rafmacalaba/datause-encoder-data huggingface dataset:
10

# 3. Sam **Ruyipt4n& StudLmm**

In putting forth his theory, MScIinnon. 1973, investigated a number of interactions between the size of the financial sector (expressed, for example, relative to GDP) and rates of output growth for specific phases in the development of eight countries, using a case-study approach. He concluded that periods of high growth were associated with expansion, or recovery, of real monetary aggregates, and that it was within the capabilities of fiscal, monetary and interest rate policies to promote such expansion. Further, at least if inflation is moderate, a policy of high interest rates could offset much of the adverse effect on financial aggregates. These results should, of course, be interpreted as applying to countries on the left of A in Figure 1.

McKinnon's empirical work offered only limited statistical support for his hypotheses.15 Later studies have estimated relationships between growth and financial policies for a wider selection of countries and time periods. This section briefly reviews a few of the most relevant studies.

Fry 1.278, tests the McKinnon-Shaw hypotheses that financial market conditions do influence saving and growth rates, using data for 7 Asian countries over 7-11 year periods, and estimating with pooled time-series cross-section data and country dummy variables. The real interest rate was found to exert a significant influence on domestic savings, and also on rates of growth. In a second paper, Fry 19fi80, estimates the cost of financial repression using a thrre-equation model based on savings and growth functions estimated on a similar data set. He concludes that the cost of financial repression appears to be around half a percentage point in economic growth foregone for every one percentage point by which the real deposit rate is below a market equilibrium rate. A pooled time-series cross-section study of the interdependence of saving, investment and growth for 61 countries over an average of 11 years (without including the interest rate in the analysis) indicates that growth, income per head, terms of trade, relative size of the mining sector and lagged savings rates affect savings positively, while foreign savings and the predictability of inflation from a three-year trend reduce it. Investment rates respond to output growth, terms of trade, "unexpected" inflation, lagged investment, domestic credit availability relative to GDP and changes in this ratio. The latter two variables can be considered as proxies for the influence of interest rate policies and/or inflation rates which affect the holding of real financial assets and thus determine the availability of credit.

15 In addition to the number of countries and periods being small, these were chosen to include episodes of wide-ranging economic reform of which financial sector reforms comprised only one component.
the density of rainfall stations can also help ameliorate basis risk. A second possible improvement to ameliorate liquidity constraints would be to sell policies at harvest time (Duflo et al., 2010) or to combine the product with a short-term loan, or equivalently, originate loans with interest rates that are explicitly state-contingent based on rainfall outcomes, to help alleviate credit constraints.<sup>23</sup> Second, the sensitivity of insurance demand to price underlines the benefits of developing ways to minimize transactions costs and improve product market competition amongst suppliers of rainfall insurance. It also suggests that government subsidies for rainfall insurance, like those now offered in several Indian states (Giné et al., 2010), would be effective in boosting participation, although it is not clear whether such subsidies improve overall welfare.

Third, the importance of trust and a history of positive past insurance payouts suggest that product diffusion through the population may be relatively slow, as the product develops a track record of paying positive returns. A potential design improvement to facilitate learning would be to amend the contract to pay a positive return with sufficient frequency. This needs to be weighed, however, against the fact that the value of the product is largest if payouts are concentrated during the most severe droughts, when marginal utility of consumption is highest.

Finally, findings that households have limited financial literacy and understanding of the product suggest that insurance policies could instead be targeted to groups, such as an entire village, a producer group or a cooperative, rather than to individuals. The insurance purchase decision would be taken by the group management, who are likely more educated and familiar with financial products, and may also be less financially constrained. The group could then decide or pre-arrange how best to allocate funds amongst its members in case of a payout.

Technological advances may improve the product offering, such as the use of satellite foliage coverage data to offer policies based on area crop yields. The degree of innovation already demonstrated by insurance providers, as well as this potential for further contract

> 23 Giné and Yang (2009) implement a field experiment in Malawi to test whether bundling insurance with credit increased farmers’ willingness to adopt a new agricultural technology. The advantage of the bundled loan over a standard loan is that it would not have to be repaid in case of a payout. As it turns out, uptake among farmers offered the bundled loan was lower than among the control group offered a standard loan. One potential explanation is that farmers were already implicitly insured by the limited liability inherent in the standard loan and hence placed little value in the insurance policy. By insuring loans, however, the lender was unambiguously better off and after the experiment was considering an increase in disbursement and a drop in the interest rate, reflecting the lower risk of lending.

24
Table C3: Results Pooled: Job Benefits - Heterogeneity by wealth

||(1)<br>Area<br>Studies|(2)<br>Contract<br>Written|(3)<br>Contract<br>Permanent|(4)<br>Job<br>Benefts|(5)<br>Registered<br>Firm|
|---|---|---|---|---|---|
|Offered Training|-0.00|0.02|0.04<sup>_∗∗_</sup>|-0.02|0.06<sup>_∗_</sup>|
||(0.04)|(0.03)|(0.02)|(0.03)|(0.03)|
|Offered * High Ubudehe|0.04|0.05|-0.01|0.04|-0.00|
||(0.05)|(0.05)|(0.03)|(0.04)|(0.05)|
|Control Mean: High Ubudehe|0.45|0.32|0.08|0.17|0.41|
|Control Mean: Low Ubudehe|0.43|0.27|0.09|0.13|0.41|
|Observations|2,597|2,597|2,597|2,597|2,597|
|p-value high Ubudehe|0.34|0.06|0.17|0.25|0.11|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

Table C4: Results Pooled: Channels - Heterogeneity by wealth

||(1)<br>Searched For Job<br>in Last Month|(2)<br>Confdence Approaching<br>Employers (0-3)|(3)<br>CV in<br>Digital Format|(4)<br>People Discusses<br>Jobs with|
|---|---|---|---|---|
|Offered Training|0.02|0.05|0.05<sup>_∗∗_</sup>|0.96<sup>_∗∗∗_</sup>|
||(0.03)|(0.05)|(0.02)|(0.25)|
|Offered * High Ubudehe|-0.01|0.02|-0.04|-0.11|
||(0.05)|(0.07)|(0.03)|(0.35)|
|Control Mean: High Ubudehe|0.66|2.52|0.87|5.49|
|Control Mean: Low Ubudehe|0.62|2.52|0.93|5.35|
|Observations|1,728|2,597|2,597|2,597|
|p-value high Ubudehe|0.80|0.13|0.53|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

44
# **8 Conclusion and Policy Implications**

We explore how numerical fiscal rules, exchange rate regimes, and institutional quality affect the cyclical behavior of fiscal policy (i.e, how government spending responds to GDP fluctuations). We perform our analysis using a panel of 153 advanced, emerging and developing countries over a 23year period, from 1993 to 2015. We proceed by a two-step econometric approach. We first compute the cyclicality coefficients of fiscal policy using the Local Gaussian-Weighted Ordinary Least Squares (LGWOLS) and secondly estimate how institutional quality, fiscal rules and exchange rate regimes affect the predicted cyclicality. In addition to basic panel regression, we use an instrumental variable method, the two-stage Least Squares (2SLS), in order to address the endogeneity issue.

We find that fiscal rules alone are not enough to promote counter-cyclical fiscal policy and should be combined with better institutions. Fiscal rules, together with strong institutions form a more optimal combination which has a disciplining effect on fiscal policy. This finding is in line with Bergman and Hutchison (2015) and Wyplosz (2012) which stipulate that to be effective, rules must be embedded in institutional arrangements. Our result is robust to the use of alternative variable of institutional quality.

Moreover, we show that the more pegged the exchange rate regime is (resp. flexible), the more counter-cyclical (resp. pro-cyclical) fiscal policy is. Hence, more fixed exchange rate regimes promote counter-cyclical fiscal policy, having thus disciplining effects on fiscal policy. Furthermore, we find that under more flexible exchange rate regimes, fiscal rules are effective in promoting countercyclical policies. But under more fixed ones, fiscal rules seem to be associated with less fiscal discipline.

We also find that the disciplining effect of fiscal rules depends on the type of rule. Specifically, while expenditure rules and budget balance rules seem to be associated with fiscal pro-cyclicality, debt rules and revenue rules seem to favor more budgetary discipline.

To sum up, fiscal rules are not a sufficient remedy against fiscal indiscipline and should be com22
while the ranking was reversed with the previous definition. Thus, the fact that consumption poverty rates are much higher in rural areas is affecting the urban-rural comparison of the profile of time and consumption poverty according to the new definition quite substantially.

When we adopt a higher threshold of 70.5 hours for the time poverty line, the time and consumption poverty headcounts are of course again lower, with the overall time and consumption poverty headcount dropping to 7.6 percent only, but again the differences between men and women become even larger – moving from the lower to the higher threshold makes time and consumption poverty rates for women decrease by a factor of about two, while time and consumption poverty rates for men decrease by a factor of more than four.

The data on children shows that for this age group, once a consumption poverty constraint is imposed on the definition of time poverty, time and consumption poverty becomes almost exclusively a rural phenomenon, especially if a high time poverty line is used. As for the profiles of time and consumption poverty observed with the poverty gap and squared poverty gap, they are similar to those observed for the headcount index.

# **_4.4. Correlates of time poverty_**

What are the determinants or correlates of time poverty? To answer this question we ran probit regressions to explain the probability of being time poor as a function of personal, household and area variables. The analysis is again carried out at the individual level, i.e. each individual is classified as time poor or not depending on his or her own individual total time worked (assuming the household is poor or at risk of becoming poor). We focus on the adult sample only, since time poverty is a less serious issues for children. Among the regressors we included, beside the usual demographic variables (age, sex, and marital status), the educational qualifications, religion, the consumption quintile of the household, the number of infants (aged 0-5) and children (aged 6-14), adults (aged 15-64) and senior people (aged over 65), and their square values. We also included dummy variables for the presence of disabled people, and for households with only women<sup>7</sup> . Finally, we included geographical dummies for rural/urban areas and for the region of residence. Separate regressions were estimated for men and women.

> 7 We preferred this variable to the alternative “female headed household”, because many female headed households include several adult men.

18
|St. Dev.|1.386<br>0.043<br>0.591<br>1.904<br>0.107|0.764|
|---|---|---|
|Mean|3.296<br>-0.015<br>0.465<br>4.733<br>0.037|8.415|
||- Center for<br>Peace|TATA rou-|
|Source|WDI<br>WDI<br>WDI<br>Polity IV<br>Systemic <br>ISTAT|Built-in S<br>tine|
||ta received in country _j_ in year _t_<br>it) as a share of GDP in country<br>f GDP in country _j_ in year _t_<br>dex in country _j_ in year _t_ (1 =<br>ve parity or subordination)<br> _MIGij_ over 2003-2011|ce between province_i_and country<br>O: World Economic Outlook (IMF).|
||i<br>fc<br> o<br>n<br>ti<br>k|an<br>E|
||cap<br> de<br>re <br>s’ i<br>cu<br>toc|ist<br> W|
||r <br> - <br>ha<br>e<br>xe<br> s|d<br>);|
|tion|ithm of ofcial aid pe<br> balance (+ surplus/ <br>ear _t_<br>nal debt stocks as a s<br>raint on the executiv<br>ited authority; 7 = E<br>th rate of the migrant|ithm of the kilometric<br>icators (The World Bank|
|ni|ar<br>al<br> y<br>r<br>st<br>m<br>w|ar<br>nd|
|Def|Log<br>Fisc<br>_j_ in <br>Exte<br>Con<br>unli<br>Gro|Log<br>_j_<br>pment I|
||_CEi,t_<br>_NAL_<br>_ONSTi,t_<br>_ANTSi,j_|_CEi,j_<br> World Develo|
|ble|_i,t_<br>_CAL_<br>_AN_<br>_ER_<br>_Ti,t_<br>_C C_<br>_GR_|_TAN_<br>WDI:|
|ia|_D_<br>_S_<br>_L_<br>_T_<br>_B_<br>_E_<br>_I_|_S_<br>|
|Var|_AI_<br>_FI_<br>_BA_<br>_EX_<br>_DE_<br>_EX_<br>∆_M_|_DI_<br>_Notes_:|

23
Ill. Customizing and understanding the simulation output

# Output Options Dialog

<!-- Start of picture text --> E n1 Options<br>-Ca,u for EMono** W'I-Y<br>7CWend tyeir: -i est'or -<br>f Mhii 0 ~~~~- 1996 , 3 R 4 r c ''<br>'<br>?Ir'2g*eo < Jv f j fbay f-N.be b,wmW^ < .<br>-et tco5erske Ye01 <10<br>F J.25 1 1996 J akMcYtg<br>F :E .- v1* {; ''Mhk' , ] .'Fl - -W -- , ,.-<br>o F#we - G-Se Chat Year.<br>~~~~~~~~~~~~~~~ Tid test run :<br><!-- End of picture text --> This dialog allows the customization of the two main types of output generated by _INFRISK:_ (i) the _simulation analysis_ and (ii) the _economic viability analysis._ _The simulation analysis_ provides informnation on the estimated probability that a given risk variable will be lower than a specified minimum level (denoted as "minimum" in the dialog). These estimates are calculated for each year of the project's lifetime. The user can also choose to generate charts showing the probability distribution estimates in a selected year for each risk variable. Figure 9 shows a sample _INFRISK_ output sheet; examples of INFRISK's chart output can be found in Figures 3-6.

Figure 9: _INFRISK_ Output Shee <!-- Start of picture text --> hca 1un96g 1997 1993 1399 2000 2001 2802 2003 2004<br>Numbf of iteralons - I 100I<br>Expected Dividierds a 8.002 9.542, 8.950 7.418 6.710 6.685 2.850. 3.337~ 2.375:<br>Eiqpeoto Cash Flow 118.362 120.151 119.357: 118.717 118.705 115.218 110.683 113.189 113.08<br>Probabit8foity.1dend below 0 0-178 0.108 0.2,27 0,264. 02312 0-200 0.433 0.3339 0.435<br>6Eted VaJueof Debt Servie Coverage 1.043 1.857 L.OW4 l.M2 1.0,03 1.006 0.949: 0.935 0.907<br>ProbabiNlt1 of Debt Service Coverage Below 1.25 0.073 0.32 0.917 0.931 2.948 0.347 0.97! 0.805 0.991<br>cvpavte.Va*of lnterest CoveTage 1.28i1 .337 1.341A, 1.314 1.334 1.333 1273 1.348 I.8<br>NJet Present Value of Cash Flow 321.5S9<br>I Internal rate of on the Expected aRetur DiVidends NIA<br><!-- End of picture text --> 24
_CARi = α + βi (PIPE) + γi (Controls) + εi_ (5) where the dependent variable is the CAR for issuer _i_ over three, five, or seven days around the announcement date. The variable of interest is _PIPE,_ a dummy variable that equals one if the equity issue is a PIPE and zero if a SEO. In case PE investors provide value enhancing certification, β is predicted to be positive and significant. _Controls_ is a vector of firm and issue specific factors including amount issued, reported sales of the issuer, operating performance, preannouncement period stock returns and market to book ratio. We also include year, industry and country fixed effects.

The regression results reported in Table 8 support our findings in the event study that the announcement effect of a PIPE issue is more positive and thus perceived as more value enhancing by the market than that of a SEO issue, even after controlling for firm and issue specific characteristics. The coefficient estimates for the PIPE dummy range from 0.033 for the three day CAR to 0.041 for the seven day CAR suggesting that the announcement of a PIPE investment by PE investors is associated with an almost four percent greater return compared to that of an SEO announcement. Among the control variables, the profitability of an issuer is negatively associated with the announcement period return suggesting that more profitable firms see a larger decline in share price. All other firm and issue specific control variables have no significant impact on the level of announcement returns.

While our finding of a positive announcement return for PIPE issuers is in line with empirical evidence from the US, as discussed earlier, and some other developed countries (Eckbo et al. 2007), it is in contrast to the findings of no significant announcement effect for all types of PIPEs in Australia and Hong Kong, Special Administrative Region of China in 2008 21
associated with a gradual increment of wage levels, presumably deriving from the accumulation of tacit professional knowledge and skills. In this setup, we analyze whether having RPL certificates helps workers enhance wage levels in a similar way that having formal education qualifications does.

First, simple comparisons of wage levels across the treatment and gender categories (figure 3.3) show no statistically significant differences, albeit the treatment group consistently earning somewhat better. The difference among females is barely not statistically significant (p-value = 0.12).

**Figure 3.3 Wage Level at End-line by Treatment and Gender (in Bangladeshi taka)**

<!-- Start of picture text --> Treatment Control<br>12,370<br>11,935<br>11,551 11,303<br>8,627<br>7,625<br>Male Female Total<br>Source: Original figure for this publication.<br>Note: Differences are not statistically significant.<br><!-- End of picture text --> The study uses the Mincerian wage model to estimate the treatment effects after controlling for other influencing factors, including education and years of experience. The dependent variable is the log values of the monthly wage. Treatment effects are ITT effects. In addition to _ordinary least square_ (OLS) regression (models 1 and 3), we also applied the Heckman regression that considers the probability of selection into wage employment (models 2 and 4) to correct the selection bias. Table 3.4 shows the estimates from the OLS and Heckman regressions. The impacts of the treatment on wages are small and not statistically significant in the standard models (models 1 and 2). As expected, those with higher levels of educational qualification or in full-time employment earn significantly larger wages. RPL certificates do not seem to supplement formal education qualifications in deciding on wage levels effectively.

**Table** **<u>3.4 OLS and Heckman Regression on Wage Earnings</u>**

||||Models<br>||
|---|---|---|---|---|
|Variables|(1)<br>OLS:<br>standard|(2)<br>Heckman:<br>standard|(3)<br>OLS: gender<br>interaction|(4)<br>Heckman: gender<br>interaction|
|**In treatment**|0.031|0.025|-0.006|-0.014|
||(0.72)|(0.59)|(-0.14)|(-0.31)|
|**In treatment * female**|||0.205<sup>*</sup>|0.219<sup>**</sup>|
|**(interaction term)**|||(1.81)|(2.06)|
|Female|-0.431<sup>***</sup>|-0.362<sup>***</sup>|-0.561<sup>***</sup>|-0.503<sup>***</sup>|
||(-6.90)|(-5.84)|(-5.89)|(-5.48)|
|Age|0.085<sup>***</sup>|0.078<sup>***</sup>|0.087<sup>***</sup>|0.077<sup>***</sup>|
||(4.74)|(4.49)|(4.88)|(4.43)|
|Age2|<br>-0.001<sup>***</sup>|<br>-0.001<sup>***</sup>|<br>-0.001<sup>***</sup>|<br>-0.001<sup>***</sup>|

18
“ALL” column), there is no apparent difference in the maximum drop in deposit growth across crisis and non-crisis countries. The picture, however, becomes much more heterogeneous when countries are grouped by income level.

First, there are no LICs that have experienced banking crises during 2006-2010. Second, there is a large difference in the maximum drop in deposit growth between crisis and non-crisis countries in the MIC group—non-crisis countries experienced, on average, a 19 percentage point drop in deposit growth, while crisis countries experienced a 53 percentage point drop. Third, there is no significant difference in the maximum drop in deposit growth between crisis and noncrisis countries in the HIC group. Interestingly, the data show a higher maximum drop in deposit growth for _non-crisi_ s HICs compared with _crisis_ HICs, by about 5 percentage points. Our conjecture is that people in richer countries diversify their saving portfolio much more than in lower income countries, allocating their savings also in investment securities. When the 2008 crisis hit, the uncertainty about future returns and general risk aversion mounted. Hence, people in HICs, especially those living through a crisis, might have liquidated their investment in securities and used bank deposits as a safe haven instrument propped by public deposit insurance. Overall, the maximum drop in deposit growth was the strongest in crisis affected MICs, and was very heterogeneous across HICs, MICs and LICs.

**Figure 4: The Drop in Deposit Growth in Crisis and Non-Crisis Countries**

<!-- Start of picture text --> 60 Median of the maximum drop in deposits growth (%)<br>Crisis countries<br>50<br>Non-crisis countries<br>40 All countries<br>30<br>20<br>10<br>0<br>All HIC-OECD MIC LIC<br><!-- End of picture text --> Source: Authors’ calculation based on data from IMF and Laeven&Valencia (2012). Note: None of the LICs in authors’ sample experienced banking crises between 2006 and 2010.

Consider the access to bank deposits prior to the 2008 crisis next. Figure 5 (left panel) plots the median access to deposits using Honohan (2008) index for all countries and their income groups. Similarly, Figure 5 (right panel) plots the use of deposits measure of Klapper and Demirguc-Kunt (2012). Both measures show a similar pattern with generally low access to or use of deposits worldwide (the “ALL” column). Although people in HICs enjoy relatively high access to bank deposits, people in MICs and LICs face much greater challenge in accessing or using bank deposits (see also Allen et al., 2012).

7