rafmacalaba/datause-encoder-data huggingface dataset:
Obstfeld, Maurice, 2005, “The Renminbi’s Dollar Peg at the Crossroads.” Mimeo, University of California, Berkeley.

- Obstfeld, Maurice and Kenneth Rogoff, 2005, “Global Current Account Imbalances and Exchange Rate Adjustments.” _Brookings Papers on Economic Activity_ , 2005(1), 67146.

- Patnaik, Ila and Ajay Shah, 2006a, “The Interplay Between Capital Flows and the Domestic Indian Financial System.” Mimeo.

- Patnaik, Ila and Ajay Shah, 2006b, “India’s Experience with Capital Flows: The Elusive Quest for a Sustainable Current Account Deficit.” Forthcoming in Edwards, Sebastian (ed.), Capital Controls and Capital Flows in Emerging Economies: Policies, Practices <u>and Consequences.</u> _University of Chicago Press_ .

- Prasad, Eswar (ed.), 2004, “China’s Growth and Integration into the World Economy,” IMF Occasional Paper No. 232.

- Prasad, Eswar, Kenneth Rogoff, Shang-Jin Wei, and Ayhan Kose, 2003, “Effects of Financial Globalization on Developing Countries: Some Empirical Evidence.” IMF Occasional Paper No. 220.

- Prasad, Eswar and Raghuram Rajan, 2005, “Controlled Capital Account Liberalization: A Proposal.” IMF Policy Discussion Paper No. 05/7.

- Prasad, Eswar and Raghuram Rajan, 2006, “A Framework for Independent Monetary Policy in China.” IMF Working Paper No. 06/111.

- Prasad, Eswar and Shang-Jin Wei, 2005, “China’s Approach to Capital Inflows: Patterns and Possible Explanations.” IMF Working Paper No. 05/79.

- Ranciere, Romain, Aaron Tornell, and Frank Westermann, 2005, “Systemic Crises and Growth.” NBER Working Paper No.11076.

- Rodrik, Dani, 2005, “Growth Strategies.” In Aghion, Philippe and Durlauf, Steven (eds.), <u>Handbook of Economic Growth.</u> _North-Holland_ .

- Rodrik, Dani, 2006a, “Goodbye Washington Consensus, Hello Washington Confusion?” Mimeo, Harvard University, Kennedy School of Government.

- Rodrik, Dani, 2006b, “The Social Cost of Foreign Exchange Reserves.” Forthcoming in _International Economic Journal_ .

- Setser, Brad, 2005, “The Chinese Conundrum: External Financial Strength, Domestic Financial Weakness.” CESifo Conference “Understanding the Chinese Economy.” Munich, June.

- Shah, Ajay, 1995, “The Indian IPO Market: Empirical Facts.” Technical Report, Centre for Monitoring Indian Economy.

- Shah Ajay and Susan Thomas, 1998, “Market Microstructure Considerations in Index Construction.” In <u>CBOT Research Symposium Proceedings, 173–193.</u> _Chicago Board of Trade_ .

- Sharma, Kishor, 2000, “Export Growth in India: Has FDI Played a Role?” Yale University Economic Growth Center Discussion Paper No. 816.

- Shen, Jian-Guang, 2001, “China’s Exchange Rate System after WTO Ascension: Some Considerations.” Bank of Finland Institute for Economies in Transition.

- Summers, Lawrence, 2006, “Reflections on Global Account Imbalances and Emerging Markets Reserve Accumulation.” L.K. Jha Memorial Lecture, Reserve Bank of India, Mumbai, March.

28
illustrations are briefly considered in section 4, based on data from Indonesia for 19932002. Concluding remarks are made in section 5. In particular, it is found that the overall poverty reduction observed in Indonesia for the period under consideration was not pro-poor because a distributionally neutral growth pattern would have done more.

# **_2.  Feasible Growth Patterns_**

Given our choice of benchmark, we need a calculus within which the effects of distributional shifts induced by the growth process can be readily quantified and analyzed. We rely on a little-noticed approach developed in Lambert (1984) to define and characterize a “growth pattern” in terms of its feasibility and pro-poorness.

Let an individual’s income be _x_ and let _y_ stand for total income in a population of size _N_ . If _f(x)_ represents the frequency density function for income, then total income is defined by the following expression.

where _mx_ is the maximum income. Denote by _q(x)_ the point elasticity of _x_ with respect to _y._ The function _q(x)_ is a measure of the responsiveness of individual incomes to changes in the overall income. In other terms, if total income _y_ grows by 1%, _x_ grows by _q(x)_ percent. This elasticity is defined formally as follows The function _q(x)_ defines a growth pattern and is the anchor of our measure of propoorness.<sup>2</sup> Ravallion and Chen (2003) define their _growth incidence curve_ (or _GIC_ **)** as the growth rate of income at the _p_<sup>th</sup> percentile point of the income distribution. Thus, expression (2.2) indicates that our growth pattern is essentially a normalized _GIC._<sup>3</sup> The function _q(x)_ must obey a feasibility constraint in order to be considered a legitimate representation of a growth pattern. Suppose the overall income increases by

> 2  Note that population growth _per se_ does not influence _q(x),_ which could alternatively be defined as the elasticity of **x** with respect to the mean income _y/N._

> _dx_ 3 Ravallion and Chen’s growth incidence curve takes the form _gRC(p) =_ when _p_ = ∫ 0<sup>_x_</sup> _f_ ( _t_ ) _dt_ . In other _x_

> _dy_ words, from (2.2), _gRC_<sup>_(p) =q(x)_</sup> . _y_

4
<mark>“Upholding the Malay Language and Strengthening the Command of English.” A</mark> lthough English would now only be learned as a second language, more emphasis would be placed on building students’ English language skills by hiring retired teachers, employing additional assistant teachers for larger classes, and having extra class hours (Chapman, 2009). Table 1 summarizes the multiple shifts in the language of instruction policies in Malaysia over the past few decades.

In this paper, we focus on the shift in the language of instruction policy in 2003 to gauge its impact on the mathematics and science test scores of Malaysian students. Prior to the implementation of PPSMI, students learned mathematics and science in BM, Mandarin or Tamil – depending on the type of primary schools they attend – and when they progressed to secondary schools, they learned the two subjects in BM. With the implementation of PPSMI, students entering primary schools for the first time since 2003 had to learn the two subjects in English regardless of the types of primary schools they attend. Similarly, students progressing to secondary schools from 2003 onwards also had to learn the two subjects in English. On the other hand, students who had already started their primary or secondary schools prior to 2003 were not affected by the policy change. How PPSMI was applied to different types of schools and levels of education is summarized in Table 2.

Since students sort into different types of primary schools based on their ethnicity, the prePPSMI language of instruction in a primary school and a student’s ethnicity are highly related. National primary schools enroll mostly ethnic Malay students, Chinese National-type primary schools enroll mostly ethnic Chinese students, while Indian National-type primary schools enroll mostly ethnic Indian students.<sup>1</sup> Nonetheless, a sizable number of ethnic Chinese (8%) and Indian

> 1 According to the Ministry of Education of Malaysia’s (2012) statistics for 2000, 98% of ethnic Malay primary school students enrolled in a National school, 92% of ethnic Chinese primary school students enrolled in a Chinese Nationaltype school, and 49% of ethnic Indian students enrolled in a Tamil National-type school.

8
population density for Mexican _municipios_ in our sample is 172 inhabitants per square kilometer.<sup>16</sup> Data on the percentage of household heads who speak an indigenous language and information on the average years of schooling of household heads also comes from the 2000 census. On average, 24.2 percent of household heads speak an indigenous language. The average years of schooling received by household heads is 4.46. Finally, our estimations also control for the distance of each _municipio_ to Mexico City, where most bank headquarters are located, and distance to the US border. We calculated the distances from data on the geographical coordinates of each _municipio_ used by López Córdova (2005), and originally obtained from INEGI.17 The average distance to Mexico City is 463 kilometers, while the average distance to the US border is 750 kilometers.

# **4. Empirical specifications and results**

Our baseline empirical specifications follow equation (2) below: _Banking Breadth_ i (or _Depth_ i) = α 0 + α _2 Remittances_ i + α _2GDP per capita_ i + α 3 _Density_ i+ ε i (2) where _i_ refers to the _municipio_ identifier. _Banking Breadth_ is measured by the number of branches and, separately, deposit accounts per capita. _Banking Depth_ refers to the ratio of the amount (in pesos) of deposits to GDP and loans to GDP. _GDP per capita_ is measured in thousands of dollars and _Density_ refers to the ratio of population to area.

We first estimate equation (2) over the entire sample of _municipios_ outside of Mexico City. Because there is a mass of _municipios_ without bank branches, deposits, etc., we estimate (2) using a Tobit specification. Given the fact that only a handful of the _municipios_ in which all of the population resides in rural communities have bank branches, we also estimate the regressions on the sample excluding these all-rural _municipios_ . We then check for robustness by including additional controls in the regressions—the percentage of household heads who speak

> 16 Population density in 2000 for the country as a whole was 51 inhabitants per squared kilometer. The larger number we obtain reflects the fact that _municipios_ with smaller land area are more densely populated.

> 17 We calculated distances in kilometers using Stata’s _sphdist_ command.

12
- **Khanna, Tarun and Krishna Palepu** , “Is Group Affiliation Profitable in Emerging Markets? An Analysis of Diversified Indian Business Groups,” _The Journal of Finance_ , 2000, _55_ (2), pp. 867–891.

- **Kyle, Albert S** , “Continuous auctions and insider trading,” _Econometrica: Journal of the Econometric Society_ , 1985, pp. 1315–1335.

- **Massa, Massimo and Zahid Rehman** , “Information flows within financial conglomerates: Evidence from the banksmutual funds relation,” _Journal of Financial Economics_ , 2008, _89_ –

- (2), 288 306.

- **Masulis, Ronald W, Peter Kien Pham, and Jason Zein** , “Family business groups around the world: financing advantages, control motivations, and organizational choices,” _Review of Financial Studies_ , 2011, p. hhr052.

- **OECD** , “Corporate Governance of Company Groups in Latin America,” _OECD Publishing, Paris_ , 2015.

- **Parrino, Robert, Richard W Sias, and Laura T Starks** , “Voting with their feet: institutional ownership changes around forced _{_ CEO _}_ turnover,” _Journal of Financial_ –

- _Economics_ , 2003, _68_ (1), 3 46.

- **Pedraza, Alvaro** , “Strategic interactions and portfolio choice in money management: Theory and evidence,” _Journal of Money, Credit and Banking_ , 2015, _47_ (8), 1531–1569.

- **, Fredy Pulga, and Jose Vasquez** , “The information advantage of domestic investors: Evidence from the Colombian stock market,” _Universidad de la Sabana, Mimeo_ , 2016.

- **Petersen, Mitchell** , “Estimating Standard Errors in Finance Panel Datasets: Comparing Aproaches,” _Review of Financial Studies_ , 2009, _22_ (1), 435–480.

- **Porta, Rafael, Florencio Lopez de Silanes, and Andrei Shleifer** , “Corporate ownership around the world,” _The journal of finance_ , 1999, _54_ (2), 471–517.

- **Porta, Rafael La, Florencio LopezdeSilanes, Andrei Shleifer, and Robert W. Vishny** , “Law and Finance,” _Journal of Political Economy_ , 1998, _106_ (6), pp. 1113–1155.

- **Puckett, Andy and Xuemin Sterling Yan** , “The interim trading skills of institutional investors,” _The Journal of Finance_ , 2011, _66_ (2), 601–633.

- **Raddatz, Claudio and Sergio L. Schmukler** , “Deconstructing Herding: Evidence from Pension Fund Investment Behavior,” _forthcoming, Journal of Financial Services Reasearch_ , 2012.

- **Rhee, S. Ghon and Jianxin Wang** , “Foreign institutional ownership and stock market –

- liquidity: Evidence from Indonesia,” _Journal of Banking & Finance_ , 2009, _33_ (7), 1312 1324.

- **Sias, Richard W.** , “Institutional Herding,” _Review of Financial Studies_ , 2004, _17_ (1), 165– 206.

24
# **1.2 OVERVIEW OF THE FINANCIAL SECTOR IN COLOMBIA**

9. **Colombia’s financial sector**<sup>**11**</sup> **underwent a significant re-shaping as a result of a banking crisis in 1998.** Following a period of strong expansion in the first half of the 1990s, excessive loan growth combined with an economic slowdown in the second half of the 1990s caused a rapid deterioration in loan portfolio quality and coverage ratios, triggering systemic liquidity problems. In order to avoid a broader systemic crisis and a deposit run, several emergency measures were introduced between 1999 and 2001. As a result of liquidation and merger activity, the number of total financial institutions decreased from 148 to 64. In particular, most of the _Cajas de Ahorro y Vivienda_ (CAVs) were absorbed by commercial banks, and the banking crisis effectively marginalized the financial cooperative sector in Colombia.

10. **Following the crisis, banking sector performance has been recovering,** mainly driven by a partial resumption in economic growth, improved earnings and portfolio quality, stable net interest margins and significant government recapitalizations. Financial system assets steadily increased from 57% of GDP in 1998 to 70% of GDP in 2003, while deposits grew even faster than total assets over the same period. For the sector as a whole, return on average equity rose from –32.6% in 1999 to 16.4% in 2003, capital adequacy recovered from 10.9% in 1999 to 14.4% in 2003, and non-performing loans decreased from 16.4% in 1999 to 10% of total loans in 2003.

**Figure 1.1  Financial depth and banking sector profitability 1998-2003**

<!-- Start of picture text --> 80 20.0% 16.4%<br>Chile<br>9.6%<br>70 10.0% 0.1% 1.8%<br>60 -2.5% -3.5% -2.3% 1.1% 1.1%<br>0.0%<br>50<br>Colombia<br>40 -10.0%<br>Mexico<br>30 Peru -20.0%<br>20 -21.0% -20.7%<br>-30.0%<br>10 Venezuela<br>-32.6%<br>0 -40.0%<br>1998 1999 2000 2001 2002 2003<br>Colombia Chile Peru Mexico Venezuela Return on Average Equity Return on Average Assets<br>Credit to GDP (%)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> _Source: World Bank calculations based on SIMA and Superintendencia Bancaria data_

> 11 The term financial sector refers to all financial intermediaries supervised by the Superintendencia Bancaria, including credit institutions (private and public banks including mortgage banks), financial cooperatives, corporate finance companies and leasing companies.

19
**Figure 29. Overall & Cross‐border Restrictions on Services Trade, Russia and Peers, 2008 – 09**

<!-- Start of picture text --> Overall Cross-Border<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20<br>20<br>0 0<br>BRA CHN IND RUS WLD ZAF BRA CHN IND RUS WLD ZAF<br>Financial Insurance Telecom Financial Insurance Telecom<br>Retail Transport Professional Retail Transport Professional<br>Overall services restrictions Overall services restrictions<br><!-- End of picture text --> Source: Borchert et al, 2012a, 2012b, and WDI; GDP = Gross Domestic Product, STRI = Services Trade Restrictiveness Index; RUS = Russia, BRA = Brazil, CHN = China, IDN = Indonesia, IND = India, TUR = Turkey, ZAF = South Africa.

53
Table A8: 25th-percentile of Ordinary Exporters’DVAR by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.909|0.928|0.897|0.884|0.876|0.922|0.911|0.931|
|06:chemical products (28-38)|0.880|0.906|0.895|0.942|0.880|0.914|0.904|0.915|
|07:plastics & rubber (39-40)|0.811|0.862|0.853|0.838|0.795|0.845|0.849|0.848|
|08:raw hides & skins (41-43)|0.792|0.846|0.876|0.894|0.870|0.792|0.803|0.777|
|09:wood & articles (44-46)|0.820|0.848|0.855|0.878|0.859|0.898|0.870|0.901|
|10:pulp of wood (47-49)|0.804|0.850|0.826|0.873|0.775|0.946|0.893|0.895|
|11:textiles (50-63)|0.802|0.852|0.855|0.873|0.858|0.890|0.893|0.891|
|12:footwear & headgear, etc. (64-67)|0.756|0.789|0.792|0.855|0.804|0.870|0.823|0.888|
|13:stone, plaster, cement, etc. (68-70)|0.942|0.889|0.912|0.907|0.861|0.876|0.877|0.892|
|14:precious metals (71)|1.000|1.000|1.000|1.000|1.000|1.000|1.000|1.000|
|15:base metals (72-83)|0.851|0.861|0.896|0.916|0.876|0.917|0.926|0.953|
|16:machinery, mech, elect eqmt (84-85)|0.830|0.833|0.841|0.893|0.836|0.900|0.910|0.915|
|17:vehicles & aircraft (86-89)|0.944|0.971|0.978|0.967|0.943|0.980|0.982|0.989|
|18:optical, photographic, etc. (90-92)|0.808|0.867|0.843|0.882|0.897|0.901|0.915|0.915|
|20:misc manufacturing (94-96)|0.730|0.804|0.892|0.901|0.899|0.912|0.932|0.923|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey.

Table A9: DVAR by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.650|0.685|0.699|0.694|0.725|0.680|0.732|0.750|
|06:chemical products (28-38)|0.386|0.463|0.500|0.481|0.384|0.452|0.564|0.443|
|07:plastics & rubber (39-40)|0.418|0.458|0.364|0.403|0.357|0.507|0.417|0.443|
|08:raw hides & skins (41-43)|0.426|0.343|0.410|0.418|0.504|0.525|0.531|0.573|
|09:wood & articles (44-46)|0.438|0.604|0.445|0.289|0.552|0.594|0.347|0.390|
|10:pulp of wood (47-49)|0.304|0.401|0.395|0.393|0.452|0.547|0.562|0.515|
|11:textiles (50-63)|0.495|0.464|0.525|0.546|0.558|0.599|0.620|0.561|
|12:footwear & headgear, etc. (64-67)|0.590|0.571|0.613|0.663|0.628|0.657|0.686|0.693|
|13:stone, plaster, cement, etc. (68-70)|0.550|0.517|0.538|0.617|0.587|0.504|0.530|0.554|
|14:precious metals (71)|0.248|0.262|0.094|0.306|0.531|0.291|0.504|0.528|
|15:base metals (72-83)|0.525|0.468|0.545|0.477|0.556|0.356|0.426|0.491|
|16:machinery, mech, elect eqmt (84-85)|0.402|0.428|0.467|0.436|0.489|0.540|0.479|0.529|
|17:vehicles & aircraft (86-89)|0.501|0.657|0.507|0.628|0.554|0.617|0.721|0.767|
|18:optical, photographic, etc. (90-92)|0.469|0.530|0.509|0.529|0.463|0.574|0.641|0.558|
|20:misc manufacturing (94-96)|0.617|0.572|0.599|0.606|0.620|0.584|0.663|0.650|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey DVAR is computed using single-industry ...rm sample and Filter 2 stated in Table 2.

23
# **Appendix: Sources and Description of the Regulation Indices’ Components**

|**ENTRY**|||||
|---|---|---|---|---|
|**Variable name in**<br>**corresponding**<br>**database**<br>**(name in our**<br>**database)**|**Scale**|**Description**|**Years**|**Source**|
|Number of<br>procedures<br>(db_entry_proc)|Actual number|The number of different procedures that a start-up has to<br>comply with in order to obtain a legal status, i.e. to start<br>operating as a legal entity. The data cover (1) procedures<br>that are always required; (2) procedures that are generally<br>required but that can be avoided in exceptional cases or for<br>exceptional types of businesses.|Survey<br>conducted<br>in 1999,<br>updated<br>to 2003|**Doing Business, The World Bank Group**<br>see Djankov, La Porta, Lopez-de-Silanes and Shleifer,<br>"The<br>Regulation<br>of<br>Entry",<br>Quarterly<br>Journal<br>of<br>Economics, 117, 1-37, Feb. 2002.<br>http://rru.worldbank.org/doingbusiness|
|Number of days<br>(db_entry_days)|Actual number|Time recorded in calendar days. It is assumed that the<br>minimum time required to fulfill a procedural requirement is<br>one day. The variable measures the average duration<br>estimated necessary to complete a procedure. The fastest<br>procedure (independent of cost) is chosen. It is assumed<br>that the entrepreneur completes the procedure in the most<br>efficient way, ignoring the time that the entrepreneur spends<br>in information gathering.|||
|Cost<br>(db_entry_cost)|% GNI|Costs associated with starting-up a business, based on the<br>texts of the Company Law, the Commercial Code, or<br>specific regulations. If there are conflicting sources and the<br>laws are not completely clear, the most authoritative source<br>is used. If the sources have the same rank the source<br>indicating the most costly procedure is used. In the<br>absence of express legal fee schedules , a governmental<br>officer’s estimate is taken as an official source. If several<br>sources have different estimates, the median reported value<br>is used. In the absence of government officer's estimates,<br>estimates of incorporation lawyers are used instead. If<br>these differ, the median reported value is computed. In all<br>cases, the cost estimate excludes bribes.|||
|Regulation<br>(ief_regulation)|1<br>Very low<br>2<br>Low|Existing regulations straightforward and applied uniformly to<br>all businesses; regulations not much of a burden for<br>business; corruption nearly nonexistent.<br>Simple licensing procedures; existing regulations relatively<br>straightforward and applied uniformly most of the time, but<br>burdensome in some instances; corruption possible but rare|1995-<br>2003<br>(annual)|**The Index of Economic Freedom, Heritage Foundation**<br>Based<br>on:<br>Economist<br>Intelligence<br>Unit,<br>Country<br>Commerce and Country Report, 2001 and 2002, U.S.<br>Department of State, Country Commercial Guide 24 and<br>Country Reports on Economic Policy and Trade Practices<br>for 2000, Office of the U.S. Trade Representative, 2002<br>National Trade Estimate Report on Foreign Trade<br>Barriers, and official government publications of each<br>country.|

37
much more pronounced in the lagging regions: for the pooled sample, the party effect in lagging regions is almost twice that in rich regions. Moreover, when we estimate that effect separately for urban and rural regions, the positive coefficient is robustly significant only in lagging economic areas, and more so in poor rural areas. Thus, access to the ruling party network appears to have especially strong payoffs in lagging rural areas.

# **_5d. Effects of Informal Finance on Components of Consumption_**

The richness of the CHFS data allows us to decompose consumption into two components: daily consumption (expenditures on food, clothing, necessities, rent, housekeeping, transportation, and communications), and long-term consumption (expenditures on health care and education). Distinguishing between them is important because, while daily consumption is more or less ‘pure’ consumption, long-term consumption has a much stronger connection to investment, which can improve future consumption. Tables 9 and 10 report regression results that use daily consumption and long-term consumption, respectively, as the dependent variable.

In general, informal loans do not appear to affect _daily_ consumption in rural areas (see 2SLS results in Table 9). However, informal loan amounts are associated with significantly _reduced_ daily consumption levels in urban regions. Increasing informal loans by 20 percent leads to a reduction in daily consumption by 1 percent. Interestingly, party affiliation is robustly associated with higher daily consumption levels, but only in rural regions.

In sharp contrast, informal finance significantly facilitates _long-term_ consumption (see Table 10). Increasing the amount of informal loans used by a household by 10 log points (i.e., 0.1) would increase long-term consumption by 4 log points in both urban and rural areas in the 2SLS models. In contrast to the patterns for daily consumption, party affiliation is associated with significantly higher long-term consumption levels only for urban residents. Thus, rural residents must resort to informal loans for longterm welfare, while urban residents are able to rely on both the party affiliation network and informal loans based on social relationships to support their long-term 20