rafmacalaba/datause-encoder-data huggingface dataset:
A number of studies have examined post-recession policy responses to these challenges. Galasso (2008) runs simulations based on the political determinants of social security contribution rates and retirement ages, and concludes that the retirement age is likely to increase in each country, regardless of political constraints. However, according to Andersen (2008), indexing benefits and retirement ages to longevity does not ensure financial viability of the social security system. Recognizing this, Poterba (2014) suggests that individuals bear a larger share of the responsibility for determining their retirement security.<sup>2</sup> According to Papke (2003), allowing participants a choice of investment options may increase their retirement savings. However, Lusardi (2003) reports that little is known about how households make saving plans and how they collect information to make savings decisions. A survey of households by the Board of Governors of the Board of Governors of the Federal Reserve System (2014) found that almost half of respondents have given little or no thought to retirement savings. Among those who have, many either do not plan to retire, expect to keep working into retirement to pay for expenses, or do not know how they will pay for their retirement.<sup>3</sup> Nearly a third had no retirement savings or pension. These retirement planning challenges were exacerbated by the recession, which resulted in many respondents delaying their planned retirement.<sup>4</sup> One way firms have responded is by shifting from defined benefit pensions to defined contribution pensions, where employees have to decide how much money to put into their retirement funds and how to allocate their pension wealth. However, such plans do not go without criticism, taking into account insights on cognitive limitations from the behavioral economics and finance literature.<sup>5</sup> For instance, Banks and Oldfield (2007) report that the

> 2 The author reviews how designing public policy toward retirement security must recognize both the heterogeneity among households saving for retirement, as well as the multiple policy objectives that are served by various policy instruments.

> 3 In similar veil, the Future of Retirement report by HSBC (2013), which surveyed 1,050 respondents in the UK and more than 16,000 people across 15 countries, revealed that almost two-fifths of retired respondents in the UK (39%) said that they had not prepared adequately or at all for a comfortable retirement.

> 4 Moreover, it was found that one-third of respondents said that they had foregone some form of medical treatment in the preceding year due to the treatment’s expected cost. Not surprisingly, the decision not to seek treatment was more common for those without savings, those indicating that they were struggling with their finances, or those without insurance.

> 5 For a detailed investigation on the heuristics and the biases that emerge in the area of retirement savings, see e.g. Benartzi and Thaler (2007). Other studies on retirement planning strategies involving relevant considerations include: Ameriks and Zeldes (2004), and Brown (2007).

5
# **1. Introduction**

The relation between the adoption of digital technologies, its impact on productivity, and the consequences on employment and labor markets has been the subject of much study in the last three decades. Investment in information and communication technologies (ICT) has been credited with the increase in US productivity in the second-half of the 1990s and the increasing productivity gap between the US and the EU. Skill-biased technological change is argued to be one of the main reasons behind the steady increase in the demand for skilled labor, wage inequality and job polarization since the 1980s.

Two main strands of literature have studied the labor market impacts of technological progress: the early literature on skill-biased technological change of Katz and Murphy (1992), Bound and Johnson (1992), Goldin and Katz (1998), among others, and the tasks approach to labor demand of Autor, Levy and Murnane (2003) and Acemoglu and Autor (2011). The literature on skill-biased technological change assumes that technology is complementary with skilled labor, therefore positively affecting the relative demand and wage of skilled workers. The tasks approach argues that the complementarity or substitutability between technology and labor does not occur at the worker category but rather depending on how susceptible different tasks are for automatization. They conclude that tasks performed by workers in the middle of the skill distribution are more likely to be substitutable by machines. Almost all of these papers describe general equilibrium models and use data on wage and employment aggregated up to the district, industry or country level.

Most notably the bulk of the literature in skill-biased technological change and the tasks approach to labor demand features homogeneous firms and competitive labor markets. While these are useful assumptions when studying employment and wages in general equilibrium, the model descriptions miss out on predictions at the firm-level. Firmlevel studies have consistently shown that there is high variance in labor force composition and wages across firms. A higher skill share and higher wages are associated with larger, more productive firms that engage in complex activities such as innovation, quality upgrading, and exporting.<sup>3</sup> Moreover, the extent to which investing in ICT boosts firm productivity has also been shown to depend on firm characteristics denominated under the umbrella term of organizational capacities.<sup>4</sup> In this paper we extend the tasks model of Acemoglu and Autor (2011) to allow for firm heterogeneity in technology and non-competitive labor markets with firm-level wages, in order to study the effects of investment in ICT on firm-level outcomes. We start by describing

> 3 Bernard and Jensen (1999), Verhoogen (2008), Kugler and Verhoogen (2012), Aw et al (2011).

> 4 Machin and Van Reenen (1998), Bloom, Sadun and Van Reenen (2012).

2
null hypothesis that Random Effects models are appropriate. Therefore, this research has utilized the Fixed Effects Model.

# **VI. Estimation Results**

Descriptive statistics for the 50 U.S. states are shown in Table 1. Among the 50 states, California has the highest property tax revenue, while New York and Texas rank second and third respectively. In terms of GDP and population, California also ranks first, while Texas takes the second place and New York takes the third.

Table 2 displays the statistics through years, to demonstrate the data characteristics from another dimension. As GDP, population, and property tax rate are all increasing over time, property tax revenue has also been increasing over the years.

Regression results of the 50 U.S. states are shown in Table 5. All the coefficients are basically in line with our predictions. Comparing between OLS and Fixed Effects Models would be helpful. In the OLS models (Columns 1 and 2), both property tax rate and governance quality have significant positive impacts on the property tax revenue. In the meantime, coefficients of federal transfer and household size are insignificant, while household size has positive impact on the dependent variable, which is a bit counter intuitive. The result might be because of running pooled OLS regressions on the sample, without dealing with differences between the states. When we run OLS regressions, we are pooling all the observations together for the estimation without controlling for time-invariant unobserved individual characteristics that can be correlated with the observed independent variables.<sup>11</sup> This could cause potential omitted variable bias due to unobserved heterogeneity when this heterogeneity is constant over time. Therefore, we use Fixed Effects Model to remove heterogeneity from the data through differencing (i.e. removing any time invariant components of the model).

When we move to Columns 3 and 4, regression results from the Fixed Effects Model, GDP and population are still significantly positive, while property tax rate and governance now turn to insignificant. The reason that coefficients of property tax rate and governance change to insignificant mainly comes from controlling for time-invariant unobserved state characteristics. When we run OLS regressions, property tax rate and governance have important impacts on the tax revenues because we consider the variations across states. But when we use the Fixed Effects Model, we estimate the impacts within states, so that impacts from the tax rate and governance diminish significantly, since tax rates and governance within each state maintain very similar levels across time. The most important changes are in the observed coefficients of federal transfer and household size. Now, both of them show negative impacts on property tax revenue as expected and are statistically significant. Since Fixed Effects Models are more sophisticated to deal with the

> 11 Some plausible indicators can be cited—especially those related to the indexation applied in perception surveys to reveal the intrinsic pricing of local benefits provided by property taxes.

9
# Leveraging Big Data to Understand Women’s Mobility in Buenos Aires

Aiga Stokenberga,<sup>1</sup> Ellin Ivarsson, Juan Ignacio Fulponi, Karla Dominguez Gonzalez JEL: R40, R41, R42 Keywords: Gender, mobility, urban transport, big data

> 1Corresponding author: <u>astokenberga@worldbank.org.</u> The authors would like to acknowledge the technical contributions to the study provided by Javier Burrieza, Luis Willumsen, María Fernanda Ortiz, Miguel Picornell, and Ricardo Herranz (CDR data analysis) and by Javier Morales Sarriera, Lucila Lavezzolo, Rodrigo Hernan Martinez, Maximiliano Daniel Roca, and Roberto Agosta (implementation of interception survey with current car and motorcycle users). The authors are also grateful for the contributions to the study provided by the Secretariat of Transportation and Public Works of the City of Buenos Aires. The authors would like to acknowledge the generous funding received from the Quality Infrastructure Investment Partnership (QII).
- Humphreys, M., Sanchez de la Sierra, R. & Van der Windt, P. 2019. Exporting Democratic Practices: Evidence from a Village Governance Intervention in Eastern Congo". Journal of <u>Development Economics, 140, 279–301.</u>

- Kling, J. R., Liebman, J. B. & Katz, L. F. 2007. Experimental Analysis of Neighborhood Effects. Econometrica, 75(1), 83–119.

- Mansuri, G. & Rao, V. 2013. Localizing Development: Does Participation Work? World Bank Policy Report. Washington DC.

- Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688–701.

- Van der Windt, P., Humphreys, M. & Sanchez de la Sierra, R. 2018. Gender Quotas in Development Programming: Null Results from a Field Experiment in Congo". Journal of <u>Development Economics, 133, 326-345.</u>

- Van der Windt, P. 2018. Can Development Aid Empower Women? Evidence from a Field Experiment in the Congo". Journal of Politics, 80(3), 1039-1044.

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# _IIIa.  Political and Macroeconomic Environment_

Even if historical factors are favorable to financial development, political turmoil may lead to macroeconomic instability and deterioration in business conditions. Civil strife and war destroys capital and infrastructure, and expropriations may follow military takeovers. Corruption and crime thrive in such environments, increasing cost of doing business and creating uncertainty about property rights. Detragiache, Gupta and Tressel (2005) show that for low-income countries political instability and corruption have a detrimental effect on financial development. Investigating the business environment for 80 countries using firm level survey data, Ayyagari, Demirguc-Kunt and Maksimovic (2005) find that political instability and crime are important obstacles to firm growth, particularly in African and Transition countries. Further, Beck, Demirguc-Kunt and Maksimovic (2005) show that the negative impact of corruption on firm growth is most pronounced for smaller firms.

Given a stable political system, well functioning financial systems also require fiscal discipline and stable macroeconomic policies on the part of governments. Monetary and fiscal policies affect the taxation of financial intermediaries and provision of financial services (Bencivenga and Smith, 1992; Huybens and Smith, 1999; Roubini and Sala-i-Martin, 1992 and Roubini and Sala-i-Martin, 1995). Often large financing requirements of governments crowd out private investment by increasing the required returns on government securities and absorbing the bulk of the savings mobilized by the financial system. Bank profitability does not necessarily suffer given the high yields on these securities, but the ability of the financial system to allocate resources efficiently is severely curtailed. Empirical studies have also shown that countries with lower and more 14
fourth example is taken from the monitoring and evaluation component of the Mexico Lifelong Learning Project, currently under preparation. The tables in annex 2 provide information on indicators developed for each of the discussed projects. They do not identify the variables or the values attributed to the indicators. This information can be found in the survey instruments, which are available on request.

- 2.2.1 Applying the Framework in Ethiopia: The Women’s Development Initiatives Project (WDIP)

The ME framework structured the design of the Women’s Development Initiatives Project’s (WDIP’s) impact evaluation.<sup>18</sup> The evaluation assesses the empowerment status of Ethiopian women in both rural and urban areas in general, and then examines whether enhancements in their empowerment associate with their participation in WDIP.

The project aims to strengthen women’s self-help groups as a mechanism to increase their economic, social, and political opportunities. The evaluation has therefore developed empowerment indicators for all three domains. These are listed in table 1 of annex 2.

# _The Project_

WDIP is a community-driven development project that seeks to enhance women’s empowerment and participation in development interventions by mobilizing women at the grassroots level and capitalizing on their potential to support development processes. It does so by facilitating the formation of self-help groups, strengthening existing grassroots groups, and enhancing women’s capacity to act collectively, thereby increasing the social and economic welfare of their households.

The Government of Ethiopia, recognizing the disadvantaged position of women, has implemented a number of policies, laws, and initiatives to promote women’s empowerment, such as removing discriminatory laws from the constitution. With the announcement of the National Policy on Women in 1993 and promulgation of the new constitution in 1995, the government highlighted its commitment to the equal development of women. However, these policies and laws are often weakly enforced, and in many cases provide contradictory or incomplete coverage in their protection of women. For example, while violations such as female genital mutilation, wife battering, domestic violence, and sexual harassment are outlawed in the Constitution, the penal code contains no provisions for adjudicating them, and existing laws are often applied by judges in a manner that does not take account of women’s rights.<sup>19</sup> The underlying rationale for launching the project is that women remain among some of the poorest and most severely disadvantaged of citizens.

WDIP aims to redress gender imbalances in development opportunity by investing in women’s skills, productivity, and organizational capacity. The project components

> 18 This section draws upon the draft interim country case report, Legovini (2004). 19 World Bank 2004 (draft).

17
15 Table 4

# Fiscal Indicators, Consolidated Government

(percent of GDP)

||1991|1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Revenue|20.1|23.4|24.6|24.1|23.2|22.2|23.2|23.7|24.3|24.7|23.5|
|Tax|13.6|16.2|16.1|16.2|15.6|15.8|16.8|17.4|17.5|18.1|17.5|
|Social Security|4.6|5.1|5.9|5.7|5.3|4.4|4.2|4.0|3.8|3.7|3.6|
|Privatization<br>|a|0.9|0.2|0.3|0.5|0.1|0.0|0.0|0.9|0.1|0.0|
|Other|1.9|1.2|2.4|1.9|1.8|1.9|2.2|2.3|2.1|2.8|2.4|
|Primary spending|20.6|21.9|23.1|24.1|23.7|23.3|22.9|23.2|25.1|24.2|25.0|
|Wages|8.0|8.4|8.8|8.9|8.9|8.3|8.2|8.3|9.4|9.5|9.9|
|Pensions|5.4|6|5.6|5.9|6|6.2|5.9|5.8|6.2|6.1|6.2|
|Other|7.1|7.5|8.8|9.3|8.7|8.8|8.8|9.0|9.5|8.6|8.9|
|Primary balance|-0.5|1.4|1.5|0.1|-0.4|-1.1|0.3|0.5|-0.8|0.4|-1.5|
|Interest|3|1.9|1.4|1.6|1.9|2.1|2.3|2.6|3.4|4|4.8|
|Overall balance|-3.5|-0.4|0|-1.5|-2.3|-3.2|-2.1|-2.1|-4.2|-3.6|-6.4|
|Off-budget debt creation|0.0|0.1|0.3|0.4|0.8|0.5|0.3|0.4|0.5|0.0|0.0|
|Balance incl. off-budget|-3.5|-0.5|-0.3|-1.9|-3.1|-3.6|-2.4|-2.5|-4.7|-3.6|-6.4|
|Memorandum:||||||||||||
|Public debt (bn pesos)|64.7|68.8|77.6|90.4|101.4|108.3|111.5|123.5|134.3|144.8|172.2|
|Percent of GDP|38.8|32.9|32.8|35.1|39.2|39.8|38.1|41.3|47.4|50.8|64.1|
|GDP (bn pesos)|166.9|209.3|236.5|257.4|258.4|272.2|292.8|298.9|283.3|285.0|268.6|

a. Included in "other" Source: Krueger (2002)
and Chile, it is generally difficult to avoid errors of _leakage_ (reaching the non-poor) and of _under-coverage_ (not reaching the poor). Improving targeting aims to reduce these errors, resulting in a more effective poverty reduction policy.

The current process for targeting the poor in Vietnam employs a “bottom up” approach. At the aggregate level, overall progress in tracking poverty over time is undertaken by the Ministry of Labor, War Invalids, and Social Affairs (MOLISA) on the basis of an income poverty line<sup>4</sup> applied it to the periodic rounds of VHLSS survey data. Identification of households eligible to receive targeted assistance is undertaken at the village level. Village committees use their own widely varying criteria to compose a list of poor households. Criteria may include food security, housing, assets, etc. This list is submitted for review to a commune-level committee of Hunger Eradication and Poverty Reduction. The committee checks that households on the list are below MOLISA’s income poverty line by conducting simple surveys of randomly selected households from the list. The refined list is then sent to the People’s Committee and the People’s Council, which are empowered to make further adjustments to it as well (Nguyen et al., 2010). Households that make it to the final ‘poor list’ are then in principle eligible for certain social assistance programs such as free health insurance or subsidies.

Previously, Vietnam would redistribute funds by means of geographic targeting. However, it is generally felt that there was a large margin of error in this approach; the “list of poor communes” that MOLISA used to target the poor was constructed somewhat arbitrarily and failed to capture the majority of poor people. One study estimates that although leakage was limited with this practice – about 8% of non-poor were misclassified as being poor – under-coverage was substantial with 80% of the poor not being reached (Baulch and Minot, 2002).

Concerns have also been voiced about the way that funding is distributed once the poor are identified. Although funds are allocated in a redistributive way to the provinces, intraprovincial distribution is to a large extent left to the discretion of provincial authorities and it

> 4  Which is different – usually lower - from the expenditure poverty line set by the General Statistics Office and The World Bank that is used by the international research community to study poverty.

22
research. Migration from Bangladesh is almost exclusively regular and temporary, mediated by officially registered agencies. Entry into the main destination countries in the Persian Gulf and Southeast Asia is strictly conditional on holding an employment contract. Due to tight enforcement and severe punishments, overstays and undocumented stays are very rare.<sup>6</sup> If an employment contract is renewed, migrants may extend their stay, but, otherwise they must return home. Temporary migrants from Bangladesh, like most migrants from other countries in South and Southeast Asia, also pay high upfront fees to the intermediary agents who match workers with employers in destination countries. We show in this paper that these costs play a critical role in the timing and duration of emigration, and, subsequently, in the self-employment patterns in Bangladesh.

The relationship between migration and self-employment under credit constraints has been investigated by Rapoport (2002) and Djaji ́c (2010) at a theoretical level. We build on their insights, and extend the empirical literature on temporary migration and entrepreneurship in several ways. First, we model emigration, the destination choice, and return migration jointly with the self-employment decision, accounting for financial constraints. Among the earlier and influential papers that link migration to entrepreneurship, McCormick and Wahba (2001) focus on emigration from the Arab Republic of Egypt, while Dustmann and Kirchkamp (2002) focus on migrants who returned from Germany to Turkey. Piracha and Vadean (2010) investigate the relation for Albania; Wahba and Zenou (2012) and Mah ́e (2019) again for Egypt; and Br ̈uck, Mah ́e, and Naud ́e (2018) for the Kyrgyz Republic. Several papers aim at isolating the causal relationships between migration and financial investments. These include Mesnard (2004) for Tunisia; Yang (2006), Yang (2008), and Khanna, Theoharides, and Yang (2020) for the Philippines; Woodruff and Zenteno (2007) for Mexico; and Batista, McIndoe-Calder, and Vicente (2017) for Mozambique.<sup>7</sup> Whereas most papers restrict attention to the link between return migration and entrepreneurship in the country of origin, Wahba (2015) accounts for the selection of migrants from the nonmigrant population and for the selectivity of returns. We draw on the insights developed in this literature, and extend them. Our paper is the first to directly estimate a dynamic model of the relationships between migration and self-employment, and to explicitly account for credit constraints, migration costs, and the risks of lower-than-expected earnings abroad, as well as for the risk of exogenous contract termination.

Methodologically, our paper contributes to the literature that uses dynamic life-cycle

> 6Southeast Asian countries such as Singapore apply very strict deportation measures to address overstays. Among the GCC countries, overstays are punished by imprisonment in Saudi Arabia and the United Arab Emirates.

> 7Also see Wahba (2014), Rapoport and Docquier (2006) and Naud ́e, Siegel, and Marchand (2017) for related surveys.

6