rafmacalaba/datause-encoder-data huggingface dataset:
28

# Notes

1. Early contributions include Corden (1967) and Snape (1977). Major contributions in the new literature are surveyed in Helpman and Krugman (1985, 1989) and in the edited volumes by Kierkowski (1984) and Krugman (1988). For a recent survey, see Harris (1989).

2. See, for example, Dixit (1988) and Venables and Smith (1988).

3. Developing country case studies include Bergsman (1974), Rodrik (1988), Gunasekera and Tyers (1988), Devarajan and Rodrik (1989a,b), Condon and de Melo (1990).

4. For further discussion of the HCI drive see World Bank (1987).

5. The market share of the 20 leading Jaebol continued to rise until the early 1980s.

6. Mean price-cost margins (PCMs) for protected sectors were a third higher than for less protected sectors in 1982.

7. The positive correlation between PCM and concentration does not necessarily support the "structuralist view" which sees in this relationship rent-seeking behavior by oligopolistic firms. It could also reflect the superior performance of large firms according to the "efficiency-based view". However, in the case of Korea, evidence indicates that the efficiency of small and medium sized firms had caught up with that of large firms by the end of the 1970s. See Kim (1985). 8. This result is known as the "import discipline" hypothesis in the industrial organization literature. See the symposia led by Caves (1980) and by Gerorski and Jacquemin (1981).

9. For a fuller description of the model, see de Melo and Tarr (forthcoming).
child” sample). Comparing these two quantities yields the relative magnitude of marriage penalty alone and the combined marriage and child penalty.

We find that South Asian women reduce their labor force participation by 12 percentage points (p.p.) following marriage, even before childbearing. Among women with children, this rises just 4 p.p to 16 p.p. As such, 75 percent of the combined family formation penalty is driven by marriage itself, rather than the burden of childbearing, at least in the first five years of marriage. The largest effects are observed in India, while more muted effects are observed in Nepal, where the majority of the combined penalty is driven by children. Dynamic event-study estimates reveal flat trends in employment status leading up to the marriage date, and sharp drops in employment in the first year of marriage. These trends lend additional support to the notion that these estimates represent the causal effect of marriage. Men, in contrast, enjoy a marriage premium. This premium does not depend on the presence of children, consistent with the existing literature showing no child penalties for men (Kleven et al., 2023).

The marriage penalty may represent an optimal solution to a joint household maximization problem. If women have limited outside options in the labor market relative to their husbands, then specialization in home-based tasks might be economically efficient, even without children. However, the value of women’s home production is greatly diminished without children, suggesting a role for social norms in driving the marriage penalty, particularly those that constrain women’s mobility outside the home (Anukriti et al., 2020). To adjudicate these hypotheses, we use heterogeneity analyses to test the sources of the marriage penalty. Despite the fact that gender norms are more progressive in urban areas in South Asia (Asher et al., n.d.), we find no significant difference between urban and rural marriage penalties. This may in part reflect the nature of urban labor markets, where employment opportunities are concentrated _outside_ the home, adding additional constraints to women’s participation in the context of conservative gender attitudes (Jalota and Ho, 2024).

We then turn to testing the impact of education and social norms on marriage penalties in South Asia by interacting our post-marriage indicator with these characteristics. We find that educated women have much smaller marriage penalties, with post-secondary 4
Table 14: Network regressions: ROA Pooled

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ROA|ROA|ROA|ROA|ROA|ROA|
|SOE|-3.749***|-3.748***|-3.748***|-3.748***|-3.749***|-3.747***|
||(0.135)|(0.135)|(0.135)|(0.135)|(0.135)|(0.135)|
|Connect|-0.825*|-0.826*|0.574|0.005|-1.334***|-0.602|
||(0.481)|(0.481)|(0.460)|(0.665)|(0.307)|(0.411)|
|BigI|0.263|1.415**||1.197|||
||(0.547)|(0.612)||(0.921)|||
|HasBetween||-2.161***||||-1.562**|
|||(0.521)||||(0.615)|
|logDegree|||-0.705***|-1.115**|||
||||(0.251)|(0.476)|||
|BigI_×_logDegree||||0.189|||
|||||(0.578)|||
|Politician|||||1.610***|1.406**|
||||||(0.462)|(0.597)|
|Politician_×_HasBetween||||||0.126<br>(0.945)|
|Constant|12.331***|12.329***|12.332***|12.332***|12.329***|12.329***|
||(0.062)|(0.062)|(0.062)|(0.062)|(0.062)|(0.062)|
|Observations|10,541,077|10,541,077|10,538,873|10,538,873|10,541,077|10,541,077|
|R-squared|0.039|0.039|0.039|0.039|0.039|0.039|
|Sector Dummies|✓|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

38
**Table T2.IR: Intergenerational Persistence in Schooling in Rural India**

|||**LINEARCEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft|**0.45*****|**0.52*****|**0.075*****|**0.52*****|**0.64*****|**0.12*****|
||(0.0064)|(0.0071)|(0.0095)|(0.019)|(0.021)|(0.028)|
|Quadratic Coefft.||||**-0.0070*****|**-0.011*****|**-0.0070*****|
|||||(0.0014)|(0.0018)|(0.0014)|
|Intercept|**6.39*****|**4.24*****|**-2.14*****|**6.32*****|**4.16*****|**-2.16*****|
||(0.044)|(0.043)|(0.062)|(0.048)|(0.046)|(0.066)|
|No. Observations|16957|17022|33979|16957|17022|33979|
|||**Estimate**|**s of Mobility**|**fromQuadra**|**tic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.32|4.16||**_IGME0 _**|0.52|0.64|
|**_ES5 _**|8.75|7.09||**_IGME5 _**|0.454|0.533|
|**_ES10_**|10.82|9.46||**_IGME10_**|0.384|0.426|
|**_ES16_**|12.85|11.58||**_IGME16_**|0.300|0.298|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
**_How Fast Has ChIat Indua_** _Gretw?_ **_27_**

# TABLE 5: CEHsUcAL INDurrRy

<!-- Start of picture text --> Averag % Change Differmtial<br>Perew Change GVIO,P80 Growth of<br>physical output OVIO, percentage<br>Nine Products<br>1979 13.51 7.01 -6.60<br>1980 7.85 10.77 2.92<br>1981 -4.06 4.66 8.72<br>1982 7.30 11.43 4.13<br>1983 8.33 12.46 4.13<br>1984 3.30 12.04 8.74<br>1985 1.05 11.61 12.66<br>1986 5.77 12.17 6.40<br>1987 14.S2 17.C8 2.56<br><!-- End of picture text --> Source: 1988 TJNJ, pp. 34546 for commodity dat; OVIO **dat** aro from Indusry 1988, p. S4. Thea figtuem ezcluds villge-level onprises. Figures for 1978-80 woro converted **from** 1970 to 1980 prime usaug th ntio of .1981 **gro** output for chemicu_ at 1980 and 1970 prioe
Policy Research Working Paper 7167

# **Abstract**

During the early 2000s, Moldova experienced strong economic growth and poverty and inequality reductions. This paper aims at uncovering the patterns behind these poverty trends by looking at economic mobility and its associated factors in Moldova. The findings build on the synthetic panel approach and allow for a non-anonymous view of the process of poverty reduction. The data used for this country study on Moldova come from the Household Budget Survey, which is conducted on a yearly basis. The paper presents transition matrices to look at the patterns of economic mobility across selected consumption thresholds, as well as descriptive statistics and a linear probability model aimed at identifying correlates of economic mobility. The findings show that the observed poverty reductions happened with little churning, and highlight the importance of education and employment on upward economic mobility in Moldova in the 2000s, as well as the role of public and private transfers.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmeyer3@ worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 7592

# **Abstract**

This paper reviews the Republic of Korea’s experience with electronic tax invoices for its value-added tax regime from the perspectives of tax policy makers and administrators. The paper evaluates Korea’s implementation of electronic tax invoicing and analyzes its effect on tax compliance through enhanced transparency of business transactions and taxpayer services. First implemented in 2011, mandatory electronic tax invoicing has been credited with lowering tax compliance costs and raising the transparency of business transactions. Effective policy design and implementation have contributed to the country’s success with electronic tax invoicing. Measured in transaction value, the electronic tax invoice adoption rate reached 99.8 percent in the first year and rose to 99.9 percent by 2013, compared with 15 percent before electronic tax invoicing became mandatory. According to a survey of taxpayers and tax practitioners in Korea that was conducted as part of this research study, 69.4 percent of the respondents agreed or strongly agreed that mandatory electronic tax invoicing has contributed to curbing value-added tax evasion by raising transaction transparency, and 72.9 percent agreed or strongly agreed that it has improved taxpayer service by facilitating the convenience of tax filing or automating the issuance of invoices. The review of Korea’s experiences gives credence to the contention that well-planned and well-executed compulsory electronic tax invoices can materially enhance tax compliance through significant institutional and perceptual changes in tax administration.

This paper is a product of the Equitable Growth, Finance and Institutions Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hlee14@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 10392

# **Abstract**

Data on global trade as well as capital and labor flows indicate a slowdown, but not reversal, of globalization post the 2008–09 financial crisis. Yet profound changes in the policy environment and public sentiment in the largest economies over the past five years suggest the beginning of a new era. Increasing anxiety about the labor market effects of import competition from low-wage countries, especially China, laid the groundwork, but was not the catalyst for the reversal in attitudes towards globalization. Similarly, the COVID pandemic provided novel arguments against free trade based on global supply chain resilience, but neither the pandemic nor short run policy response had enduring effects on trade flows. Global trade was remarkably resilient during the pandemic and that supply shortages would likely have been more severe in the absence of international trade.

After a temporary decline in 2020, global trade in goods and services increased sharply in 2021. Russia’s invasion of Ukraine raised new concerns about national security and the exposure of supply chains to geopolitical risk. This was followed by demands to diversify away from “non-friendly” countries and to the employment of trade policy, export restrictions in particular, to halt China’s technological development. The future of globalization is highly uncertain at this point, but these new policies will likely slow global growth, innovation, and poverty reduction even if they benefit certain industries in certain countries. Regarding resilience, the main goal of recent trade policy changes, measures of trade volatility or concentration can be helpful, but resilience will be elusive as long as we lack benchmarks against which policy performance can be measured.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at treed@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 8522

# **Abstract**

This paper examines the impact of the quality of the business environment as well as the monitoring capacity of the tax agency on firms’ tax evasion and production decisions. First, the paper uses firm-level data for 30 African and Latin American countries to show that tax evasion and distortions stemming from the business environment are positively and significantly correlated, while sales not reported for tax purposes and institutional quality are negatively and significantly correlated. Second, the paper develops a general equilibrium model where heterogeneous firms make tax evasion decisions based on their assessment of the quality of their business environment as well as the monitoring capacity of the tax agency. The model simulations for each country in the African and Latin American sample show that the model can explain 35 percent of the variation in tax evasion and more than 49 percent of the dispersion in output per worker across the sample countries. Finally, a series of counterfactual experiments shows that, at the current level of deterrence, governments could decrease sales not reported for tax purposes by 21 percent, by reducing distortions stemming from the business environment by half. The paper presents empirical supporting evidence consistent with testable predictions of the model.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at wkouame@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Figure E1: Unconditional quantile regression of height-for-age z-scores on food security measures, limited controls (Bangladesh)</u>**

<!-- Start of picture text --> HFIAS Score FCS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>quintile quintile<br>95% confidence interval OLS 95% confidence interval OLS<br>RIF-OLS RIF-OLS<br>Child DDS Mother's DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>quintile quintile<br>95% confidence interval OLS 95% confidence interval OLS<br>RIF-OLS RIF-OLS<br>-.1 .25<br>-.15 .2<br>-.2<br>.15<br>-.25<br>.1<br>0 .2<br>-.05 .15<br>-.1 .1<br>-.15 .05<br><!-- End of picture text --> 52
WPS3784 Porto, G (2005) ‘Informal Export Barriers and Poverty’, _Journal of International Economics_ , 66, 447-470 Rosenbaum, P., and D. Rubin (1983). “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” _Biometrika_ , 70, 41-55.

Rubin D. (1977) “Assignment to a Treatment Group on the Basis of a Covariate,” _Journal of Educational Statistics_ , 2, 1-26.

Smith, Jeffrey and Petra Todd. 2001. “Reconciling Conflicting Evidence on the Performance of Propensity Score Matching Methods,” _American Economic Review Papers and Proceedings_ , 91, 112-118.

Winters L.A.; McCulloch N.; McKay A. (2004) “Trade Liberalization and Poverty: The Evidence So Far” _Journal of Economic Literature_ ; 42, 72-115.

World Bank (2005). _Rwanda: Diagnostic Trade Integration Study_ . May 2005. The World Bank.

19
complex as the interacting roles of institutions and trade for growth. At best<sup>this</sup> approach can summarize interesting partial correlations in the data while at<sup>least</sup> partially controlling for obvious endogeneity problems. Combining this approach<sup>with</sup> more microeconometric evidence, case studies, and descriptions of<sup>historical episodes</sup> is likely to constitute fruifful research agenda. In the meantime, the empirical<sup>examples</sup> we provide here illustrate a broader problem in the cross-country empirical growth literature, which only in recent years has begun to take serious the problem<sup>of</sup> endogeneity of many of the key determinants of growth. Finding compelling<sup>instruments</sup> for these endogenous variables is difficult to begin with. If in addition we<sup>are interested</sup> in sorting out the partial effects of multiple endogenous determinants of growth,<sup>the</sup> search for instruments is further complicated by the need to find exogenous _and_ independent sources of variation in order to identify partial effects of interest.

20
# **<u>Table 4. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 3 using 2020/21 Poverty Line, Tanzania (percentage)</u>**

|**Method**||||**TA**|**3**||||
|---|---|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 4.1**|**Model 4.2**|**Model 8**|**Model 9**|
||**21.7**<sup>**a**</sup>|**22.6**<sup>**a**</sup>|38.5|**20.5**<sup>**a**</sup>|48.6|**22.0**<sup>**a**</sup>|**23.4**|**23.7**|
|1) Normal linear regression model|(2.5)|(2.6)|(2.6)|(2.1)|(2.9)|(2.5)|(2.6)|(2.6)|
|2) Empirical distribution of the error|**21.6**<sup>**a**</sup>|**22.6**<sup>**a**</sup>|39.7|**19.8**<sup>**a**</sup>|48.9|**22.0**<sup>**a**</sup>|**23.5**|**23.8**|
|terms|(2.5)|(2.6)|(2.6)|(2.1)|(2.9)|(2.5)|(2.6)|(2.6)|
|_Control variables_|||||||||
|Food expenditures|||Y|Y|||||
|Non-food expenditures|||||Y|Y|||
|Utilities: water, kerosene, lighting|||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.40|0.54|0.85|0.86|0.79|0.71|0.55|0.44|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|N2 (target survey)|698|698|698|698|698|698|698|698|
|**True poverty rate in TA 1**<br>||||21.0<br>(2.2|<br>)||||

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2020/21. Food and non-food expenditures, household size, and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 3 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

35
Table 2: Coastal protection from storm surges due to mangroves in the top 10 mangrove countries under current climate conditions

|**Country**|**Mangrove**<br>**area, sq**<br>**km**|**Storm surge**<br>**area without**<br>**mangroves (sq**<br>**km)**|**Storm surge**<br>**area with**<br>**mangroves**<br>**(sq km)**|**Reduction in**<br>**area subject**<br>**to storm**<br>**surge due to**<br>**mangroves**|
|---|---|---|---|---|
|Indonesia|26,705|37,904|27,865|27%|
|Mexico|6,358|12,819|6,478|50%|
|Myanmar|4,935|7,873|5,612|29%|
|Papua New<br>Guinea|4,705|5,123|4,763|7%|
|Bangladesh|4,290|4,849|4,365|10%|
|Cuba|4,241|5,724|4,463|22%|
|India|3,821|7,875|4,159|47%|
|Venezuela,<br>RB|3,309|3,928|3,398|14%|
|Mozambique|2,891|4,076|3,071|24%|
|Philippines|2,482|3,947|2,849|28%|
|Remaining 36<br>countries|16,019|23,952|16,856|30%|
|**Total**|**79,756**|**118,070**|**83,879**|**29%**|

Source: Table 1 and authors estimates described in the text.

In unison, while not in the top 10 of mangrove coverage, there are a number of additional countries with significant mangrove coverage (at least 1,000 square kilometers) that benefit considerably, achieving at least a 25 percent reduction in the surge inundation. China (84 percent), Vietnam (54 percent), Pakistan (58 percent), Nicaragua (45 percent), and Honduras (35 percent) are illustrative examples. These findings illustrate the importance of careful review of the site selection for mangrove plantation to achieve effective coastal protection, as well as careful consideration in converting existing mangroves to other land uses.

# **4. Assessing the Impact of Sea‐Level Rise on Mangroves**

12