rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**<sup>**2**</sup>

Investment plays a critical role for growth and social development. Investment in human capital and high-quality, sustainable infrastructure lays the foundation for output and productivity growth, provides basic services to households and market access for firms; enables sustainable urban development; and opens corridors of trade to link into the global economy (Global Infrastructure Facility 2015). Competitiveness rests on strong human capital and quality infrastructure (World Economic Forum 2016). Infrastructure mitigates the challenges to economic activity imposed by distance, helps integrate domestic markets, and established critical connections to international markets. Strong human capital increases productivity and earnings.

Investment growth in emerging market and developing economies (EMDEs) has slowed sharply since 2010. However, investment growth performance has varied significantly across different regions (Figure 1). The objective of this paper is to examine the regional dimensions of recent weakness in investment.<sup>3</sup> Specifically, the paper addresses the following three questions. First, how has investment growth in the six EMDE regions evolved? Second, what are the remaining investment needs across these regions? Third, which policies can help address investment needs?

Investment growth in EMDEs slowed from 10.2 percent in 2010 to 3.4 percent in 2015 and, based on partial data, likely slowed further in 2016. This slowdown exceeded 5 percentage points in almost half of EMDEs. In 2015, investment growth was below its long-term average in more than 60 percent of EMDEs and negative in about 30 percent of EMDEs.

Developments have varied widely across regions, however, reflecting the presence of commodity importers or exporters, the degree of political stability, and spillovers from key trading partners and investors. In the EMDE regions with a substantial number of commodity-importing economies—East Asia and Pacific (EAP), which accounted for one-quarter of global investment during 2010-15 and South Asia (SAR), which accounted for 4 percent—investment growth has been stronger than the average across EMDEs, but also declined steadily in 2010-14. Investment growth has since stabilized in EAP and SAR, in part reflecting reduced policy pressures for rebalancing, lower political tensions and sharply lower oil prices.

> 2 This paper draws from background studies featured in World Bank (2017a). Figures and background data presented here are available at www.worldbank.org/gep.

> 3 The six regions include East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MNA), and South Asia (SAR) and Sub-Saharan Africa (SSA).
|Table 6: Nested Logit Model of Veh<br>Variables|icle Owne<br>Coefs.|rship and Mode Choice<br>T-value|
|---|---|---|
|Constant: Rail|-1.01|-11.47|
|Constant: Bus|-1.09|-10.45|
|Constant: Rail+Bus|-1.73|-11.54|
|Constant: 2-wheeler|-0.14|-1.62|
|Constant: Car|-0.21|-2.09|
|Constant: Own 2-wheeler|-3.23|-10.50|
|Constant: Own Car|-3.78|-5.05|
|Constant: Own Both|-5.13|-5.29|
|Walking time|-0.02|-11.93|
|In-vehicle time|-0.02|-8.90|
|#workers*Own 2-wheeler|-0.09|-1.29|
|#workers*Own Car|-0.25|-1.88|
|#workers*Own Both|-0.26|-1.47|
|Child*Own 2-wheeler|0.27|2.50|
|Child*Own Car|0.05|0.23|
|Child*Own Both|0.17|0.60|
|Suburb*Own 2-wheeler|-0.61|-5.49|
|Suburb*Own Car|-0.62|-2.78|
|Suburb*Own Both|-0.70|-2.01|
|Years of Edu*Own 2-wheeler|0.14|7.37|
|Years of Edu*Own Car|0.28|6.92|
|Years of Edu*Own both|0.27|5.37|
|Business Owner*Own 2-wheeler|1.23|11.16|
|Business Owner*Own Car|0.98|4.47|
|Business Owner*Own Both|1.73|4.62|
|Hicksian bundle|7.63|18.34|
|Inclusive Value: None|0.57|11.75|
|Inclusive Value: Own 2-wheeler|0.65|10.40|
|Inclusive Value: Own Car|0.39|9.01|
|Inclusive Value: Own Both|0.69|4.17|
|Log likelihood|-4412||
|Sample size|3724||
|Value of time (Rs./hour at mean)|||
|Walking|64||
|In vehicle|59||

28
# **<u>Table 2</u>** <u>:continued</u>

|Variable|All<br>(N=618)|DS Beneficiary<br>(N=108)|<br>PW Beneficiary<br>(N=186)|<br>Non-Beneficiary<br>(N=344)|
|---|---|---|---|---|
|Landholding 0-0.25 ha (=1)|0.56|0.72|0.61|0.50|
|Landholding 0.25-1 ha (=1)|0.32|0.23|0.30|0.35|
|Landholding >1 ha (=1)|0.11|0.05|0.09|0.15|
|Index of housing structure|0.21|0.29|0.41|0.08|
|Ubudehe 1 (=1)|0.25|0.44|0.33|0.17|
|Ubudehe 2 (=1)|0.35|0.35|0.36|0.33|
|Ubudehe 3 (=1)|0.34|0.19|0.26|0.42|
|Ubudehe 4 or higher (=1)|0.06|0.03|0.06|0.08|
|Owns goat (=1)|0.40|0.41|0.39|0.40|
|Owns rabbit (=1)|0.09|0.06|0.10|0.09|
|Owns pig (=1)|0.17|0.11|0.17|0.18|
|Owns chicken (=1)|0.21|0.13|0.16|0.25|
|Owns sheep (=1)|0.09|0.10|0.09|0.08|
|Owns cow (=1)|0.36|0.14|0.30|0.44|
|Livestock (TLU)|0.80|0.35|0.65|1.00|
|**Panel C: 2014**|||||
|Head is male (=1)|0.57|0.34|0.58|0.63|
|Age of head (yrs.)|52.47|61.92|48.52|51.92|
|No schooling (=1)|0.44|0.64|0.42|0.38|
|Primary started (=1)|0.24|0.13|0.26|0.26|
|Primary completed (=1)|0.27|0.20|0.27|0.29|
|Secondary or more (=1)|0.05|0.03|0.04|0.06|
|Disabled (=1)|0.27|0.49|0.22|0.24|
|# of HH members|4.59|3.06|4.92|4.83|
|Share of adults (18-65 yrs.)|0.39|0.33|0.49|0.48|
|Share of children (0-5 yrs.)|0.10|0.05|0.12|0.10|
|Share of children (6-18 yrs.)|0.29|0.24|0.32|0.30|
|Share of elders (65+ yrs.)|0.11|0.30|0.05|0.09|
|Landholding 0-0.25 ha (=1)|0.58|0.65|0.57|0.56|
|Landholding 0.25-1 ha (=1)|0.30|0.27|0.30|0.31|
|Landholding >1 ha (=1)|0.12|0.08|0.13|0.13|
|Index of housing structure|-0.12|-0.12|-0.07|-0.15|
|Ubudehe 1 (=1)|0.23|0.53|0.25|0.14|
|Ubudehe 2 (=1)|0.38|0.31|0.45|0.36|
|Ubudehe 3 (=1)|0.33|0.12|0.25|0.44|
|Ubudehe 4 or higher (=1)|0.06|0.00|0.01|0.01|
|Owns goat (=1)|0.26|0.29|0.03|0.26|
|Owns rabbit (=1)|0.04|0.03|0.03|0.05|
|Owns pig (=1)|0.12|0.13|0.13|0.11|
|Owns chicken (=1)|0.11|0.06|0.10|0.12|
|Owns sheep (=1)|0.05|0.06|0.05|0.05|
|Owns cow (=1)|0.28|0.17|0.24|0.32|
|Livestock (TLU)|0.56|0.37|0.54|0.62|

_Source:_ VUP household survey, 2009, 2011, 2014.

17
Table 1 provide summary statistics of married women in the data, broken down by whether are outside the home. Since women are on they currently working working average younger (27.7 years old) than nonworking women (who are on average 34.1 years old), I also display the difference in means (worker vs. nonworkers) after controlling for age. Taking into account these age differences is important. For instance, while women who work on average have more 0.7 years more education than those who do not, a woman who works has on average 0.4 years less education than a woman of the same age who does not work. Despite less education, women who work have a marginally higher age at marriage, fewer children, and a smaller age difference between husband and wife, relative to nonworkers. The houses of working women are also more likely to have a cement floor. Finally, women who work are considerably more likely to be migrants than nonworkers.

Table 1 also reports variables that reflect the women’s status within the home. While it is unsurprising that women workers leave the household compound (bari) much more often than workers due to their work schedules, they also report a greater say in household decisions and a smaller likelihood of needing a husband’s permission to buy something for themselves. However, women workers are no less likely than nonworkers to say that it is ever justified for a husband to beat his wife and actually 4.7 percentage points more likely to have ever been beaten in their life than a woman of the same age who does not work ( _P_ = 0.109). The empirical evidence in this paper will further investigate why women who work report control over household resources but do not seem to be able to translate this over income into reductions in the violence that higher bargaining power they face.

Table 2 provides summary statistics of the nature of the work done by women who participate in the labor force. Seventy-eight percent of women who are working work in the garment industry. These workers typically work long hours, an average of 11.78 hours versus 8.34 hours for workers. The and standard per day per day nongarment average deviation of wages is similar for garment and other workers. The average wage of 3000 taka month is 36 dollars US and twice the minimum per approximately approximately at the time of the of 1662 taka month. The worker wage survey per typical garment has been working for a total of 3.9 years and the typical non-garment worker has been working for 5.9.

10
outcomes. The main explanatory variable we wish to correlate with women’s economic empowerment is the gap between the leave days allocated to mothers and to fathers. As noted in section 2, motivated by the work of Eckberg et al. 2013, we wish to investigate whether there is any correlation between the leave allocation to both parents and women’s labor market outcomes. Because there are some extreme values in this relationship, we express this variable in logarithmic form.<sup>9</sup> Additionally, because the relationship between the leave day gap and women’s outcomes may vary based on the number of days that a mother receives—for example, a gap of five days may have very different implications if mothers are allocated ten days, or if they are allocated 100—we also control for the number of days allocated to mothers. As leave policies may not be correlated with any changes in labor market statistics in the year in which the policies are implemented or changed, we lag the policy variables by three years.

There are several control variables that are included in our estimations. Firstly, because countries that have a more equal allocation of leave between parents may have a more gender-inclusive legal framework in general, we control for the overall level of legal equality within a country using the _Women, Business and the Law_ index score, recalculated without including the datapoints capturing family leave. Secondly, we account for the overall level of economic development in a country by including the log of gross domestic product (GDP) and its square term. This variable is measured with a three-year lag to lessen endogeneity concerns. Next, because we expect family leave policies to be most effective when there are more women in a country of child-bearing age, we include the percentage of women in a country between the ages of 15 and 44. Finally, we also control for the fertility rate within a country – while countries with relatively lower fertility rates have been at the forefront of the development of leave policies, more women in countries with higher fertility rates may be impacted by leave policies. We also include country and year fixed effects to control for unobservable differences between countries as well as over time. By including country fixed effects, we are measuring the correlation between leave policies and women’s economic outcomes based on variation over time within each country, as opposed to cross-country comparisons. The model is summarized by equation (1) below. L L L ) + γ γ L l l + ii,tt β l l 2 ii,tt−3 L + μii,tt−3 ii,tt (1) ii,tt−3 yy = αα+ β ββ(L hLLee_ββL + δδW LL_iiββiiLLii + ρρβ ll ii,tt−3 ii,tt ii,tt ii tt ii,tt θθβ ll + θθllLLeePPllhiiββi eeiiββLLiiL LLeeγγiiββiiγγyyμμLLγγLL + φφ + ττ + εε

> 9 For those few country-year observations where there is no gap between leave days for mothers and fathers, the value of the dependent variable will be zero. As such, we transform the dependent variable using the hyperbolic inverse sine. This transformation can be interpreted as a normal logarithmic transformation, but it takes account of zero values (Witte, Burger and Ianchovichina 2020).
Differences in Efficiency under Competition," _Bell Journal of Economics,_ 13:2, 418-438.

Loayza, Norman V. (1995), "The Economics of the Informal Sector: A Simple Model and Some Empirical Evidence from Latin America," mimeo.

Lucas, Robert E., Jr. (1978), "On the Size Distribution of Business Firms," _Bell Journal of Economics,_ 9:2, 508-23.

Ozorio de Almeida, A.L. and S.E.M Graham and L.F. Alves (1994). "Poverty, Deregulation and Informal Employment in Mexico," ESP Working Paper, World Bank.

Palay, Thomas M. (1984), "Comparative Institutional Economics: The Governance of Rail Freight Contracting," _Journal of Legal Studies,_ 13, 265-287.

Palay, Thomas M. (1985), "Avoiding Regulatory Constraints: Contracting Safeguards and the Role of Informal Agreements," _Journal ofLaw, Economics, and Organization,_ 1, 155-175.

Peattie, Lisa (1987), "An Idea in Good Currency and How It Grew: The Informal Sector," _World Development,_ 15:7 851-860.

Porter, Michael E. (1995), "The Competitive Advantage of the Inner City," _Harvard Business Review,_ 73:3, 55-71.

Portes, Alejandro (1994), "The Informal Economy and Its Paradoxes," in Neil J. Smelser and Richard Swedberg, eds., _The Handbook of Economic Sociology,_ Princeton, N.J.: Princeton University Press.

Rauch, James E. (1991), "Modeling the Informal Sector Formally," _Journal of Development Economics,_ 35:1, 33-47.

Roberts, Mark J. and James R. Tybout (forthcoming), _Industrial Evolution in Developing Countries: Micro Patterns of Turnover, Productivity and Market Structure,_ New York: Oxford University Press.

Schaffner, Julie Anderson (1996), "Premiums to Employment in Larger Establishments: Evidence from Peru," mimeo, forthcoming _Journal of Development Economics._ de Soto, Hemando (1989), _The Other Path: The Invisible Revolution in the Third World,_ New York: Harper and Row.

Thomas, J.J. (1992), _Informal Economic Activity,_ Ann Arbor, University of Michigan Press.

Tokman, Victor E. (1992), "The Informal Sector in Latin America: From Underground to Legality," 22
# **Poverty and Income Seasonality**

# **in Bangladesh**

## **By**

## **Shahidur R. Khandker**<sup>**1**</sup> **World Bank**

> 1 This paper is part of a research project funded by the World Bank on “Seasonality, hardcore poor and microfinance” in Bangladesh.  An earlier version of the paper was presented in a National Seminar on Monga organized jointly by the Institute of Micro-finance (InM) and Palli Karma Shahayak Foundation (PKSF) on January 2-3, 2008, in Dhaka.  I would like to thank Mesbahuddin Ahmed, Gershon Feder, Baqui Khaliliy, Gayatri Koolwal, Wahiduddin Mamud, Hussain Samad, Umar Serajuddin, Tara Vishwanath, and Hassan Zaman for very useful comments.  Views expressed are, however, entirely mine, and do not reflect the views of the World Bank or its affiliated organizations.
# WPS3362

# Education, Information, and Smoking Decisions Evidence from Smoking Histories, 1940-2000

## Damien de Walque

Development Research Group, World Bank <u>ddewalque@worldbank.org</u>

### World Bank Policy Research Working Paper 3362, July 2004

_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. Policy Research Working Papers are available online at http://econ.worldbank.org._ The author thanks Gary Becker, Mark Duggan, Michael Greenstone, Steven Levitt and Tomas Philipson for useful comments, suggestions and encouragements. I am grateful to Daniel Parent for suggesting the Vietnam draft as an instrumental variable for college education. I also thank Raphael De Coninck, Michael Grossman, Ted Joyce, Fabian Lange, Ellen Meara, Chris Rohlfs and participants of the Applications Workshop (May 5, 2003) at the University of Chicago, as well as participants at a NBER Health economics seminar for helpful comments and discussions.
autonomy in raising their own revenues. They are also managed by different parties, elected through multiparty electoral contests, unlike in China.

In China, a first set of reforms in the 1980s cut government tax revenue as a share of GDP and sharply reduced the share of the central government. A second set of reforms, implemented in 1993–94, led to the recentralization of revenue-raising powers. Meanwhile, spending responsibilities were progressively devolved to provincial and municipal governments. In 1978, the central government collected only 15 percent of total revenue while incurring 47 percent of total expenditure; by 2007 these shares had changed to 54 percent and 22 percent, respectively. The ratio of central transfers (both mandatory and discretionary) to sub-national revenue, called the _dependence ratio_ , rose to 43.6 percent by 2007<sup>3</sup> .

States in India have the power to levy and collect a state value added tax (VAT), liquor excise duty, stamp duty on real estate transactions, road transport taxes and registration fees, entertainment taxes, and electric power tax. They are still significantly dependent on central transfers, with the average dependence ratio at 37 percent in 2008. The dependence ratio ranged from 22 percent in the relatively prosperous state of Punjab to 64 percent in the poor state of Bihar. It was less than 30 percent in 7 of the 14 major states.

In Brazil, state governments are less dependent on transfers from above than they are in India or China. The average dependence ratio of Brazilian states is 24 percent. States have the single largest tax instrument at their command, the ICMS ( _imposto sobre a circulacao de mercadorias e servicos_ ), a value added tax. Municipalities have important spending responsibilities and are highly dependent on transfers from higher-level governments, with an average dependence ratio of 77 percent. Municipalities with fewer than 20,000 inhabitants— more than 70 percent of all municipalities—raise less than 5 percent of their expenditure needs from their own taxes.

The 1993–94 reforms in China attempted to move away from ad hoc negotiated intergovernmental transfers toward a more rules-based and somewhat more transparent mechanism. In particular, a system of general purpose grants was introduced aimed at fiscal

> 3 Based on data provided in Table 3 on page 16, IMF 2009.

15
<mark>WPS3565</mark>

# Trade Preferences to Small Developing Countries and the Welfare Costs of Lost Multilateral Liberalization<sup>∗</sup>

Nuno Limão<sup>†</sup> Marcelo Olarreaga<sup>‡</sup> University of Maryland and CEPR World Bank and CEPR

## Abstract

The proliferation of preferential trade liberalization over the last 20 years has raised the question of whether it slows down multilateral trade liberalization. Recent theoretical and empirical evidence indicates this is the case even for unilateral preferences that developed countries provide to small and poor countries but there is no estimate of the resulting welfare costs. To avoid this stumbling block effect we suggest replacing unilateral preferences by a fi xed import subsidy. We argue that this scheme would reduce the drag of preferences on multilateral liberalization and generate a Pareto improvement. More importantly, we provide the fi rst estimates of the welfare cost of preferential liberalization as a stumbling block to multilateral liberalization. By combining recent estimates of the stumbling block effect of preferences with data for 170 countries and over 5,000 products we calculate the welfare effects of the United States, European Union and Japan switching from unilateral preferences to Least Developed Countries to the import subsidy scheme. Even in a model with no dynamic gains to trade we fi nd that the switch produces an annual net welfare gain for the 170 countries ($4,354 million) and for each group: the United States, European Union and Japan ($2,934 million), Least Developed Countries ($520 million) and the rest of the world ($900 million).

JEL classi fi cation: D78; F13; F14; F15 Keywords: Preferential trade agreements; multilateral trade negotiations; MFN tariff concessions, preference erosion.

> ∗We thank Stephanie Aaronson, Bernard Hoekman, Aaditya Mattoo, Petros Mavroidis, Stefano Inama, Chris Stevens, Alan Winters and participants of the seminar on the WTO and developing countries (Columbia Law School) for helpful comments and discussions. The usual disclaimer applies. The Policy Research Working Paper Series disseminates the fi ndings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the fi ndings 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 fi ndings, 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. Policy Research Working Papers are available online at http://econ.worldbank.org.

> †University of Maryland and CEPR. E-mail: Limao@econ.umd.edu. Part of this research was conducted while the author visited the World Bank Research Group in the summer of 2004.

> ‡World Bank and CEPR. E-mail: molarreaga@worldbank.org.
# **Figure 1 - Methodology Underpinning the Debt Reporting Heatmap**

||**1. P**|**ublic debt reporting publ**|**ished**|||**2. Public D**|**ebt Management**|**3. Other debt statistics /**|
|---|---|---|---|---|---|---|---|---|
|**_Data accessibility_**||**_Completeness_**||**_Timeli_**|**_ness_**|**Debt Management**|**Annual Borrowing Plans**|**contingent liabilities (CLs)**|
||Instrument coverage|Sectorial coverage|Information on new|Periodicity|Time lag|**Strategy (DMS)**|**(ABP)**||
||||loans|||||**N.A./ No information**|
|No publicly available<br>debt reporting<br>or<br>incomplete/ outdated<br>debt reports|N.A. or incomplete<br>coverage|N.A. or incomplete<br>budgetary central (CG)|N.A|N.A.|N.A.|No DMS published|No ABP published or a partal<br>plan with a delay > 3 months|No reporting or insufficient<br>reporting of existing CLs|
|Multiple websites|Limited coverage:<br>external or domestic<br>debt only|Limited coverage :<br>complete CG|Limited information:<br>lender's name or<br>purposes of borrowing,<br>amount|> 1 year<br>(e.g. every<br>two years)|> = 6<br>months|Yes, but no targets|Partial: only for domestic or<br>external debt) with a<br>delay<3 months or full<br>coverage with delay > 3<br>h|Limited reporting: guaranteed<br>debt by beneficiary (if applicable)|
|Single website &<br>multiple docs|Partial coverage:<br>external and<br>domestic (if<br>applicable)|Partial coverage:<br>complete general<br>government (GG) or<br>complete public<br>corporations|Partial information:<br>lender's name, amount,<br>purpose, but no<br>financial terms|Annual<br>basis|> 3<br>months|Yes, with target for<br>total debt|mont<br>Full coverage within 3 month|Partial reporting:<br>audited/recognized fiscal arrears<br>and collaterilized debt (if<br>applicable)|
|Single document|Full coverage:|Full coverage (GG and|Full information:|< 1 year|< = 3|Yes, with targets for|Full coverage before the|Comprehensive reporting:|
||external and<br>domestic and<br>guarantees (f<br>applicable)|public corporations)|lender's name, amount,<br>purpose, and financial<br>terms|(e.g.<br>quarterly<br>update)|months|domestic & external<br>debt|fiscal/calendar year starts|publication of a framework<br>covering existing CLs|

Source: Author’s elaboration.