rafmacalaba/datause-encoder-data huggingface dataset:
6 Mexico MFA Mexico from U.S. components. This provision<sup>allows American apparel firms to export cut cloth fcr</sup> assembly in Mexico's in-bond industry (the<sup>_maquiladora_industry) and re-import the final goods,</sup> paying tariffs only on the value added in Mexico.<sup>In most cases, the facilities in Mexico used for</sup> assembly are owned by U.S. firms, so that<sup>Mexican apparel exports entering the U.S. under HTS</sup> 9802.00 consticute movements of goods within<sup>the same firm, even though they Are registered as</sup> imperts into the U.S. under the MFA (USITC,<sup>1991). HTS 9802.00 is essentially a tariff provision,</sup> but it has implications for rent sharing. If<sup>rents exist, then U.S. firms with assembly operations in</sup> Mexico owning a large snare of the quota will<sup>be the principal beneficiaries of the quota restrictions.3</sup> The evidence in Table 1 shows that for Mexican<sup>apparel exrports to the U.S. this is probably the case.</sup> From 1988 to 1991 between 85 and 90 percent<sup>of all Mexican appare' exports under the MFA entered</sup> the U.S. under HTS 9802.00 provisions, with<sup>between 60 and 70 percent of their value added in the</sup> U.S.

Table **1.** Percentage of MFA Exports from<sup>Mexico to the U.S.</sup> Entering Under HTS 9802.00

||1988||1989||1990||1991||
|---|---|---|---|---|---|---|---|---|
|||U.S.||U.S.||U.S.||"J.S.|
||9802.00|Value<br>Added|9802.00|Value<br>Adided|9802.00|Value<br>Added|9802.00|Value<br>Added|
|AllMFA|72.2|69.3|76.87|61.5|73.2|65.7|75.5|66.2|
|Apparl|84.9|69.4|90.68|61.5|88.32|66.3|89.52|66.3|
|Other|28.3|68.9|30.08|61.5|28.01|63.5|29.27|66.01|

Source: U.S. Department of Commerce, Office of Textiles.

The second institutional arrangement affecting<sup>Mexican exports of apparel into the U.S. is a</sup> special provision of the MFA known as the Special<sup>Regime. It effectively eliminates quotas under the</sup> MFA for apparel assembled in Mexico from<sup>fabric cut and formed in the U.S. The test for eligibility</sup> for the Special Regime quota treatment is more<sup>stringent than that for HTS 9802.00 tariff provisions</sup> 3This is not a case particular to Mexico. Large U.S. importers that also manufacture and assemble appaiel overseas, such as Liz Claiborne, often<sup>have claims to large fractions of available quota in developing countries.</sup>
ing economies. Specifically, we exploit the fact that (a) loans from official creditors (such as the World Bank, and other multilateral and bilateral aid agencies) finance a significant fraction of government spending, particularly in the low-income countries we study, and (b) disbursements of these loans, and the spending they finance, are spread out over many years following the approval of the loans. This permits the isolation of a predetermined source of variation in government investment that reflects only loan approval decisions made in previous years, and well before the associated spending takes place. Under the plausible identifying assumption that loan approval decisions made by official creditors do not anticipate future macroeconomic shocks that matter for private investment and output, this predetermined component of spending can be used as an instrument for government investment.

Using data on government and private investment in a panel of 39 lowincome countries, we find that government investment crowds in private investment: an extra dollar of government investment leads to 2 additional dollars of private investment, and 1.5 additional dollars of output in the subsequent period (though the 95% confidence intervals for both estimates are large). We then use these estimates to calibrate the CES production function. In contrast with the existing literature, which has mostly focused on the Cobb-Douglas case, we find that the data suggest a substantially higher degree of complementarity between government and private capital.

This higher degree of complementarity tends to imply more crowding in. The key point, however, is that for a given amount of crowding in, the implied rate of return on government investment is decreasing with the degree of complementarity. We use our estimated functional form to calibrate rates of return on government investment. Even with high complementarity, we find that, for most countries in our sample, the return to government investment exceeds the world interest rate, with excess returns between 5- 170%. However, this conclusion does not apply to countries in our sample 5
This paper is structured as follows. The next section describes the city profile, rainfall pattern and recurrent floods. Methodology and data sources are outlined in Section 3, followed by the discussion on findings on exposure, vulnerability and ability to respond in Section 4. Section 5 brings out the policy implications of the study, followed by the summary and conclusion in Section 6.

# 2. Profile of Colombo city, rainfall and flooding

Colombo is the largest city and commercial capital of Sri Lanka and is located adjacent to the administrative capital Sir Jayewardenepura-Kotte. Colombo city has been developed along the lowlying river estuary overlooking the western seafront and occupies an area of 37 sq. km.<sup>.2</sup> The city, with six administrative districts as shown in Figure 1, is under the jurisdiction of Colombo Municipal Council (CMC). CMC, with an annual budget of Lankan Rupees (LKR) 9 billion, is the largest local authority in Sri Lanka and one of the oldest in South Asia with operations since 1865. CMC has 15 departments with functional responsibility of public health, solid waste management, roads development, water, drainage, land, environmental development and social services. The role of CMC assumes great importance for the economy of the island nation, since Colombo metro area generates almost half of the country’s GDP.<sup>3</sup> Colombo district forms a major part of the Western province of Sri Lanka which is the most densely populated province in the whole country. The gross domestic product of this province was LKR 4,610,509 million (USD 24.78 billion<sup>4</sup> ) at current prices in 2015, which is 41.2% of the national GDP.<sup>5</sup> The per capita GDP of the Western province was LKR 771,117 (USD 4246.38) in the same year.<sup>6</sup> Important statistics for Colombo are given in Table 1 below.

_Table 1: Important statistics for Colombo_

|Totalpopulation of Colombo District|2.3 million<sup>7</sup>|
|---|---|
|Population in Colombo city|10,00,000(approx.)<sup>8</sup>|
|Residential|555,031 asper Census 2012|
|Floating|400,000(approx.)|
|Household monthlyincome(Colombo District)|LKR 77,723 in 2012-13<sup>9</sup>|
|Per capita monthlyincome(Colombo District)|LKR 19,346<sup>10</sup>|
|Officialpovertyline|LKR 4044perpersonper month<sup>11</sup>|

> 2 <u>http://www.cmc.lk.</u>

> 3 http://www.worldbank.org/en/news/feature/2014/07/02/helping-colombo-hold-back-next-flood.

> 4 1 USD = 132.8 LKR is the exchange rate used throughout this paper. This was the average exchange rate in 2015 when the study was carried out in Colombo.

> 5 Central Bank of Sri Lanka (2016), Sri Lanka Socio-Economic Data 2016, Vol. XXXIX, June 2016.

> 6 _Ibid._

> 7 Central Bank of Sri Lanka (2016), Economic and Social Statistics of Sri Lanka 2016, Vol. XXXVIII, June 2016.

> 8 City Profile available on Colombo Municipal Council website http://www.cmc.lk.

> 9 Govt. of Sri Lanka (2015), Household Income and Expenditure Survey – 2012/13, Department of Census and Statistics, Ministry of Policy Planning Economic Affairs, Child Youth and Cultural Affairs.

> 10 _Ibid._

> 11 http://www.statistics.gov.lk/poverty/monthly_poverty/.

4
**Table 1: Sample means of village explanatory variables**

|**Variables**|**Total**|**RPRP**|**DPIP**|
|---|---|---|---|
|**Village Demographics**||||
|Number of households (1000s)|0.61|0.64|0.55|
|Scheduled tribe (%)|0.18|0.16|0.22|
|Scheduled caste (%)|0.18|0.21|0.14|
|Other backward caste (%)|0.45|0.42|0.51|
|Other caste (%)|0.18|0.21|0.14|
|Economically active females (%)|0.10|0.02|0.24|
|Schooling by household head (years)|2.41|2.37|2.49|
|**Initial village conditions**||||
|Availability of public bus|0.66|0.74|0.53|
|Availability of telephone|0.55|0.60|0.46|
|If child marriage existed|0.48|0.47|0.48|
|If untouchability existed|0.27|0.24|0.32|
|If temple prostitution system existed|0.06|0.04|0.08|
|Ifcaste panchayats existed|0.39|0.32|0.49|
|**Number of observations**|659|409|250|

_Source:_ Own computation from CESS impact evaluation survey.

17
<!-- Start of picture text --> Compared with the ready alternatives, our count data approach to measuring export diversifica-<br>tion would appear to provide acceptable results. Given its ease of interpretation and direct link to<br>the theoretical model we develop in the next section, we focus on that measure in the remainder of<br>this paper. We are conscious, however, that its robustness is an important issue, and we therefore<br>return to it below in the context of our empirical model.<br>2.2 Measuring the Costs of Trade and Domestic Market Entry<br>Full details of the remaining elements in our dataset are presented in Table 2. Our bilateral<br>applied tariff data from the ITC-CEPII Market Access Map (MAcMap) database take full account<br>of preference schemes, and are available for 2004 only. We aggregate them from the HS 6-digit<br>level to the 2-digit level using the reference group methodology due to Laborde et al. (2007). This<br>aggregation scheme minimizes endogeneity bias by calculating weights based on import shares<br>in groups of similar countries, rather than in each country individually. We also draw historical<br>and geographical data from CEPI (Mayer and Zignago, 2006), while our macroeconomic data are<br>extracted from the World Development Indicators.<br>In addition to applied tariffs, we use new data from the World Bank’s Doing Business database<br>to measure trade costs. For the first time in 2006, the "Trading Across Borders" component of<br>Doing Business captures the total official cost for exporting a standardized cargo of goods ("Export<br>Cost"), excluding ocean transit and trade policy measures such as tariffs. The four main compo-<br>nents of the costs that are captured are: costs related to the preparation of documents required for<br>trading, such as a letter of credit, bill of lading, etc.; costs related to the transportation of goods<br>to the relevant sea port; administrative costs related to customs clearance, technical controls, and<br>inspections; and ports and terminal handling charges. The indicator thus provides a useful cross-<br>section of information in relation to a country’s approach to trade facilitation, in the broad sense<br>in which that term is used by Wilson et al. (2005). The data are collected from local freight for-<br>warders, shipping lines, customs brokers, and port officials, based on a standard set of assumptions,<br>including: the traded cargo travels in a 20ft full container load; the cargo is valued at $20,000; and<br>8<br><!-- End of picture text -->
<!-- Start of picture text --> (b) Linear GIC (a) Convex GIC<br>an 2 _ «gs 14<br>aa” 2 207 =<br>oo 40 — oo 12TM=<br>Bu £2 8a 2<br>22 = 22 10 fy<br>as 8 Pas<br>aa 7 2 3B a 2<br>33 6 S 33 7 a<br>zor 5 zen 5<br>44<br>33<br>TM ml<br>2 -1 0 1 2 -2 -1 0 1 2<br>Annual change in Gini (96) Annual change in Gini (90)<br>(c) $3.20 (d) $5.50<br>TM ~TM<br>45<br>ge_ 25 _ ge_ to _<br>aa” 25¢ 88° 375 =<br>Sto 75 5 Sto 325 5<br>38 18 3 S83 30 8<br>ge 6 3s 275 5<br>Ea 5c Ea_ 2<br>ae"£ 10 ae"5 22.5<br>7.5 20<br>TM TM<br>-2 -1 it} 4 2 -2 -1 0 1 2<br>Annual change in Gini (9) Annual change in Gini (9%)<br>(e) Poverty Gap (f) Squared Poverty Gap<br>“ 8 “ 6<br>2a 7 _ $8 5<br>ag7 6 & 8g~ =<br>= = 4<br>5S * Be 5S° 358<br>gsva45jEs7a 1 .5<br>55<br>TM “<br>-2 A 0 1 2 2 A it] 1 2<br>Annual change in Gini (9) Annual change in Gini (9)<br><!-- End of picture text -->
# **Figure A.4 Evidence from the Census on School Enrollment Rates of Young Children Aged 6, 7 and 8**

<!-- Start of picture text --> Rural Boys<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 2000 2005<br>6 Years 7 Years 8 Years<br><!-- End of picture text --> <!-- Start of picture text --> Urban Boys<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 2000 2005<br>6 Years 7 Years 8 Years<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girls Urban Girls<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>1990 2000 2005 1990 2000 2005<br>6 Years 7 Years 8 Years 6 Years 7 Years 8 Years<br><!-- End of picture text --> Sources: 1990 and 2000 Population Census and 2005 One Percent Population Sample. National Bureau of Statistics (Beijing).

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<!-- Start of picture text --> Percent<br>-2<br>3<br>-4<br>5<br>-6<br>-7<br>t t+1 t+2 t+3<br><!-- End of picture text --> <!-- Start of picture text --> Percent m Advanced Advanced economics s» EMDEs EMDEs<br>2<br>-4<br>6<br>.55<br>-10<br>t t+1 t+2 t+3<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent m Advanced Advanced economics s» EMDEs EMDEs<br>00<br>4<br>2<br>-2<br>3 -4<br>-4 6<br>5<br>-6 .55<br>-7 -10<br>t t+1 t+2 t+3 t t+1 t+2 t+3<br>Percent mBanking Currency Sovereign debt Percent0 mBanking Currency Sovereign debt<br>0 -4<br>-4<br>-8<br>-8<br>12 -12<br>-16 -16<br>-20 -20<br>World Advanced EMDEs World Advanced EMDEs<br>economics economics<br><!-- End of picture text --> <!-- Start of picture text --> Percent mBanking Currency Sovereign debt<br>0<br>-4<br>-8<br>12<br>-16<br>-20<br>World Advanced EMDEs<br>economics<br><!-- End of picture text -->
# **Table 5-a: Bank Regression Results (2000-2006)**

||Model (1)<br>Private credit<br>to GDP|Model (2)<br> <br>Log total<br>private credit<br>to GDP|Model (3)<br>Deposits to<br>GDP|Model (4)<br>Log total<br>assets of<br>deposit<br>money bank<br>to GDP|Model (5)<br>s<br>Net-interest<br>margin|Model (6)<br>Log interest<br>spread|Model (7)<br>Log overhea<br>to totalasset|Model (8)<br>d<br>s<br>ATMs per<br>100,000<br>people|Model (9)<br>Log deposits<br>per 1,000<br>people|Model (10)<br> <br>Log bank<br>branches per<br>100,000<br>people|
|---|---|---|---|---|---|---|---|---|---|---|
|Log GDP per capita|0.487|0.48|0.35|0.441|-0.25|-0.239|-0.17|1.016|0.787|0.708|
||[0.013]***|[0.014]***|[0.011]***|[0.012]***|[0.010]***|[0.011]***|[0.012]***|[0.065]***|[0.113]***|[0.051]***|
|Offshore|0.259|0.274|0.454|0.375|-0.014|-0.316|-0.144|0.141|0.388|0.52|
||[0.085]***|[0.087]***|[0.069]***|[0.074]***|[0.063]|[0.059]***|[0.075]*|[0.509]|[0.784]|[0.376]|
|Log fuel|-0.049|-0.044|-0.047|-0.017|0.012|0.002|-0.013|0.061|-0.027|-0.064|
||[0.009]***|[0.009]***|[0.007]***|[0.007]**|[0.008]|[0.006]|[0.010]|[0.048]|[0.075]|[0.038]*|
|Log poverty gap|-0.054|-0.078|-0.047|-0.023|0.099|0.094|0.078|0.09|-0.286|-0.165|
||[0.029]*|[0.030]***|[0.023]**|[0.025]|[0.021]***|[0.020]***|[0.024]***|[0.118]|[0.154]*|[0.095]*|
|Log population density|0.088|0.08|0.146|0.106|-0.077|-0.042|-0.079|-0.043|0.059|0.066|
||[0.013]***|[0.013]***|[0.010]***|[0.011]***|[0.009]***|[0.010]***|[0.011]***|[0.056]|[0.079]|[0.046]|
|Log population size|0.028|0.017|-0.006|0.046|-0.006|-0.08|-0.013|-0.061|0.067|0.04|
||[0.012]**|[0.013]|[0.010]|[0.011]***|[0.009]|[0.009]***|[0.011]|[0.058]|[0.094]|[0.046]|
|GDP per capita *|-0.006|0.004|0|-0.006|0.004|-0.003|0.003|0.003|-0.066|-0.008|
|population size|[0.002]***|[0.002]*|[0.002]|[0.002]***|[0.002]**|[0.003]|[0.002]|[0.007]|[0.057]|[0.006]|
|Year-fixed effects?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|986|985|981|984|984|815|993|88|54|97|
|Adjusted R-squared|0.65|0.65|0.66|0.68|0.5|0.48|0.3|0.8|0.61|0.71|
|<br>Standard errors in brackets<br>* significant at 10%; ** signi|ficant at 5%; **|* significant at|1%||||||||

19
average of father and mother‘s education as an indicator of parental education and re-estimate all of the regressions. The results in Table 10 show that if anything, the magnitudes of estimates are slightly larger in this new formulation. Thus our estimates of intergenerational correlations presented in the previous tables can be taken as conservative estimates of the effects of family background. The gender and geographic patterns and trends also remain unchanged with this alternative definition of parental education.

Our main empirical results indicate that sibling and intergenerational correlations in education in India remained largely unchanged over a period of almost a decade and a half (19932006) after the economic liberalization in 1991. The only group that experienced significant decline in the sibling and intergenerational correlations are women in urban areas. These results seem to contradict the evidence presented by Jalan and Murgai (2008) who find substantial improvements in educational mobility in India over time. They use 1998-99 NFHS data and find that the magnitude of the _intergenerational regression coefficient_ declines substantially for younger age cohorts. We discussed earlier the pitfalls in relying on cohort based analysis when data consists of only the coresident children. In fact, Jalan and Murgai (2008) are well aware of the limitations of the cohort analysis and discuss many of the same points we raised earlier. Our results thus can differ on two grounds: (i) intergenerational correlations take into account the declining variance in education in children‘s generation, and (ii) we compare the same age cohorts (16-27 years) across two surveys, instead of relying on the different age cohorts in a single survey round. It is, however, important to check if we get estimates similar to those of Jalan and Murgai (2008) from a cohort based analysis. We replicate the analysis in Jalan and Murgai (2008) and report the estimated intergenerational regression coefficients for both rural and urban areas for three age cohorts [15-19, 20-24 and 25-29 year] in Table 11. It is interesting that the estimates show a declining effect of parental education for the younger age cohorts for both the survey years which is consistent with the estimates in Jalan and Murgai (2008). This seems to justify the worry that the estimates for younger cohorts may be biased downward. As noted before, the estimated intergenerational regression coefficients are likely to be smaller for the younger cohorts simply because of the fact that some of the children have not completed their education. Also, since geographic mobility has increased over time, and better educated children usually migrate first, the downward bias in the estimate would be more pronounced in younger cohorts. On the other hand, estimates tend to be biased upward for older age cohort when parents co-reside with better educated children. This highlights the need for using separate survey rounds and multiple measures to understand the trends in intergenerational persistence given the data constraints in developing countries.

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- Steven J Davis, John Haltiwanger, Ron Jarmin, Javier Miranda, Christopher Foote, and Eva Nagypal. Volatility and dispersion in business growth rates: Publicly traded versus privately held firms [with comments and discussion]. _NBER macroeconomics annual_ , 21:107–179, 2006.

- Jan De Loecker and Jan Eeckhout. The rise of market power and the macroeconomic implications. NBER Working Paper 23687, National Bureau of Economic Research, August 2017. URL `http: //www` _._ `nber` _._ `org/papers/w23687` .

- Jan De Loecker and Frederic Warzynski. Markups and firm-level export status. _American Economic Review_ , 102(6):2437–71, 2012.

- Jan De Loecker, Pinelopi K Goldberg, Amit K Khandelwal, and Nina Pavcnik. Prices, markups, and trade reform. _Econometrica_ , 84(2):445–510, 2016.

- Amy Dittmar and Jan Mahrt-Smith. Corporate governance and the value of cash holdings. _Journal of Financial Economics_ , 83(3):599–634, 2007.

- Craig Doidge, G Andrew Karolyi, and Ren ́e M Stulz. The US listing gap. _Journal of Financial Economics_ , 123(3):464–487, 2017.

- Craig Doidge, Kathleen M Kahle, G Andrew Karolyi, and Ren ́e M Stulz. Eclipse of the public corporation or eclipse of the public markets? _Journal of Applied Corporate Finance_ , 30(1):8–16, 2018.

- Andrea L Eisfeldt and Dimitris Papanikolaou. The value and ownership of intangible capital. _American Economic Review_ , 104(5):189–94, 2014.

- Antonio Falato, Dalida Kadyrzhanova, and Jae Sim. Rising intangible capital, shrinking debt capacity, and the US corporate savings glut. _FEDS Working Paper No.2013-67_ , 2013. Available at SSRN: https://ssrn.com/abstract=2350863.

- Michael Faulkender and Mitchell Petersen. Investment and capital constraints: repatriations under the american jobs creation act. _The Review of Financial Studies_ , 25(11):3351–3388, 2012.

- Michael Faulkender, Kristine Hankins, and Mitchell Petersen. Understanding the rise in corporate

32
ownership measures. The standard ownership measure used in the literature is constructed as the number of shares held by certain investors (e.g. domestic or foreign investors) divided by the total number of outstanding shares at a given point in time. As described in Section II, Worldscope reports the number of outstanding shares at year-end. However, our database on holdings is not as consistent – portfolio holdings are reported on different dates. Therefore, we are not able to construct confidently an ownership measure given that numerator and denominator would not be consistently measured at the same point in time. Hence, we focus on two other variables. First, we analyze a dummy variable capturing mutual fund ownership – it equals one when a firm is held by a certain mutual fund type and zero otherwise. In this case, maximum-likelihood logit models are estimated.<sup>22</sup> Alternatively, we use count data: the number of funds with a certain mandate that holds assets of a given firm in a certain year. The count variables however contain evidence of over-dispersion, and thus negative binomial maximumlikelihood models are estimated.<sup>23</sup> Both variables capture the relevant information for the purposes of the analysis conducted here. We are able to quantify the impact of firm-level attributes on the portfolio choice of mutual funds, although we are not able to measure their impact on the size of mutual fund investments. Multivariate models dividing mutual funds according to their mandate, namely global and specialized funds, are estimated. The multivariate setup accounts for any residual correlation between the equations for different investor types. We are thus able to perform hypothesis tests on the estimated coefficients across equations.

> 22 We do not report estimates based on a linear probability models as it predicts probabilities outside the 0-1 range for a significant number of observations in our sample.

> 23 A poisson model assumes that the mean and the variance of the count data are equal. However, in our dataset, the variance is considerably larger, making the negative binomial model more appropriate.

25