rafmacalaba/datause-encoder-data huggingface dataset:
29 Table 2.7 Comason of Lending Practices of Institutional Sources of Credit

|...............................................................................|ADBP<br>...................................................................................|Commercial Banks<br>..............................|..............................|
|---|---|---|---|
|Purpose of the loan<br>(1991-92)|25% production & 75 %<br>development loans.|78% productions loans|NA|
|Credit targeted at<br>subsistence framers|42.8%|76.7%,but suveys show many<br>of these to be proxy loans for<br>big landlords|96.6%, butsurveysshow that<br>many coop societies were one<br>man operations.|
|Collateral<br>requirement|Tangible suity<br>reqmured<br>for medium term loans|63% of disbursement through<br>personal sureties, passbooks<br>(land) also common|land and other immovable<br>property|
|Loan recovery rate|600/o|NA|Good in Punjab & Northern<br>areas (about 900/o of the<br>rescheduled loans). Loan<br>operations were suspended in<br>Sindh and NWFP.|
|Treatment of default|Laws exist on paper but<br>not enforced|Existing laws not enforced|Existing laws not enforced.<br>Special Coop Tribunals<br>proposed to improve<br>enforcement.|
|Supervision of credit<br>utilization|In theory, 1400 MCO's are<br>supposed to supervise<br>credit closely, MCO's in<br>turn are monitored by<br>credit officers|A small number of Agricultural<br>officers results in inadequate<br>supervision|Poor coordination between<br>provincial government<br>departments and the Federal<br>Coop Bank renders<br>supervision inadequate.|
|Cost of credit delivery<br>(esfimated)|9.1%|18.6%|15.8%|

Source: Agricultural Statistics of Pakistan. 1991-92 and various other sources.

# B: **Informal** Credit

The credit needs of the majority of rural enterprises are met in large part by informal credit sources, based essentially on personal contact and local sanction. The informal market can be divided into four categories. Socially based arrangements, commercial arrangements, a land-based system, and money lenders Sociallv Based Arrangements. One-third of noninstitutional credit comes from friends and relatives who do not charge interest and operate on the basis of reciprocity.
× GFC0810 and the TANG × CREDIT/GDP × GFC0810 interaction terms in the same regression to capture simultaneously the reliance of firms on external finance and their ability to raise outside finance. Coefficients on the interaction terms are statistically significant and have the expected signs, confirming that a sector’s financial vulnerability is shaped both by firms’ reliance on external finance and the ability of these firms to gain access to external finance.

In this section, we have found that the impact of the global crisis on the relative volume of outward FDI in financially vulnerable sectors has been greater in more financially developed countries.

# **4.2 Extensions:**

Table 2 presents additional results. We notably deepen our focus on a credit channel effect of the global financial crisis on outward FDI by looking at the mediating role played by banking crises.

Model (2) is estimated in columns (1)-(3). cial crisis on the relative volume of outward FDI in financially vulnerable sectors in more financially developed countries was magnified by the occurrence of a banking crisis. The negative and statistically significant coefficient on the interaction term between the triple interaction term and a banking crisis dummy variable implies that the ratio of relative outward FDI in financially vulnerable sectors during the global financial crisis to relative outward FDI in financially vulnerable sectors during noncrisis years was about 44% lower in a highly financially developed country _experiencing a banking crisis_ like the United States than in a less financially developed country _not experiencing a banking crisis_ such as Italy, i.e. a 14 percentage points fall relative to a situation where the highly financially developed country did not experience a banking crisis. In column (2), we make a distinction between

> portation Equipment sector (a typical low TANG sector; 25<sup>th</sup> percentile of TANG) to outward FDI in the Beverages sector (a typical high TANG sector; 90<sup>th</sup> percentile of TANG).

17
month ($6.89 per day) at 2005 PPP; 15 of our sampled countries have PCE less than $60 per month, or about $2.00 a day; the poorest country is Malawi with a PCE per capita of $1.03 a day. Figure 1 also gives the density function for PCE per capita; the modes are not far apart, but otherwise the distributions are very different, with PCE showing much greater spread.

The alternative to PCE from the NAS is to use mean household consumption or income from the relevant survey. However, in many cases the poverty line was calculated from that same survey, so the relationship seen between the national poverty line and the survey mean may be spurious, being driven by common measurement errors. Consider, for example, the most popular method of setting a national poverty line, in which a predetermined food bundle is valued in the country (or each region) and an allowance added for non-food spending based on the food Engel curve. An under-estimation of non-food spending in the survey will shift the Engel curve and so automatically adjust the poverty line downwards. The measurement error will generate a positive correlation between the poverty line and the survey mean.<sup>19</sup> As long as the measurement errors in the NAS are largely independent of those in the surveys, PCE will be a better indicator. However, we also test the sensitivity to using the survey means instead.

Figure 2 plots the poverty lines against log PCE for the survey year. The least squares estimate of the elasticity of the poverty line to PCE is 0.655 (with a t-ratio of 13.68, based on a robust standard error).<sup>20</sup> This is significantly less than unity (t=7.21), as used in some relative poverty lines for developed countries,<sup>21</sup> although, our elasticity is similar to some past estimates based on subjective poverty lines for developed countries.<sup>22</sup> However, Figure 2 suggests that the economic gradient only emerges strongly once mean consumption is above a critical level. Figure 2 also gives a nonparametric regression of the national poverty lines against log mean consumption.<sup>23</sup>

> 19 The same would happen if the poverty line is derived by the alternative method of finding the total consumption expenditure level at which predetermined food-energy requirements are met on average. If non-food spending is underestimated by the survey then the poverty line will be automatically adjusted downwards, reflecting the measurement error. A spurious correlation will result.

> 20 The estimate is quite robust to outliers; a median quantile regression gave 0.647 (t=9.57).

> 21 A poverty line of half the mean or median is not uncommon in comparative studies of poverty in developed countries; see, for example, Smeeding et al. (1990) and Atkinson (1998).

> 22 Hagenaars and van Praag (1985) estimate an elasticity of 0.51 for eight European countries. Kilpatrick (1973) estimated an elasticity of about 0.6 for subjective poverty lines in the US.

> 23 All nonparametric regressions in this paper use STATA’s Locally Weighted Scatter Plot Smoothing method with the default bandwidth (0.8). We also tested alternative bandwidths in the interval

10
size firms are defined as those having 5 to 99 employees.<sup>4</sup> Non-exporters are defined as those whose export revenue is less than 10 percent of sales. Table 4 reports that investment of a small firm drops by 2.3 percent for each 1 percent increase in central government debt. The declines are 3 percent for service firms, 2.4 percent for non-exporter firms, and 2.5 for domestic firms. The estimated coefficients for larger firms, manufacturing firms, exporting firms, and foreign-owned firms are either smaller or comparable to those by other firms. Still, in all cases, the coefficients are less precisely estimated, indicating a less statistically significant impact of rising debt on these firms. In addition, the distributional effects are stronger for investment in equipment than for land and buildings (see Appendix Tables A2 and A3).

It is important to note that the effects nested the potentially positive effects of government spending associated with the rising debt. In other words, the effects captured here are the net effects. <mark>Appendix Table A4 presents the findings for the universe of firms and without firm fixed effects. Again, the findings remain very robust.</mark>

# 3.3 On the channels of impacts

Understanding the channel by which higher public debt reduces a firm’s investment is important for policy makers. The evidence can guide governments’ support for firms when they have to increase public debt. The literature has well established the “access to finance” channel. Higher debt makes access to finance for the private sector more difficult. Hence firms are forced to reduce investment (see Huang et al., 2018 and 2020). We also find evidence for the access to finance channel in the following way. When government debt is higher, firms are more likely to rely on internal funds for working capital and investment (see columns [1] and [2] of Table 5). Working capital from other sources (such as microfinance institutions, credit cooperatives, credit unions, finance companies, moneylenders, friends, relatives) is significantly

> 4 Recall that firms with fewer than 5 workers are not included in the Enterprise Surveys.

13
changed as public investment strayed into areas (e.g. manufacturing) which could have been left to the private sector, and which failed to generate the income required to service its debt financing.

Table 10: Public Investment In Cote d'Ivoire and Comparators (as a Percentage of Total Investment)

|Sector<br>/<br>Year|1975|1976|1977|1978|1979|11980|1981|11982|1983|1984|11983|196|1987|198|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Metod'lvo1re|41|41|39|45|48|48|39|52|52|63|72|85|78|69|
|SSAfrIca|40|42|42|41|44|43|46|45|49|48|44|4|<br>7t|46|
|OECD|18|17|17|17|||**1**6|17<br>1|17|16|16|15|15|14|

Source: DEC AnatytIcal Database Public Expenditure -- Crowding Out: The high level of government spending maintained after the end of its economic boom of the mid 1970s began to bid scarce resources away from the private sector. Public expenditure reached a record level in response to revenues associated with the 1976-8 boom and stayed high, resulting in a fiscal deficit that grew to 12% of GDP in 1981. Government investment began to drive up costs to the manufacturing sector, reducing its competitiveness. By initially failing to reduce its investment in response to the terms of trade shock following the 1976-78 boom, the government drove up the price of nontradables (e.g. construction services), raising costs to both import-substituting and export-oriented industries. The result was a less competitive manufacturing sector and an effective appreciation of the real exchange rate -- further aggravating the balance of trade deficit. Although the Government eventually managed to reduce investment expenditure, other expenditures continued to grow, in large part because of the large cadre of politically influential public employees. As the financial crisis deepened, public investment virtually cannibalized private investment. By 1989, the estimated fiscal deficit amounted to a record 16% of GDP. By the mid-1980s (see table 10), the public sector's share of total investment was at record levels for a non-socialist economy.

Financing Expenditure -- Borrowing: Government spending in C6te d'Ivoire is financed in three principal ways: through borrowing in financial markets, through taxation (discussed in section V), and through the accumulation of arrears.' Each appears to have behaved as a mechanism for crowding out private sector activity. One legacy of excessiv **e** xpenditure has been a tremendous debt burden. The government's voracious need for credit was supplied in part by private international and domestic capital markets, crowding out private borrowing.

Service on the debt now requires revenues that unproductive investment did not yield. Debt service skyrocketed to an estimated 17% of GDP in 1989, consuming nearly half of the nation's I Because of its membership in the CFA Franc Zone, the C6te d'Ivoire cannot unilaterally resort to printing money with which to finance its puAlic expenditures.

21
# **5. Welfare simulation impacts**

In this section, we report simulations results to assess the welfare impacts of the different shocks affecting cashew farm-gate prices that we studied above. The underlying theory builds on Deaton (1989, 1997) and Nicita, Olarrega and Porto (2014). Provided cashew sales are an important source of income for Guinea-Bissau, changes in cashew prices can have potentially large effects on household welfare. As it is well-known, these welfare effects can be well approximated by multiplying the size of the price shock and the share of cashew income in total household income. This quantity gives the proportional change in real income, or expenditures, caused by a change in cashew prices.<sup>15</sup> We begin with a brief description of the variables needed for the analysis. The data used here come from the Guinea-Bissau household survey, the QUIBB, _Questionário de Indicadores Básicos de Bem-estar_ , 2010. Figure 5 plots the density of the log of per equivalent adult expenditure in the country. This is the measure of household welfare typically adopted in studies of this type. As expected, the density has an approximate Normal shape. It is noteworthy that the tails are long, indicating the presence of potential outliers. Figure 6 plots the densities for the rural and the urban populations. The urban density lies to the right of the rural density, thus indicating that urban households tend to enjoy higher levels of living, on average, than rural households. This is a standard observation from household survey data.

**Figure 5. Density of log per equivalent adult expenditure**

<!-- Start of picture text --> 5 10 15 20<br>log per equivalent adult expenditure<br>.5<br>.4<br>.3<br>density<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: QUIBB 2010.

15 This kind of first order approximation often works very well in giving a short-run assessment of the impact of a price shock. To measure farmer adjustments, a second order approximation can be done but, in practice, these second-order terms are generally very small. With distortions and imperfections, the first order approximation can be inaccurate but the estimation of such a model imposes very heavy data and modeling burdens.

24
values of _X_ . Any swap of treatment status among participants who belong to the same pre-randomization strata of gender and age are just as likely to occur as the realized vector. Hence, under exchangeability we can permute treatment status between individuals with the same pre-program variables _X_ and the joint distribution of ( _Y, D_ ) will remain the same under the null hypothesis of no treatment effects.

Under exchangeability we can generate the exact distribution of a conditional test statistic _T_ ( _Y, D|X_ ). Specifically, we generate the conditional distribution of the statistic given by all of the values that _T_ ( _Y, D_ ) takes as we fully permute the elements of the vector of treatment status _D_ within strata formed by _X_ . The distribution generated in this way does not depend on any distributional or asymptotic assumption. We use this generated distribution for inference to test the null hypothesis that there is no difference between the treated and the untreated population means. We report a _p_ -value that is simply the proportion of the test statistic values that are bigger than the ones computed using the actual data.

# **4.1.3 Baseline Imbalance**

While randomization guarantees that any baseline variable _Z_ is independent of the vector of treatment status _D_ conditional on variables _X_ used in the randomization protocol, the realization of baseline variables can turn out to be imbalanced across treatment groups. In the case of the Jamaica Study, three potentially important characteristics were not balanced at baseline. In order to control for potential bias, we estimate treatment effects by linear regression controlling for these variables when relevant for explaining outcomes.

The conditioning variables may also be used to increase the power of the statistical inference. Heckman et al. (2010b) address this problem by assuming partial linearity as suggested in Freedman and Lane (1983). Essentially, this involves permutation using the residuals from a multivariate linear regression. However, we would like to avoid linearity as this assumption is unlikely to hold for categorical outcomes and imposes a specific functional form.

11
(Carcasson, 2016). The results of the experiment discussed in this paper also illustrate that digital technology in general, and VR in particular, can be used as a tool for inclusion and stakeholder engagement in a fast and cost-effective manner. Physical spaces should facilitate civic engagement and civic interaction. VR technology creates opportunities for simulating a close-to reality setting of infrastructure projects, at a low cost and in a short timeframe, before large investments are made on the ground. This can enable urban designers to adopt “tactical urbanism” without having to build or tear down actual physical investments. Further, the approach has the potential to be quickly deployed in other cities and regions. With VR, city administrators can develop a suite of options for a transformational investment, test out ideas, seek input from diverse groups, and quickly adjust virtual designs. This is a more efficient process than the traditional options of using detailed architectural drawings for public consultations. For better data collection and monitoring and evaluation during implementation, VR can also be combined with complementary technologies including drones, aerial photography, satellite mapping, cellphone geofencing, and social media big data mining tools and trends.

This paper found that the effects of increased exposure to diverse groups on trust and perceptions toward outgroups, and willingness to interact with outgroups, are mixed. While these findings are inconclusive, the variations in participants’ responses as a result of a short VR intervention nevertheless demonstrate the potential of VR as a stimulus tool to promote social exposure as a first step toward addressing social biases. Further, negative effects of exposure for some groups indicates that exposure to diversity might not be enough, and that future research needs to be conducted. This study thus serves as a test case to showcase a technology-based method of studying social cohesion.

Future research on this topic can expand the post-VR activity beyond a computer-assisted survey. One suggestion is to include an observable and measurable behavior as part of the study, as opposed to only focusing on self-reported survey responses. An example can be to ask people to donate time to help clean a park and measure attendance rates and the time spent, or to ask people to donate money to local organizations known for supporting diverse ethnic groups. Similarly, as the VR technology advances, it may be possible to embed opportunities for behavioral response and interaction within the VR experience itself. Finally, the measures used in this study primed participants directly about ethnic and gender diversity (for example, asking participants whether they spotted men or women or a Muhajir or a Sindhi), which might have induced a response bias. Future studies can explore indirect measures that can reveal attitudes and preferences toward diverse groups by prompting them in subtler ways, such as by running choice experiments that are quite common in the behavioral sciences.

23
significant 2.7 percentage points (p-value < 0.001). And although the point estimate is then lower than the estimated effect of the savings treatment, the difference is not statistically significant. For grades 9-11, the results are different. The results for the basic and savings treatments in San Cristobal are similar, but the results for the Suba experiment (the tertiary treatment) are an increase in attendance by 5.0 percentage points.

The next two rows divide students based on the predicted attendance measure estimated from Equation 3. (In other words, we divide the sample based on whether students’ baseline characteristics are similar to those characteristics of control group students who either met the attendance target or did not.) As shown in Figure 5, the treatment was slightly more effective for students whose projected baseline attendance was below the attendance target – the difference varies by treatment, although the difference is not statistically significant at conventional significance levels for any of the treatments. Only the basic treatment in Suba does not follow this trend, revealing a higher attendance effect for students likely to attend without receiving the treatment.

In Panel B, we move from all of the students in the 68 schools to just focus on those individuals who were found at school in the baseline survey. In the baseline, we attempted to survey every child who was registered as attending one of the 68 schools, and we were able to locate and survey 90 percent of those students at the time. Not surprisingly, the students who took the survey were, on average, higher attending students. In fact, as shown in Panel B, even those students who were projected to attend less than 80 percent of the time based on their household characteristics (the fifth row) would have met the attendance target on average without the subsidies.

As would be expected from the higher average attendance rates, the estimated treatment effects are slightly smaller for this sample of survey participants. Most of the point estimates are still statistically significant at conventional levels, but the average effects are around one percent rather than three percent for the entire sample.<sup>13</sup> The pattern of results, however, is similar to the pattern obtained when all of the students are included. The results are evenly distributed across grades, and those that are projected to attend less than 80 percent of the time show a stronger

> 13 Given the relatively small sample of students who did not give the baseline survey, we have not been able to estimate treatment effects for this sample consistently.  The estimates are very sensitive to the specification used, but the difference in treatment effects between students that did and those that did not give a baseline survey are never statistically significant at conventional levels.

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