rafmacalaba/datause-encoder-data huggingface dataset:
primary channel through which protection has been ratcheted up around the world. We refer to such accords as Intellectual-Property-Related PTAs, or IPAs.

The TRIPS Agreement has been studied empirically in broad terms, with papers asking whether its adoption by members has affected such economic outcomes as trade and foreign direct investment (FDI). The role of PTAs that feature strong IPRs standards has attracted much less attention, however. These agreements, which have steadily proliferated since the mid-1990s, are a potentially significant determinant of trade and investment patterns and innovation activities. As such, they deserve systematic study, which we undertake in this paper. Specifically, we build on an existing working paper (Maskus and Ridley, 2020), which studied the impact of membership in IPAs, where one partner is the US, the EU, or EFTA, on the composition of aggregate and bilateral trade, controlling for compliance with TRIPS standards. Here we delve deeper by using specific aspects of such agreements, as measured by the World Bank’s database on the depth of trade agreements (Hofmann, et al, 2017).

The relationship between intellectual property standards and trade is theoretically ambiguous and depends on context. Most important, patents and copyrights are different from import barriers. A cut in import tariffs is a reduction in trade costs, implying generally higher trade. Lower trade barriers expose domestic firms to competition, typically reducing market power and markups. Intellectual property rights, however, create temporary monopolies in the use, including trade, of technologies and goods. These exclusive rights permit their owners to decide where, when, and how they will produce, export, license, and sell protected products and technologies.

A small number of studies have estimated the trade effects of TRIPS, using broad measures. Thus, Ivus (2010) found that a group of developing countries (those that were not British or French colonies) that were obligated by membership in the WTO agreement to adopt stronger patent reforms than a similar group that did not have to reform as much, experienced significantly higher import growth in high-technology products. Delgado et al. (2013) studied a more comprehensive set of developing countries and found that, after implementation of TRIPS patent rules, there was a significant rise in imports of patent-intensive goods. Maskus and Yang (2018) found a significantly positive effect of changes in an index of national patent reforms in the TRIPS era on the growth of R&D-intensive sectoral exports in both emerging and developed 3
# When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries<sup>1</sup>

M. Shahe Emran IPD, Columbia University William Greene New York University Forhad Shilpi DECRG, World Bank Key Words : Coresidency, Truncation Bias, Intergenerational Mobility, Developing Countries, Intergenerational Regression Coefficient (IGRC), Intergenerational Correlation (IGC), LSMS, HIES, Bangladesh, India JEL Codes : O12, J62

> 1 We would like to thank Matthew Lindquist, Hector Moreno, Tom Hertz, Claudia Berg and seminar participants at NEUDC 2015 at Brown University for helpful comments on earlier versions, and Gabriela Aparicio for help with data at the early stage of this project. An earlier version of the paper was circulated under the title "When Measure Matters: Coresident Sample Selection Bias in Estimating Intergenerational Mobility in Developing Countries". Email for correspondence: shahe.emran@gmail.com, fshilpi@worldbank.org.
upper-consumption quintiles (44.5 percent), while in the southern region this figure corresponded to only 16.7 percent (Figure 5b). Households in the lowest consumption quintiles are more likely to be less prepared to face a natural disaster (see section 4), which shows the particular vulnerability of this area of the country to the August 2021 earthquake.

**Figure 4** **_:_ Percentage of households in the southern region affected by the 2021 earthquake according to consumption quintile groups (2021-2023)**

<!-- Start of picture text --> 78 76.6<br>71.9<br>65.6 61.1 65.4 63.3 68<br>58.1<br>Low consumption quintiles (Bottom Middle & High consumption quintiles Total<br>40 percent) (Top 60 percent)<br>Nov/Dec 2021 Nov/Dec 2022 Mar/Apr 2023<br>Note: Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated.<br>Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Results for 2023 are from entire sample of<br>Figure 5 : Percentage distribution of households in the two bottom and the middle -and high-consumption<br>quintiles by region (2021-2023)<br>a) Two bottom quintiles b) Middle and high quintiles<br>Nov/Dec 2021 29.6 22.9 24.1 23.5 Nov/Dec 2021 42 23.4 18.6 16.1<br>Nov/Dec 2022 25.6 24 27.4 23 Nov/Dec 2022 45.7 22.4 17.5 14.3<br>Mar/Apr 2023 27.1 22.7 25 25.2 Mar/Apr 2023 44.5 22.7 16.7 16.1<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Ouest Central Southern Northern Ouest Central Southern Northern<br><!-- End of picture text --> Note: Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Results for 2023 are from entire sample of wave 4.

**Figure 5** **_:_ Percentage distribution of households in the two bottom and the middle -and high-consumption quintiles by region (2021-2023)**

Note: Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Results for 2023 are from entire sample of wave 4.

8
53

**Table 6**

**Tests of Unit Roots for Series with Structural Breaks**

||Log of stock<br>market prices in<br>real terms<br>(LGPRLCR)|6-month moving<br>average of capital<br>flows proxy<br>(KFLO6)|Domestic credit<br>in real terms<br>(DCR)|Domestic<br>credit growth<br>in real terms<br>(DCRG)|
|---|---|---|---|---|
|Argentina|1) 89:02|1) 91:09|1) 89:06|91:05|
||2)|2)|2) 84:05, 90:04||
||3) 89:02|3) 91:10|3) 82:08, 89:06||
||SETAR* 89:02<br>& 91:06||||
|Brazil|1) 86:11|1)|1) 89:11|92:02|
||2) 90:10|_|2) 91:09|_|
||3)|3) 92:01|3)||
||SETAR* 93:04||||
|Chile|1)|1) 91:09, 96:07|1) 89:05|82:06?|
||2) 91:07|2) 97:09|2) 83:07, 91:02||
||3)<br>_|3) 91:09, 97:12|3)||
|Colombia|1) 91:11|1|1)|91:10|
||2) 94:05|2) 97:02|2) 97:01||
||3) 91:06,98:02|3)|3)||
|Mexico|1)|1) 94:03|1)|88:08|
||2) 91:11|2) 91:10|2) 92:07||
||3) 86:05, 94:10|3)|3)||
|Peru|1) 97:10|1) 94:03|1) 88:02|90:08|
||2)<br>|2) 96:09<br>|2) 89:12||
|____________|_3)|3)|3)||

Note: Numerals on the table denote the equation used below. The methodology followed for unit root tests is that as described in Zivot and Andrews (1992) and Vogelsang and Perron (1998). Initially we use the "innovational outlier" (10) model where a dummy for a break in the level is allowed along with a dummy for a break in the trend at an unknown time (equation 1). We used two restricted models (equation 2 and 3), when only a break in the trend or a break in the level were more appropriate. Also to complement the break in the level model, we made use of the SETAR method as described by S.M Potter (1995).

where _DU=_ I if **t>Tbt** and 0 otherwise, _DT =_ t - Tb if t > Tb, and 0 otherwise, k **=** number of lags according to the ADF general to specific methodology for lag selection.
Khandker, Shahidur R, Hussain A Samad, Rubaba Ali, and Douglas F Barnes. 2014. "Who benefits most from rural electrification? Evidence in India." _The Energy Journal_ :75-96.

Khanna, Madhulika, Nishtha Kochhar, and Nethra Palaniswamy. 2015. "A retrospective impact evaluation of the Tamil Nadu empowerment and poverty alleviation (Pudhu Vaazhvu) project." _The Journal of Development Studies_ 51 (9):12101223.

Khatun, Dilruba, and BC Roy. 2012. "Rural livelihood diversification in West Bengal: determinants and constraints." _Agricultural Economics Research Review_ 25 (347-2016-16910):115.

Lance, Peter M, David K Guilkey, Aiko Hattori, and Gustavo Angeles. 2014. "How do we know if a program made a difference? A guide to statistical methods for program impact evaluation." Macours, Karen, Patrick Premand, and Renos Vakis. 2012. "Transfers, Diversification and Household Risk Strategies: Experimental evidence with lessons for climate change adaptation." _World Bank Policy Research Working Paper_ (6053).

Majumdar, Shruti, and Vijayendra Rao. 2017. "On the frontlines of scaling-up: a qualitative analysis of implementation challenges in a CDD project in rural India." Ministry of Rural Development. 2019. "Deendayal Antyodaya Yojana - NRLM." Accessed 25-02-2019. <u>https://aajeevika.gov.in/.</u> Parajuli, Dilip, Gayatri Acharya, Nazmul Chaudhury, and Bishnu Bahadur Thapa. 2012. "Impact of social fund on the welfare of rural households: evidence from the Nepal poverty alleviation fund." Prennushi, Giovanna, and Abhishek Gupta. 2014. "Women's empowerment and socio-economic outcomes: impacts of the Andhra Pradesh rural poverty reduction program." _World Bank Policy Research Working Paper_ (6841).

Rosenbaum, Paul R, and Donald B Rubin. 1983. "The central role of the propensity score in observational studies for causal effects." _Biometrika_ 70 (1):41-55.

Sanghi, Sunita, A Srija, and Shirke Shrinivas Vijay. 2015. "Decline in rural female labour force participation in India: A relook into the causes." _Vikalpa_ 40 (3):255-268.

Sanyal, Paromita, Vijayendra Rao, and Shruti Majumdar. 2015. "Recasting culture to undo gender: A sociological analysis of Jeevika in Rural Bihar, India." Sen, Amartya. 2001. "The many faces of gender inequality." _New republic_ :35-39.

Sherwood, Ben, Lan Wang, and Xiao‐Hua Zhou. 2013. "Weighted quantile regression for analyzing health care cost data with missing covariates." _Statistics in medicine_ 32 (28):4967-4979.

Titus, Marvin A. 2007. "Detecting selection bias, using propensity score matching, and estimating treatment effects: An application to the private returns to a master’s degree." _Research in Higher Education_ 48 (4):487-521. World Bank. 2011. India - National Rural Livelihoods Project. Washington D.C.: The World Bank.

World Bank. 2018. India - National Rural Economic Transformation Project : additional financing (English). Washington D.C. : The World Bank.

26
with the export tariff exposure.<sup>17</sup> The coefficient of the imported input tariff exposure does not change at all, while the export tariff exposure coefficient is marginally reduced in magnitude. There is some weak evidence that the subsidy directly had a positive impact on job postings when included alongside the interaction term in column (3), but this effect does not appear to be any stronger for commuting zones that were more exposed to retaliatory tariffs as was the declared intention of the US Administration.<sup>18</sup> Here a one-standard-deviation increase in subsidies ($25,100,000), led to a 2.5 percent increase in postings.

The weakness of the subsidy results may be due to several factors. The first is that the effect is small and difficult to identify, especially given the few months included in the sample after the announcement of the subsidies. The second is that the online job adverts data may insufficiently capture farm labor, and particularly informal farm labor. Third, due to the construction of the export tariff exposure measure using CBP data, which excludes farm labor, the results may insufficiently capture the part of retaliatory tariff exposure which affected farm labor. Hence, the agricultural subsidies would be less effective at pushing back against this negative impact in this specification.

# **5.3 Impact on different skill groups and occupations**

Table 5 displays the baseline regressions run separately for high skilled and low skilled job adverts. The classification here categorises 2-digit SOC codes into skill levels based on whether the average number of years of education required is greater than or equal to 14 (discussed further in Section 3). Panel A shows the results for low skilled job adverts, while Panel B shows the results for high skilled job adverts.

Relative to the aggregate baseline coefficients, presented for reference in column (6), the low skilled results remain significant and increase slightly in magnitude for both imported input and export tariff exposure measures. The opposite is seen for high skilled results, with coefficients that are smaller in magnitude than the aggregate results. It therefore appears that it is lower skilled workers that are disproportionately affected by both the imported input

> 17All specifications use the slightly reduced dataset with non-missing subsidy values which covers 615 instead of 625 CZs

> 18If anything, the coefficient on the interaction term is negative although it has a p-value of 0.135. If the coefficient were significant, it would lead to a negative overall effect for export tariff exposures of over 2.69, is the case in only 164 CZ-year-month observations spread across 21 out of 615 commuting zones.

27
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income relative to men’s income. Since, on average, women earn less than men, deregulation helped reduce total income inequality by reducing the gender income gap.

This paper relates to an enormous literature on the determinants and consequences of the distribution of income. First, the international policy community increasingly emphasizes the benefits of providing the poor with greater access to financial services as a vehicle for fighting poverty and reducing income inequality. While the experience of branch deregulation within U.S. states may not generalize perfectly to developing economies, the results do raise the possibility that financial development helps the poor primarily by boosting wage income, not by increasing the business income of the poor. This warrants further research. Second, many authors examine income redistribution as a mechanism for reducing the inefficient propagation of relative incomes across generations (Aghion, Caroli, and Garcia-Penalosa, 1999; Galor and Moav, 2006). Unlike financial liberalization, however, redistribution has adverse incentive effects. Thus, our work contributes to the policy debate on how to address socially inefficient income inequality. Third, we contribute to recent cross-country analyses of finance and the distribution of income. Beck, Demirguc-Kunt and Levine (2007) find that banking sector development reduces income inequality and poverty. In contrast, we analyze the impact of a specific, exogenous policy change on the distribution of income, rather than examining an overall index of financial sector development. By using the differences-in-differences approach across U.S. states, a consistent source of income inequality data, and a specific policy event, we increase the power of the econometric tests, reduce potential biases due to measurement error, and reduce concerns about omitted variables and endogeneity.

This paper also relates to a substantive body of work on the effects of branch deregulation. Researchers examine the impact of intra-state branch deregulation on economic growth (Jayaratne and Strahan, 1996; Huang, 2007), the entry of non-financial companies (Black and Strahan, 2002), 6
analysis first proceed by using spectral indices derived from these bands. These measure certain land use patterns and can be easily interpreted. The regressions use the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) which are non-linear combinations of two Landsat bands each.<sup>9</sup> where _Red_ corresponds to the Landsat band 3 (red light), NIR is the Near Infrared measurement of band 4, and SWIR is the value of the Shortwave Infrared band 5. These indices are designed to measure urban areas and vegetation by capturing typical spectral signatures of these features. Their values range between -1 and 1, and a higher index value corresponds to more vegetation and built-up area presence respectively. For example, the NDVI measure is designed to capture live green vegetation on the ground. For photosynthesis, live plants absorb visible light (low wavelengths) but reflect infrared light (higher wavelengths), thus a higher value of _NIR − Red_ indicates presence of vegetation. The NDVI has been used in the environmental sciences to distinguish vegetation from other land uses. Similarly, NDBI is designed to detect land cover typical of urban areas (Zha et al., 2003).

These indices are chosen for several reasons: 1) They are simple transformations of the raw Landsat data that can be easily calculated by applied researchers. 2) They directly correspond to land uses that are man-made (cities, agriculture) and are plausibly correlated with economic activity. 3) They have been commonly used in other fields and can in principle be replicated with other satellite products that measure spectral light such as Sentinel-2. All measures follow a distribution which can be approximated by a normal distribution, although there are thick tails in the right (NDBI) and the left (nightlights, NDVI) end of the distributions (see density figures in the appendix).

> 9A third commonly used indicator is the normalized difference water index (NDWI) measuring water bodies. In this setting, NDVI and NDWI are highly negatively correlated. The latter is excluded to avoid issues of multicollinearity.

13
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# Tae 13: Rural **hooWome** MutWUe Acros Stats with _Dtrffhg_ **Agricultural** hcomes

||Change in Nonfarm<br>One Rupee Increase<br>Rural<br>Areas|Income Resulting<br>In Agricultural In<br>Rural<br>Townsal|from<br>come<br>Rural Areas<br>Plus Rural Townsb;|
|---|---|---|---|
|PunJab/Haryana|.34|.59|.93|
|All India average|.25|.39|.64|
|Karnataka/Gujarat|.24|.40|.63|
|Madhya Pradesh/Blh|ar<br>.18|.28.|.46|

a/ Rural towns are localities betwen 5,000 and 100,000 in population.

b/ Sirc separate equatns are obtained from the 2SLS-PH district regressions as described in Table A.13. The state-specific muftipiwrs are simizy derived. They vary because the average rod desity, per capita income and population density all differ across states. For details, see Table A.14.
# **Figure 1** Exporter premia and GDP per capita

<!-- Start of picture text --> COLOMBIA<br>CHINA<br>BELGIUM<br>SPAIN<br>FRANCE<br>CHILE IRELAND<br>GERMANY DENMARK<br>AUSTRIA<br>SLOVENIA<br>UK<br>ITALY<br>SWEDEN<br>5 10 15 20 25 30<br>GDP per capita based on PPP (1,000 Current international dollars)<br>20<br>15<br>10<br>Exporter premia<br>5<br>0<br><!-- End of picture text -->

## Footnote:

The exporter productivity premium shows the average percentage difference in labour productivity between exporters and non-exporters. It is computed for each country from the β coefficient estimated of model (1) in the text, including a whole set of fixed firm effects. The estimate of Germany is the simple average of the estimates of East and West Germany. Gross Domestic Product per capita (GDPpc) corresponds to year 2000. It is based on purchasing-power-parity (PPP) values and expressed in 1,000 current international dollar. It has been obtained from: International Monetary Fund, World Economic Outlook Database, April 2007. The estimated equation of the fitted regression line is: Export Premia=12.48-0.25 GDPpc; R2 =0.35 (p-value in parenthesis) (0.00) (0.03) If Colombia and Sweden are excluded, the estimated equation is: 2 Export Premia=9.34-0.11 GDPpc; R =0.18 (p-value in parenthesis) (0.00) (0.16) 60
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**Table 8: Portfolio Composition of MPFs (% of total), 1998 – 2004**

||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|---|---|---|
|Cash|10.8|2.8|1.3|1.1|3.3|0.7|0.6|
|Time Deposits/CDs|3.5|0.3|0.1|0.1|0.7|0.4|0.5|
|Government securities|76.7|83.4|77.6|80.0|68.0|69.9|72.7|
|Direct Equity|6.6|9.8|14.8|11.6|8.9|9.3|7.8|
|Corporate and FI Bonds|1.7|1.7|2.3|3.0|4.2|3.0|1.7|
|Investment notes|0.3|1.7|2.6|2.3|7.1|7.2|8.2|
|Other|0.4|0.3|1.3|1.9|7.8|9.5|8.5|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Memo:||||||||
|Est. Total Equity|6.8|11.2|16.7|13.4|14.6|15.1|14.4|
|Est. Foreign Assets|3.5|5.6|8.3|6.7|7.3|7.5|7.9|

Source: HFSA

**Table 9:   Portfolios of Pension Funds in Hungary and Other Reforming Countries (%)**

|**Country**|**First**<br>**Year of**<br>**Operation**|**Gov**<br>**Bon**<br>**7**<sup>**th**</sup><br>**Year**|**t.**<br>**ds**<br>**2004**|**Finan**<br>**Institu**<br>**7**<sup>**th**</sup><br>**Year**|**cial**<br>**tions**<br>**2004**|**Equ**<br>**7**<sup>**th**</sup><br>**Year**|**ity**<br>**2004**|**Fore**<br>**Ass**<br>**7**<sup>**th**</sup><br>**Year**|**ign**<br>**ets**<br>**2004**|
|---|---|---|---|---|---|---|---|---|---|
|**Hungary**|**1998**|**72.7**|**72.7**|**2.2**|**2.2**|**14.4**|**14.4**|**7.9**|**7.9**|
|Argentina|1994|56.0|62.0|15.6|6.6|12.3|12.7|4.5|10.3|
|Bolivia|1997|64.1|67.5|7.3|5.6|8.6|7.5|1.7|1.5|
|Chile|1981|41.4|18.7|49.4|29.5|6.2|42.8|0.0|27.2|
|Colombia|1994|-|48.5|-|16.6|-|6.2|-|6.7|
|Costa Rica|2001|-|73.1|-|14.2|-|0.1|-|0.0|
|El Salvador|1998|83.5|83.5|10.5|10.5|0.2|0.2|0.0|0.0|
|Mexico|1997|82.3|85.5|4.5|4.9|0.0|0.0|0.0|0.0|
|Peru|1993|7.1|24.2|39.3|11.4|37.1|37.7|0.0|10.2|
|Poland|1999|-|59.6|-|-|-|32.8|-|2.2|
|Uruguay|1996|55.5|57.9|39.6|36.9|0.0|0.0|0.0|0.0|

Source: HFSA, AIOS, FIAP
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