rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

India has a poor track record on gender equality, even among lower-middle income countries. According to the UNDP’s 2013 Gender Inequality Index, India ranks 136<sup>th</sup> of 186 countries; as per World Economic Forum’s 2012 Global Gender Gap Index, it ranks 105<sup>th</sup> of 135 countries. An important contributor to the low status of women in India is their low level of participation in the labor force: for instance, the “economic participation and opportunity” subcomponent of the Gender Gap Index ranks India 123<sup>rd</sup> of 135 countries (see Appendix Figure 1).

This is worrisome on two counts: economic empowerment is an important aspect of women’s bargaining power both within the household and in society at large, and female labor force participation (LFP) rates in India have been showing a declining trend in recent years. According to the ILO’s Global Employment Trends 2013 report, LFP for women in India has gone down from 37 percent in 2004–2005 to 29 percent in 2009–2010. These figures are much lower than the average rate of female LFP across countries, which is around 60% (Bhalla and Kaur 2011). The downward trend in India is especially striking given the huge expansion of economic opportunities in India since the liberalization of the economy in 1991.

There are at least two reasons to care about gender equality. One is for its own sake. The other is that gender equality can benefit economic development. While there is some support for the view that economic development may itself promote gender equality, evidence from various countries suggests that this effect is not strong enough (Duflo 2012). The figures cited above provide evidence for the case of India. Therefore, continuous, creative, and pro-active policy commitment to equality, and specifically to increasing economic opportunities for women, may be needed to promote gender equality. In this paper, we investigate the role of a somewhat unconventional policy tool – political empowerment for women – in enhancing economic opportunity. We evaluate the effect of political empowerment of women on female LFP using the context of a natural experiment: the phased implementation of a 1993 reform mandating political representation (PR) for women in local government.

In what way(s) may political empowerment of women affect their economic opportunities and LFP? We believe that there are two broad, distinct channels through which this can happen. The first is through direct authority that female political representatives (PRs) and leaders may have 2
# **Table A.1. First stage instrumental variables regressions using migrants’ characteristics**

This table shows the results of the first stage regressions of the instrumental variables estimations using migrants’ characteristics as instruments. The instruments used are _Average time abroad, Average time abroad_<sup>_2_</sup> _,_ and _Kinship relationship_ . In columns (1)-(3), the dependent variable is a dummy for whether the household received remittances. In columns (4)-(6), the dependent variable is the per capita amount of remittances received from overseas. Columns (1) and (4) are the first stage estimations for the likelihood that the household has a deposit account. Columns (2) and (5) are the first stage regressions for the likelihood of receiving a loan, while columns (3) and (6) shows the first stage estimations for the likelihood of applying for a loan. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables|Deposit<br>account|Loan<br>received|Loan<br>requested|Deposit<br>account|Loan<br>received|Loan<br>requested|
|---|---|---|---|---|---|---|
||Dependent<br>household r<br>(1)|variable: dum<br>eceives remitt<br>(2)|my equal 1 if<br>ances<br>(3)|Dependent<br>amount of r<br>(4)|Variable: per<br>emittances rec<br>(5)|capita<br>eived<br>(6)|
|Average time abroad|0.035|0.034|0.034|0.066|0.069|0.066|
||[3.27]***|[3.15]***|[3.21]***|[2.16]**|[2.24]**|[2.14]**|
|Average time abroad<sup>2</sup>|-0.002|-0.002|-0.002|-0.004|-0.004|-0.004|
||[-2.93]***|[-2.84]***|[-2.88]***|[-2.21]**|[-2.34]**|[-2.20]**|
|Kinship relationship|0.367|0.368|0.367|0.691|0.688|0.693|
||[19.08]***|[18.91]***|[18.94]***|[10.73]***|[10.58]***|[10.66]***|
|Real per capita income|-0.003|-0.004|-0.004|-0.004|-0.004|-0.004|
||[-1.50]|[-1.60]|[-1.57]|[-0.47]|[-0.48]|[-0.50]|
|House and land ownership|0.019|0.020|0.019|0.033|0.034|0.035|
||[1.85]*|[1.93]*|[1.88]*|[0.95]|[0.95]|[1.00]|
|Adults average education|0.001|0.001|0.001|0.011|0.012|0.011|
||[0.24]|[0.42]|[0.32]|[1.36]|[1.45]|[1.37]|
|Agriculture|-0.012|-0.009|-0.010|-0.008|-0.006|-0.006|
||[-0.84]|[-0.68]|[-0.71]|[-0.17]|[-0.13]|[-0.13]|
|Electricity|0.024|0.025|0.024|0.130|0.127|0.129|
||[2.10]**|[2.16]**|[2.11]**|[3.65]***|[3.54]***|[3.60]***|
|Returned migrant|0.237|0.240|0.237|0.542|0.550|0.541|
||[5.11]***|[5.15]***|[5.12]***|[3.38]***|[3.39]***|[3.37]***|
|Constant|-0.018|-0.004|0.005|-0.136|-0.193|-0.139|
||[-0.76]|[-0.13]|[0.19]|[-1.53]|[-2.22]**|[-1.55]|
|Observations|2122|2097|2103|2098|2073|2079|
|R<sup>2</sup>|0.706|0.705|0.705|0.398|0.397|0.397|
|Cragg-Donald F-stat|678.9|662.6|668.9|113.7|111.9|112.9|

33
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_Table 8: Disaggregated trade flows between country 1 and country2 in sector GTAP_1_

|**Origin**|**Destination**|**GTAP Sector**|**HS Category**|**Τ**|**Trade Flow**|
|---|---|---|---|---|---|
|Country 1|Country 2|GTAP_1|HS_1|τ<sup>HS1</sup>|5|
|Country 1|Country 2|GTAP_1|HS_2|τ<sup>HS2</sup>|20|
|Country 1|Country 2|GTAP_1|HS_3|τ<sup>HS3</sup>|10|
|Country 2|Country 1|GTAP_1|HS_1|τ<sup>HS1</sup>|30|
|Country 2|Country 1|GTAP_1|HS_2|τ<sup>HS2</sup>|10|
|Country 2|Country 1|GTAP_1|HS_3|τ<sup>HS3</sup>|5|

For each directed pair of countries, we then build the “value of time” associated with the trade of GTAP_1 from the origin to the destination, by taking the trade‐weighted average of the underlying HS estimates from Hummels and Schaur (2013). Using the numerical example above, the value from country 1 to country 2 and from country 2 to country 1 are then computed as: To build the appropriate trade‐related weights, we use UN Comtrade data on trade flows which we aggregate at the 4‐digit or 2‐digit levels to construct sector and origin‐destination specific weights for the aggregation.<sup>15</sup> One particular issue when building the aggregation is the treatment of missing values. While Hummels and Schaur (2013) provides “value of time” estimates for many sectors at either HS2 or HS4 levels, the data also contains many missing observations. Since the US imports only a subset of all HS categories, it is not surprising that data are missing for many HS categories. As discussed in Minor and Hummels (2013), the treatment of those missing values is an important component of the aggregated results.

One solution could simply be to discard, in the aggregation, HS sectors for which we do not have estimates of the value of time. Doing so yields the possible issue that GTAP aggregated values might be computed based on an arbitrarily small fraction of actual trade flows. This, in turn, can then bias aggregated values if the only HS4 sectors for which we have non‐missing data for the cost of time feature an abnormally high

> 15 Regarding which level of aggregation is the most useful in our case, our preference goes with the HS2 level estimates. HS4 level results present the advantage to cover more disaggregated sectors which can be important in some applications, the associated estimates are more likely suffer from a low number of observation which makes the results more sensitive to measurement errors and outliers. Estimates at the HS2 levels pool together sectors that might not be homogenous, but are based on a higher number of observations within each category and, hence, feature more realistic values.

21
28

# Appendix 1:

# Definition and Data Sources

The basic sources for the data used in this study are International Financial Statistics (IFS) published by the International Monetary Fund and the Statistical Appendix of the World Bank Country Study :"Argentina : Reforms for Price Stability and Growth" (WB). These sources was augmented, when necessary, by data from the Central Bank of Argentina (BCRA), the "Fundaccion Meditarranea" (FM), FIEL and Dornbusch and de Pablo's study : "Debt and Macroeconomic Instability in Argentina" (DP). All real variables have been deflated by the WPI index (1970-100).

We present in Table 6 the complete model used for Argentina and the definitions of the variables used in this paper with the source of data where relevant. Note, as described in section 2.1, that there are some differences between the general framework proposed in section 1 and the model estimated for Argentina. In particular, the private sector does not hold public bonds and the interest rate on public bonds is implicitely determined through the bank deposits markets. These both assumptions are explained in details in appendix 2. Also we prefered to estimate the inflation rate (equation 15') directly rather than estimate the demand for money (equation 15). Finally, discrepancies in the data prevailed to apply to Argentina a consistency approach for the public and the financial sector budget constraints.

# Table 6 :

# The Complete Model aBnlied to Argentina and Definition of Variables

# Behavioral Eauations

Private Consumotion CP- c10 +<sup>**6**</sup> **llYd** + 12r + 113im + 1 1 5 (eD$/P) + 1 1 6 (pfyP) + 1 2 7 (eOD$p/P) + 118cp-1 Inflation **ir** - _ro_ + **_r?jS_** + **72**<sup>**0**</sup> **im** + r3irf + r4(P/pf)-1 - r 5dum2

# Bank DeBosits demand

d * o30 + 131Yd + 32'r + &33i, + £35(eD$/P) + 136(OLp/P) + A3 7(eOD$p/P)<sup>8</sup> + 138v- 1 + 9 3 9 duml Private Investment ip - Aso + B 51(Y/YT) + £5 2 r + **£ 5 3im** + 955(eD$/P) + £56(OLp/P) + A57(eOD$p/P) + 158kp-l Exports x - e 0 + e 1 (eP*/P) + e2 k-1 + 93 ay + 84x 1 + 95dum3 Public Investment is = TO + **vl(ibB-1/P)** + r 2 (fds.) + r 3 (eOD 88<sup>/P)</sup> r4Y-1 **_Ty_** r 5 ig-1
# **The People’s Voice and Access to Sanitation**<sup>*</sup>

John Nana Francois<sup>†</sup> Kwabena Gyimah-Brempong<sup>‡</sup> Johnson Kakeu<sup>§</sup> World Bank University of South Florida and IZA University of Prince Edward Island Cristelle Kouame<sup>¶</sup> World Bank _JEL Classification_ : C26, D72, H41, H42, O18, P16 _Keywords_ : Sanitation; Voice and accountability; Awareness and visibility; Rural and urban; Instrumental variable; Instrument-free estimator

*We are grateful to Michael Bleaney, Robert Johann Utz, Chiara Bronchi, Yaw Nyarko, Zalda Feliciano, Maty Konte, Leonce Ndikumana, Mina Baliamoune-Lutz, Jesse Mora, Grace Phillips, Eric Hoffmann, Anne C. Barthel, Kodama Toyoko, Berta Macheve, and Guy Tchuente for useful comments, feedback, and discussions on earlier versions of the paper that have significantly improved the paper. We would also like thank conference participants and discussants at the 2021 American Economic Association (AEA), the 2022 97th Western Economic Association International meeting in Portland, Oregon, and University of Prince Edward Island for useful discussions and feedback. We are solely responsible for any remaining errors.

**Disclaimer** : The findings, interpretations and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

†Fiscal Policy and Sustainable Growth Unit, World Bank, 701 18th Street NW, Washington, DC20433, US.

‡Department of Economics, University of South Florida, Tampa, FL 33620, Tel: (703) 798-2438 email: kgyimah@usf.edu and IZA-Institute of Labor Economics, Bonn.

- §Email: jkakeu@upei.ca. Department of Economics, 550 University Avenue Charlottetown, Prince Edward Island Canada C1A 4P3 ¶Email: ckouame1@worldbank.org. World Bank, 701 18th Street NW, Washington, DC20433, US (J10-042)
- Easterly, W., and R. Levine. 1996. “Africa’s Growth Tragedy: Policies and Ethnic Divisions.” World Bank Policy Research Department, Washington, D.C. Mimeograph.

- Easterly, W., N. Loayza, and P. Montiel. 1997, ‘Has Latin America’s Post-Reform Growth Been Disappointing?” Journal ofInternational Economics. Forthcoming.

- Easterly, W., and S. Rebelo. 1993. ‘Fiscal Policy and Economic Growth: An Empirical Perspective.” Journal ofMonetary Economics, December. a

- Echavarria, J. J. 1997. ‘Liberalization, Integration, and Trade in the Western Hemisphere.” Organization ofAmerican States Trade Unit, Washington, D.C. Mimeograph.

- Edwards, S. 1992. ‘Trade Orientation, Distortions and Growth in Developing Countries.” Journal of Development Economics. (39) 31-57.

# Edwards, S. 1995. Crisis andReform in Latin America. New York: Oxford University Press.

- Fernandez-Arias, E., and P. Montiel. 1997. “Reform and Growth in Latin America: All Pain, No Gain,” Inter-American Development Bank, Washington, D.C. Mimeograph.

- Fields, G.S. 1990. “Labour Market Modelling and the Urban Informal Sector: Theory and Evidence.” In The Informal Sector Revisited, edited by D. Tumham, B. Salome, and A. Schwarz. Development Centre Seminars, OECD, Paris.

- Harris J., and M. Todaro. 1970. ‘Migration, Unemployment and Development: A Two-Sector Analysis.” American Economic Review, March.

- Harrison, A. 1996. ‘Openness and Growth: A Time-Series, Cross-Country Analysis for Developing Countries.” Journal ofDevelopment Economics, March.

- Hubbard, G.R., A. Kashyap, and T.M. Whited. 1995. ‘International Finance and Firm Investment.” Journal ofMoney Credit and Banking. (27) 3.

- Inter-American Development Bank. 1996. Economic and Social Progress in Latin America. Washington, D.C.: Inter-American Development Bank.

|

- International Finance Corporation. Emerging Stock Markets Factbook. Various editions . Washington, D.C.: International Finance Corporation. .

- International Labour Office. 1996 Labour Overview. Lima, Peru: International Labour Office.

- King, R., and R. Levine. 1992. ‘Financial Indicators and Growth in a Cross-Section of Countries.” PRD Working Paper Number 819. World Bank, Washington, D.C., January.

a 31
have private credit bureaus. The survey from the World Bank finds that private credit bureaus exist in at least half of the countries surveyed (26/57 reported the existence of a private credit bureau).<sup>16</sup> See Figure 1 in the Appendix for a map of public credit registries and private credit reporting worldwide. Here, we strongly suggest that PCRs and private credit bureaus are complements and not substitutes.

In the three countries studied in this paper, Argentina, Brazil and Mexico, private credit bureaus exist and are the primary sources of information for many lending institutions. However, the public registries have played an important role in supplementing the data available from the private sector. For example, in Brazil most private sector credit reporting is focused on negative information. Since loans in good standing are not regularly reported to the private registries, private credit reports do not provide a full picture of the total indebtedness of borrowers. Brazil’s public registry provides this important information to the banking community. In Argentina, the PCR has helped to fill in gaps in coverage in the private reporting system since all regulated financial institutions are required to report to the central bank on virtually all loans. Lenders can obtain both positive and negative information on a case-by-case basis from the public registry for borrowers they are evaluating. They also receive information on all borrowers who are in arrears or who are in default on a monthly basis. In Argentina, the negative information from the PCR is also available to the private credit bureaus. In Mexico, the public registry was the only source of centralized credit information available to financial institutions until the mid-1990s when private credit bureaus were established. The development of the private industry led to a decision by the Mexican central bank to end the distribution of the public registry data to banks in 2002.

# **_2. Banking supervision_**

A second main objective for establishing a public credit registry is to strengthen banking supervision. In a prior survey of PCRs from 2000-2001, approximately half indicated that banking supervision was the most important reason for operating the public registry. In that same survey, 65% stated that the PCR had been “very important” in strengthening supervision of

> 16 PCR data is shared with private credit reporting firms or bureaus in Bolivia, Chile, the Dominican Republic, Peru and Slovenia. In Argentina, negative data from the PCR is publicly available and included in private credit reports.

16
# **Table A2 OLS Estimations based on country specific variables**

||**1985 a**<br>|**rrival **<br>||**1975 a**<br>|**rrival**<br>||
|---|---|---|---|---|---|---|
|**PRESTIGE INDEX**|**1990**|**-2000**|**198**|**0-1990**|**199**|**0-2000**|
|||**(1/10)**||**(1/10)**||**(1/10)**|
||**predicted**<br>**(lnOP1990)**|<br>**Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|<br>**Δpredicted**<br>**(lnOP1980-1990)**|**predicted**<br>**(lnOP1990)**|<br>**Δpredicted**<br>**(lnOP1990-2000)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Log of GDP per capita**|**0.0681*****<br>**[0.0130]**||0.0344<br>[0.0212]||0.0126<br>[0.0147]||
|**Openness Index**|-0.00018<br>[0.000431]|0.00002<br>[1.05e-05]|-0.00017<br>[0.000643]|-0.00002<br>[2.19e-05]|-0.00028<br>[0.000500]|0.00001<br>[1.42e-05]|
|**Log of Distance to the US**|**0.0610*****<br>**[0.0218]**|**0.000740***<br>**[0.000406]**|0.023<br>[0.0256]|0.000924<br>[0.000834]|**0.0395***<br>**[0.0233]**|0.000242<br>[0.000525]|
|**English**|0.0399<br>[0.0247]||**0.0559***<br>**[0.0325]**||0.0318<br>[0.0260]||
|**Military conflict**|**-0.0692****<br>**[0.0285]**|-0.000638<br>[0.000782]|0.00504<br>[0.0525]|0.00039<br>[0.00136]|-0.0212<br>[0.0353]|-0.00113<br>[0.000891]|
|**Communism index**|0.0218<br>[0.0304]|**0.00243****<br>**[0.00117]**|0.0241<br>[0.0489]|0.0000863<br>[0.00174]|-0.0174<br>[0.0449]|-0.0000822<br>[0.000838]|
|**Predicted Log of OP in 1990**||**-0.0279*****<br>**[0.00378]**||||**-0.0188****<br>**[0.00864]**|
|**Predicted Log of OP in 1980**||||**-0.0339*****<br>**[0.00503]**|||
|**Constant**|5.101***<br>[0.221]|0.175***<br>[0.0216]|5.747***<br>[0.301]|0.209***<br>[0.0304]|5.866***<br>[0.226]|0.119**<br>[0.0522]|
|**Observations**|71|71|72|72|72|72|
|**R-squared**|0.649|0.672|0.203|0.463|0.153|0.217|
|***** p<0.01, ** p<0.05, * p<0.1**<br>**Robust standard errors in bracke**|**ts**||||||

40
26 trade was settled at the internal rate of 2.8 yuan per dollar; the official rate of 1 .53 yuan per dollar continued to apply to non-commodity transactions. After 1 985,.the yuan was gradually devalued.

Additionally, the proportion of retained foreign exchange, which was introduc d in 1 979, was gradually raised. Enterprises were allowed to swap their foreign exchange entitlements with each other through the Bank of China at rates above the official exchange rate. Restrictions on trading foreign exchange were further relaxed with the establishment of a "foreign exchange adjustment center" in Shenzhen in 1985 where enterprises could trade foreign exchange at negotiated rates. By the late 1 980s, similar centers had been established in most provinces of China, and more than 80 percent of the foreign exchange earnings was traded in them (Sung 1993). The climax of foreign exchange rate policy reform was the establishment of a managed floating system and unification of the dual rate system as of January 1, 1994.

Interest rate policy is the least affected area of the traditional macro-policy environment. Under the heavy industry oriented development strategy, the interest rate was kept artificially low to f acilitate the expansion of capital-intensive industries. Afterthe reforms started in 1979, the government was forced several times to raise both the loan rates and the savings rates. **16** Still, throughout the reform process, both active and passive interest rates were maintained at levels far below the opportunity cost of capital.

In late 1993 the government announced a plan to establish a development bank with the function of financing long-term projects at subsidized rates and to turn the existing banks

**16** To stop runs on banks, savings rates were indexed to inflation rates in October 1988. The policy was revoked three years later. In May 1993, the interest rate for a one-year time deposit was 9.18 percent. For a one-to-three-year basic investment loan, it was 10.80 percent (China Statistics Yearbook 1993, pp. 670-71). However, the market rate for a commercial loan was between 15 and 25 percent.
origins and destinations are occupational inheritance (with differentiated strength by stratum of origin) and barriers to long-distance mobility.

**Table 8. Adjustment of log-linear models. Youths from 18-35 years of age of ENCEL 2017.**

|Type of model|g.l.|G<sup>2</sup>|X<sup>2</sup>|BIC|Value of P|
|---|---|---|---|---|---|
|1. Independence|25|251.97|268.98|162.38|0.00|
|2. Homogeneous diagonal|24|46.76|47.27|-39.24|0.00|
|3. Diverse diagonal|22|39.68|39.83|-39.16|0.01|
|4. Symmetry (2p) with homogeneous diagonal|23|42.43|43.13|-39.99|0.01|
|5. Symmetry (3p) with homogeneous diagonal|22|42.42|43.15|-36.42|0.01|
|6. Symmetry (4p) with diverse diagonal|21|37.57|37.42|-37.69|0.01|
|7. Symmetry (5p) with diverse diagonal|20|37.55|37.42|-34.12|0.01|
|8. Corners (2p) with homogeneous diagonal|23|43.49|44.23|-38.93|0.01|
|9. Corners (3p) with homogeneous diagonal|22|40.13|41.20|-38.71|0.01|
|10. Corners (4p) with diverse diagonal|21|34.25|33.84|-41.00|0.03|
|11. Corners(5p)with diverse diagonal|20|28.97|28.78|-42.70|0.09|

Note: g.1. = degree of freedom; G<sup>2</sup> = Deviance; X<sup>2</sup> = Pearson’s chi square; BIC = Bayesian Index Criteria. Source: Own elaboration from ENCEL 2017 (n=3,084).

The parameters of both models are presented in table 9. For our interpretation, we will use model 11, which provides a more detailed explanation. According to the parameters of this model’s main diagonal, occupational inheritance is strongest for those who have an agricultural origin ( _dp6=2.92, p<0.001_ ), followed by those with a NM origin ( _dp1=2.27, p=0.003_ ) and, finally, those with an origin in the intermediate strata ( _dp23=1.80, p<0.001_ ). Youths with an agricultural origin are 2.9 times more likely to inherit their provider’s occupation than to have a different occupation. In the case of those of NM origin and of the intermediate strata the probability is 2.27 and 1.80 times, respectively. We found no evidence of barriers to long-distance downward mobility from an NM origin ( _esq1=0.68, ls_ ), but there are barriers to long-distance upward mobility from the agricultural stratum _(esq2=1.32, p<0.001_ ).

Continuing the analysis, we studied whether relative mobility is different between males and females. To do this, we fitted the three-way log-linear models (with both constant fluidity, which assumes there are no differences between groups, and non-constant, which assumes there are) and the log-multiplicative models. Table 10 shows the results of log-linear models by sex. Our statistical findings do not allow us to conclude with certainty which is the better fit, because the differences in BIC between some of the fluidity models in their constant and non-constant versions are very small (between 1 and 5 points in the case of the models 2, 4, 8 and 9).

35