rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 5. Effect of displacement and gender norms around women’s mobility</u>

|||Attit|udes||||Beha|viors||
|---|---|---|---|---|---|---|---|---|---|
|||D rest|mobility||||Say|visits||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Displaced|0.05*|0.04*|0.03|0.04|0.05|0.06**|0.06**|0.06***|0.04|
||(0.03)|(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.02)|(0.03)|
|**Household characteristics**||||||||||
|Children under 5||-0.00|-0.00|0.00|-0.05*||0.06***|0.05**|0.03|
|||(0.02)|(0.02)|(0.02)|(0.03)||(0.02)|(0.02)|(0.03)|
|Female headed||-0.02|-0.00|-0.02|-0.03||-0.04*|-0.04*|-0.06**|
|||(0.02)|(0.02)|(0.02)|(0.03)||(0.02)|(0.02)|(0.03)|
|**Wealth quintile**||||||||||
|Poor||0.05|0.04|0.02|0.03||0.06*|0.07**|0.08**|
|||(0.03)|(0.03)|(0.03)|(0.04)||(0.03)|(0.03)|(0.04)|
|Middle||0.06*|0.04|0.03|0.04||0.04|0.06*|0.05|
|||(0.04)|(0.04)|(0.04)|(0.04)||(0.04)|(0.04)|(0.05)|
|Rich||0.11***|0.09***|0.09**|0.07||0.05|0.08**|0.06|
|||(0.04)|(0.04)|(0.04)|(0.05)||(0.04)|(0.04)|(0.05)|
|Richest||0.13***|0.10***|0.10***|0.09**||0.01|0.04|0.03|
|||(0.03)|(0.03)|(0.04)|(0.04)||(0.04)|(0.04)|(0.05)|
|**Context**||||||||||
|Reference network|||0.20***|0.14**|0.06|||0.27***|0.38***|
||||(0.07)|(0.07)|(0.09)|||(0.08)|(0.10)|
|Royalties (ln)|||||||||-0.00|
||||||||||(0.00)|
|Social investment (% exp)|||||||||0.00<br>(0.00)|
|Constant|0.72***|0.67***|0.54***|0.62***|1.21***|0.69***|0.63***|0.42***|<br>0.32|
||(0.01)|(0.03)|(0.06)|(0.06)|(0.36)|(0.01)|(0.03)|(0.06)|(0.41)|
|Observations|15,462|15,508|15,464|15,464|9,008|15,462|15,508|15,500|9,032|
|R-squared|4,195|4,196|4,152|4,152|2,358|4,195|4,196|4,188|2,382|
|Department FE|No|No|No|Yes|Yes|No|No|Yes|Yes|
|Yeardummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Source:_ Author based on DHS 2000/2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Reference group for wealth quintile is poorest.

22
# 2.2 _Explanatory variables_

Our main explanatory variable is the number of adult non-workers per household in the city. The variable equals total number of adult non-workers in the city divided by the total number of households in the city ( _Non workers_ ). We use (lagged) 1991 values of the variable taken from Census of India (1991).<sup>8</sup> Shopping for many products such as grocery items is typically done for the entire household which motivates our preference for non-workers per household over, for example, non-workers as a proportion of city population.<sup>9</sup> We follow the Census definition of adults and household. The former include all agents above seven years of age and the latter is defined as the set of individuals living in a common house and sharing a common kitchen.<sup>10</sup> For the cities in our sample, _Non workers_ varies between 2 and 3.9 with a mean value of 2.9 and a standard deviation of 0.38.

Reverse causality from the level of competition to non-workers is unlikely given that the latter is lagged by 15 years. A relatively more serious problem with our results could be a bias due to omitted variables.

It is natural to expect some correlation between non-workers and income levels of households and overall development of cities. This is both, an advantage and a

> 8 Data on non-workers is available every ten years with the latest year being 2001. Our results do not change much if we use 2001 values of the variable. The correlation coefficient between 1991 and 2001 values of non-workers per household is .875.

> 9 For the cities in our sample, the correlation coefficient between _Non workers_ as defined above and nonworkers as a proportion of total city population equals .768. The high correlation between the two variables makes it difficult to infer whether the distribution of non-workers across households within cities (as opposed to the distribution of non-workers across cities) matters for the level of competition. However, in some specifications where our results are particularly strong such as the sample of traditional stores (selling grocery items), the effect of Non workers on competition remains significant even if we control for the proportion of non-workers in total city population.

> 10 Data on non-workers by other age groups are not available.

7
<!-- Start of picture text --> and the use of instrumental variables based on a simple political economy framework. They are<br>consistent with a view of the world in which product standards impose fixed costs of adaptation, of<br>which harmonization tends to reduce the multiplicity.<br>These are significant findings from a development point of view, given the importance of the<br>textiles, clothing, and footwear sectors to economies in the early stages of industrialization. Based<br>on a heterogeneous firms framework, these results would tend to suggest that harmonization can be<br>an effective way of promoting foreign market access for firms with lower productivity than incum-<br>bent exporters, since it induces an upwards shift in the export marginal cost cutoff. International<br>harmonization could therefore be expected to encourage exports by small and medium enterprises<br>in developing countries—a prediction that future work using firm level data could test. Importing<br>countries looking to provide an impulsion to non-traditional exports from developing countries<br>could perhaps use international standards harmonization as a complement to more generous tariff<br>preferences and more open rules of origin.<br>An alternative way of interpreting the results presented here is in terms of export diversifica-<br>tion, an important policy issue for many developing countries. By equating variety growth and<br>diversification, a case can be made that international standards harmonization could be one way<br>in which the large, rich country import markets could help support export diversification in de-<br>veloping countries. These results therefore complement recent work on diversification, which has<br>highlighted the importance of policies such as trade facilitation and the rationalization of barriers<br>to domestic market entry within developing countries (Dennis and Shepherd, 2007).<br>The main obstacle to future empirical work in the area of product standards and their trade<br>effects remains limited data availability. While the World Bank’s EU Standards Database provides<br>information on the textiles, clothing, and footwear sectors, there is clearly a need to expand on<br>this. One direction for possible expansion would be the addition of further countries, including<br>data on national standards in EU-15 member states. At the current time, the data do not permit<br>an assessment of the impact of product standard harmonization on geographical export diversifica-<br>tion; however, the relatively weak (but statistically significant) elasticity found here suggests that<br>24<br><!-- End of picture text -->
**Stage 4: Decoupling payment systems** . This includes digital wallets where payment is initiated in a connected device, and the money remains in a bank or prepaid account.

**Stage 5: Decoupling currency account** . This includes digital wallets where money is converted into a private or local currency available only on the device or application.

<u>Source: Adapted from Natarajan (2019)</u>

# **3. Empirical strategy**

As mentioned, the empirics are limited by the lack of historical data about the incidence of access to bank accounts, use of cashless payments and mobile money. Consequently, it is virtually impossible to analyze the movement between stages over time. In addition, data for stages 4 and 5 can be hard to come by. For the first three stages of payments, we rely on pooled crosssectional data from two years of the Findex data, 2014 and 2017. Importantly, we work with a common sample of country observations for the three estimations, which allows for comparisons of the key estimated coefficients across the three models.

The main estimation equation is: 1 ∗log�G G (1) cc,tt cc,tt tt cc,tt YY = CC+ ββ cc �+ νν + εε The variable represents the incidence of one of the three indicators of payments adoption and is observed for country in year with being either 2014 or 2017. is the intercept term, which YY reflects payment systems adoption when GDP per capita ( G G is equal to 0. We also include cc tt tt CC as a dummy variable taking the value of 1 for the latest year in the survey, namely 2017. cc) tt νν We estimate this model for each indicator in order to assess the different stages of payment systems – see the data section below for the descriptions of the variables. Regarding inference, YY a negative sign on 1 would indicate a pro-poor bias of payments adoption in each stage of the payments system, thus we interpret such evidence as indicating absolute leapfrogging. In ββ addition, a comparison between the three stages would indicate that there is leapfrogging if more recent payment technologies see a higher adoption rate at low levels of development than, say, the incidence of bank accounts. This is estimated by looking at the intercept level or by estimating fitted values when G G is at its minimum and 20<sup>th</sup> percentile value. cc,tt YY<sup>�</sup> cc Equation (1) presumes that the functional form is log-linear, but it is not guaranteed, and it could also be non-linear. Choosing the best-fitting functional form might be critical for precise comparisons of the magnitudes of the intercept coefficients across the three models. Hence, we explore this possibility by estimating quadratic functional forms: 5
Nonetheless, most municipalities need assistance to develop their capacity for planning and execution of programs. Figure 5 shows per capita transfer to municipalities as of 2001, and a projection of own revenues based on actual numbers collected by INEGI for 1999. Within each state there are also large variations in per capita allocations to municipalities.<sup>6</sup> The largest municipalities have the property tax base, from which they can and occasionally do raise significant revenue, but the overall effect has been that their tax effort has declined since the municipal transfers were increased in Ramo 33 after 1997.

The municipal governments urgently need more capacity and time to plan for good use of the resources they receive. With mayors elected every three years and no chance for re-election, municipal governments lack the opportunity and incentive to do much that is meaningful —the same problem as for federal congressional deputies. Allowing re-election of mayors would benefit the cities greatly, enhancing the accountability of the mayors to the local populace and giving them time to plan something and, if the people like it and re-elect them, to implement it. A phase-in schedule similar to that for congressional deputies would make sense.<sup>7</sup>

# _Figure 5. Municipal Government Resources (2002)_

<!-- Start of picture text --> 1,800<br>1,600<br>1,400<br>1,200<br>1,000 Aportaciones<br>Participaciones<br>800 Own Revenues<br>600<br>400<br>200<br>0<br>MxP per capita<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Weighted Average<br><!-- End of picture text --> _Source_ : SHCP and Bank staff calculations.

# **III. Bargaining Packages**

For both economic rationality and political feasibility, it makes sense to think of packaging the reforms and bargaining along two main tracks—one concerning tax assignments, revenue sharing and tax administration, and another concerning the conjunction of earmarked transfers and accountability for service provision. Not coincidentally, the two main

> 6 Appendix C shows some data and analysis of municipal transfers in two of the southern states.

> 7 For instance, those elected in 2003 (or 2006) could be given the opportunity to run for a second term, and new deputies elected in say 2009 and thereafter could be allowed a maximum of three terms.  Experience in other countries suggests the benefit of having some term limits.

9
> - 31 -

billion, 192 percent of total bank lending to residents in 1982 and slightly above total commercial bank assets.

FIGURE 7: DOMESTIC LIQUIDITY (In billions of Kuwaiti dinars) <!-- Start of picture text --> 6 -<br>4<br>I<br>3/79 5/80 3/81 3/82 5/83 3/84 3/85 3/86 3/87<br>0 MONEY + 0-MONEY 0 UOUIDITY a LENDING<br>Source: International Financial Statistics, various editions.<br><!-- End of picture text --> While the increase in domestic liquidity was the main impetus behind the initial increases in asset prices, postdated cheques took over as the driving force as higher and higher premiums were offered. The external shocks, the termination of future trading, and the subsequent restrictions in bank lending all contributed to the rapid decline in asset prices.

_The Role of Regulatory Instltutlons._ From 1979 to 1982 the CBK took a number of measures that ultimately had little effect on bank lending AQ/ and may have contributed to increasing domestic liquidity, including the following: jQ/ Central Bank of Kuwait. op.cit. p. 41.
Table A.4: Descriptive statistics on wedding expenditure from the 2005 India Human Development Survey

|||||**P**|**anel A: **|**By caste**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||||Urban|||||Rural|||
||Brahmin|OBC|SC|ST|Others|Brahmin|OBC|SC|ST|Others|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Expenditure by bride’s family|186,683|120,818|96,038|74,689|165,710|140,575|89,973|64,194|35,007|120,038|
|Expenditure by groom’s family|128,166|74,673|63,616|58,691|108,188|92,365|56,380|43,539|29,176|74,162|
|Diference|58,617|46,145|32,423|15,997|57,522|48,210|33,589|20,655|5,835|45,875|
|N|1,313|5,452|2,322<br>**P**|499<br>**anel B: B**|4,956<br>**y religio**|1,108<br>**n**|10,834|6,011|2,939|6,118|
|||Ur|ban|||Ru|ral||||
||Hindu|Muslim|Christian|Sikh|Hindu|Muslim|Christian|Sikh|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|||
|Expenditure by bride’s family|137,992|119,959|143,553|194,341|84,014|100,849|103,211|161,513|||
|Expenditure by groom’s family|90,649|76,515|66,274|132,988|55,309|57,894|48,767|107,471|||
|Diference|47,350|43,444|77,278|61,353|28,706|42,956|54,443|54,042|||
|N|11,286|2,215|514|258|22,239|2,573|862|732|||

NOTES: This table provides means of wedding expenditure in the 2005 India Human Development Survey (IHDS). The survey asks “At the time of the marriage in your community ( _jati_ ) for a family like yours, how much money is usually spent by the girl(boy)’s family?” The IHDS data set has five broad social groups: (1) Brahmin (2) OBC (3) SC (4) ST (5) Others.
Fredrikeen, Birger. "Increased Foreign<sup>Aid for Primary Education: The Challenge</sup> for Donors." PHREE/90/30, Washington, D.C.:<sup>World Bank, 1990.</sup>

- Haddad, Wadi. "Educational and Bconomic<sup>Effects of Promotion and Repetition</sup> Practices." Staff Working Paper, no. 317,<sup>Washington, D.C.: World Bank,</sup> 1979.

- . "Teacher Trainings A Review of world Bank<sup>Experience." EDT Discussion</sup> Paper, no. 21, Washington, D.C.s World Bank,<sup>1985.</sup>

- Hawkridge, David. "General OperatLonal Review of **Distance** EducatLon." EDT Discuseion Paper, no. 68, Washington, D.C.:<sup>World Bank, 1987.</sup>

- Johanson, Richard. "Sector Lending in Education." ZDT Discuseion<sup>Paper, no.</sup> 18, Washington, D.C.: World Bank, 1985.

- Lockheed, Marlalne, John Middleton and<sup>**Greta**Nettleton. "Educational Technology:</sup> Sustainable and Effective Use." PHREE/91/32,<sup>Washington, D.C.: World Bank,</sup> 1991.

- , and B. Hanushok. "Improving Educational<sup>Efficiency in Developing</sup> Countries: What Do We Know?" Compare 18,<sup>no. 1: 21-36.</sup>

- and Adriaan Verapoor. "Improving Primary<sup>Education ln Developing</sup>

- Countries: A Review of Policy Options." Draft Prepared for the World Conference on EducatLon for All, Jomtien, Thailand, March 5-9, Washington, D.C.: World Bank, 1990.

- _ , and Alastair Rodd. "Lending by the<sup>World Bank for EducatLon Research."</sup> PRE Working Paper, no. 583, Washington, D.<sup>C.: World Bank, 1991.</sup>

- Middleton, John. "Management in World Bank Zducation Projectss<sup>Analysis of</sup> Experience." EDT Discussion Paper, no. 42, WashLngton,<sup>D.C.: World Bank,</sup> 1986.

- , James Terry, and Deborah Bloch. "BuildLng<sup>BducatLonal Evaluation Capacity</sup> in Developing Countries." PPR Worklng<sup>Paper, no. 140, Washington, D.C.:</sup> World Bank, 1989.

- Moor, Abdun. "Strengthening ZducatLon<sup>Management: A Review of World Bank</sup> Assistance, 1963-83." Washington, D.C.s World<sup>Bank, 1985.</sup>

- Paxman, B., C. Denning, and A. Read. "Analysis of **Research** on Textbook As- ilablilty and Quallty ln Developing Countrles."<sup>PHR33/89/20, Washington,</sup> D.C.s World Bank, 1989.

- Romain, Ralph. "Lending _ln_ Primary EducatLons Bank Performance Review,<sup>1962-</sup> 83." ZDT DLicussion Paper, no. 20, WashLngton,<sup>D.C.: World Bank, 1985.</sup>

41
skills, and health a country’s population accumulates over their life-cycles. In addition, we explore how skill-mobility (proxied by a country’s net migration-to-population ratio) impacts on country GVC participation. The following subsections discuss each of these three skill indicators and how they are constructed.

# **_a. Skilled and unskilled labor content in exports_**

Skilled labor data are very difficult to obtain especially for African countries. Even data on the education level of workers, as a proxy for skill-level, are not available for most SSA countries. We therefore generate skilled and unskilled labor content in exports (LACEX) using data from GTAP’s Input-Output (IO) tables and a methodology developed by Cali et al. (2016). Based on the International Labor Organization’s (ILO) classification, skilled labor comprises professional workers who consist of managers and administrators (including far managers), professionals, and para-professionals. Unskilled workers (production workers) category consists of tradespersons, clerks, salespersons, personal service workers, plant and machine operators and drivers, laborers and related workers, and farm workers (Liu et al. 1998; Vo and Tyers 1996; Dimaranan and Narayanan, 2009 and 2014). Since there is no complete global data set with disaggregate employment by worker type, the GTAP team initially used labor force surveys and national censuses of 15 different economies where data are available to develop a statistical model to explain labor payment shares in the sample regions. In a second stage, they use the results to predict labor payment shares in 37 aggregated sectors for unobserved regions. The authors subsequently mapped individual country data to GTAP concordance to address inconsistencies in occupational classifications used by different governments. The GTAP approach in splitting total labor payment into payment to skilled and unskilled labor has been used in several GTAP IO database version (GTAP 1-7) which are used in this analysis. Liu et al. (1998) provide a detailed description of the methodology used in GTAP to disaggregate labor payments by skills level.

The IO tables in the GTAP data set allow one to exploit a form of social-accounting matrix (SAM) where incomes are shown in the rows of the SAM while expenditures are shown in the columns (Hertel, 2013 and McDougall, 2001 cited in Cali et al. 2016). The structure of the data is based on a general equilibrium principle – every income has a corresponding expenditure. The advantage of using this framework is that it provides a comprehensive record of the interrelationships of an economy, including intermediate and final demand linkages (Cali et al. 2016). This database 11
14

# Table I

|UNEMPL|OYMENT RATES IN URBA|N AREAS BY SEX 198|0-1991|
|---|---|---|---|
||TOTAL|MEN|WOMI cN|
|1980|4.68|3.83|5.90|
|1981|4.20|3.53|5.58|
|1982|4.21|3.87|4.94|
|1983|6.06|5.33|7.56|
|1984|5.57|4.85|7.03|
|1985|4.36|3.63|5.76|
|1986|4.29|3.74|5.34|
|1987|3.88|3.42|4.77|
|1988|3.54|3.03|4.51|
|1989|2.92|2.59|3.55|
|1990|2.81|2.63|3.04|
|1991<br>Jan-Jun|2.55|2.47|2.68|
|Jan-Oct|2.75|2.62|2.97|
**Figure 9: Currency composition of central government debt stock (December 2016)**

<!-- Start of picture text --> JPY Other<br>SDR 2.60% 0.20%<br>0.60%<br>EUR<br>9.80%<br>USD<br>25.20%<br>TL<br>61.60%<br><!-- End of picture text --> Source: Public Debt Management Report, Undersecretariat of Treasury, January 2017 Since a significant part of the country’s economic activity is linked to Europe, the Turkish Treasury finds it convenient to have a portion of the government FX debt denominated in EUR.<sup>25</sup> Should the Turkish lira depreciate (appreciate) versus the EUR, Turkish exports are likely to increase (decrease), boosting (knocking) government revenues in a manner that mitigates the foreign currency exposure of the government debt.

Turkey has also found that the EUR offers an avenue to reach real money accounts in Germany, Austria, and other countries. These institutional investors do not usually participate in the market in USD and are more selective, given the number of high-credit issuers in the region.<sup>26</sup> Turkey also sees a spillover effect when accessing the Euro market as it attracts part of the investors from the USD market, making the placements in USD easier to execute and more attractive (scarcity value).

> 25 It should be noticed that the international bond program is not the only tool Turkey uses for achieving its benchmark; it also strategically employs project financing in EUR having in mind the total portfolio exposure. 26 In this context, having an investment grade is very helpful to be able to regularly access this market.

17