rafmacalaba/datause-encoder-data huggingface dataset:
to 19% (Ghani et al. 2013c). Therefore it is of crucial importance to understand more deeply the factors that have contributed to this sharp emergence of female entrepreneurship and business ownership over a relatively short time period.

Our empirical strategy follows Iyer et al. (2012) by using differences in the timing of the implementation of reservations across Indian states. We conduct a panel analysis at the stateindustry level that allows us to exploit this state-level timing variation and differences in traits across industries. Industries are defined at the three-digit level within the manufacturing sector. Empirical results yield a mixed message regarding the extent to which these reforms improved women’s participation in the workforce. On one hand, we do not identify that women’s employment in manufacturing increased after the implementation of reservations. This limited response is true in both the unorganized and organized sectors, is observed in multiple datasets, and it holds for employment in both female- and male-owned establishments.

On the other hand, we find significant evidence that women’s entrepreneurship in India increased with the implementation of political reservations. This increase occurs with a statelevel timing that follows the implementation of the reservations, and the growth persists even after controlling for contemporaneous male entrepreneurship by state-industry. We also show that the state-level timing of the implementations was not linked to pre-existing differences in entrepreneurship by state, further adding to the plausible exogeneity introduced by Iyer et al. (2012), and that there is no evidence of pre-trends in the age structures of women-owned manufacturing businesses in 1994. We describe below, however, some important limitations in the degree to which we can fully measure pre-trends, with our evidence often being indirect.

We also see evidence of this entrepreneurship response when looking along state borders as to how long adjacent district pairs have been exposed to the reservations. This state border strategy helps with omitted concerns, but it is not a panacea for all potential identification challenges. The approach better controls for local differences in income, climate, culture, social norms, and demographics, for example, to the degree to which these factors are common between neighboring districts. Also, the approach requires that identification come through differential changes for states on one border versus another that are appropriately timed with the adoption of reservations. However, the approach may not isolate the impact of the reservations from other changes that states may have been made with similar timing. Thus, if the political 3
0.01 0.01 <!-- Start of picture text --> 0.005<br>@ Agriculture = Min. = Manuf.<br>0 m OtherOther serviceindustry = Trade = Trans.and Fin.<br>= a<br>-0.01<br>1975-2017 (60) 1975-1995 (60) 1995-2018 (91)<br><!-- End of picture text --> <!-- Start of picture text --> 0.01<br>-0.01<br>oe aoe<br>0.02<br>= < & = rs} s s Pa<br>cS) = = 5 ~ = B<br>D 2 . o<br><< fe) 2 x<br>O © 5<br>FE<br>mTM 1975-1995 (60) 1995-2018 (91) TM=1975-2018(60)<br><!-- End of picture text -->
Figure 7: Heterogeneity in the Prevalence of Shocks in Sudan: Household Livelihoods <!-- Start of picture text --> 2009<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Crop farming Animal Wages and Owned business Property income Remittances Pension Aid Others<br>husbandry salaries enterprises<br>Floods/Drought Crop Disease Livestock Loss Severe Illness/Accident Death of a hh. Member<br>Fire Robbery/Assult Dwelling Damaged Severe Water Shortage Other Shocks<br>2014/15<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Crop farming Animal Wages and Owned Property Remittances Pension Aid Others Transfers from<br>husbandry salaries business income Hh. members<br>enterprises<br>Floods/Drought Crop Disease Livestock Loss Severe Illness/Accident<br>Death of a hh. Member Fire Robbery/Assult Dwelling Damaged<br>Severe Water Shortage Other Shocks Death of a hh. Head<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS .

20
# Table of Contents

|I.|Sununary|2|
|---|---|---|
|II.|Background|3|
|III.|Objectives of Bank Regulatory Policy|4|
||Fundamental Objectives and Financial Soundness<br>Transitional Objectives<br>Balancing Conflicting Objectives|4<br>5<br>6|
|IV.|Financial Sector Structure|7|
||Key Issues for Financial Conglomerate Viability|8|
|V.|An Alternative Approach to Financial Sector Policy|12|
||The Nature and Role of Banks<br>The Nature and Role of Non-Banks<br>Relationship of Banks, Non-Banks and Government|13<br>16<br>18|
|Appe|ndix|20|
education, occupation, and some other demographic characteristics. These variables combine information from a thoughtful merger of responses to different sections of the 2013 HBS questionnaires.

We estimate different logistic regression models for three sets of predictors: income and access to facilities, other demographic characteristics, and region of residence. Table 7 shows estimated coefficients and corresponding standard errors for the three models. Income is mean-centered and scaled by two times its standard deviation, so that the resulting coefficient can be interpreted like those of binary predictors (Gelman 2008) and the relative importance of each predictor can be evaluated.

**Table 7: Estimated Likelihood of Being in the Middle Class, 2013,**

**Logistic Regression Results**

||**Mode**|**l A**|**Mode**|**l B**|**Mode**|**lC**|
|---|---|---|---|---|---|---|
||Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|
|Income|0,926|0,067|***<br>1,7 31|0,090 ***|2,297|0,103 ***|
|Safe district|0,850|0,068|***<br>0,765|0,071 ***|0,501|0,07 5 ***|
|Chronic disease|-0,215|0,041|***<br>-0,230|0,043 ***|-0,054|0,048|
|Access togas|0,385|0,039|***<br>0,400|0,043 ***|0,125|0,061 **|
|Land line or cellphone|0,311|0,125|**<br>0,244|0,129 *|0,451|0,138 ***|
|Access to PC at home|0,345|0,039|***<br>0,144|0,044 ***|0,247|0,048 ***|
|Age: 20-35|||-0,136|0,054 **|-0,078|0,059|
|Age:50-60|||0,170|0,052 ***|0,144|0,057 **|
|Age: 60-80|||-0,054|0,080|-0,106|0,087|
|Education:primary|||0,115|0,272|0,063|0,325|
|Education: tertiary|||0,254|0,047 ***|0,288|0,052 ***|
|Urban|||-0,208|0,046 ***|-0,060|0,053|
|Female|||-0,27 5|0,042 ***|-0,219|0,048 ***|
|Ret1|||0,168|0,060 ***|0,155|0,065 **|
|Ret2|||0,244|0,091 ***|0,284|0,099 ***|
|Emp0|||-0,182|0,060 ***|-0,264|0,065 ***|
|HH size: 1 component|||-0,835|0,087 ***|-0,980|0,095 ***|
|HH size:3components|||0,340|0,062 ***|0,354|0,068 ***|
|HH size:4components|||0,518|0,066 ***|0,524|0,07 3 ***|
|HH size:5components|||0,699|0,076 ***|0,647|0,085 ***|
|HH size: 6 or more components|||0,7 34|0,078 ***|0,600|0,089 ***|
|Aktobe|||||2,710|0,140 ***|
|Almaty|||||-0,494|0,132 ***|
|Almaty city|||||0,154|0,126|
|Astana city|||||0,051|0,129|
|Atyrau|||||1,129|0,139 ***|
|East Kaz|||||0,922|0,113 ***|
|Jambyl|||||1,539|0,125 ***|
|Karaganda|||||-0,860|0,125 ***|
|Kostanay|||||-1,256|0,148 ***|
|Kyzlorda|||||1,196|0,126 ***|
|Mangystau|||||0,412|0,139 ***|
|North Kaz|||||0,918|0,121 ***|
|Pavlodar|||||1,624|0,114 ***|
|South Kaz|||||1,282|0,123 ***|
|West Kaz|||||0,7 39|0,130 ***|
|(Intercept)|-1,47 4|0,141|***<br>-1,389|0,156 ***|-1,093|0,114 ***|

*** Significant at 0.1%; **significant at 1%; *significant at 5%.

17
and the exchange rate of a country vis-a-vis the dollar is the correlation between the price of that good and the value of the dollar in international markets vis-a-vis other major currencies.

## 2.4 Stationary versus transition equations

The empirical specications in equations (7) to (8) describe the long run equilibrium of a country's trade pattern, conditional on other characteristics. Since the force that leads sectors with natural hedge to become more prevalent in more volatile environments probably operate over long periods, this is an accurate representation of the long run steady state composition of exports. However, since the available trade data covers at most 17 years, during which many countries engaged in structural reforms that might have aected the steady state composition of exports, such as trade liberalization, from an empirical perspective it may be also appropriate to estimate the transitional version of the equation that relates natural hedge to the growth of the export share of a commodity over the sample period, given by, where the notation is as above, except that _T_<sup>_−_1</sup> ∆log � _Xi,W_<sup>_h_</sup> � _t,t_ + _T_<sup>istheaveragegrowthrateof</sup> the global export share during the sample period of length _T_ , and log � _Xi,W_<sup>_h_</sup> � _t_<sup>isthe(log)initial</sup> value of this share for each country-good pair. The long run, stationary equilibrium of this equation corresponds to equation (7), where the long run coecients correspond to the short run ones divided by _−η_ . The long run coecient associated with the interaction would, therefore, be ( _−β/η_ ) _._ A similar transition equation applies to the bilateral export shares model of equation (8).

In addition to allow for non-stationarity, this specication is also less likely to be aected by the reverse causality problem discussed above, since it controls for initial size dierences across sectors and convergence eects. Finding similar evidence on growth rates would strongly support the view that it is the natural hedge oered by the correlation that favors certain sectors.

# 3 Data

Data on export composition come from Feenstra et al. (2005), NBER-UN World Trade Flows database, which reports bilateral trade ows (imports and exports) for a large number of countries at detailed 4-digit SITC (Revision 2) level during the period 1962-2000. This database has been broadly used in the empirical trade literature, specially in the estimation of gravity models of disaggregated trade (Chaney (2008), Colacelli (2008), Do and Levchenko (2007)). Because of restrictions to the availability of other variables, the analysis focuses on the 1984-2000 period. Our nal sample includes 12
<!-- Start of picture text --> Figure 3<br>Gasoline Prices minus Import Parity<br>$025<br>$020<br>$0.15<br>$0,10 'A<br>$00.6 ......<br>40.0S<br>1994 1995 1996 1997 1998 1993 20D<br>|-Plemim iur - ar sDieOsa<br><!-- End of picture text -->

Source: Secretariat of Energy, Argentina In Argentina, from 1994 to 2001, the variable "Price minus Import Parity" was positive for prernium, regular and diesel gasoline'<sup>2</sup> . oil companies argued that the difference between the domestic price and the import parity was a consequence of a "brand premium" that the Argentine consumer was willing to pay. Moreover, the existence _of_ a brand premium is consistent with the existence of a competitive market (Argentine Chamber of Oil Industry, 2000). This argumnent could be valid if the behavior of the variable "Price - Import Parity" is constant over time or if it changes from time to time as new products are introduced or the average consumer changes her perception about the quality of a brand. From figure 3 it is unmistakable that the oil companies' argument is not consistent with the data; the series "Price minus Import Parity" have huge fluctuations, explained by the rigidity of domestic prices relative to the movements in the price of crude oil'<sup>3</sup> . Sumnmarizing, the evidence suggests that oil companies in Argentina are not price takers and they seem to be isolated from changes in gasoline prices in other

**32** Except for two months for diesel and one month for regular gasoline. Gasoline prices are the average for the whole _country_ and were provided by the Secretariat of Energy. 13 Changes in crude oil prices are fully reflected in the import parity.
-51-

# Capital

An alternative constraint to expansion could, however, come from the capital side in so far as capacity may be insufficient at present. Be it because of the excessive real wage levels of the late 19709, or because of sluggish demand so far in the 1980s, investment has been very low for the last 15 years. Since low investment appears, in addition, to have been accompanied by accelerated scrapping consequent upon either more rapid technological progress and/or sharper shifts in relative prices and demand patterns, the OECD economies may have been left with insufficient capacity for anything but present sluggish growth rates. For an individual country this may not be a very severe constraint, given the possibility of importing capital equipment to remedy such shortages (though the problem would then manifest itself in either a worsening balance of payments or in higher inflation via currency depreciation). If, on the other hand, the expansion was generalized to the whole OECD area, the external problem would virtually disappear, but the inflationary danger would remain. The evidence on whether capacity is, or is not, available i,i by necessity imperfect. The existing estimates of capacity utilization in the major countries (Table 8), suggest that, in mid-1987, utilization was still below what it had been at the time of the 1979-80 peak (let alone in 1973), but well above the levels recorded in the trough of 1982-83, despite the weakness of the intervening recovery.

This picture could be more worrying should the slow growth rate of investment of recent years have also been accompanied by a deceleration in the growth of technological progress. This may sound surprising. Journalistic observations on the variety of new technologies and products, as well as theories which argue that recessions eradicate inefficiencies
81

# **Table A5a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Regressions with Institutions**

|**Years : 1960 - 2000**|**Reg A5a.1 **|**Reg A5a.2 **|**Reg A5a.3**|**Reg A5a.4**|**Reg A5a.5 **|**Reg A5a.6 **|**Reg A5a.7 **|**Reg A5a.8 **|**Reg A5a.9 **|**Reg A5a.10 **|**Reg A5a.11**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**per worker**|**0.2119*****|**0.2330*****|**0.2367*****|**0.2526*****|**0.2392*****|**0.2384*****|**0.2286*****|**0.2316*****|**0.2471*****|**0.2338*****|**0.2338*****|
|**Initial GDP (1960)**|**-3.7103*** **|**-3.8461*** **|**-3.6899*****|**-3.5075*****|**-3.6970*** **|**-3.6529*** **|**-4.0116*** **|**-3.8621*** **|**-3.7000*****|**-3.8801*****|**-3.8673*****|
|**Institutions**||**3.9795*****|**3.0796*****|**4.0017*****|**4.0743*****|**4.6475*****|**4.1509*****|**3.2376*****|**4.1720*****|**4.2608*****|**4.8122*****|
|**HC15**|**4.4661****|**2.8568***|**2.9652***|**2.7237**|**2.5302**|**2.6163**||||||
|**Sq of HC15**|**-1.0965****|**-0.6290**|**-0.6791***|**-0.5859**|**-0.5547**|**-0.5234**||||||
|**HC25**|||||||**2.6073**|**2.7239***|**2.4351**|**2.2220**|**2.3954**|
|**Sq of HC25**|||||||**-0.5589**|**-0.6127**|**-0.5045**|**-0.4635**|**-0.4518**|
|**Log (Educational**<br>**Quality)**|**0.3719**|**0.7744**|**0.8912***|**0.7831**|**0.8438**|**0.8389***|**0.7802**|**0.8921***|**0.7915**|**0.8563**|**0.8431***|
|**Log (Patents)**|**0.1956*****|**0.0815**|||**0.0887**|**0.0726**|**0.0762**|||**0.0808**|**0.0642**|
|**Log (Journals)**|||**0.1414*****|||||**0.1397*****||||
|**Royalties per**<br>**capita**||||**0.000003*****|||||**0.000003***|||
|**Phones**|**0.0040*****|**0.0009**|**0.0012**|**0.0009**|||**0.0008**|**0.0011**|**0.0008**|||
|**Computers**|||||**0.0010**|||||**0.0007**||
|**Internet Users**||||||**-0.0017**|||||**-0.0021**|
|**Constant**|**-4.5304** **|**-6.5086*** **|**-7.1021*****|**-6.4873*****|**-6.4777*** **|**-6.9488*** **|**-6.2809*** **|**-6.8520*** **|**-6.2340*****|**-6.2399*****|**-6.7406*****|
|**R squared**|**0.5912**|**0.7858**|**0.8045**|**0.7903**|**0.7871**|**0.7852**|**0.7858**|**0.8050**|**0.7896**|**0.7875**|**0.7859**|
|**No. of Countries**<br>*, **, *** denotes significa<br>Robust standard errors o|**85**<br>nce at the 90, 9<br>btained using W|**74**<br>5 and 99 perc<br>hite's correct|**74**<br>ent confidence<br>ion for heteros|**74**<br>level respective<br>cedasticity.|**72**<br>ly|**74**|**73**|**73**|**73**|**71**|**73**|
**Decoupling and recoupling**

global financial crisis (i.e. betas increased even more during this financial turmoil). However, Dooley and Hutchinson (2009) analyze the financial recoupling-decoupling hypothesis comparing the period going from early 2007 to summer 2008 with the Lehman and post-Lehman period of the crisis.<sup>13</sup> In here, we go a little further, by findng that during the 2000s EM have not decouple, financially speaking, from global assets. Furthermore, our evidence points towards a financial recoupling during this period, that was taking place even before the global financial crisis of 2008/2009.

# **5. Is Financial recoupling the result of financial globalization?**

Why have market betas to global drivers remained so high despite the more diversified economic pattern displayed by EM in the 2000s? In principle, stable to higher betas could be seen as the natural consequence of financial globalization, to the extent that the latter tends to increase the global nature of EM ́s investor base, thereby making it more homogeneous. As the global investors increasingly participate in emerging markets ( **Figure 11** ), the importance of global factors coming from the developed world increases at the expense of within-EM factors that represented the typical source of contagion in the 1990s.

On the other hand, in the late 2000s, emerging assets prices have exhibited a clear high-beta, high-alpha pattern relative to global assets ( **Figure 12** ); namely, a tightly correlated oscillation around clearly diverging trends (in line with results in **Table 7** ).<sup>14</sup> In this section, we explore whether this pattern (particularly, the high beta) is related to (or is a consequence of) financial globalization. To do that, we focus on the three EM assets classes previously discussed: credit, currencies and equity.

> 13 As we do, they analyze equity, debt and FX markets in EM. They use daily data and analyze whether news emanating from the US had more or less impact comparing these two phases of the US subprime crisis.

> 14  By alphas and betas we refer to the coefficients of a simple regression of return of the EM asset on the return of a comparable market portfolio (both in excess of the risk free rate), which in turn are associated, respectively, with (diversifiable) idiosyncratic and (non diversifiable) systemic returns.

<u>10</u>
stands at 3.5 pp, ranging from 1.3 pp in El Salvador to 6.6 pp in Nicaragua. Still, the efficiency gains from this industrialization remain trivial. The average increase is below 2 percent, with the figures for individual countries lying between 0.3 to 3.2 percent.

**Table 4: Aggregate impact of wedge-led industrialization**

|||**Change in**<br>**employm**<br>**(p**|**industrial**<br>**ent share**<br>**p)**|**Change i**<br>**(**|**n output**<br>**%)**|
|---|---|---|---|---|---|
|||**Wedges e**<br>**i**<br>|**liminated**<br>**n**|**Wedges e**<br>**i**<br>|**liminated**<br>**n**|
||**Initial**|**Industry**||**Industry**||
|**Country**|**wedges**<br>**from**|<br>**&**<br>**services**|**Industry**<br>**only**|<br>**&**<br>**services**|**Industry**<br>**only**|
||(1)|(2)|(3)|(4)|(5)|
|CRI|2017|3.24|10.88|0.30|-0.33|
|GTM|2017|3.84|19.25|3.16|-2.37|
|HND|2017|1.65|14.02|2.23|-1.64|
|NIC|2012|6.64|13.47|1.63|0.07|
|PAN|2017|4.42|32.34|2.48|-6.70|
|SLV|2017|1.33|18.31|1.65|-2.59|
|Average<br><br>||3.52<br><br>|18.04<br><br>|1.91<br><br><br>|-2.26<br>|
|Columns (2) an<br>wedges in the<br>ΔYpa1<br>I(in %) ar<br>|d (3) report th<br>yeart1(colum<br>e reported in c<br>|e change in indu<br>n (1)) are elimi<br>olumns (4) and<br>|strial employmen<br>nated. The corres<br>(5), respectively.<br>|t shareΔlpa1<br>I<br> <br>ponding chan<br>|(in pp) when<br>ge in output<br>|

As services wedges also lie above zero, eliminating them causes labor to move into the sector. Some of these resource gains occur at the cost of the industrial sector. Thus, industrial employment can expand more than in column (2) if I only eliminate the industrial wedges and keep those in services fixed. Column (4) reports the industrial share gain from this selective removal of wedges. Industrial employment share grows by about 18 pp on average, more than five times the expansion borne by a non-selective treatment. Each regional economy experiences robust double-digit gain, with Panama outperforming the others.

But does this massive industrialization entail equally healthy economic growth? The figures in column (5) imply that it isn't so. Instead of generating efficiency gains, this selective elimination affects a contraction in five of the six economies. This finding contrasts with the previous result in column (4), where each country saw its output grow. The reason is that a selective elimination still leaves the economy distorted. Removing wedges in both industry and services leads to complete efficiency at which the output attains a maximum. But the wedges in either sector do not individually bear an 24
Figure 1: Average investment growth, by EMDE region <!-- Start of picture text --> A. EAP investment growth B. ECA investment growth<br>Percent Percent<br>14 2000-21 8 2000-21<br>12 6<br>10<br>4<br>8<br>2<br>6<br>0<br>4<br>2 -2<br>0 -4<br>2000-10 2011-21 2022 2023 2000-10 2011-21 2022 2023<br>C. LAC investment growth D. MNA investment growth<br>Percent Percent<br>6 2000-21 10 2000-21<br>8<br>4<br>6<br>2<br>4<br>0<br>2<br>-2 0<br>2000-10 2011-21 2022 2023 2000-10 2011-21 2022 2023<br>E. SAR investment growth F. SSA investment growth<br>Percent Percent<br>10 2000-21 10 2000-21<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>2000-10 2011-21 2022 2023 2000-10 2011-21 2022 2023<br><!-- End of picture text --> Sources: Haver Analytics; World Bank, World Development Indicators database; World Bank. Notes: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC =Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa.

A.-F. Investment growth rates are estimates for 2022 and forecasts for 2023. Regional investment growth rates are calculated using real annual fixed investment in constant U.S. dollars as weights. Growth rates for 2000-10, 2011-21, and 2000-21 are geometric averages of regional annual investment growth. Sample includes 11 EAP, 13 ECA, 20 LAC, 11 MNA, 5 SAR, and 38 SSA economies.
<!-- Start of picture text --> Percent, year-on-year<br>0<br>“4-2 mDemand - L Cd CT]<br>6 mSupply<br>-8 —GDP<br>-10<br>-12<br>-14<br>-16<br>2019 2020 2021<br><!-- End of picture text --> <!-- Start of picture text --> Percent, year-on-year<br>.e mDemand mSupply —Inflation<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>2019 2020 2021<br><!-- End of picture text --> <!-- Start of picture text --> Percent, year-on-year<br>0<br>“4-2<br>“6<br>-8 mDemand<br>-10 mSupply<br>-12 —GDP<br>-14<br>-16<br>2008 2009 2010<br><!-- End of picture text --> <!-- Start of picture text --> Percent, year-on-year<br>.e mDemand mSupply —Inflation<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>2008 2009 2010<br><!-- End of picture text -->