rafmacalaba/datause-encoder-data huggingface dataset:
- _Liquidity and funding of the banking system_ : In addition to assessing the banking sector’s resilience in the face of credit losses, it is also important to analyze the capacity of the banking system to absorb a shock on the liability side of the balance sheet through higher costs or reduced availability of funding. In deciding on the range of supporting indicators, it is important to take into consideration the funding profile of banks. In most developing countries, deposits are the main funding source, with wholesale funding (including the interbank and repo market) following at considerable distance. Among the relevant liquidity indicators are loan-to-deposit ratios, the ratio of liquid assets to short-term liabilities, and the reliance of the banking sector on back-up liquidity sources (such as parent bank funding). Where available, Basel III liquidity indicators, including the net stable funding ratio (NFSR) and the liquidity coverage ratio (LCR), could be constructed.<sup>33</sup> The usual pattern is that liquidity imbalances build up in the upturn of the financial cycle, when the expansion of the loan book usually outpaces the growth of stable sources of funding, particularly deposits. Once the financial cycle turns, the liquidity imbalances unwind as credit growth slows down. For countries characterized by high levels of currency substitution, the metrics above may also be calculated in the relevant national and foreign currencies.

- _Soundness of NBFIs_ : This index is meant to measure the financial soundness of various kinds of non-deposit taking financial institutions, such as insurance companies and pension funds. A frequent problem is that NBFIs report less often than the banking sector and that prudential data have a longer time lag. It may therefore be necessary to revisit the reporting requirements that apply to the NBFI sector to ensure the timeliness and comprehensiveness of the reports. Among the indicators to be included are measures of solvency. This typically involves the backing of liabilities to policyholders (technical provisions), taking into account the level of guarantee with conservatively valued eligible assets. Other relevant indicators include the RoE and RoA, and measures of the volatility of investment returns. For the insurance sector, the ratio of paid insurance claims to received premiums is relevant. For the pension sector, the ratio of contributors to beneficiaries is relevant, particularly for defined-benefit pay-as-you-go systems.

> 33 The NSFR has a time horizon of one year and measures the sustainability of the maturity structure of assets and liabilities. The LCR measures the sufficiency of the buffer of high-quality liquid assets in the face of a stress scenario lasting for one month.

36
**Appendix 2:** _Calculation of US inpatient prices_

|_Procedure_|_DRG_<br>_Code_|_Hospital_<br>_Payment($)_|_Representative_<br>_CPT codes_|_Physician_<br>_Payment($)_|_Anesthetist_<br>_units_|_Anesthetist_<br>_fee ($)_<sup>_38_</sup>|_Inpatient_<br>_price($)_<sup>_39_</sup>|
|---|---|---|---|---|---|---|---|
|Knee surgery|209|9,261|29882, 29873,<br>27437,27446,27447|879|11|195|10,335|
|Shoulder Arthroplasty|223,224|4,267|23470,23472|1,443|13|230|5,940|
|TURP|337|3,271|52601|696|9|159|4,127|
|Tubal Ligation|361|5,124|58670, 58671|362|10|177|5,663|
|HerniaRepair|161,162|4,206|49650|388|9|159|4,753|
|Excision of skin lesions|270|5,861|11426|238|8|142|6,240|
|Tonsillectomy|59|2,924|42820,42821|315|9|159|3,398|
|Hysterectomy|358, 359|4,492|58550, 58553|1,113|10|177|5,783|
|Haemorrhoidectomy|158|4,436|46255|349|9|159|4,945|
|Rhinoplasty|56|4,137|30400|754|9|159|5,050|
|Bunionectomy|225|5,456|28290|448|8|142|6,046|
|Cataract extraction|39|2,861|66983|592|8|142|3,595|
|Varicose vein surgery|119|6,524|37720|382|9|159|7,065|
|Glaucoma procedures|46,47|3,020|66150|702|9|159|3,882|
|Tympanoplasty|55|4,150|69631|701|8|142|4,993|

_Sources:_ DRG Expert 2005, Current Procedural Terminology (2004), Medicare Physician and Anesthetist Fee Schedules

> 38 Anesthetist fees were calculated as the national average anesthetist fee of $17.71 per unit multiplied by the number of units. 39 Inpatient prices equal the sum of hospital payment, physician and anesthetist fees.

30
**Table 2: Regression Results on Likelihood of a FDI Surge and Stop – Complementary Logistic Regression Estimates**

|||**Surges**<br>|||**Stops **<br>||
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|0.015(.013)|0.010(.014)|0.045(.028)|0.023(.010)*|0.013(.013)|0.046(.031)|
|Global Liquidity|0.014(.003)**|0.013(.004)**|0.015(.007)|0.003(.003)|0.002(.003)|0.003(.006)|
|Global Growth|0.150(.055)**|0.076(.058)|0.542(.097)**|-0.127(.042)**|-0.112(.053)*|-0.235(.112)*|
|**Regional Contagion**|0.020(.006)**|0.021(.008)*|0.032(.015)*|0.000(.006)|-0.007(.010)|0.028(.017)|
|**Domestic Factors**|||||||
|Stop previousyear|0.217(.180)|0.263(.231)|0.685(.501)|-0.391(.218)|-0.735(.332)*|0.848(.612)|
|Surgepreviousyear|-0.182(.183)|-0.014(.205)|-0.195(.540)|1.623(.141)**|1.533(.153)**|3.915(317)**|
|GDPper Capita(ln)|-0.082(.023)**|-0.148(.034)**|0.173(.068)*|-0.008(.028)|-0.067(.036)|0.106(.068)|
|Natural Resources|0.001(.003)|0.007(.003)*|-0.036(.018)*|0.002(.003)|0.006(.003)*|-0.007(.009)|
|Δ Financial Openness|0.181(.132)|0.118(.165)|0.433(.212)*|-0.069(.156)|-0.247(.195)|0.708(.247)**|
|Δ Economic and Financial<br>Stability|0.002 (.013)|0.020 (.015)|-0.068 (.027)*|-0.004 (.017)|-0.011 (.018)|0.026 (.031)|
|Δ Political Stability|-0.030(.018)|-0.038(.020)|0.011(.042)|-0.025(.019)|-0.031(.021)|0.013(.041)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|264|207|57|282|225|57|
|Pseudo R<sup>2</sup>|0.034|0.033|0.102|0.125|0.104|0.340|
|*p<0.05,**p<0.01;cluster-robust|standard errors inpare|ntheses.|||||

21
# Table 3.  Panel fixed-effects regression of output growth on commodity prices

|||**D**|**ependent var**|**iable: GDP**|||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Explanatory variables**|**EMDE**|**EMDE**|**EMDE**|**Full**|**Full**|**Full**|
|**Commodity Export Price Index (EPI)**|**0.085 *****|**0.063 *****|**0.080 *****|**0.024**|**0.018**|**0.026**|
|**Terms of Trade**||**0.029**|||**0.026**||
|**GDP (-1)**|||**0.367 *****|||**0.366 *****|
|**Commodity Export Price Index x EMDE**||||**0.062 ****|**0.046 ***|**0.054 ****|
|**Observations**|**381**|**364**|**370**|**533**|**436**|**518**|
|**Countries**|**11**|**11**|**11**|**15**|**15**|**15**|
|**F-Test joint Commodity EPI and EMDE intera**|**ction**|||*******|*******|*******|
|Notes: Panel least squares with country fixed effects. All var<br>statistical significance at 10, 5, and 1 percent, respectively.|iables are in log-|differences. Full|refers to the samp|le with AE and|EMDE. *,**, a|nd *** indicate|
Figure 6 provides a graphical overview of our results that are obtained when we vary both the target survey sample and the base survey sample. The former ranges from 10 to 100 percent of the sample, while the latter ranges from 10, 20, 30 to 100 percent of the sample (which helps make the graph less crowded). Since the combined TA sample contains 2,110 observations, we consider a range of 211 to 2,110 observations for the target survey sample size. Given the sample size of 4,644 households for the TZNPS 2020/21, we explore the predictive accuracy of imputation models using a base survey sample ranging from 464 to 4,644 observations.

Figure 6 illustrates that when using only 10 percent of the base survey (464 observations) to construct the models, most of the estimates for different target survey samples (indicated by the dotted blue line) fall outside the 95 percent CI of the true poverty rate (represented by the light gray zone). These estimates also display more variation, especially for smaller samples of the target survey. Increasing the sample size of the base survey from 10 percent to 20 percent (930 observations) (the dotted light green line) results in a dramatic improvement in the performance of the models, with almost all the estimates falling inside the 95 percent CI of the true poverty rate. Further increasing the base survey to 30 percent (1,393 observations) (the dotted orange line) slightly improves the imputation accuracy. Yet, estimates using larger sample sizes of the base survey (not shown) result in marginal improvement, as seen with the 100 percent sample of the base survey (the dotted black line).<sup>21</sup> In general, the predictive performance of the models stabilizes at the minimum sample size of around 1,000 observations for the target survey (close to 50 percent of the sample) and a similar

> 21 For example, a further increase in the size of the base survey from 20 percent to 60 percent (2,786 observations) does not appear to significantly improve the accuracy of the poverty estimates in all models, although there is a marginal improvement in Model 9 by bringing estimates closer to one standard error of the true poverty rate (the dark gray zone). Moreover, there is only a marginal difference between the behavior of Models 1 and 2 and that of Models 8 and 9, as Models 1 and 2 display less fluctuation starting from 844 observations (40 percent of the target survey), while Models 8 and 9 become more stable starting from 1,055 observations (50 percent of the target survey).

25
the Indian context. We also consider the share of the district population in a scheduled caste/tribe and the female labor force participation rate (e.g., Iyer et al. 2011, Klapper and Parker 2011).

Education and infrastructure are two factors consistently linked to India’s regional development.<sup>5</sup> We measure education level as the district’s percentage of adults with a graduate (post-secondary) degree and through literacy rates. Our infrastructure measures are the share of villages in a district with electricity access or paved roads. Finally, spatial locations relative to major population centers are frequently found to be important.<sup>6</sup> We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to the nearest of India’s ten largest cities<sup>7</sup> as a measure of physical connectivity and across-district infrastructure. This is calculated based on data on India’s road networks gathered using GIS software. We finally model the strength of the household banking sector for each district.

The univariate correlations in Table 4a are weak. The upper panel considers changes in the unorganized sector share. These basic traits do not connect systematically with unorganized share adjustments—the correlations except three are all less than 0.1 and statistically insignificant. The bottom panel considers growth in unorganized sector employment. Some of the correlations are stronger, with growth drivers identified in past work like education and infrastructure being again linked to unorganized sector growth. The comparison of the two panels reveals, however, that this is a general district growth effect that is not decreasing the unorganized sector’s share. Table 4b finds similar results when looking at industry traits and changes in the level of unorganized activity by industry.

The stability of these patterns suggests that the persistence of India’s unorganized sector is not due to a particular subset of regions or industries. For example, it is not the case that there are rapid shifts out of the unorganized sector in advanced technology industries, while lagging technologies are becoming more unorganized and holding the aggregate transformation back. Likewise, similar stories about different regions, city classes, or related spatial dynamics are difficult to support. Instead, given the limited movement at the aggregate level across both of these spatial and industry dimensions, we turn to state-industry analyses to identify conditions that have been associated with the transition. Before doing so, we pause briefly to provide simple evidence on why a very large unorganized sector can be worrisome.

> 5 For example, Lall (2007), Amin and Mattoo (2008), Mukim (2011), and Ghani et al. (2011a).

> 6 For example, Partridge and Rickman (2008), Volpe Martincus (2010), and Lall et al. (2011).

> 7 These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna.

9
# **Correlates of Participation in Employer-Based Health Insurance**

# Dependent Variable: Participation (1=Yes, 0=No)

Probit Marginal Effects

|Regressors|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Age|0.021***|0.027***|0.025***|0.027***|0.028***|0.028***|
||(0.005)|(0.005)|(0.006)|(0.005)|(0.005)|(0.005)|
|Age-Squared|-0.000*|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Schooling|0.029***|0.023***|0.019***|0.025***|0.023***|0.025***|
||(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|(0.003)|
|Post-Secondary Education|0.061***|0.019|0.020|0.011|0.017|0.009|
||(0.020)|(0.022)|(0.022)|(0.022)|(0.022)|(0.022)|
|Married|0.037**|0.012|0.011|0.010|0.013|0.012|
||(0.017)|(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|
|Female|-0.007|0.004|0.007|0.007|-0.017|-0.016|
||(0.011)|(0.013)|(0.013)|(0.013)|(0.015)|(0.015)|
|Urban Migrant w/ Other Urban|-0.342***|-0.287***|-0.288***|-0.230***|-0.306***|-0.231***|
|Hukou|(0.016)|(0.019)|(0.019)|(0.035)|(0.024)|(0.045)|
|Rural Hukou Migrant|-0.499***|-0.469***|-0.459***|-0.378***|-0.497***|-0.388***|
||(0.012)|(0.015)|(0.015)|(0.026)|(0.018)|(0.031)|
|Year 2010|0.107***|0.156***|0.175***|0.191***|0.155***|0.191***|
||(0.012)|(0.013)|(0.013)|(0.015)|(0.013)|(0.015)|
|Female x Rural Migrant|||||0.074**|0.025|
||||||(0.029)|(0.049)|
|Female x Urban Migrant|||||0.043|-0.002|
||||||(0.036)|(0.069)|
|Urban Migrant x Year 2010||||-0.089**||-0.119**|
|||||(0.043)||(0.054)|
|Rural Migrant x Year 2010||||-0.155***||-0.200***|
|||||(0.034)||(0.042)|
|Female x Rural x 2010||||||0.086|
|||||||(0.054)|
|Female x Urban x 2010||||||0.063<br>(0.073)|
|Has Labor Contract|||0.266***<br>(0.013)||||
|_Alternate Controls_|||||||
|Industry|no|yes|yes|yes|yes|yes|
|Work Unit Ownership|no|yes|yes|yes|yes|yes|
|Observations|9,830|9,830|9,820|9,830|9,830|9,830|
|Average Participation|0.583|0.583|0.583|0.583|0.583|0.583|

Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes 9830 working age adults, aged 18-60, who are employed for a wage. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables. Models (2) through (6) control for 17 industries and 8 work unit ownership types.

**Table A.5**

43
# **Entrepreneurship Capital and Technical Efficiency: The role of new business/firms as a conduit of knowledge spillovers**<sup>**1**</sup>

Leopoldo Laborda<sup>*</sup> World Bank and Institute of Latin American Studies University of Alcalá (Spain) Jose Luis Guasch<sup>**</sup> World Bank and University of California, San Diego (USA) Daniel Sotelsek<sup>***</sup> Institute of Latin American Studies University of Alcalá (Spain)

**Keywords:** Technical Efficiency, Entrepreneurship Capital, Stochastic Frontier Analysis (SFA).

> * Research Associate at the Institute of Latin American Studies, University of Alcalá c/ Trinidad no1, Colegio de Trinitarios, Alcalá de Henares, 28801, Madrid. (Spain).  Phone: +34 91 8820399. e-mail: llabordacastillo@gmail.com

** Senior Adviser at the World Bank and Professor of Economics at University of California, San Diego (USA) e-mail:  jguasch@worldbank.org

> *** Professor of Economics at the University of Alcalá (Spain). e-mail: <u>dsotelsek@uah.es</u>

> 1 This research is part of the program in LAC Private Sector and Finance Unit in supporting private sector development and entrepreneurship in Latin America and Caribbe.  This paper is forthcoming in the Entrepreneurship Research Journal. For additional information contact Jose Luis Guasch.
Policy Research Working Paper 6264

# **Abstract**

In previous literature, social capital has been hypothesized as a substitute for other forms of capital, such as physical and human capital. This paper contributes to this literature, studying the association between mothers’ access to social capital via participation in community organizations and their children’s nutritional status at 1 and 5 years. Using the Peruvian sample of the _Young Lives_ project, this study suggests that, where human capital is scarce, social capital might have important implications for child development. Maternal social capital is positively associated with height at 1 year old for those children whose mothers have no formal education. No significant association is found at 5 years of age.

This paper is a product of the Poverty, Equity and Gender Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mfavara@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
These subjective outcomes are somewhat consistent. Research on happiness over decades has shown that both absolute and relative positions matter for one’s satisfaction with life. There is ample empirical evidence that relative position matters more, but it is hard to generalize this proposition on individual satisfaction question. Thus it becomes difficult to put exact weights on these two factors. In this case relative position is directly asked in ranking on the ladder question. So one could expect the weight for relative position is higher in the ranking question. A moderate positive correlation between these two factors indicates the co-existence and significance of both absolute and relative positions in satisfaction with life.

# **[Figure 3.4 is about here]**

We now turn to historical life events. In a series experimental research retrospective evaluations of life events was found to play an important role in one’s satisfaction with life. Kahneman and Thaler (2005) provide a review of adaptation and learning from the past in subjective judgment. As mentioned by Stone et al (1997), evaluations of the past are anchored on the individual’s emotion when the evaluation is made. To them people must sometimes predict the hedonic effect of a long-term change in life circumstances.

Life in Transition Survey has a retrospective section where respondents were asked in which year(s), starting from 1989 until 2006, the transition has affected their work trajectory and life in general. The answers were binary - that is, yes or no, if someone experienced any live events (marriage, divorce, etc) or job events (worked, received unemployment benefits, etc) or economic distress (cut down on food consumption, etc). We grouped these variables into five categories, which are, life cycle events, labor market shocks, general economic distress, mobility intention and migration spanning over 18 years (1989 to 2006). In table 3.1 we show summary outcomes of these historical events under each broad group. On average mobility intention is found to be the most frequent event in life since transition for them.

# **[Table 3.1 is about here]**

11
internal trade costs has significant quantitative implications for the effects of the standard variables used to proxy for trade costs in empirical gravity models.

# **4.1 Gravity Estimates for Services Trade**

We start with a specification of the gravity model that imposes the standard assumption of equal internal trade costs.<sup>12</sup> Table 2 reports gravity estimates for each service category in our sample<sup>13</sup> which, as discussed above, are obtained with 2-year lagged panel data, the PPML estimator, and time-varying, directional, country-specific fixed effects from the following econometric model: Here _SMCTRYij_ is the Kronecker delta, _β_ 6<sup>_k_isthecommon(acrosscountriesandovertime)</sup> _SMCTRY_ coefficient, _ηi,t_<sup>_k_denotesthesetoftime-varyingexporterdummies,whichcontrol</sup> for outward multilateral resistances and countries’ output shares in sector _k_ , and _θj,t_<sup>_k_denote</sup> time-varying importer dummies that account for inward multilateral resistances and expenditure shares n sector _k_ , respectively. The relationships between the gravity fixed effects from specification (7) and those from equation (5) are _x_<sup>_k_</sup> _i,t_<sup>=exp[</sup><sup>_η_</sup> _i,t_<sup>_k_]and</sup><sup>_mk_</sup> _j,t_<sup>=exp[</sup><sup>_θ_</sup> _j,t_<sup>_k_],</sup> respectively. Standard errors are clustered by country pair.<sup>14</sup> Overall, we find that the disaggregated gravity model works well with services data. Estimated coefficients on standard trade cost variables in Table 2 almost always exhibit expected signs and reasonable magnitudes. We discuss the effects of standard gravity variables in greater detail below when _SMCTRY_ coefficients are allowed to vary across countries,

> 12Since the gravity model can only ever identify relative trade costs, our specification is equivalent to imposing the constraint of internal trade costs being equal and equal to zero.

> 13In Table 2 trade in ‘research and development’ services (RSRCH) is listed as a separate sector even though the EBOPS taxonomy treats it as a part of ‘miscellaneous business services’ (BUSIN). We think, though, that the results for both categories are of distinct economic interest. Our empirical results offer evidence for heterogeneous trade cost estimates in these two categories.

> 14Comparison between estimates obtained with and without clustering reveal that the clustered standard errors are a bit larger. This suggests positive intra-cluster correlations, as expected.

12