rafmacalaba/data-use-mentions-extended huggingface dataset:
farm gate level. The reference price at the farm gate level is defined as the net price of the product when it leaves the farm, after marketing costs have been subtracted. The undistorted farm gate price is defined as the price prevailing in competitive world markets.

B. Percent of respondents based on a survey of 80 EMDEs.

C. Estimates based on the Error Correction Model described in Annex 6.1. The coefficient of price insulation ranges from 0 for countries that do not insulate against the rise in world prices, to -1 for countries that adopt policies that fully insulate domestic markets. The error correction term represents the cost of being out of equilibrium or the speed with which polices achieve the target level of protection or at which policy makers move back toward this equilibrium after being forced away from it by a shock to world prices. Based on data for 82 countries, of which 26 advanced economies, 44 EMDEs, and 12 LICs for the period 1955-2011.

D. Real terms. Estimates derived based on the methodology described in the Annex.
Notice from Figures 7(a) and (b), which show savings and growth rates for China and India, that the need for high savings rates for sustained fast growth is hardly extravagant.

**Figure 7. National Savings and Growth: China and India**

<!-- Start of picture text --> (a). China: Growth rate and Savings Rate (b). India: Growth rate and Savings Rate<br>18 60 12 40<br>16<br>50 10 35<br>14<br>30<br>12 8<br>40<br>10 25<br>6<br>8 30 20<br>4<br>6<br>15<br>20<br>4 2<br>10<br>2<br>10<br>0 0 5<br>-2 0 -2 0<br>Growth rate-L/Axis Gross Savings/GDP - R/Axis Growth rate-L/Axis Gross Savings/GDP - R/Axis<br>Source: World Bank WDI data Source: World Bank WDI data<br>% % % %<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br><!-- End of picture text --> This exercise brings out two points: first, achieving 8 percent growth over the medium term in Sri Lanka will call for a huge fiscal effort to increase public savings substantially. Second, even assuming the required level of savings is achieved, it will not suffice to maintain an 8 percent growth rate over the long run. Since achieving the desired level of public savings may not be feasible, Figures 8 (a) and (b) shows the growth dynamics consistent with the targeted level of public savings of 1.5 percent of GDP to be attained by 2012. This will constrain the
# Acknowledgments

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Heinrich Bofinger on air transport, Michael Minges on information and communication technologies, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank teams; notably Yusupha Crookes (director of the Africa Regional Integration Department), Andrew Roberts (Africa Regional Integration Department), Gael Raballand (transport), and Charles Kunaka (International Trade Department). The paper benefited from feedback provided by REC staff at a May 2010 Workshop organized by the Infrastructure Consortium for Africa in Tunis and at other meetings.
al. 2020).

# Table 1. Panel regressions

> 7 The impacts of voluntary shifts in behavior and government-imposed lockdowns on economic activity are likely to vary significantly over time, depending on the severity and duration of the pandemic, as well as household adherence to pandemic-control policies.

> 8 The estimated number of new daily cases from the Imperial College of London model is accessed via the Our World in Data (OWID) COVID-19 database.

8
few differences stand out for smaller transactions, possibly pointing to different behavior between larger or institutional and retail investors. Focusing on Model 5 (and 10), the magnitude of U.S. inflation expectations is about half compared to Table 5 but remains economically very relevant. At the same time, the bitcoin price coefficient is about twice as large, while the ether price results are weaker. Further, the pandemic dummy implies that retail volumes have grown less compared to total volumes: 105 percent and 119 percent for all countries and EMDEs only, respectively. Finally, the factors are able to explain somewhat less of the variation volumes for smaller transactions: the adjusted R-squared is about 0.58-0.65 compared to 0.70-0.75 in Table 5.

# 5.2 Robustness and extensions of the main results

## _Robustness_

This section documents several robustness checks of the main results in Tables 5 and 6. Tables 7 and 8 report the results using alternative measures for three indicators. First, we use the countrylevel change in Broad Money instead of inflation (ρ = 0.69). Note that our sample size drops significantly due to missing observations for this indicator. However, the results are broadly similar: when considering small transactions, Model 1 (and 5) shows that an increase in broad money is also associated with an increase in crypto volumes at the 10-percent level of significance, but it loses significance once global factors are included. Second, we use the U.S. 10-year Breakeven Inflation Rate, the expected average inflation rate over the next 10 years which is also derived from Treasury yields, instead of the U.S. 5-year, 5-year forward Inflation Expectation Rate (ρ = 0.97). The coefficient on the 10-year breakeven rate has a similar sign and magnitude and is also significant at the 1-percent level. Finally, we use the U.S. BBB Corporate Bond Spread as an alternative proxy for risk appetite (higher values indicate higher risk aversion), instead of
participates in the paid labor force; for example, women‘s participation rate in MGNREGS is about twice their share of other (non-PW) casual wage work.<sup>28</sup> The variation across states is striking; between the two extremes, only 7% of the work goes to women in Jammu & Kashmir as compared to 88% in Kerala (Table 1). The female share on MGNREGS work is greater than their share of the work in the casual wage labor market in all states.

Women are less likely to participate (relative to men) in MGNREGS in poorer states. Figure 7 plots the share of person days of employment going to women against the poverty rate. We see a negative correlation (r = –0.47). By contrast, women‘s share of casual (non-PW) wage work tends to be slightly higher on average in poorer states, though the difference is not statistically significant (r=0.09). While the scheme is clearly bringing women into the paid workforce, it is less effective in doing so in states with higher poverty rates.

Why do we see less of the available work going to women in poorer states? The NSS does not allow us to identify rationing at the individual level (it is a household variable). However, we can gain some clues to how rationing has affected women from the inter-state comparisons. It would seem unlikely that the effect of household poverty on demand for work amongst women is any different than amongst men. Assuming that the effect of being a poorer state on demand for work is the same for men and women, the pattern in Figure 7 suggests that the rationing process is less favorable to women in poorer states.

Do women have equal access to the scheme, when they need it? Again, we cannot give a direct answer from the survey data. However, the patterns in the inter-state data are suggestive. We observe a negative correlation between the female share of work and the
0.22|0.75|6.46|
|F29 non-overlap (mn. ha)<br>20.34|0.31|0.21|1.14|18.68|
|SAR only (mn. ha)<br>3.36|0.45|0.27|0.33|2.32|
|Informal (mn. ha)<br>14.51|||||

_Source:_ Computation based on data from the SAR, 2020 Form 29 data from SSSU, and crop maps based on satellite imagery for 2020, as discussed in the text. _Note:_ Region is not reported in the SAR for 1,629 farms cultivating a total of 39,735 ha. FOP = family-owned business; ha = hectare; mn = million; SAR = State Agrarian Register; SSSU = State Statistics Service of Ukraine.

37
# **Political Reservations and Women’s Entrepreneurship in India**

Ejaz Ghani, William R. Kerr and Stephen D. O’Connell January 2014

**Abstract:** We quantify the link between the timing of state-level implementations of political reservations for women in India with the role of women in India’s manufacturing sector. While overall employment of women in manufacturing does not increase after the reforms, we find significant evidence that more women-owned establishments were created in the unorganized/informal sector. These new establishments were concentrated in industries where women entrepreneurs have been traditionally active and the entry was mainly found among household-based establishments. We measure and discuss the extent to which this heightened entrepreneurship is due to channels like greater finance access or heightened inspiration for women entrepreneurs.

**Keywords:** Women, female, gender, entrepreneurship, political reservations, development, informal sector, India, South Asia.

**JEL Classification:** D22, E26, H11, J16, L10, L26, L60, M13, O10, R00, R10, R12.

_Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Kerr:</u> Harvard University, Bank of Finland, and NBER, <u>wkerr@hbs.edu; O’Connell: CUNY Graduate Center, soconnell@gc.cuny.edu.</u> _Acknowledgments_ : We thank Nina Pavcnik, David McKenzie, Petia Topalova, and two referees for helpful comments on this work. We also thank Mani Shankar Iyer, Pratap Banhu Mehta, Meera Chatterjee, Prabir De, Ria Mukherji, Reema Nanavaty, Bhishma Rout, and Nupur Tiwary for extremely useful discussions on Panchayati Raj Institutions. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. We are particularly indebted to Shanthi Nataraj for sharing her wisdom regarding the industrial survey data during the nascent stages of this work. Funding for this project was provided by
||||||-1.737|
||||(0.312)||||||(1.521)|
|Non-colony|||-0.234||||||-0.333|
||||(0.308)||||||(0.577)|
|Landlocked|||0.328||||||-0.778|
||||(0.315)||||||(1.376)|
|Latitude|||-0.000910||||||0.00459|
||||(0.0122)||||||(0.0168)|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Authors’ estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

17
half as profitable, and has slightly lower capital adequacy but similar asset quality when compared to the average private bank. Average size, as measured by total assets, is 41 billion USD for private banks, and 97 billion USD for state-owned banks. Profitability, as measured by average Return on Assets (ROA), is 1.1 percent for private banks, and 0.5 percent for state-owned banks. Asset quality, as measured by the share of non-performing loans over gross loans, is remarkably consistent across the subsamples, i.e., at 9.1 percent and 9.9 percent for private and state-owned banks, respectively. Finally, the average Tier 1 Capital ratio stands at 16.6 for private banks and 15.5 for state-owned banks.

# Latent Variables

State-owned banks are supposed to target credit constrained firms or firms that generate positive externalities. This section describes the construction of three indexes aimed at capturing the presence of credit constraints, innovation, and green activities.

The Enterprise Survey contains several variables that indicate the potential presence of credit constraints. For instance, one question asks whether access to finance is an obstacle; another question asks whether access to finance is the major obstacle faced by the firm (hence, unlike the first question, this question focuses on the relative importance of credit constraint); yet another question asks if the firm is credit constrained (the possible answers are fully constrained, partially constrained, and unconstrained) and a further question (included in a different set of surveys) asks if the firm is partially credit constrained. Finally, the survey includes a variable that measures the share of investment financed with bank loans.

Each of these variables is likely to be correlated with some latent continuous variable that measures whether a firm is credit constrained. We assign numerical scores to each of these questions and use factor analysis to extract this latent variable.<sup>7</sup> One problem with applying factor analysis to the variables listed above is that not all questions are
<!-- Start of picture text --> Sovereign Debt Rating and Liquidity Space (by country)<br>21.0 CAN woh PacHe aNd “Eiyoeu<br>> FIN Nz AUKG<br>My 19.0 KOR ARE KWT FRA, CBR<br>s 17.0 wae SR.CHL RL ypn & CHN<br>@ 450 MET itu POL MYS<br>D MUS MERER pha ESP<br>6 13.0 weet IT PHPAN BGR<br>3 440 © OMN . ot zMAR<br>2 ‘ @ HRV @ CYP<br>D 90 © TUR © SRRpy OO aco<br>@ - ee NcA @ LKA @ KEN © 40 TUN<br>a ® PAK@® ARG © oe JAM<br>50. © UKR<br>1.0 1.2 1.4 1.6 1.8 2.0 2.2<br>Current ratio (LTM)<br><!-- End of picture text -->
current backhaul composition is 4% fixed fiber, 6% fixed copper, 84% wireless and the remaining 6% using satellite. A least-cost design is also used to connect areas via a backhaul link into the main fiber network. Using a minimum spanning tree, the cheapest network structure to connect all regional nodes and sites is estimated, which can either be linked using fiber or a wireless technology (as illustrated in Figure S4 of the Supplementary Materials).

Once the additional sites required and the distance of the backhaul links are known, costs can be developed using estimates from a literature survey (5G NORMA, 2016; Frias and Pérez, 2012; Johansson et al., 2004; Markendahl and Mäkitalo, 2010; Paolini and Fili, 2012; Smail and Weijia, 2017), combined with validation by local operators. Mean per site capex costs include $40k for all active equipment, $30k to build a full 30m tower and $30k for installation. Mean per site opex costs include operation and maintenance of $7.4k, power of $3k, along with site rental of $10k (urban), $5k (suburban) and $3k (rural). For the backhaul, mean fiber costs per meter are $25, $15 and $10 for urban, suburban and rural respectively. Mean wireless backhaul costs are based on $15k, $20k and $45k for each small (<10km), medium (<20km) and large (<40km) backhaul unit (of which two are required to form a wireless connection). Connections over 40km require multiple hops (Kusuma and Boch, 2021; Oughton et al., 2022a). Core and backhaul links use an annual opex of 10% of the initial capex required for all active equipment to cover network maintenance and operation (Vannieuwenborg et al., 2018). An administration cost of 10% of the RAN cost is added to cover all necessary operations activities (subscriber acquisition, marketing, R&
<br>Sep 80|<br>676 F<br> <br>8320 J|U.N. 1988 revision<br>(prelim.)<br>UNPVSR 7/86 (official<br>est.)|U.N. 1988 revision<br>(prelim.)<br>Based on INED 10/87|U.N. 1988 revision<br>(pretim.)<br>Based on UNPVSR<br>4/87|
|Switzerland|Dec 80|<br>6366 J|Bank projection<br>from official<br>1984 est., UNPVSR<br>7/86|<br>Based on INED 10/87|Based on UNPVSR 4/87|
|Syrian<br>Arab Rep|. Sep 81|<br>9053 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Taiwan, China|Dec 80|17969F|Bank projection<br>from census|Based on USBOC 1985|Based on USBOC 1985|
|Tanzania|Aug 78|17513 F|Bank projection from census|Bank est.|Bank est.|
|7,284|7,284|
|R-squared|0.870|0.870|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

In fact, the evidence suggests a contrary strategy since it shows that labor standards thus promote economic upgrading through GVC integration as a seller. All six indicators have the expected sign and four are statistically significant (Basic ILO conventions, vulnerable employment, child labor, and wage dispersion). Minimum wages in our sample show no mediating effect, thereby rejecting the positive impact hypothesized by Mayneris and others (2014). Similarly, female intensity in our sample has no moderating effect, opposing the negative relationship hypothesized by Tejani and Milberg (2010). Finally, we conclude that raising labor standards is a sound and recommendable strategy for countries that are engaged in GVCs. Furthermore, the income group specific results show that, not only high, but also upper middle-income countries benefit from such standards, indicating that their facilitating potential is not dependent on the stage of development.

28
on how much bribes firms pay to “get things done”. While the measure has been used in the literature, there may be some concern since it is not clear what types of corruption experiences firms have in mind in responding to the question. One way to address such concerns, at least to some extent, is see how well the overall corruption measure correlates with the petty corruption measures. Since petty corruption is part of overall corruption, we should find that the two are positively correlated. Of course, the correlation is unlikely to be perfect since petty corruption is only a fraction of overall corruption, and countries may be more corrupt in certain types of petty corruptions and less so in the others.

For our baseline sample, we find that there is a high correlation between the overall corruption and the two petty corruption measures. The Spearman’s rank correlation coefficient between overall and petty corruption (incidence and depth) equals about 0.48, significant at the 1 percent level. At the country-level, the correlations are about 0.55.

Another validity check for our corruption measures is how well they correlate with the macro-level corruption measures typically used in the literature. Thus, we check for the correlation between our overall and petty corruption measures and other corruption measures including the Control of Corruption (Worldwide Governance Indicators, World Bank), Corruption Perception Index (CPI) (Transparency International), and the corruption measure from International Country Risk Guide (ICRG). All these external corruption measures are rescaled so that higher values imply higher corruption. Doing so does not affect the analysis that follows. The correlations between the ES corruption measures (averaged at the country level) and the external ones is large and statistically significant (at the 1 percent level and 5 percent level). Table A4 in the online appendix provides the details. For instance, the rank correlation between overall corruption and the Control of Corruption variable equals 0.54, 0.44 with CPI and 0.21 with the ICRG corruption measure
7

# **2 Spending and Earnings in Rural Tanzania and China**

To explore the link between spending behavior and sources of income in Tanzania, the study uses the information collected under the Vulnerability Household Panel conducted in rural Kilimanjaro and Ruvuma, two cash crop producing regions in eastern and southern Tanzania respectively. Kilimanjaro is a richer and better connected region in Tanzania, situated in the north east and bordering Kenya. Ruvuma is in the south, bordering Malawi, and is poorer, geographically more remote, and ethnically distinct.<sup>14</sup> About 900 households, representative of all agricultural households, were surveyed in each region in 2003/4, 2004/5 and 2009.<sup>15</sup> Most modules, including the income and expenditure modules used here, are identical across survey rounds.

Income is coded into two categories, earned and unearned income, based on the effort involved in obtaining it. Earned income includes farm income, wage income, and business income. Unearned income includes transfers, remittances, gifts and pension<sup>16</sup> . Farm income makes up about two thirds of earned income in both regions (62% and 65% in Kilimanjaro and Ruvuma respectively) (Tables 1 and 2)<sup>17</sup> . Remittances are the most important unearned income, accounting for about half of the unearned income (47% in Kilimanjaro and 53% in Ruvuma). About 57% of the households in Kilimanjaro and 25% of the households in Ruvuma report having received unearned income. Unearned income exceeds earned income only for a few households (11% of the households in Kilimanjaro and 2% of the households in Ruvuma). Among those whose unearned income is strictly positive, but smaller than their earned income, it amounts on average to between 8% and 10% of earned income.

Both earned non-farm income and unearned income can be split up by the gender of the person receiving it. This is
, which coincided with the lockdown policies. The total value of sales and the number of transactions fell by almost 60% during April-June of 2020 compared to 2019. For reference, the fall in the value of sales was only 25% after the strict centralized lockdown was over (July-September) and only 15 _._ 6% before the lockdown (January-March) compared to the corresponding months in 2019.

The characteristics of the network, including measures of network in-degree (i.e., number of sellers per buyer) and outdegree (i.e., number of buyers per seller), are in Tables A2 and A3. Similar to other production network datasets, our network is sparse. In terms of network indegree, for 4-digit HS products, the median buyer has 2 suppliers per product but 6 suppliers on average with a standard deviation of 17. In terms of network outdegree, the median seller has 5 buyers

> 16See the Economic Times. On April 30, one _Red_ zone district was reclassified to the _Green_ zone, but we maintain the initial classification as it is likely to be more exogenous.

8
<!-- Start of picture text --> Figure 3. Share of Municipalities with Seguro Popular<br>2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1<br>date<br>5 households with SP 2% population with SP<br>5% population with SP<br>Source: Seguro Popular Registry for municipalities in Labor Surveys<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Table 1 shows the summary statistics of the main variables in the analysis. On average 45 percent of the households are covered and 27 of the individuals are directly affiliated by _SS_ . Most households have at least 6 people (61 percent). Regarding the shares in different labor market status, in addition to the 27 percent in salaried formal jobs, there are 18 percent of the population in salaried jobs without _SS_ , and another 14 percent who are self-employed, 2 percent who are unemployed and 4 percent in other informal jobs. Finally, 38 percent of the population is out of the labor force.

# **6. Identification Strategy**

The analysis will examine the impact of _Seguro Popular_ ( _SP_ ) on the probability of a household being covered by _SS_ by estimating the following specification.

## P( _SS_ ijt) = + λZ _t_ + _SPjt_ +ψX _ijt_ + ε _ijt  ,_ where E[ε _ijt\jt_ ]=0,

where _i_ identifies the household, _j_ the municipality, and _t_ the period, SS takes the value of 1 if the household head or the spouse of the head are contributing to _SS_ through their jobs, _Zt_ is a vector of time dependent variables, and _Xijt_ is a vector of household characteristics that vary over time.

15