rafmacalaba/datause-encoder-data huggingface dataset:
concerns regarding endogeneity and omitted variables bias by isolating the effect of the EZ (EC) from city fixed effects because it makes an across-time comparison. It also isolates the effect of the EZ (EC) from time variant citywide effects because it makes an intra-city comparison. Moreover, the comparison group used (i.e., EC) is similar to the EZ areas but is not likely subject to spillover effects from the policy because they are made up of tracts located in different cities than the EZs. As both the comparison and the treatment groups applied for EZ designation and met the requirements for unemployment and poverty, there would be no unobservable differences caused from going through the application process or being qualified. This estimation strategy requires the assumption that the difference between EZ and EC areas and their surrounding cities would have grown the same in the absence of the tax incentives and does not require the assumption that the EZ and EC areas would have grown the same.

Thus the remaining concern is zone-specific time-variant unobserved factors that might bias the result as the EZ areas could be those areas which would have been economically successful even in the absence of EZ tax incentives. To deal with this issue, the authors use congressional representation on the U.S. House of Representatives Ways and Means Committee as IV for EZ designation as previous work has shown this committee (and no others) to be a significant determinant of EZ designation. They thus find that the effect of location-based tax incentives has a heterogeneous effect across industries based on the ability of capital-labor substitutability in the production technology of different industries. In the short term, the retail and service sectors seem to benefit most from the program, increasing the share of establishments in the designated area by between 0.16 and 0.30 percentage points. These gains from the program seem to be offset by the losses in other sectors as the share of establishments in the transportation and finance, insurance, and real estate industries declined by between 0.16 and 0.19 percentage points. Interestingly, the industry level affects demonstrated in this study are not the goal of the EZ program but rather its unintended consequences. This implies that policy makers should also consider potential unintended consequences. Wherever possible, the impact evaluation should not only focus on partial equilibrium effect, but also should address the general equilibrium effects.

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POLICY RESEARCH WORKING PAPER 2299

# Summary findings

During the transition from central planning to market economies now under way in Eastern Europe, output levels first collapsed by 40 to 50 percent in most countries, then staged a modest recovery in the last two years. Longer-term revival of growth requires a resumption of investment and thus, realistically, of domestic savings.

To explore the determinants of household savings rates in transition economies, Denizer, Wolf, and Ying studied matching household surveys for three Central European economies: Bulgaria, Hungary, and Poland.

They find that savings rates strongly increase with _relative_ income, suggesting that increasing income inequality may play a role in determining savings rates.

Savings rates are significantly higher for households that do not own their homes or that own few of the standard consumer durables - possibly because, with no retail credit or mortgage markets, households must save to purchase houses and durables.

The influence of demographic factors broadly rnatches earlier findings for developing countries.

Perhaps surprisingly, variables associated with the - household's position in the transition process including either sector of employment (public or private) - or form of employment do not play a significant role in determining savings rates.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Centrai Asia Region - is part of a larger effort in the region to understand determinants of savings, at both the household and the aggregate level. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactAla Cubukcu, room H4-347, telephone 202-473-8449, fax 202-522-2754, email address acubukcu@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Cevdet Denizer may be contacted at cdenizer@worldbank.org. March 2000. (14 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
**Table 7: Heterogeneity according to self‐control**

Dependent variable: Real profits

|Interaction Category:|Used a<br>Susu at<br>Baseline|Said they<br>Save<br>regularly|Discount<br>rate above<br>median|Hyperbolic<br>Discounter|Lacks<br>Self‐control|Says there is<br>pressure to<br>share with hh|Can spend<br>freely without<br>spouse|Household<br>Size|Number of<br>Siblings<br>in Area|
|---|---|---|---|---|---|---|---|---|---|
|**Panel A: Pooling Treatment Effects across Ge**|**nder**|||||||||
|Cash Treatment|‐5.117|‐34.97**|13.26|6.219|2.768|1.187|‐8.618|2.191|0.414|
||(11.05)|(14.58)|(13.92)|(10.33)|(8.579)|(9.886)|(18.29)|(8.350)|(8.624)|
|In‐kind Treatment|25.24|‐4.260|13.97|40.35***|29.80***|31.54**|8.972|28.79***|25.61**|
||(15.34)|(9.341)|(11.20)|(14.49)|(10.81)|(12.49)|(13.33)|(10.62)|(10.40)|
|Cash Treatment * Interaction|14.54|50.95***|‐21.97|‐18.70|‐16.13**|‐0.705|13.44|5.937|2.166|
||(16.70)|(17.69)|(17.15)|(16.82)|(8.102)|(17.82)|(22.54)|(4.238)|(3.284)|
|In‐kind Treatment *Interaction|10.35|49.41***|31.00|‐38.88**|‐6.587|‐23.61|20.68|0.451|6.469*|
||(20.80)|(17.91)|(20.66)|(18.09)|(6.273)|(20.36)|(20.09)|(5.022)|(3.835)|
|p‐value for testing interactions jointly zero|0.6355|0.001|0.1024|0.0708|0.0983|0.501|0.535|0.371|0.198|
|Observations|4,170|4,157|4,169|4,157|4,145|3,880|2,702|4,169|3,750|
|Number of firms|758|755|758|755|753|675|490|757|652|
|**Panel B: Females Sub‐sample only**||||||||||
|Cash Treatment|‐8.313|‐20.90|9.207|0.955|‐0.608|‐3.369|0.710|‐3.461|0.0197|
||(12.94)|(17.44)|(15.23)|(10.69)|(9.100)|(10.76)|(19.53)|(8.711)|(9.261)|
|In‐kind Treatment|24.79|‐2.700|12.62|41.12**|31.20**|35.56**|‐4.982|30.52**|33.84**|
||(21.96)|(8.508)|(11.47)|(17.56)|(13.04)|(17.04)|(12.88)|(13.40)|(13.80)|
|Cash Treatment * Interaction|12.37|28.15|‐19.78|‐11.43|‐11.65|16.55|2.671|10.49**|5.174**|
||(17.51)|(20.15)|(18.47)|(18.97)|(7.963)|(16.63)|(24.14)|(4.602)|(2.092)|
|In‐kind Treatment *Interaction|14.44|50.76**|38.26|‐34.74|‐3.526|‐10.42|42.57**|‐1.392|2.144|
||(27.10)|(20.86)|(25.00)|(23.17)|(7.445)|(23.13)|(17.84)|(6.528)|(2.694)|
|p‐value for testing interactions jointly zero|0.704|0.033|0.139|0.297|0.318|0.479|0.051|0.064|0.045|
|Observations|2,588|2,586|2,587|2,580|2,574|2,398|1,730|2,592|2,375|
|Number of firms|471|470|471|469|468|418|312|471|414|

Notes: Results from Fixed effects estimation on trimmed sample. *, ** and *** denote significant at the 10%, 5% and 1% levels. All regressions include wave effects which vary with the interaction.
Trends in urbanization and implications for upper secondary enrollment China’s dramatic demographic change is also accompanied by its drastic urbanization since the 1980s. The urban population was only 190 million in 1980, which accounted for 19.4 percent of the total population. With the launch of a series of economic and political reforms in the 1980s, rural residents were gradually endowed with the freedom to migrate to cities for better economic opportunities and higher standards of living, leading to an overturn in the composition of the rural and urban populations. In 2010, about half of the total population in China was identified as urban residents. The urban population continued to grow to 803 million in 2017, which accounted for 57.9 percent of the total population.

Figure 2.8 China’s percentage of population identified as urban residents, 1960–2017 <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br><!-- End of picture text --> _Source_ . World Bank DataBank.

China’s urbanization process has allowed more of the population to become urban residents, including children under the age of 17. The urban-rural composition of the population ages 0–17 mirrors the pattern in the total population. While urban children accounted for only 16.6 percent of the population ages 0–17 in 1982, this percentage increased to about 50 percent in 2015. Over the past decade, while incomes for both urban and rural residents have increased significantly, those who live in urban areas continue to earn more than twice as much as the rural residents.

External Factor 2: Global trends and common debates in upper secondary education Around the globe, upper secondary education<sup>3</sup> is typically designed as an immediate step after basic or compulsory education in preparation for tertiary education or to provide skills relevant to employment, or both. Programs at this level offer students more varied, specialized, and in-depth instruction than programs at lower secondary education.<sup>4</sup> They are more differentiated, with an increased range of options and streams available. Some countries stress vocational education that develops specific job-related skills to prepare students to work in specific occupations, while others emphasize general education that provides students with broad knowledge and basic skills in mathematics and communication and serves as the foundation for further learning and on-thejob training.

There is a wide variety of approaches countries have adopted in terms of upper secondary schooling structures depending on their focus on the job transition. On the one hand, the United States and Canada have largely eliminated vocational education as a separate track in secondary 16
<mark>conducted (Table 1).</mark><sup>5</sup> <mark>Hereafter any reference to “small states” should be understood as excluding those classified as high income, absent any indication to the contrary.</mark> Based on 2011 data the average per capita income of small states was US$4,759 and only 9 percent were classified as low income. By comparison, as a group low and middle income countries had an average per capita income of US$3,631 and 27 percent were classified as low income. Small states often stand out as the richest country in a region, for example in South Asia the per capita income of Maldives (US$6,530) is more than four times that of India, the largest economy in region. There is however, a marked disparity between the per capita income levels of small states. They range from The Gambia, the poorest, with a GNI per capita of US$610 to Antigua and Barbuda with a per capita income of $12,060.

Examined separately, small states exhibit disparate public debt levels. Several including Antigua and Barbuda, Grenada, Seychelles and St. Kitts and Nevis built up very sizeable public debt on account of expansionary fiscal policies and fixed exchange rate regimes. St. Kitts and Nevis has the highest nominal public debt-to-GDP ratio of any small state, 154 percent at end 2011, and the comparable ratio for Antigua and Barbuda and Grenada is in excess of 90 percent, after substantial debt restructuring with official and private creditors<sup>6</sup> . However, almost half of the 35 small states classified as low and middle income have ratios of public debt to GNI below 50 percent and Timor-Leste has no public debt whatsoever. The composition of public debt portfolios also differs. In general small states rely more heavily on external sources rather than domestic markets for their public financing needs but there are some important exceptions. For example in Antigua and Barbuda, Fiji, Mauritius, Namibia and St. Kitts and Nevis domestic debt constitutes by far the largest share of public debt (Figure 1).

**Figure 1: Public Debt Composition and Ratio to GNI 2011**

_Percent_

> 5 <mark>Small states classified as low and middle income for FY13 are: Antigua and Barbuda, Belize, Botswana, Bhutan, Cape Verde, Comoros, Djibouti, Dominica, Fiji, Gabon, The Gambia, Grenada, Guinea-Bissau, Guyana, Kiribati, Lesotho, Maldives, Marshall Islands, Mauritius, Federated States of Micronesia, Montenegro, Namibia, Palau, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Seychelles, Solomon Islands, Suriname, Swaziland, Timor-Leste, Tonga, Vanuatu</mark>

> 6 St Kitts and Nevis concluded a rescheduling agreement with official and private creditors in 2012.

4
# **References**

- **Agarwal, Sumit, Sashwat Alok, Pulak Ghosh, Soumya Kanti Ghosh, Tomasz Piskorski, and Amit Seru** , “Banking the Unbanked: What do 255 Million New Bank Accounts Reveal about Financial Access,” _Working Paper_ , 2018.

- **, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel** , “Regulating Consumer Financial Products,” _Quarterly Journal of Economics_ , 2015, _130_ (1), 111–164.

- **Aker, Jenny C, Silvia Prina, and C Jamilah Welch** , “Migration, Money Transfers, and Mobile Money: Evidence from Niger,” in “AEA Papers and Proceedings,” Vol. 110 2020, pp. 589–93.

- **Anagol, Santosh, Shawn Cole, and Shayak Sarkar** , “Understanding the Advice of Commissions-Motivated Agents: Evidence from the Indian Life Insurance Market,” _Review of Economics and Statistics_ , 2017, _99_ (1), 1–15.

- **, Vimal Balasubramaniam, and Tarun Ramadorai** , “Learning from Noise: Evidence from Indias IPO Lotteries,” _Journal of Financial Economics_ , 2020.

- **Anderson, Michael L.** , “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” _Journal of the American statistical Association_ , 2008, _103_ (484), 1481–1495.

- **Annan, Francis** , “Gender and Financial Misconduct: a Field Experiment on Mobile Money,” _Working Paper_ , 2019.

- **Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira** , “How Debit Cards Enable the Poor to Save More,” _NBER Working Paper 23252_ , 2017.

- **Badarinza, Cristian, Vimal Balasubramaniam, and Tarun Ramadorai** , “The Household Finance Landscape in Emerging Economies,” _Annual Review of Financial Economics_ , 2019.

- **Batista, Catia, Pedro C Vicente et al.** , “Is Mobile Money Changing Rural Africa? Evidence from a Field Experiment,” _Working Paper_ , 2018.

- **Bertrand, Marianne and Adair Morse** , “Information Disclosure, Cognitive Biases and Payday Borrowing,” _Journal of Finance_ , 2011, pp. 1865–1893.

- **Beshears, John, James J. Choi, Christopher Harris, David Laibson, Brigitte C. Madrian, and Jung Sakong** , “Which Early Withdrawal Penalty Attracts the Most Deposits to a Commitment Savings Account?,” _Journal of Public Economics_ , 2020, _183._

- **, , David Laibson, and Brigitte C. Madrian** , “Behavioral Household Finance,” in Douglas Bernheim, ed., _Handbook of Behavioral Economics: Foundations and Applications_ , Vol. 1, Amsterdam: Elsevier, 2018, chapter 3, pp. 177–276.

- **Blumenstock, Joshua, Michael Callen, and Tarek Ghani** , “Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan,” _American Economic Review_ , October 2018, _108_ (10), 2868–2901.

29
deregulation process, followed by de novo branching. Table A1 presents the deregulation dates.<sup>1</sup> Twelve states deregulated before the start of our sample period in 1976. Arkansas, Iowa and Minnesota were the last states to deregulate, only after the passage of the Riegle-Neal Act in 1994.

# **1.2. Income distribution data**

Information on the distribution of income is from the March Supplement of the Current Population Survey (CPS), which is a survey of about 60,000 households across the states of the U.S. The CPS provides information on total personal income, income from wages, proprietor income, as well as detailed demographic information. The CPS is not a true panel; it is cross-sectional survey that is repeated each year. The CPS does not trace individuals through time, but rather replaces 50% of the sample each year, so that each household is included for two consecutive years. Each individual is assigned a sample weight corresponding to his or her representativeness in the sample, which we use in our analyses.

We measure the distribution of income for each state and year over the period 1976-2005 in four ways. First, the natural logarithm of the Gini coefficient of income distribution is derived from the Lorenz curve (Gini), where larger values imply greater income inequality. Our second measure of income distribution is the coefficient of variation of income, i.e. the variance of income divided by the mean (Variance). Third, we use the difference between the natural logarithm of incomes of those at the 90<sup>th</sup> percentile and those at the 10<sup>th</sup> percentile (90/10). Finally, we use the difference between the natural logarithm of incomes of those at the 75<sup>th</sup> percentile and those at the 25<sup>th</sup> percentile (75/25).

> 1 We have data for 50 states and the District of Columbia. Consistent with the literature on branch deregulation, we drop Delaware and South Dakota because the structure of their banking systems were heavily affected by laws that made them centers for the credit card industry, and because South Dakota lacks some financial and banking data.

9
> among other macro variables. Much of the traditional 'disctission of fiscal policy centers around

> the financing decisions of a particular spending level. The recent revival of interest in the expenditure-composition issue (Aschauer **[1989],** Morrison **[1991],** Holtz-Eakin **[1991])** has been

> based on theoretical models but the focus has been on the productivity of public expenditures in the United States.

In this paper, we develop (in section 2) an analytical framework which links the composition of public expenditure with economic growth. Government expenditures are divided into two categories: "productive" and "unproductive". The former complements private-sector productivity while the latter does not. We show that an increase in the share of productive expenditure leads to a higher steady-state growth rate of the economy. Next, in section **3,** we estimate the model with different components of public expenditure as candidates for productive expenditure. Using data on **69** countries over twenty years, we assess whether current or capital expenditure on the one hand, and health, education, transport and communications, or defense expenditure on the other, can be classified as productive in the sense defined above. Section 4 presents our concluding remarks.

# **2. The Model**

Since the 1960's, researchers have been looking at the relationship between fiscal policy and the economy's growth rate. The seminal contribution was **by** Arrow and Kurz **[1970],** who developed a model where consumers derive utility from private consumption as well as the public capital stock. In addition, pri-ate production benefits from the services **of** this capital

**3**
19 López Bóo, F. 2010. Returns to Education and Macroeconomic Shocks: Evidence from Argentina. IZA Discussion Paper No. 4753: <u>https://www.iza.org/publications/dp/4753/returns-toeducation-and-macroeconomic-shocks-evidence-from-argentina; Fiszbein, A., Giovagnoli, P., and</u> Patrinos, H.A. 2007. Estimating the Returns to Education in Argentina using Quantile Regression Analysis: 1992-2002. Económica 53(1-2): 53-72: https://ideas.repec.org/a/lap/journl/555.html 20 Albornoz, F., and Menendez, M. 2002. Analyzing income mobility and inequality: the case of Argentina during the 1990s. DELTA Working Paper. Paris: DELTA: <u>http://www.depeco.econo.unlp.edu.ar/wp/wp-content/uploads/2017/06/semi061202.pdf; Cruces,</u> G., and Wodon, Q. 2003. Transient and chronic poverty in turbulent times: Argentina 1995-2002. Economics Bulletin 9(3): 1-12: <u>https://ideas.repec.org/a/ebl/ecbull/eb-03i30003.html</u> 21 Psacharopoulos, G., Velez, E., Panagides, A., and Yang, H. 1996. Returns to education during economic boom and recession: Mexico 1984, 1989 and 1992. Education Economics 4(3): 219230: <u>https://www.tandfonline.com/doi/abs/10.1080/09645299600000022; Patrinos, H.A., and</u> Metzger, S. 2005. Returns to education in Mexico: An update. Processed, The World Bank, Human Development Network; Patrinos, H.A., and Sakellariou, C. 2005. Economic volatility and returns to education in Venezuela: 1992-2002. Applied Economics 38:1991-2005: <u>https://www.researchgate.net/publication/24075775_Economic_Volatility_and_Returns_to_Educ ation_in_Venezuela_1992-2002; Vilerts, K., Krasnopjorovs, O., and Brekis, E. 2017. Returns to</u> education during and after the economic crisis: Evidence from Latvia 2006–2012. Comparative Economic Research 20(1): 133-157; <u>https://www.semanticscholar.org/paper/Returns-toEducation-During-and-After-the-Economic-VilertsKrasnopjorovs/91f584e6f6a0ee7c5cb13eb1c0e722e8738e5bec; Cholezas, I., Kanellopoulos, C.,</u> Mitrakos, T., and Tsakloglou, P. 2013. The impact of the current crisis on private returns to education in Greece. Economic Bulletin, Bank of Greece 38: 33-63: <u>https://ideas.repec.org/a/bog/econbl/y2013i38p32-63.html</u> 22 DeCicca, P., and Krashinsky, H. 2020. Do Differences in School Quality Generate Heterogeneity in the Causal Returns to Education? NBER Working Paper 27089, Cambridge, MA: National Bureau of Economic Research (https://www.nber.org/papers/w27089)

> 23 <mark>Sadique, M.Z., Adams, E.J. and Edmunds, W.J. 2008. Estimating the costs of school closure for mitigating an influenza pandemic. BMC Public Health 8: 135:</mark> <u><mark>h</mark> ttps://doi.org/10.1186/1471-24588-135</u>

> 24 <mark>Lempel, H., Epstein, J.M., and Hammond, R.A. 2009. Economic cost and health care workforce effects of school closures in the U.S. PLoS Currents1:RRN1051:</mark> <u><mark>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762813/</mark></u>

25 Andrabi, T., Daniels, B., Das, J. 2020. Human Capital Accumulation and Disasters: Evidence from the Pakistan Earthquake of 2005. RISE Working Paper Series. 20/039. <u>https://doi.org/10.35489/BSG-RISEWP_2020/039.</u> 13
In terms of empirical analysis, the Sub-Saharan Africa study may stand on firmer ground given the larger sample size with numbers of observations across specifications ranging from 83 to 176, compared to a corresponding range of 9 to 42 in East Asia. As mentioned earlier, our analysis tells a preliminary story about the comparative influence of infrastructure sectors on per capita growth across East Asian countries, and in different institutional and physical settings. Clearly, more detailed and better quality data would be useful to further test and confirm this preliminary story. Finally, as already pointed out, country-specific assessments are required to formulate country-specific policy priorities.

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