rafmacalaba/datause-encoder-data huggingface dataset:
regulation create scope for corruption, connected lending, and gambling for redemption.

- Are stronger prudential measures on the claims of foreign investors justified, assuming that the claims are more volatile?

- Empirical evidence is not clear. Experiences of financial crises in several emerging markets have suggested that domestic residents contributed importantly to the short-term capital outflows that occurred in the run-up to the crises in question. (Frankel and Schmukler, 1996, 1997)

- Fischer, 1998 and Mussa, 1998 suggest that prudential regulation should address threats not only to the stability of the banking system but also to general financial stability created by the assumption of large volumes of short-term debt, particularly that denominated in foreign currency, by financial institutions, firms and the sovereign. This could be justification for extending the standard arguments for prudential supervision and regulation beyond the short-terrn foreign currency exposure of the banking system to at least to the monitoring of other short-term flows, and perhaps to tax-like measures to discourage excessive exposure for the economy as a whole. (Eichengreen, Mussa, et al., 1998, p. 26)

- Can capital controls on capital inflows have a significant effect on the composition of capital inflows?

- The empirical evidence is mixed. Studies that distinguish between short-term and long-term suggest that controls are likely to become progressively less effective the longer they remain in place. Still, a number of studies conclude that measures adopted by countries like Chile, Colombia, and Israel to influence the level and composition of portfolio capital inflows have not been without effect.

- On the other hand, in 1990, Korea started a series of reforms designed to liberalize the capital account. The sequencing of the reforms led to an early liberalization of conmnercial credit and short-term flows, while it left in place significant restrictions on foreign direct investments and portfolio investmnents in domestic equities.<sup>40</sup>

- To some extent, the Korean capital controls program should have had the opposite effect of the Chilean program. Data from BIS confirm this. Chile's short-term borrowing from banks in the BIS area represents 43 percent of total borrowing, while the corresponding figure for Korea is 68 percent.<sup>4</sup> '

# **3.6. Proper market infrastructure**

- Market infrastructure includes the systems and institutions that facilitate the trade and custody of securities. The necessary functions comprise matching buyers and sellers,

> 40 See Park and Song, 1996, and Johnston, Darbar, and Echeverria, 1997.

> 41 However, we should wait until we could collect more pervasive empirical evidence before we reach any strong conclusions.

14
# **Figure 1:  “Policy Uncertainty” is commonly identified by firms as a “severe” or “major” obstacle to firm growth in Africa**

<!-- Start of picture text --> Major/Severe Moderate Minor<br>Electricity<br>Policy Uncertainty<br>Tax Rate<br>Macroeco. Instability<br>Access to Finance<br>Anticompetitive<br>Crime<br>Transportation<br>Tax Administration<br>Corruption<br>Skills Availability<br>Licenses<br>Access to Land<br>Customs Clearance<br>Telecoms<br>Labor Availability<br>0 20 40 60 80 100<br>Percent of firms reporting constraint<br><!-- End of picture text --> _Source: Author’s analysis using Enterprise Surveys in 2002-2005_<sup>_1_</sup> One view is that firms are concerned about a lack of political and policy making stability which leads to frequent and unpredictable changes in the formal rules and _de_

> 1 “Policy uncertainty” as one of the potential constraints was not included in the later surveys; it was changed to “political uncertainty” which gets much lower ratings.  This chart reflects the responses of 3317 firms in 13 countries.  Data presented in the rest of the paper uses the full sample of firm responses in SubSaharan Africa.

3
54

# **Table A3.4: Colombia**

|||<br>Pre-FRL<br>|Post-F|RL|
|---|---|---|---|---|
||Date|<br>_(Dt-1)/GDP - (Dt-5)/GDP_<br>|_(Dt+5)/GDP_|_- (Dt)/GDP_|
|**Subsovereign Debt**|2003|0.58%||-1.07%|

Source: Ministry of Finance and Public Credit

# **Table A3.5: India**

|||Pre-FRL<br>|Post-FRL|
|---|---|---|---|
|States|Date|_(Dt-1)/GSDP - (Dt-_<br>_5)/GSDP_<br>|_(Dt+5)/GSDP -_<br>_(Dt)/GSDP_|
|**Karnataka**|2002|3.80%|1.10%|
|**Kerala**|2003|8.70%|-3.50%|
|**Punjab**|2003|8.10%|-8.50%|
|**Tamil Nadu**|2003|7.40%|-3.90%|
|**Uttar Pradesh**|2004|12.90%|-3.90%|
|**Andhra Pradesh**|2005|7.30%|-4.50%|
|**Chhattisgarh**|2005|0.90%|-7.30%|
|**Gujarat**|2005|6.00%|-8.00%|
|**Haryana**|2005|0.20%|-7.70%|
|**Madhya Pradesh**|2005|4.50%|-2.70%|
|**Maharashtra**|2005|7.70%|-6.20%|
|**Orissa**|2005|7.10%|-15.90%|
|**Rajasthan**|2005|9.30%|-8.00%|
|**Assam**|2005|8.30%|-3.00%|
|**Himachal Pradesh**|005|13.80%|-17.30%|
|**Manipur**|2005|<br>11.90%|4.90%|
|**Nagaland**|2005|-2.20%|1.30%|
|**Tripura**|2005|12.70%|-20.90%|
|**Uttarakhand**|2005|12.20%|-2.90%|
|**Bihar**|2006|6.30%|-12.60%|
|**Goa**|2006|-3.30%|-2.80%|
|**Arunachal Pradesh**|2006|31.10%|-9.60%|
|**Jammu and Kashmir**|2006|10.90%|-0.60%|
|**Meghalaya**|2006|6.50%|0.50%|
|**Mizoram**|2006|39.80%|-2.80%|
|**Jharkhand**|2007|2.30%|0.00%|

Notes: 1) 2009 data are budget estimates and 2010 data are revised estimates; 2) Due to limited data, Pre-FRL data of Chhattisgarh and Uttarakhand only date back 4 years before enactment of FRLs. Source: Reserve Bank of India.
# **Table A.14. Predicted Poverty Rates Based on Imputation, from TA 1 to TA 2 using 2019/20 Poverty Line, Tanzania (percentage)**

|<br>**Method**|||**T**<br>|**A 2**<br>|||
|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 8**|**Model 9**|
|1) Normal linear regression model|**16.5**<sup>**a**</sup><br>(2.4)|**16.0**<sup>**a**</sup><br>(2.3)|24.4<br>(2.3)|**15.6**<br>(1.8)|**15.3**<br>(2.3)|**15.9**<sup>**a**</sup><br>(2.5)|
|2 Eiil ditibti f th t|**16.5**<sup>**a**</sup>|**15.5**|24.5|**15.5**|**15.0**|**16.0**<sup>**a**</sup>|
|) mprca sruon o e error erms|(2.4)|(2.3)|(2.3)|(1.8)|(2.3)|(2.5)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.27|0.43|0.89|0.89|0.47|0.35|
|N1(base survey)|711|711|711|711|711|711|
|N2(target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjusted for c<br>linear regression model with the theoretical distribution o<br>cluster random effects. Model 3.1 employs non-standardi<br>household size and age in both surveys are transformed t|omplex survey design<br>f the error terms and<br>zed variables. Model<br>o normality using the|. All estimates<br>Method 2 uses t<br>3.2 employs va<br>Box-Cox meth|1<br>(1<br>are obtained with<br>he empirical dist<br>riables in TA 2 st<br>od before standar|7.7<br>.9)<br>the population we<br>ribution of the err<br>andardized by th<br>dization. The imp|ights. Method<br>or terms. Both<br>ose in TA 1. F<br>uted poverty r|1 uses the normal<br>methods employ<br>ood expenditures,<br>ates for TA 2 use|

**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. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in TA 1. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 2 use the estimated parameters based on the TA 1. 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 2019/20 deflated to 2022 prices with 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.7.

58
Table 2. Descriptive statistics of variables of the production frontier model 20022005

|**Variable**|**Year**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|GDP (in millions)|2002|456684|1419363|9997600|2371|
||2003|466695|1451176|10249800|2703|
||2004|483326|1503767|10651700|2987|
||2005|497255|1547630|10995800|3402|
|Gross Fixed Capital|2002|91219|274765|1835000|448|
|Formation (GFKF) (in|2003|93539|280281|1890700|600|
|millions|2004|98363|293680|2004600|637|
||2005|103604|309418|2132800|664|
|Labor Force (LF)|2002|20738|55685|408342|168|
|(in miles)|2003|21005|56640|416285|169|
||2004|21353|57546|422759|173|
||2005|21656|58537|430607|177|
|Research and|2002|10336237|38943279|265884666|215|
|Development Expenditure|2003|10553995|39853975|272239510|354|
|(R&D)|2004|10714079|40311619|275048835|988|
|(in miles)|2005|11210290|42355334|287770196|1168|
|New Businesses|2002|49496|101292|650843|31|
|Registered (NBR)|2003|52404|104156|618503|57|
||2004|57433|111332|657195|24|
||2005|59459|114565|676830|23|

Notes: Number of observations = 61 countries. Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

# **4.- Empirical Results**

There is a wide difference and variation of expenditure on Research and Development – R&D (as a percentage of GDP) across countries (see Appendix tables 6 and 7). There are also differences on New Business Entry Rate – NBER (New Businesses Registered - NBR as % of total business) across countries when grouped in terms of High-Medium and Medium-Low-Tech (see Table 3).

Some examples are Sweden<sup>4</sup> (high expenditure on R&D and low NBER), Turkey<sup>5</sup> (high NBER and low expenditure on R&D), India<sup>6</sup> (low expenditure on

> 4 The industrial sector plays an important role in Sweden’s economy. Recently the Agricultural sector has also contributed a lot to the country's Gross Domestic Product. The major industries in the country are iron, steel, wood pulp, paper products, and motor vehicles. The important agricultural products in the country are wheat, barley, sugar and milk.

> 5 In the case of Turkey the strong and rapidly growing private sector is a landmark of its economic success.

> 6 Agriculture is a major component of India’s economy. The industrial sector  includes manufacturing industries, textiles and handicrafts, etc. However, the service sector is greatly expanding and has started to assume an increasingly important role (e.g., India has become a hub

11
22 urban municipalities that surround the city.

More details about the COMIPEMS assignment mechanism can be found in Bobba and Frisancho [2014] and Dustan [2014]. Here we stress some aspects of the context that make students’ rankings particularly informative about their underlying preferences over schooling alternatives. First, administrative records capture individual revealed preference rankings over a wide range of available high school alternatives, which allows us to accurately measure student-specific ranked orderings over high school tracks. Second, the matching mechanism in place and the large portfolio size that can be submitted imply that the assignment mechanism is strategy-proof [Pathak, 2011]. Since admission probabilities to each school do not play a role when choosing an application portfolio, any effect on choices due to the receipt of the cash transfers is enabled through changes in net expected benefits across high school tracks.<sup>5</sup> Third, official information about school-level academic quality is publicly available. Past cutoff scores for each schooling option have been made available through the COMIPEMS website since 2005.<sup>6</sup> Since the cutoff scores and the mean score of admitted students are almost perfectly correlated, students have access to an excellent proxy for the average peer ability in each school.

# **2.2 The** **_Oportunidades_ Program**

_Oportunidades_ is a conditional cash transfer program that targets poor households in both rural and urban areas of Mexico.<sup>7</sup> The program was first initiated in 1997 in rural areas of Mexico, and by 2001 it began to expand at a rapid rate into semi-urban and urban areas reaching the urban area under study in the second half of 2004. Cash transfers are given on a bimonthly basis to one of adult household members (usually the mother) and come in two forms. The first is a fixed food stipend which is worth 175 MX$ (around 16 US$) per month as of the first half of 2005 and it is conditional on family members obtaining preventive medical care. The second is an educational grant which is offered to each child aged 6-21 in the household. To receive the school subsidy, children or youths in participating households have to attend school in one of the subsidy-eligible

> 5Even though students are allowed to rank up to twenty options only a minor percentage of them fills up the entire preference sheet; students seem to be choosing a portfolio without binding constraints on the number of options that can be chosen.

> 6This site is frequently checked by the applicants because it has become the main channel to pre-register and it is also a key source of information about the system.

> 7Details of the program can be found in Hoddinott and Skoufias [2004].

6
7 of the Koreans had experience in a related business, almost 60 percent of them in a management position. A substantial majority of the Japanese sample also reported having had prior experience in a related business.

The exceptions to the overall pattern of high education are revealing. The export entrepreneurs from Jepara, Indonesia are shown in Table 3 to be substantially less educated tha in the other countries and subsectors; uniquely among the subsec-tors studied, their source of competitive advancage lies in unusually well-developed wood-carving skills. Also, although not shown in the table, the owners of the Japanese SMEs samnpled, espec-ially those in their fifties or older, tend to have no more than high school education. However, as the diLscussion Of marketing supportsystems below will highlight, they appear to face fewer marketing demands tha the other entrepreneurs, inoar as they operate within a well-developed network of interfirm relatins.

Finally, subcontracting relations are ubiquitous in all four countries. As thie cotuntry~ case describes in detail, all of the Japanese firms surveyed are embedded in vertical, multi-tier prodution and marketing relations. In Korea, 82 percent of the firms surveyed subcontract, out either specific- production tasks or fabrication of the entire product; the corresponding figures for Indonesia and C-olombi **a** re 76 percent and 67 percent. What is remarkablc about the Korean experience in particular is the rapidity of change in the role of subcontracting, in industrial production. In 1973, only 25 percent of all Korean SMEs earned any-revemue from subcontracting; by 1990, subcontracting was a source of revenue for 70 percent of Korean firms, and fulfly 90 percent of these earned over 80 percent of their revenue in this mannerFor all of the pervasiveness of subcontracting, and for all that its expansion (as in Kore) doubtless multiplies the opportunities available to small business, the results of the: Indonesian and Colombian firm surveys sound a note of' caution as to whether subcontracting can serve straightforwardly as a stepping-stone to SME success mn export markets. Fewer than 14 percent of the Colomobian SME exporters surveyed, and fewer tha 22 percent of the hidoneian SUE exporters, had themselves had prior experience as subcontractors prior to becomning direct exporters. Considered together with the evdneeneucto lees, these
under these conditions.<sup>5</sup> The model is closely related to Ben-David (1998), which analyzes the implications of a model with subsistence consumption under exogenous growth.

The representative household chooses a path for consumption to maximize its intertemporal utility:

# (3)

where _c_ is the consumption per member of the household and the utility function is: (4)

> The minimum subsistence consumption level is captured by _~~c~~_ . The representative household is endowed with a constant returns production technology _F_ ( _K_ , _L_ ) and with an endowment of labor that grows exogenously at rate _n_ . Capital depreciates at a constant rate δ . Under these conditions, the evolution of the capital per worker is given by

> where _k_ denotes capital per worker, and _f_ ( _k_ ) = _F_ ( _K_ / _L_ ,1) represents the production function in intensive form. The problem of the representative household is therefore

(6) which is the standard Ramsey problem.

The first-order conditions of this problem are given by the following two differential equations: 5 This is of course not the only way to generate a saving rate that increases with the level of development. For example, Chakraborty (2004) develops a two-period overlapping generations growth model in which the probability of survival to the second period is endogenously determined as a function of public health care expenditures. The model exhibits an (unstable) low-level equilibrium in which the capital stock, income, and health spending are low. This in turn means that the probability of survival is low, and individuals rationally respond to this by saving less for their old age.

12
with expenditure and income measures (Rustein, 1999; Filmer and Pritchett, 2001).<sup>12</sup> We also include a control for whether the family resides in a rural area. Rural areas in India typically have significantly higher rates of infant mortality than urban areas. We finally add dummy variables for states and the region in which an individual resides. This is intended to capture state- or region-fixed effects.

The results of the fertility regression are presented in Table 3. As expected, and based on the summary statistics seen in Table 2, the coefficients for _Muslim_ and _Hindu Non-Dalit_ take opposite signs: Muslims have more children, and Hindu Non-Dalits have fewer children than the omitted group (Dalits) and these differences remain statistically significant even when all the control variables are included. Muslims have about 1 child more than Dalits and Non-Dalits have about 0.3 fewer children than Dalits. We interepret this as evidence that Muslim fertility is higher than Hindu fertility overall. Additionally, most measures of socioeconomic status have the expected negative sign in the fertility regressions. The household wealth index as well as parental age and education are all associated with decreased fertility. As widely seen in the empirical literature, maternal attributes have a stronger effect on fertility than paternal attributes.

In results not shown here, we examine the robustness of the relationship between religion and fertility by employing additional socio-economic and demographic variables such as the use of contraception (traditional as well as modern), duration of the use of contraception, exposure to mass media and nutritional indicators. Inclusion of these additional explanatory variables had little to no effect on the two variables _Muslim_ and _Hindu Non-Dalit_ that are central to our interest in this paper.

> <u>12The</u> wealth index is constructed by combining information on 33 household assets and housing characteristics such as ownership of consumer items, type of dwelling, source of water, and availability of electricity into a single wealth index. These 33 assets are as follows: household electrification, type of windows, drinking water source, type of toilet facility, type of flooring, material of exterior walls, type of roofing, cooking fuel, house ownership, number of household members per sleeping room, ownership of a bank account, ownership of a mattress, a pressure cooker, a chair, a cot/bed, a table, an electric fan, a radio/transmitter, a black and white television, a color television, a sewing machine, a mobile phone, another other telephone, a computer, a refrigerator, a watch or clock, a bicycle, a motorcycle or scooter, an animal-drawn cart, a car, a water-pump, a thresher, and a tractor. Each household asset is assigned a weight (factor score) that is generated through principal components analysis. The resulting asset scores are standardized in relation to a normal household and is then assigned a score for each asset, and scores were summed for each household; individuals are ranked according to the score of the household in which they reside.

9
Table 4 presents the dominant mode of transportation used by people to commute from home to work and reveals how different the travel patterns are across Amman, Beirut, and Cairo.<sup>32</sup> First, a sizeable share of people commuting from home to work in all three cities only use walking as a transportation mode. This share is larger for women than men in both Beirut and Cairo (respectively 19% of men versus 24% of women, and 24% of men versus 30% of women), while there are fewer women using only walking than men in Amman (25% of men versus 18% of women).

Second, in terms of public transportation, there are stark differences by city. Both men and women in Cairo tend to use much more public transportation than in either Amman or Beirut. In each city, taxi, call cabs, or equivalent are more frequently a dominant mode of transportation for women than for men. Private buses provided by employers are not very developed in Beirut, while they represent up to 10.6% of commuting women in Amman. The use of cars for commuting is much more developed in Amman and Beirut than in Cairo.

**Table 4: Dominant mode used for commuting from home to work, by city and gender (percent of total)**

||Am|man|Be|irut|Ca|iro|
|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|
|Walking|24.9|17.8|19.3|24.1|23.9|29.6|
|Bicycle|0|0|2.9|1.0|0.3|0|
|Bus/Minibus|3.9|3.8|7.8|7.9|3.0|5.1|
|Microbus|7.5|5.8|1.9|2.6|31.2|31.0|
|Metro|-|-|-|-|9.3|10.8|
|Train|-|-|-|-|0.4|0|
|Taxi, Call Cabs or equivalent*|10.4|12.7|4.1|7.3|6.7|10.5|
|Private bus (School/ Work/...)|3.0|10.6|0.2|0.5|3.3|3.6|
|Own motorcycle|0.9|0.3|15.2|1.0|6.7|0.4|
|Someone else's motorcycle|0.1|0|1.5|1.0|0|0|
|Own car|42.8|39.7|44.0|50.8|13.8|5.1|
|Someone else's car|5.6|9.2|3.2|3.7|0.1|2.2|
|Other|0.7|0|0|0|1.2|1.8|
|_Observations_|694|292|587|191|734|302|

Notes: * In Amman, services (shared white-taxi with fixed routes) are included in the category Taxi, Call Cabs or equivalent, but are included in the public transport network. Figures are percent of respondents within gender and city. This table includes only respondents who work outside of home (the total is in the row “Observations”). The dominant mode is defined as the motorized mode on which the respondent spends most of their usual work commute, bicycle if no other motorized mode is used, and walking if it is the only mode used. Bicycles include other two-wheeler non-motorized vehicles. Motorcycles include other two- or three-

> 32 The survey asks for all modes of transportation used during the commuting trip. The dominant mode is defined as the motorized mode on which the respondent spends most of their usual work commute, as bicycle if no other motorized mode is used, and as walking if it was the only mode of transport reported. Where there was more than one potential dominant commute mode (rare occurrences), precedence was given to larger modes (specifically precedence will be given in the following order: metro – bus/minibus – microbus – taxi – own car – someone else’s car – motorcycle – someone else’s motorcycle).

26