rafmacalaba/datause-encoder-data huggingface dataset:
between regions that are above a threshold level of financial development compared to those that are not.

A key empirical challenge in addressing this question is that large-scale infrastructure investment is typically endogenous, making it extremely difficult to causally identify whether a strong financial market needs to be in place first or whether financial development appropriately mirrors and develops alongside major investment efforts. We study this question using India's Golden Quadrilateral (GQ) highways investment as a natural experiment, examining the spatial development of banking at the district level before and after. The GQ network connects the four major cities of Delhi, Mumbai, Chennai, and Kolkata and is the fifth-longest highway in the world. Conceived in 1999, the GQ upgrades began in 2001, with a target completion date of 2004, and 95% of the work was completed by the end of 2006.

Several studies have subsequently documented the importance of the GQ upgrades for Indian manufacturing development along the highway system but have not focused on the role of finance.<sup>2</sup> This project connects the GQ work to the financial sector and makes two main contributions: First, we use comprehensive and detailed data on bank lending across India over an extended period of time, drawn from the Reserve Bank of India. This database gives us detailed information on each outstanding loan above a small threshold, reported annually by every branch of every scheduled commercial bank in India. We have invested substantially over this project in accessing and preparing these data. They constitute a major new tool for the economic and financial development and growth literatures. Second, the context of the GQ infrastructure project allows us to generate strong causal results of the relationship between infrastructure investment and local financial development, using straight line IV analyses and comparing results to the planned, but not completed NS-EW corridor. To understand the interaction between infrastructure and finance, we examine how the results vary based on the pre-existing financial development of districts adjacent to the highway. This allows us to speak directly

> 2 Using a very short time window, Datta (2011) finds almost immediate evidence of improved inventory efficiency and input sourcing for businesses situated along the GQ network. Ghani et al. (2016, 2017) demonstrate greater formal sector manufacturing growth and entrepreneurship in districts located within 10 kilometers from the GQ network compared to those farther away. They further highlight urban-rural differences, changes in allocative efficiency, and causal assessments. In total, organized manufacturing output increased by 15%-20% due to the highway system. Khanna (2014) examines changes in night-time luminosity around the GQ upgrades, finding evidence for a spreading-out of economic development.

2
Figure 7 depicts fitted lines from 5<sup>th</sup> order local polynomial regressions of saving for old age on log (GDP per capita)<sup>13</sup> in Panel A and household income per capita percentile (by country) in Panel B. The figure in Panel A indicates a positive and mildly concave relationship between real GDP-per-capita levels of USD $400 and $3,000, corresponding to rates of saving for old age between 8% and 17%, respectively. The relationship then becomes steeply convex, with the maximum occurring at real GDP per capita levels of around 50,000 and rates of saving for old age just under 50 percent. The rates of saving for old age then drop for figures greater than 50,000 to figures between 40 and 48 percent. In comparison, Panel B depicts a more or less linear relationship between saving for old age and household income per capita percentile by country. The rates of saving for old age are around 13% at the bottom household-income-per-capita percentile, to around 30% at the 97<sup>th</sup> percentile.

Finally, Figure 8 presents the distribution of saving for old age across different labor market groups—self-employed, wage employed, out of workforce, and the unemployed. We find striking differences between employed adults versus adults out of the workforce. The weighted global figures for saving for old age by labor market status are 33% for those in paid employment, 27.4% for the self-employed, 17.1% for those out of workforce, and 11.2% for the unemployed.

# **4. Empirical analysis**

# **4.1 Empirical strategy**

Our empirical strategy entails three stages. In the first stage, we regress saving for old age on a rich set of individual characteristics. These include: gender, urban/rural region of residence, age [6 groups], education [3 groups], marital status [3 groups], having financially dependent children, household income per capita centile (by country), and labor market status [4 groups]. In the primary specifications we incorporate region fixed effects

> 13 Data on real GDP per capita, PPP (constant 2011 international dollars) are from the World Development Indicators.

11
# **Figure B8: Tin**

<!-- Start of picture text --> Log scale<br>11<br>Log price Permanent component<br>10<br>9<br>8<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.00<br>Medium-term cycle Transitory component<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 49 —_
# **3.2 Exchange rate Management in India since the early 1990s—a steady move towards more flexibility**

India’s exchange rate regime has become progressively more flexible over time. After the breakdown of the Bretton Woods System in 1971, the rupee was initially pegged to the pound sterling, and then from 1975 with a basket of currencies. Following the balance of payments crisis in 1991 the exchange rate was depreciated twice in quick succession, by 9 per cent and 11 per cent on July 1, 1991 and July 3, 1991 and was floated subsequently. Even though officially the exchange rate was floated in 1993, in practice it continued to be managed. As per the Reinhart and Rogoff’s defacto classification of exchange rates, as well as the IMF’s classification, India’s exchange rate is broadly characterized as a pegged or managed float from the early 1990s until late 2000s, and as floating since 2010, Appendix Table 1. Increased flexibility of India’s exchange rate regime over time is confirmed in monthly percent changes in Figure 9 below as well as in higher frequency data (which we have not presented here for brevity).

**Figure 9: Monthly percent change in Nominal Exchange Rate confirm defacto Managed Float until late 2000s, and increased flexibility since 2008**

<!-- Start of picture text --> De facto peg and De facto crawling band<br>25 two devaluations De facto crawling peg/ that is narrower than or<br>managed floating equal to +/-2% Floating<br>20 (managed Floating)<br>15<br>10<br>5<br>0<br>‐5<br>‐10<br>Source: RBI Handbook of Statistics on the Indian Economy<br>% Chg. m-o-m<br>Jan‐91 Oct‐91 Jul‐92 Apr‐93 Jan‐94 Oct‐94 Jul‐95 Apr‐96 Jan‐97 Oct‐97 Jul‐98 Apr‐99 Jan‐00 Oct‐00 Jul‐01 Apr‐02 Jan‐03 Oct‐03 Jul‐04 Apr‐05 Jan‐06 Oct‐06 Jul‐07 Apr‐08 Jan‐09 Oct‐09 Jul‐10 Apr‐11 Jan‐12 Oct‐12 Jul‐13 Apr‐14 Jan‐15<br><!-- End of picture text --> Looking at the movement in exchange rate across the three phases of capital flows, the nominal exchange rate depreciated during the 1990s, more than offsetting the impact of inflation on the real exchange rate, which depreciated as well. Subsequently, during the capital surge phase, even as the burden of maintaining the exchange rate shifted to reserves, the nominal exchange rate appreciated mildly. With inflation running high as well, this reflected in a sharp appreciation in the real exchange rate. The nominal exchange rate then depreciated starting in 2008-09, but not sufficiently to offset the impact of high inflation on 13
|Applied _<_ 0.7 Bound<br>(4)<br>0.011<br>[0.071]<br>0.223***<br>[0.048]<br>29,798<br>69<br>65<br>0.583<br>Applied _<_ 0.7 Bound<br>(4)<br>0.065<br>[0.098]<br>0.265***<br>[0.064]<br>28,726<br>69<br>65<br>0.411<br> 1995-2006. Each model includes<br>_≤_0_._05, * _p ≤_0_._1|
|---|
|Dep. variable: ln simple tarifs<br>Applied _<_ 0.9 Bound<br>Applied _<_ 0.8 Bound<br>(2)<br>(3)<br>0.004<br>0.007<br>[0.071]<br>[0.071]<br>0.228***<br>0.225***<br>[0.050]<br>[0.049]<br>31,128<br>30,538<br>69<br>69<br>65<br>65<br>0.582<br>0.582<br>Dep. variable: ln weighted tarifs<br>Applied _<_ 0.9 Bound<br>Applied _<_ 0.8 Bound<br>(2)<br>(3)<br>0.069<br>0.072<br>[0.094]<br>[0.095]<br>0.277***<br>0.272***<br>[0.066]<br>[0.065]<br>29,980<br>29,403<br>69<br>69<br>65<br>65<br>0.415<br>0.414<br> of data for bound tarifs, the period of analysis is restricted to <br>ndard errors clustered by country in brackets. *** _p ≤_0_._01 ,** _p _|
|Full sample<br>(1)<br>0.022<br>[0.070]<br>0.218***<br>[0.053]<br>33,454<br>69<br>65<br>0.580<br>Full sample<br>(1)<br>0.071<br>[0.087]<br>0.271***<br>[0.068]<br>33,114<br>69<br>65<br>0.416<br> Due to availability<br> efects. Robust sta|
|_Panel A_<br>ln rain_jt_<br>ln rain_jt ∗_agric_i_<br>Observations<br>Number of countries<br>Number of products<br>R-squared<br>_Panel B_<br>ln rain_jt_<br>ln rain_jt ∗_agric_i_<br>Observations<br>Number of countries<br>Number of products<br>R-squared<br>The estimation method is OLS.<br>product, country and year fxed|

4
||||**Table 21: D**|**ID Regressio**|**n(7)**||||
|---|---|---|---|---|---|---|---|---|
||Car for ho<br>Linear pro|spital<br>bability|Probit||Car for sch<br>Linear pro|ool<br>bability|Probit||
|D|-0.004|(0.016)|-0.023|(0.116)|-0.005|(0.008)|-0.116|(0.215)|
|T|-0.028|(0.020)|-0.174|(0.134)|-0.018|(0.010) *|-0.336|(0.238)|
|D*T|0.076|(0.025) ***|0.447|(0.163) ***|0.040|(0.013) ***|0.717|(0.302) **|
|lnHH size|0.016|(0.016)|0.107|(0.104)|0.002|(0.008)|-0.020|(0.178)|
|Adult share|0.093|(0.034) ***|0.611|(0.220) ***|0.058|(0.021) ***|1.167|(0.459) **|
|HH head sex: Male|-0.005|(0.020)|-0.002|(0.130)|0.002|(0.011)|0.092|(0.211)|
|HH head education: Elementary|-0.007|(0.014)|-0.065|(0.093)|0.012|(0.007) *|0.273|(0.173)|
|HH head education: Secondary|0.057|(0.032) *|0.286|(0.152) *|0.026|(0.017)|0.495|(0.258) *|
|HH head education: University|0.149|(0.081) *|0.596|(0.273) **|0.123|(0.065) *|1.073|(0.347) ***|
|Appliance ownership: Refrigerator|0.005|(0.018)|0.023|(0.125)|0.008|(0.008)|0.210|(0.185)|
|Appliance ownership: Color TV|0.011|(0.018)|0.074|(0.116)|-0.003|(0.008)|-0.062|(0.170)|
|Appliance ownership: Radio|0.039|(0.012) ***|0.270|(0.088) ***|0.005|(0.007)|0.107|(0.153)|
|Appliance ownership: Gas stove|0.000|(0.016)|0.003|(0.120)|0.016|(0.006) ***|0.716|(0.344) **|
|Appliance ownership: Washing|||||||||
|machine|0.038|(0.020) *|0.203|(0.103) **|0.009|(0.011)|0.137|(0.166)|
|Gas use for cooking|0.016|(0.017)|0.090|(0.098)|0.009|(0.010)|0.133|(0.149)|
|Power use for lighting|0.001|(0.017)|0.019|(0.117)|-0.007|(0.008)|-0.140|(0.182)|
|Tap water for cooking|0.007|(0.014)|0.026|(0.085)|0.008|(0.008)|0.133|(0.151)|
|Constant|-0.042|(0.053)|-2.271|(0.355)***|-0.056|(0.029)**|-4.114|(0.828)***|
|Obs.|2,064||2,064||2,064||2,064||
|R-squared|0.033||||0.035||||
|F-stat|3.12||||1.99||||
|Pseudo-R2|||0.051||||0.145||
|Wald-stat|||63.48||||45.58||
|Z-statfor interactioneffect (mean& s|td.dev.)||2.522|(0.193)|||1.496|(0.433)|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

40
practices of establishments; the 2017 data for LAC represent some of the first available results from these newly added questions.

# 3.2 Description of the sample

As Table 1 shows, establishments differ across the seven countries in many respects. The sample sizes are notably larger in the three largest economies—Argentina, Colombia and Peru, where approximately 1,000 establishments were surveyed. In Argentina, most establishments in the sample are engaged in manufacturing, whereas, in all other economies, the highest proportion of establishments surveyed operate in the other services sector. Establishments are largest (as measured by the number of employees) in Peru, where the average establishment employs 90 workers, and the median establishment employs 20, and smallest in Bolivia (average number of employees is 31, and median is nine). Multinationals have the strongest presence in Ecuador, where 12 percent of establishments are foreign owned, and the weakest presence in Argentina, where only 2.8 percent of establishments are. The most open economy, measured as the proportion of establishments engaged in exporting activity, is Peru where 12 percent of establishments are exporters, whereas the least open economy is Ecuador, where only 3.2 percent of establishments are exporters. Establishments that are part of a larger multi‐establishment firm are most prevalent in Colombia (23 percent) and least prevalent in Paraguay (7.5 percent).

The data show heterogeneous levels of female empowerment in the private sector (as measured by the proportion of establishments with a female top manager). In Bolivia, just over 26 percent of establishments are managed by women, whereas only 8 percent of establishments in Argentina are. In Bolivia, establishments are also characterized by highly concentrated ownership structures—here, 45 percent of establishments are sole proprietorships. In Ecuador, on the other hand, in only 1.6 percent of establishments is the ownership concentrated in the hands of a single owner. In all countries, the average establishment employs experienced top managers, with the average number of years of managers’ experience being close to, or greater than, 20 years in all countries.

8
||**Increase in Import Value**<br>|**Country with Largest Increase**<br>**Country with Largest Decrease in**|
|---|---|---|
|**Product**<br>**Product Description**|**(US$, thousands)**|**in Import Value**<br>**Import Value**|
|850440<br>Static converters|1,106|Lithuania<br>Russian Federation|
|||Change in import value<br>363<br>-510|
|||Import value in 2008<br>5,977<br>4,625|
|||% Change in import value<br>6<br>-11|
|830241<br>Base metal mountings suitable for buildings|1,027|Poland<br>Russian Federation|
|||Change in import value<br>931<br>-461|
|||Import value in 2008<br>15,161<br>4,312|
|||% Change in import value<br>6.14<br>-10.69|
|210690<br>Other miscellaneous edible preparations|1,006|Germany<br>Russian Federation|
|||Change in import value<br>763<br>-706|
|||Import value in 2008<br>19,006<br>13,447|
|||% Change in import value<br>4.01<br>-5.25|
|80830<br>Pears|955|Belgium<br>Russian Federation|
|||Change in import value<br>306<br>-2|
|||Import value in 2008<br>4,542<br>12|
|||% Change in import value<br>6.74<br>-17.03|
|441011<br>Particle board|896|Poland<br>Russian Federation|
|||Change in import value<br>2,239<br>-1,850|
|||Import value in 2008<br>17,331<br>40,500|
|||% Change in import value<br>12.92<br>-4.57|
|300420<br>Medicaments containing other antibiotics|748|Lithuania<br>Russian Federation|
|||Change in import value<br>333<br>-215|
|||Import value in 2008<br>7,692<br>2,366|
|||% Change in import value<br>4.33<br>-9.09|
|390120<br>Polyethylene having a specific gravity of 0.94 or more|733|Poland<br>Russian Federation|
|||Change in import value<br>418<br>-828|
|||Import value in 2008<br>12,144<br>27,868|
|||% Change in import value<br>3<br>-3|
|390210<br>Polypropylene|729|Lithuania<br>Russian Federation|
|||Change in import value<br>619<br>-1,147|
|||Import value in 2008<br>14,848<br>50,078|
|||% Change in import value<br>4.17<br>-2.29|
|854449<br>Other insulated wire, cable, and other insulated electric conductors, whether or not fitted with connectors;|717|Germany<br>Russian Federation|
|optical fibre cables, made up of individually sheathed fibres||Change in import value<br>753<br>-1,761|
|||Import value in 2008<br>6,448<br>74,297|
|||% Change in import value<br>11.68<br>-2.37|
|940600<br>Prefabricated buildings|704|Poland<br>Russian Federation|
|||Change in import value<br>546<br>-1,362|
|||Import value in 2008<br>3,969<br>22,815|
|||% Change in import value<br>13.76<br>-5.97|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 55
too often, though, crisis resolution will fall short of this ideal, in which case the distributional improvements will not be seen either.

Both short and long-term perspectives foresee potential improvements in income distribution around financial sector crises, though on different timescales, and depending (in the first instance) on the nature of the economic system’s crisis absorption capacity, including the safety-net response of government and (in the second instance) on whether the crisis does in fact achieve the hoped-for cathartic effect.

# _2.3 Channels of effect_

Banking crises come in different varieties and with different degrees of severity (Honohan, 2000). Some are resolved without the economy being pitched into a macroeconomic recession. These would include several cases where government-owned banks were revealed to have incurred severe losses on their loan portfolio, losses which were eventually made good by government recapitalization without an intervening loss of confidence by depositors or by the holders of local currency or government debt. In these cases, the costs flow through fiscal channels, affecting the needed balance between tax revenues and expenditures which can have distributional consequences as well as potentially affect long-term growth rates through the deadweight cost of taxation.

Other banking crises have been associated with major macroeconomic volatility in a way which, as mentioned, often defies analysis of causality: economic downturns trigger the banking crisis, or its recognition; the scale of the banking losses in turn affects confidence and may deepen the recession and drive down the external and internal value of the currency. Here some distributional effects may still come through fiscal channels (including the transfer of fiscal resources _to_ persons who are, directly or indirectly, bailed out), but there are several additional channels: loss of employment; loss of current and prospective business profits; relative price changes, including changes in real wages, capital losses and gains on unhedged foreign exchange positions.

Taking stock, then, consider the following checklist of channels through which households can be adversely affected by a financial crisis.<sup>4</sup> We rank them in order starting with those most closely related to the banking failure itself.

- (a) Loss of deposits in a failed banking institution

- (b) Loss of employment or earnings directly due to (i) disruption of the payments process, (ii) the bankruptcy of financial institutions (for employees and other stakeholders of these institutions) or (iii) the interruption of credit flows (for borrowing clients with information capital invested in the failed financial institutions)

- (c) Tax increases or curtailment of public spending due to fiscal cost of bail-outs of financial firms or their customers

> 4 Many previous studies provide a similar checklist. The present one is specifically adapted to banking crises.

6
2002, 422,000 in 2004, 498,073, in 2005.<sup>2</sup> Growth in commercial vehicles has been especially important, with light commercial vehicles in particular accounting for roughly 70% of the market. On the other hand, even prior to the 1997 crisis, this growth has been highly erratic, with auto production falling in 1982, 1988, and 1992 as a result of unstable business cycles, industrial policies, and credit conditions. And even in periods of expansion, the auto industry has been highly fragmented (discussed below).

Owing to the auto industry's overall expansion, the number of parts producers has grown from very few to 150-200 firms in a sector characterized by significant market and geographical concentration: The four largest component firms accounted for almost 60% of value added in 1996, and greater Jakarta contributed 93% of the subsector's value added.<sup>3</sup> The quantity and range of local products have expanded significantly over the past 30 years with quality improving as well. Until the early 1980s, the components sector, consisting largely of replacement parts of uneven quality, was actually larger than assembly in value added. This changed in the 1980s, as government promotional incentives stimulated growth in assembly, which accounted for almost 70% of the auto industry’s value added in 1990. But this trend was reversed in the 1990s, as the share of components rose from 30% to 40% (Aswicahyono, Basri and Hill - ABH 2000: 225-226; Okamoto and Sjoholm 2000: 62). The resiliency of components production seemed to be evident in its post-crisis rebound: from a value of $1.6 billion in 1997, components production fell to $563 million in 1998 but then rose to $2.3 billion in 1999 and $3.1 billion sin 2001.<sup>4</sup> Component exports also rose, doubling from roughly 2 Figures for 2000-2002 from PT Data Consult 2003, Table 1, p. 9. Figures for 2004-2005 from CEIC Data / http://site.securities.com.proxy.library.emory.edu/doc_pdf?pc=ID&doc_id=102335246 3 Figures on numbers of parts firms in the late 1990s vary from 158 (Ito 2003: 9) to 200 (PT Data Consult 2002: 2). Note that auto assembly was even more concentrated (92% in 1996) than auto parts (ABH 2000: 236-237).

4 "Post crisis condition of automotive component industry" 2002: Table 5. The Indonesian market for components (as opposed to production volumes) also rose to $4.13 billion in 2001 from a low of $1.04 billion in 1998 and a previous peak of $3.23 billion in 1997 (Ibid.).

53