge, access to information, civic participation, local governance, trust in public and non-public institutions and perception about main public services. These are believed to be important intermediate mechanisms for the information legislation to ultimately have positive results impacts on public transparency and accountability. Specifically, we investigate the ATI impacts on knowledge about the FOI law, existence of offices of access 10
estimates for gaps in growth rates using different weighting schemes are available in Appendix 2, Table A2.2.

The general pattern of a larger gap in growth in middle-income countries is reasonably consistent with the findings regarding the gap in levels. The gap in levels is increasing across the middle-income countries, suggesting that surveys also grow slower than national accounts means over this segment. For high-income countries, the point estimate suggests that growth rates of consumption in surveys is higher than that in national accounts, which contributes to explaining the narrowing gap seen for high-income countries (Figure 3). The narrowing of the gaps as countries get richer could be due to the increased integration of survey data with national accounts in richer countries, noted earlier. Notably, in US and Canada, this integration between survey and national accounts measures is not practiced to the same extent, and a larger and widening gap is seen in these countries.

In addition to the direction of bias (or gaps), we are often interested in the precision (efficiency) of growth rates of national accounts as a predictor of survey growth rates, given that national accounts data are frequently used to interpolate and extrapolate household survey consumption or income to obtain estimates of poverty in years surveys are not conducted, as done by the World Bank. The mean absolute error and the root mean square errors presented in Table 2 gives an assessment of the precision, which is relevant for using national accounts in predicting survey growth. Again, there is a clear economic gradient with the precision increasing with income, but it should be noted that high-income countries typically experience lower growth rates and the gradient of relative errors may be smaller. The mean absolute error is large, more than 2 percentage points for all sub-groups except for North America and high-income countries, highlighting the large average differences in growth rates observed between HHS and NAS data sources.

Ravallion (2003) uses an alternative way to assess the degree of correspondence of growth rates, using a simple no-constant OLS regression of survey means on national account means. Table 3 presents results using this approach. The resulting coefficients on the national accounts mean have typically been used when extrapolating income and consumption distributions for the World Bank’s poverty projections beyond the latest official reference year, 19
same survey round, but not linked to the same households), The abbreviated version tends to give lower consumption levels and hence higher poverty measures.

The rural sample includes about 61,000 households, of which the vast majority (96.5%) have at least one able-bodied adult. We base our analysis on the sample of 178,000 adults (15 to 59 years) from the 15 major states.<sup>10</sup> The analysis is done at the level of the NSS region, and all results are then aggregated up with appropriate weights. The set of beneficiaries from an EGS is assumed to include all adults who are likely to gain from an increase in labor market wages, either directly (as EGS participants) or indirectly due to an increase in wages paid in the market as a whole. The beneficiaries need not be currently in the labor force.

Amongst potential beneficiaries, roughly 65% participated in the labor force, 24% were employed as casual wage labor (either on public works or private) and 8% were unemployed for at least part of the time during the week preceding the survey. In terms of time allocation, on average, a person spent four days of the week in the labor force. The time spent in the labor force is much higher for males who spent 5.5 days on average employed and an additional half day seeking employment in the week preceding the survey. The survey gives daily wages earned by those employed as either casual or salaried labor during the week. The average wage earned by casual laborers in the sample is about Rs 40 per day.

There are two obvious ways one might define a “living wage” in rural India. The first is anchor the EGS wage rate to the official poverty line. At the average household size of 5 persons and average number of adults in the labor force of 2, a daily wage rate per working adult of Rs 38 would be sufficient to reach the mean official poverty line in 1999-2000. Using instead

> 10 These comprise Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal.

12
# **Figure 4a and 4b Transport Costs from Selected Cities to a European Port**

<!-- Start of picture text --> Transport costs from selected cities to Rotterdam (standard container,<br>textiles). Source: Maersk<br>8,000<br>Additional charges<br>7,000 Inland Haulage Import, USD<br>6,000 Basic Ocean Freight, USD<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>City<br>Transport costs from selected cities to Algeciras, Spain (standard container,<br>textiles). Source: Maersk<br>9,000<br>Additional charges<br>8,000<br>Inland Haulage Import, USD<br>7,000<br>Basic Ocean Freight, USD<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>City<br>Gaborone,BWAAbidjan, CIVNairobi, KENMaseru, LSOBamako, MLIMaputo, MOZDakar, SENLome, TGODar es Salaam, TZAKampala, UGACapetown, ZFADurban, ZFAPort Louis, MUSNew Delhi, INDIzmir, TURBeirut, LBNSantos, BRABuenos Aires, ARGAsuncion, PRYMontevideo, URY<br>Gaborone,BWAAbidjan, CIVNairobi, KENMaseru, LSOBamako, MLIMaputo, MOZDakar, SENDar es Salaam, TZAKampala, UGACapetown, ZFADurban, ZFAPort Louis, MUSNew Delhi, INDIzmir, TURBeirut, LBNSantos, BRABuenos Aires, ARGAsuncion, PRYMontevideo, URY<br>Costs, USD<br>Costs, USD<br><!-- End of picture text --> Source: Maersk. Transport costs corresponding to July 2008, see: http://www.maersk.com/en/Pages/Welcome.aspx Recent research also identifies poor infrastructure as a significant barrier to trade expansion (e.g., Limao and Venables, 2000). Buys, Deichmann, and Wheeler (2006) investigate the potential trade benefits of investing in upgrading and maintaining a transAfrican highway network. The proposed network links 83 major cities at a length of about 100,000 km, and the estimated benefits are found to be significant. Buys, Deichmann, and Wheeler find that intra-African trade, as a whole, can be expected to increase from 10 billion to about 30 billion U.S. dollars per year, while initial investments and annual maintenance costs would be relatively moderate over the course of the investment cycle. For instance, an upgrade of the road from Bangui in the Central 14
# Appendix IV.A Brazil

# Percentage of Demographic Group in Poverty or Absolute Poverty

|Demographic|Percent in|Percent in|
|---|---|---|
|Group|Poverty|Absolute Poverty|
|Young (14 or under)|31.8|20.8|
|Prime Age 1 (20-25)|19.4|10.9|
|Prime Age 2 (26-35)|18.4|10.9|
|Prime Age 3 (36-60)|20.2|12.1|
|Over 60 Total|24.2|12.7|
|Female|23.5|12.2|
|Male|25.0|13.3|
|Over 75 Total|28.6|13.9|
|Female|26.9|13.1|
|Male|31.2|15.2|

0 Poverty _-<_ 50< median income per family member; Absolute Poverty - < 35% median income per family member. Source: Calculated using househod survey data explained in Psacharopoulos: 'Poverty and Income Distribution in Latin America: The Story of Lhe 1980s.'

# Appendix IV.B Chile

# Percentage of Demographic Group in Poverty or Absolute Poverty

|Demographic<br>Group|Percent in<br>Poverty|Percent in<br>Absolute Poverty|
|---|---|---|
|Young (14 or under)|18.3|7.6|
|Prime Age 1 (20.25)|11.8|4.8|
|Prime Age 2 (26-35)|11.5|4.2|
|Prime Age 3 (36-60)|12.8|5.2|
|Over 60 Total|13|4.8|
|Female|13.4|5|
|Male|12.7|4.6|
|Over 75 Total|12.8|4.4|
|Female|12.5|4.2|
|Malc|13.2|4.7|

Poverty - < 50%/ median income per family member; Absolute Poverty - < 35% median income per family member. Source: See table Appendix IV.A 119