rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Private investment (%GDP) Private investment growth rates<br>30 IND EMDE (median) 40 IND EMDE (median)<br>EMDE (25-75%)<br>EMDE (25-75%)<br>25 30<br>20 20<br>15 10<br>10 0<br>5 -10<br>0 -20<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Private investment in India is likely constrained by several factors. There are issues related to past leverages as well as subdued market demand.<sup>40</sup> Going forward, de-risking the private sector may be important, as it may be to further ensure an environment of policy certainty. Understanding and relieving the generic, spatial, or sector-specific constraints to investment growth may be important too. Reviving private investment in areas such as infrastructure to finance India’s long-term investment needs would be useful.<sup>41</sup>

# **4.3 Reviving bank credit and resolving asset quality issues to support growth**

The last few years have been challenging for the Indian banks, as the pace of credit growth remains subdued, and the stress on asset quality continues. Bank credit growth has consistently declined since the GFC, after increasing briskly for a few years before that. The annual average growth rate of bank credit was 9.5 percent in 2014-17 compared to 26.3 percent in 2004-08. As a result, credit to GDP ratio has declined in recent years, after peaking at 56 percent in 2014, and after doubling within a span of 7 years from 25.5 percent in 2001 to 52 percent in 2008.

In addition, after a decade long declining trend, the ratio of gross non-performing assets to advances (GNPA) of Scheduled Commercial Banks (SCBs) increased from 2 1⁄2 percent in 2007 to 9.3 percent in 2017. The asset quality of SCBs deteriorated across sectors, with the largest deterioration in the industrial sector. The level of stressed assets (NPAs and restructured loans) has

> 40 Deleveraging could be one reason behind the slow pace of investment growth--Indian businesses over invested and over leveraged during the boom years. Yet, due to the slow pace of resolution, businesses have been unable to deleverage quickly and start investing afresh. There may also be sectoral constraints to investments in sectors such as construction, leather, infrastructure, telecom, and energy. If the investment slowdown is concentrated in export-oriented firms, it may be indicative of specific constraints related to the size of the external markets, and to their competitiveness. 41 The World Bank has recently suggested a “Maximizing Finance for Development (MFD)” approach to crowd-in private financing through the use of public instruments such as guarantees, and by removing policy or regulatory gaps. The idea is to leverage more private investment, while reserving scarce public financing for areas where private sector engagement is unavailable or not optimal.

37
FIGURE 11 Effects of adverse events on growth of employment, TFP, and investment in advanced economies and EMDEs <!-- Start of picture text --> A. EMDEs: Response of potential TFP growth<br>5 years later<br><!-- End of picture text --> B. EMDEs: Response of investment growth 5 years later <!-- Start of picture text --> Percentage points Percentage points<br>0.5 9<br>6<br>0.0<br>3<br>0<br>-0.5<br>-3<br>-6<br>-1.0<br>-9<br>-1.5 -12<br>Recessions Banking crises Epidemics Recessions Banking crises Epidemics<br>C. EMDEs: Response of employment growth 5 D. Advanced economies: Response of potential<br>years later TFP growth 5 years later<br>Percentage points Percentage points<br>2 0.2<br>0.0<br>1<br>-0.2<br>0<br>-0.4<br>-1 -0.6<br>-0.8<br>-2<br>Recessions Banking crises Epidemics -1.0<br>Recessions Banking crises Epidemics<br>E. Advanced economies: Response of F. Advanced economies: Response of<br>investment growth 5 years later employment growth 5 years later<br>Percentage points Percentage points<br>6 2<br>4<br>2<br>0<br>0<br>-2<br>-4 -2<br>-6<br>-8<br>-4<br>-10 Recessions Banking crises Epidemics<br>Recessions Banking crises Epidemics<br>Source: World Bank.<br><!-- End of picture text --> Note: Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2012, 2018, 2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

31
not in the US.

Comparing Figures 1 and 5, it is clear that the high relative volatility of consumption in Mexico is driven by Mexican shocks rather than by US shocks. This is the case because Mexican shocks have a much larger e¤ect on Mexican interest rates than US shocks. Intuitively, US shocks trigger a more persistent decline in Mexican output than Mexican shocks. As a result, Mexican companies want to cut their investment more drastically in response to them. This leads to a larger initial increase in the price of installed capital (PS<sup>I) which reduces the increase</sup> in the slope of PS<sup>Iduetothegradualincreaseinthepriceofnewcapital(P K</sup> S<sup>).23Hence,the</sup> lower increase in interest rates following a recessionary shock in the US vs. Mexico.

Before moving on to the quantitative analysis of the model it is worthwhile making a remark about the relative volatility of consumption and output in the US. Note that, according to Figure 3, consumption is more volatile than output in the model. This is a counter-factual prediction that follows from the assumption that the US only trades with Mexico. This assumption implies that the price of capital in the US increases very gradually in response to a decline in FDI to Mexico. (Recall that transferring the production of intermediate goods reduces their price in the US.) As argued above, the prospect of a higher future price of capital in the US reduces the decline in current investment and increases current and future interest rates leading to a larger decline in US consumption.

In reality, the US imports a large share of its intermediate goods from many countries in addition to Mexico including many developed economies that do not obtain their intermediate goods only from the US. As a result, the actual number of intermediate goods imported by the US is more independent from US shocks than in the model and the price of capital in the US increases less gradually than in Figure 3. This intuition is illustrated in Figure A2 were we present the impulse responses (to the three types of shocks) of the version of the model without the international di¤usion of intermediate goods. In this extreme case where US shocks do not a¤ect the technology available for production in other countries, the initial response of investment is much more pronounced than in Figure 3 and the response of the US price of capital is less gradual. This results in a consumption process that is less volatile than US the US, the correlation between these variables over the medium term is -0.55. For the full post-war period the correlations in the US are slightly larger (in absolute value). -0.18 for HP-...ltered data and -0.66 over the medium term.

> 23A decline in investment leads to an increase in the price of installed capital because the adjustment costs embedded in (3) imply that lower levels of investment today increase the costs of investment tomorrow.

22
|Services<br>Other||-0.258<br>[0.006]***<br>-0.260<br>[0.006]***||-0.077<br>[0.008]***<br>-0.231<br>[0.008]***||0.311<br>[0.008]***<br>0.446<br>[0.008]***|
|---|---|---|---|---|---|---|
|Occupation-Medium skill||0.023<br>[0.003]***||0.148<br>[0.006]***||-0.220<br>[0.007]***|
|Occupation-High skill||0.021<br>[0.004]***||0.040<br>[0.007]***||-0.262<br>[0.008]***|
|Constant|0.235<br>[0.009]***|0.455<br>[0.008]***|0.304<br>[0.010]***|0.239<br>[0.010]***|0.159<br>[0.009]***|0.009<br>[0.009]|
|Numberofcountries|27|17|27|17|27|17|
|Observations|156,850|107,922|156,850|107,922|156,850|107,922|
|R-squared|0.218|0.318|0.384|0.384|0.075|0.137|

Source: Own estimations using GMD. Notes: Linear probability models estimated by Ordinary Least Squares. Omitted categories: 15-24 years old, no education, being household head, agriculture, low-skilled occupations. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.

# **6. Profiling Urban Informal Firms**

We now turn to the firm data to understand the nature of informal jobs from a firm perspective. We rely on the IES, keeping in mind that the harmonized data are from only three countries: Zambia, Zimbabwe, and Mozambique.

The IES sample differs from the GMD sample in a few important ways. The IES sample has fewer female firm owners in comparison to the share of women in our GMD sample, informal firm owners are more likely to be in the prime-age group than they were in the GMD sample, and informal firm owners are less educated than in our GMD sample. Though 72 percent of urban informal firms in the IES data are owned and operated by one person (selfemployment) who has a low level of education, (Figure 12), the share of employers is much larger (25 percent) than in the GMD sample. This suggests that the findings based on worker analysis in the above section might underestimate the level of informality in urban Africa, since that analysis had assumed all employers were in the formal sector.

27
30 a price increase. He then applies the model to Bangladesh, using an incomeexpenditure survey and econometric estimates of the wage-price elasticity, and finds that in the short run, the rural poor are likely to be worse off after a price increase, a;' in the long run, their welfare will be neutral to such price increases.

A second paper incorporates consumer demand responses to changes in prices. Ravallion and van de Walle (1989) use a microdata set to estimate the effect on poverty of a change in the administered price of a staple food. Their method incorporates behavioural demand responses by households to changes in food prices and income. This is done by estimating demand functions, and then using these to calculate a household's "equivalent income", a money metric of household welfare. The equivalent income is the amount of income a household requires if they had the reference household's characteristics, to achieve the same utility they had before prices were changed and with their own household characteristics. This measure of equivalent income is then used to construct a poverty index, which will be behaviourally consistent since consumer demand responses have been included. Simulations of food price increases and decreases, with varying assumptions about producer input subsidies are performed.

These last several studies demonstrate that future research on adjustment and income distribution requires a closer link between macroeconomic and sectoral research, and work on microeconomic issues. The latter studies that analyze behavioural responses of consumers and producers need to be considered when tracing the effects of macropolicies.

# 4. Conclusion

This paper has provided a review of the methodologies used to trace the
**Table 5: Testing the significance of banking sector vulnerabilities in the transmission of the 2007-2008 crisis** Table shows results from regressing local market returns expressed in US dollars. Estimations include time dummies and country fixed effects. Robust standard errors, clustered at the country level, are shown in parentheses.  *, **,*** denote significance at 10, 5 and 1 percent significance level, respectively.

|Variables||Re|turns in US $||
|---|---|---|---|---|
|US Stock Returns* Bank Capital Adequacy Ratio*Pre-Lehman|-0.171*<br>(0.089)||||
|US Stock Returns*Bank Capital Adequacy Ratio*Post-Lehman|-0.069<br>(0.054)||||
|US Stock Returns*Bank Liquidity Ratio*Pre-Lehman||-0.183***<br>(0.068)|||
|US Stock Returns*Bank Liquidity Ratio* Post-Lehman||-0.042<br>(0.046)|||
|US Stock Returns*Private Credit to GDP Ratio*Pre-Lehman|||0.237***<br>(0.062)||
|US Stock Returns*Private Credit to GDP Ratio*Post-Lehman|||0.129**<br>(0.049)||
|US Stock Returns*Banking Activities Index*Pre-Lehman||||-0.161**<br>(0.067)|
|US Stock Returns* Banking Activities Index*Post-Lehman||||-0.146***<br>(0.046)|
|F-test null Pre-Lehman interaction=Post-Lehman interaction|1.55|5.35|3.56|0.04|
|F-test(p-value)|0.217|0.023|0.063|0.850|
|Observations|1730|1714|1587|1503|
|R-squared|0.511|0.511|0.545|0.571|
|Number of countries|83|82|76|72|

29
Figure 6: Rolling Correlations of Maize Nominal Price Returns - 24-Month Window <!-- Start of picture text --> Crude oil Coal Natural gas<br>Soybean oil Barley Sorghum<br>Rice Wheat Sugar<br>1970m1 1980m1 1990m1 2000m1 2010m1 1970m1 1980m1 1990m1 2000m1 2010m1<br>Coffee<br>1970m1 1980m1 1990m1 2000m1 2010m1<br>1<br>0<br>-1<br>1<br>0<br>-1<br>1<br>0<br>-1<br>1<br>0<br>-1<br><!-- End of picture text --> � Note. This figure reports a graphical representation of the evolution of<sup>�</sup> _ρxz,w,t_ – i.e., the rolling correlation between the nominal price returns of commodities _x_ (maize) and _z_ , where _w_ is the length of the window used to compute the rolling correlations ( _w_ = 24 months, in this case). 95% confidence bands are also plotted.

34
availability as a measure of the reliability of electricity supply, we find that outages have a negative effect on economic outcomes and that the effects of gaining access to electricity become stronger after controlling for outages. For example, per capita income decreases by 0.5 percent for each additional hour of outages. And while electrification combined with a 24‐hour‐a‐day power supply is associated with a 17 percent increase in income, electrification alone (with outages not controlled for) is associated with only an 11 percent increase. The aggregate benefit of obtaining reliable electricity in rural India is estimated to be US$11 billion a year, with US$4.7 billion from increasing the access rate and US$6.5 billion from improving the reliability of existing supply.

Several studies have examined the benefits of rural electrification in India. For example, van de Walle et al. (2015) estimate the long‐run effects of electrification on household consumption in rural India based on data for 1981–98; Burlig and Preonas (2016) investigate the effect of India’s national rural electrification program on labor force participation, living standards, and other village‐wide outcomes using census data for 2001 and 2011; and Khandker et al. (2014) estimate the benefits of electrification projects in rural India based on cross‐sectional data for 2005. A study by Chakravorty, Pelli, and Marchand (2014) is one exception that examines the effects of reliability on the benefits of electrification. Also relying on IHDS data, the authors find that a grid connection and a higher quality of electricity increased nonfarm income by 28 percent during 1994–2005, while a grid connection alone increased nonfarm income by 9 percent.

Our study differs from the one by Chakravorty, Pelli, and Marchand (2014) in several ways. First, we analyze the effects of rural electrification based on a more recent two‐round panel survey conducted in 2005 and 2012. In addition to providing updated evidence, the survey data for this period are particularly interesting for understanding rural electrification in India. An important reason is that 2005 was the year in which the Indian government launched its national rural electrification program committed to connecting all 100,000 unelectrified villages. By 2010 the program had connected 283 million people to the grid. But despite the improvement in the connection rate, fewer homes benefited from uninterrupted power supply in 2012 than in 2005, suggesting that outages became more common during this period. The relatively large variation in both connection rates and reliability over time provides a unique opportunity to identify how lack of reliability interferes with achieving the benefits of electrification. In addition to analyzing the effect of electrification on households’ income, as done in Chakravorty, Pelli, and Marchand (2014), we also analyze its effect on a broad range of households’ social and economic outcomes, including expenditure, education, employment, and poverty status.

3
_Nj_<sup>respectivelyin</sup><sup>_Y_</sup> _t_<sup>_s_andin12</sup><sup>_Y_</sup> _t_<sup>_s_</sup> +1<sup>.</sup> The individual OGIC provides information on the impact of growth on IOp. Consider the Lorenz curve of _Yt_<sup>_s_:</sup> The individual OGIC defined in eq. (3) can be decomposed in such a way that it becomes a function of the Lorenz curve defined in eq. (4), as follows: where ∆ _LYt_<sup>_s_</sup> � _Nj_ � = _μ_ ( _μy_<sup>_t_</sup> _<u>jt</u>_ )<sup>isthefirstderivativeof</sup><sup>_LY_</sup> _t_<sup>_s_</sup> � _Nj_ � with respect to _Nj_<sup>,and</sup><sup>_γ_=</sup><sup>_<u>μ</u>_</sup> _μ_<sup><u>(</u></sup><sup>_<u>y</u>_</sup> (<sup>_t_</sup> _y_<sup><u>+</u></sup> _t_ )<sup>1)</sup><sup>_−_1</sup> is the overall mean income growth rate.

Thus, when growth is proportional, it does not have any impact on the level of IOp: ∆∆ _LLY sY st_ <u>+</u> _t_ 1<sup>((</sup> _N_<sup>_<u>i</u>_</sup> _N_<sup>_<u>j</u>_))=</sup> 1, and _gY_<sup>_os_</sup> � _Nj_ � will just be an horizontal line, with _gY_<sup>_os_</sup> � _Nj_ � = _γ_ for all _j_ . On the contrary, when growth is progressive (regressive) in terms of opportunity, growth acts by reducing (worsening) IOp: ∆∆ _LLY sY st_ <u>+</u> _t_ 1<sup>((</sup> _N_<sup>_<u>i</u>_</sup> _N_<sup>_<u>j</u>_))= 1,and</sup><sup>_g_</sup> _Y_<sup>_os_</sup> � _Nj_ � will be a decreasing (increasing) curve.

The main aspect that distinguishes the individual OGIC from the standard GIC is represented by the distributions used to construct that curve. This variation allows us to establish a link between growth and IOp. Note that the smoothed distribution at the base of the individual OGIC is the same used by Checchi and Peragine (2010) and Ferreira and Gignoux (2011) to measure ex ante IOp. Therefore, our evaluation of growth based on the individual OGIC is, by construction, consistent with the IOp index they proposed; other things being equal, an individual OGIC curve that is downward sloping in all of its domain implies a reduction in IOp.

However, the individual OGIC is unable to track the evolution of each type during the growth process. In the smoothed distribution, types are ranked according to the value of their opportunity set at each point in time. Thus, the shape of the curve depends not only on the change in the

> 12Note that, given the assumption of anonymity implicit in this framework, the individuals ranked _Nj_<sup>in</sup><sup>_t_canbe</sup> different from those ranked _Nj_<sup>in</sup><sup>_t_+ 1.</sup>

12
# **Property Rights Institutions and Investment**<sup>**1**</sup>

## Jahangir Saleh

St.Mary’s College of MD

## Abstract

This paper examines the channels through which alternative property rights institutions affect investment. These institutions are defined by a society’s enforced laws, regulations, governance mechanisms and norms concerning the use of resources. A transaction cost framework is used to analyze the incentive impact of various types of property rights, liability rules, and rules regarding contracts. This framework is used to discuss the legal and cultural conditions necessary for the formation of productivity-enhancing organizations and the proper role of government in providing the infrastructure for private investment. A brief section examines the role of cultural and religious norms in determining the economic effectiveness of legal systems, with a focus on Islamic countries. The final section evaluates empirical approaches used to discover the specific ways property rights structures affect investment and growth.

## World Bank Policy Research Working Paper 3311, May 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 I am grateful to Shahrokh Fardoust , Jean Jacques Dethier, Rick Messick and Mustapha Nabli for their very valuable and insightful comments.
<!-- Start of picture text --> 180000 7000<br>160000<br>6000<br>140000 ; oes<br>= 5000 ==<br>= 120000 c<br>=<br>s-<br>“Q 100000 4000<br>9 as<br>© =<br><!-- End of picture text --> <!-- Start of picture text --> 3000 »J<br>o<br>o<br>60000 2<br>2000 2<br>40000 a<br>20000 1oo «8<br>&<br>0 0<br>2010 2015 2020 2025 2030 2035 2040<br>— GDP —«- Energy right axis CO2 right axis<br><!-- End of picture text -->
Table 6. Decomposition of City Size Growth

||Coef. of<br>Table 5-1<br> <br><br>||Mean( )<br>_i_<sup>_b_</sup><br>L|Sll|Decompo<br>(<br>_a_|sition of ci<br>)<br>/<br>_i_<br>_i_<br><br>_b_<br>_c_<br>×<br>,<br>L|ty growth<br>%<br>Sll|
|---|---|---|---|---|---|---|---|
||(3),(<br>)<br>_i_<sup>_a_</sup>|Total|arge<br>cities<sup>b</sup>|ma<br>cities<sup>b</sup>|Total|arge<br>cities<sup>b</sup>|ma<br>cities<sup>b</sup>|
|No. cities||123|61|62||||
|Δ Ln(city pop)||0.226|0.264|0.188||||
|Δ Ln(ruralpop. supply|9.429|-0.006|-0.005|-0.008|-8.5|-6.5|-10.6|
|marketpotential)||||||||
|Δ Ln(marketpotential)|1.284|0.346|0.346|0.345|63.8|62.2|65.5|
|Average schooling (t-1)|0.071|4.568|4.773|4.366|46.7|47.4|45.9|
|Δ Average schooling|0.384|1.208|1.215|1.201|66.7|65.3|68.2|
|Δ Ln(inter-citytransport|-0.089|-0.215|-0.191|-0.239|2.8|2.4|3.1|
|costs)||||||||
|State capital dummy|0.154|0.171|0.344|0.000|3.8|7.4|0.0|
|Ln(population) (t-1)|-0.047|12.339|13.172|11.520|-83.4|-86.6|-80.1|
|Manu / service(t-1)|0.140|0.406|0.428|0.385|8.2|8.4|8.0|
|_i_<br>_i_<br>_i_<br>_c_<br>_a_<br>_b_<br>=<br>×<br>∑||0.695|0.715|0.676||||
|sum|||||100.0|100.0|100.0|

a. Means are for 2000-1991 and 1991-1980. For average schooling (t-1), it is for 1991 and 1980.

b. We define large (small) cities if they have greater (less) than median city population in each year.

36
# **Tables and Figures:**

**Table A1: Correlations between economic and political indices, 1970 and 2000**

|||**19**|**70**|||
|---|---|---|---|---|---|
||prop|reg|polity4|przdemaug|fhpolrigau|
|prop|1|||||
|reg|0.2424|1||||
||0.1606|||||
|polity4|0.5156|0.569|1|||
||0.0002|0.0001||||
|przdemaug|0.4296|0.4506|0.868|1||
||0.0023|0.0027|0|||
|fhpolrigaug|0.6278|0.6219|0.9017|0.8446|1|
||0|0|0|0||
|||**20**|**00**|||
||prop|reg|polity4|przdemaug|fhpolrigau|
|prop|1|||||
|reg|0.6644<br>0|1||||
|polity4|0.377|0.4439|1|||
||0|0||||
|przdemaug|.|.|.|||
||1|1|1|||
|fhpolrigaug|0.5431|0.5662|0.9004|.|1|
||0|0|0|1||

40