rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6 Regressions results on lagged Altman’s Z” score components as predictors of fiscal injections**

|_Item_|_(1)_<br>_Diffe _<br>_Subsidies +_<br>_government equity_<br>_injections +_<br>_Δ government loans_<br>_+_<br>_Δ state-owned bank_<br>_loans + Δ deferred_<br>_tax liabilities(DTL)_|_(2)_<br>_rencefrom Δ total a_<br>_Subsidies_|_(3)_<br>_ssets(aspercent_<br>_Government_<br>_equity_<br>_injections_|_(4)_<br>_of total assets), coun_<br>_Subsidies +_<br>_government_<br>_equity injections_|_(5)_<br>_try and industry xy_<br>_Δ Government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans_|_(6)_<br>_earfixed effects_<br>_Government_<br>_equity_<br>_injections +_<br>_Δ government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans_|_(7)_<br>_Government_<br>_equity injections_<br>_+_<br>_Δ government_<br>_loans +_<br>_Δ state-owned_<br>_bank loans +_<br>_Δ DTL_|
|---|---|---|---|---|---|---|---|
|(Current assets –|2.070|5.444***|0.219|6.799***|–0.655**|0.622|–1.377**|
|current<br>liabilities)/assets (lag)|(3.579)|(1.687)|(0.511)|(2.551)|(0.260)|(2.030)|(0.644)|
|Retained.<br>|4.118**|5.861***|–0.001|7.775***|–0.299**|–2.648**|–0.880***|
|earnings/assets (lag)|(1.838)|(0.880)|(0.258)|(1.317)|(0.134)|(1.042)|(0.331)|
|Adjusted EBIT/assets<br>|–63.759***|–46.843***|–3.780***|–67.170***|0.187|–11.095***|–0.108|
|(lag)|(3.867)|(1.662)|(0.502)|(2.559)|(0.249)|(1.999)|(0.696)|
|Book value of<br>|0.070|–0.036|–0.023|–0.057|–0.008|–0.104*|–0.019|
|equity/liabilities (lag)|(0.119)|(0.050)|(0.015)|(0.076)|(0.008)|(0.061)|(0.021)|
|Observations|608|843|795|788|734|691|608|
|Number of SOEs|92|118|114|112|110|106|92|
|_R_-squared|0.474|0.638|0.280|0.626|0.161|0.358|0.169|
|Adjusted_R_-squared|0.418|0.609|0.220|0.594|0.0851|0.296|0.0815|
|F-statistic|8.516|22.53|4.665|19.90|2.117|5.747|1.928|
|Log likelihood|–2,558|–3,031|–1,892|–3,140|–1,229|–2,566|–1,515|
|Mean of dependent<br>variable|5.035|3.519|0.412|4.793|0.266|2.545|0.756|
|Standard deviation of<br>dependent variable|22.40|14.66|3.083|21.27|1.411|12.38|3.209|

26
elasticities for the two periods is much smaller (2.63 and 2.94 respectively) and is not statistically significant. Finally, for the squared poverty gap measure, the elasticities are the same for the two periods (about 3.48). The pattern is similar when we use our IVE method to control for correlated measurement errors, however the difference between the two periods is narrower, and for the squared poverty gap measure the post-91 elasticity of 3.28 is in fact lower that the pre-91 elasticity of 3.52. The vanishing difference in post- and pre-91 elasticities for the higher-order measures of poverty is consistent with the increase in inequality during the latter period.

As a check on the internal consistence of our estimates, the estimated elasticities of poverty measures with respect to survey mean can be multiplied with the trend growth rates of survey mean to yield an estimate of the trend rates of decline in poverty measures. Table 4 reports the results of this calculation, which indicate that the trends in poverty measures estimated by using the elasticities track the actual trends in poverty measures reasonably well.

In contrast to the growth rates based on the survey means, we find that both NAS-based growth rates indicate lower (absolute) elasticities in the post-1991 period, although the difference between the two periods is generally not statistically significant; the exceptions to this pattern are for the “unadjusted” elasticities of PG and SPG which are significantly lower in the post-reform period. It is nevertheless notable how much difference there is in the elasticity based on the NSS consumption growth rates versus the NAS rates for the post-1991 period. The much lower NAS elasticities are reflective of the much faster NAS-based growth relative to that based on the NSS. Since the NAS-NSS growth divergence is more pronounced post-91, for the PG and SPG measures it even yields lower (absolute) elasticities for this period relative to the pre-91 period.

We also estimated the semi-elasticities, from the regression of<sup>_P_</sup> _t_<sup>on</sup> ln  _t_ . We found that the poverty impact of growth in the survey mean is lower in the post-91 period. The estimated semi-elasticities for the post-1991 period were -0.73 (t=-45.8) for H, -0.34 (t=-32.3) for PG, and - 0.17 (t=-25.3) for SPG as compared with -0.63 (t=-15.7), -0.20 (t=-9.82) and -0.08 (t=-7.24) respectively in the pre-91 period.

The regressions also incorporate a kink at NSS round 47 (July-December 1991) such that there is no discontinuity in the predicted values of log poverty measures between the pre- and post-1991 periods.

20
hunger prevalence (almost 60%), while the household diaries report hunger just slightly higher than with the individual diary (23%-27% depending on the level of supervision, compared with 19% for the individual diary).

One of the arguments for favoring the HCES-direct method over the FBS-CV method is that it allows for within-country comparisons across geographical zones or socio-economic or demographic groups, which is a key concern for policy makers at the national level. A natural question to ask then is whether such intra-country comparisons might also be affected by survey design variations. We adopt the following framework:

# (1) _Yik = βkMk +βxXik +γkMkXik+ eik_

where _Yik_ is either the (log) of kilocalories per capita (estimated with an Ordinary Least Squares regression – OLS) or a variable indicating that the household is categorized as hungry (estimated with a Linear Probability Model – LPM). Households are indexed _i_ and questionnaire assignments _k_ , with _M_ k a vector of six dummy variables for module type, omitting the resource-intensive personal diary which is the base category, and _Xik_ is a single household characteristic. Randomization of module assignment ensures that the error term, _eik_ , is orthogonal to both _Mk_ and _Xik_ and to their interaction. We estimate Equation (1) separately for each of four selected household characteristics, with results reported in Table 3.

Many of the interaction terms are statistically significant, which suggests that estimated within-country patterns of hunger are sensitive to the type of HCES that is used. For example, for each standard deviation increase in the asset index, a household given the usual month survey method will have a 13 percentage point lower chance of being measured hungry compared to a household at the same level of wealth but assessed through personal diary (this result is of course conditional on the mean effect of the questionnaire design, which is captured by the _Mk_ term). Other recall modules have a similar sign but the interaction effect is smaller in magnitude. In other words, recall modules would increasingly underestimate hunger prevalence as the household grows richer. On the other hand, recall modules tend to overstate hunger vis-à-vis diaries with respect to household size. For every one-person increase in the number of household members, the relative likelihood of a hunger diagnosis with recall surveys increases by 2-4 percentage points.

15
As for HBS, NPS data indicate a decline in returns to households’ endowments, but contrary to HBS findings, the decline seems more marked for the poorest groups. Here again, this decline masks divergent trends across the different attributes. Returns to education and assets seem to have improved, while returns to access to markets appear to have declined. These results deserve further investigation and confirmation.

**Figure 11: Returns Effect and Endowments Effects over Time, NPS 2008/09–2012/13**

<!-- Start of picture text --> Returns effects and endowment effects over time for Tanzania NPS 2008/09-2012/13<br>.1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles<br>Confidence interval / endowment effect Confidence interval /returns effect<br>Endowment effect Returns effect<br>.4<br>.2<br>0<br>-.2<br>Difference in log real per capita total expenditures<br>-.4<br><!-- End of picture text --> 44
In terms of salient administration features, the testing is school-based, with students tested in their school during normal school hours. The test papers are written, formatted, sorted, counted, and sealed in envelopes by ADU. PEF also administered the tests at program schools in QAT 1−QAT 3. Starting from QAT 4, the administration of the test was competitively outsourced to independent testing agencies, namely the Board of Intermediate and Secondary Education (BISE), Bahawalpur and the Punjab University Education Testing Service, Lahore.<sup>11</sup> The testing agencies are expected to strictly follow the test administration guidelines prepared by PEF and are responsible for the administration steps starting from securely transporting the test materials to the schools and ending with hand-scoring the test following the provided scoring guides and the delivery of the student- and subject-wise test score database and filled-in and unused question papers, as well as brief test administration reports and test administration checklists for each school, to ADU. PEF is not completely divorced from test administration however—it sends a PEF staff member to be present at each test to monitor test agency compliance with the guidelines; if any problems arise, the staff member is authorized to take corrective steps. The staff member is expected to fill in and submit a monitoring report (accompanied by photographs and video footage [see Figure 2 for examples]) for each program school covered.

Although the QAT is a pre-announced test, it attempts to control for test pool selection by the school at the grade level. Prior to administering the QAT, PEF sends an intimation letter to the school some two-three weeks in advance of the planned test date, with key test planning information. The testing agency also communicates the same information to the school a few days before the planned test date, either by letter or personal visit. Importantly, schools are notified that 100% attendance is required on the test date, with attendance numbers checked against the grade-wise enrollment statement submitted to PEF for the latest subsidy benefit payment. Consistent with the test administration guidelines, in practice, the QAT is administered at the program school if the student attendance rate is at least 80% (PEF reports that test cancellation due to lower-than-required student attendance is a rare event). The number of test papers in each grade-specific packet is determined by ADU based on the latest submitted monthly enrollment statement. If the number of students in the grade selected for the QAT

> 11 PEF reportedly made the decision to outsource the testing in order to (i) increase program school confidence in the integrity of the testing process and the test results as well as (ii) release the organization from the administrative burden of performing these critical activities during a period of significant program scale-up.

12
responses are incomplete and some smaller programs may not be included. Much of the data presented in the report draws from the ILO data supplemented with other sources.

The Organization for Economic Cooperation and Development (OECD) also collects data on the pension spending of its members. For many of the industrial countries, data obtained directly from the OECD in Paris were used in the report. The statistical arn of the European Economic Community, Eurostat, also publishes detailed pension spending data which were sometimes used to modify the OECD figures, mainly to add spending on early retirement and "redundancy" benefits to the OECD figures.

A third major source of data were a number of World Bank reports which typically covered the social sector or public expenditures or were part of a comprehensive country economic report. Many of these reports are unpublished and some were in draft form at the time of writing. The report drew heavily on these World Bank documents for pension data covering Eastern Europe and the former Soviet Union. Finally, some data were taken directly from the statistical records of administering agencies in the countries themselves. In the case of the Nordic countries (Norway, Iceland, Sweden, Denmark and Finland), the data published by the Nordic Statistical Secretariat in its tri-annual statistical yearbook were used.

For many countries, data from different years and from different sources were available. In general, statistics of the administering agencies in the countries were chosen over secondary sources. When more than one secondary source was available, the choice was based on the perceived quality of the data. Otherwise, an attempt was made to find the most recent year available. In some cases, multiple sources were used in order to get a complete picture of the various pension outlays being made in different programs.

Table A. 11 reports pension spending ratios for the most recent year available (at the time of publication) for Eastem Europe and are sometimes of a preliminary nature. Although most correspond to the data presented in Table A.5, the regression results in the book which use pension share of GDP refer to pre-1991 data in all cases. In other words, the regressions are generated with pension spending ratios prior to the major declines in GDP registered in the region. These declines in national income combined with inelastic pension spending tend to increase the ratio after 1991, as shown in Box Figure 4.6. With the 5
De Tray, Dennis. 1983. “Children's Work Activities in Malaysia,” _Population and Development Review_ , 9(3/Sep):437-455 Desai , Sonalde and Devak Jain. 1994. “Maternal Employment and Changes in Family Dynamics: The Social Context of Women's Work in Rural South India,” _Population and Development Review_ , 20(1/Mar):115136.

Desai, Sonalde and Soumya Alva. “Maternal Education and Child Health: Is There a Strong Causal Relationship _?” Demography_ , 35(1/Feb):75-81.

Duflo, Esther. 2001. “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Experiment,” _The American Economic Review,_ 91(4/Sep):795–813.

Fiszbein, Ariel and Norbert Schady. 2009. Conditional Cash Transfers: Reducing Present and Future Poverty, The World Bank: Washington D.C.

Gonzales, Patrick, Trevor Williams, Stephen Roey, David Kastberg and Summer Brenwald. 2009. Highlights <u>From TIMSS 2007: Mathematics and Science Achievement of U.S. Fourth- and Eighth-Grade Students in an International Context, National Center for Education Statistics, U.S. Department of</u> Education.

Guryan, Jonathan, Erik Hurst and Melissa Kearney. 2008. “Parental Education and Parental Time with Children,” _Journal of Economic Perspectives_ , 22(3/Summer):23-46 Heckman, James and Richard Robb. 1986. “Alternative Methods for solving the Problem of Selection Bias in Evaluating The Impact of Treatments on Outcomes,” in H. Wainer, ed., Drawing Inferences <u>from Self-Selected Samples, Berlin: Springer-Verlag.</u> Heckman, James J. 1978. “Dummy Endogenous Variable in a Simultaneous Equation System,” _Econometrica_ , 46(4/July):931-959.

Holmes, Jessica. 2003. “The Determinants of Completed Schooling in Pakistan: Analysis of Censoring and Selection Bias,” _Economics of Education Review_ , 22:249-264 _._ Kremer, Michael, Edward Miguel and Rebecca Thornton. 2009. “Incentives to Learn,” _Review of Economics and Statistics_ , 91(3):437-456.

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observations from item-district cells that have insufficient representation to construct a price schedule (fewer than 15 observations). This reduces the sample size by roughly 1%.

A further cleaning step was required to standardize units. Respondents reported many quantities in kilograms and liters, but others in bunches, heaps, tins, ladles, buckets or bundles. For some items these units were measured during the market price survey. For other commonly reported units, the team went back specifically to measure the kg or liter conversion. We use the median, district-level conversion rates to convert local units into kilograms or liters.<sup>7</sup> The final data set contains details for 48,501 purchase transactions. Descriptive details for the final set of 19 study items are reported in Table 1. Maize and cooking bananas— staple carbohydrates—are the items purchased in the largest kilogram quantities. Comparing transaction quantity to total 2-week consumption in Table 1, it is clear that households tend to buy items multiple times over two weeks. Table 2 provides more details on this and other patterns in the diary data. The total number of observed transactions ranges from 688 (maize) to 5319 (cooking oil). The average item was purchased by just under half of the sample (733 households), and was purchased multiple times by just over a third of the sample (505 households). Some items, such as cooking oil, kerosene, sugar, dried sardines, and onions, were purchased more than once by a majority of households. Among the households that purchase each item, the highest average expenditure is on maize at 7,354 TZS/household, and the lowest is on matches at 187 TZS/household. The average number of purchases per item is 3.6.<sup>8</sup> The SHWALITA team also conducted a market price survey in each village, in conjunction with the household survey. Markets are relatively dense in Tanzania: 97% of study households live within 10 kilometers of the nearest market, and the median distance is 1.15 km. For 42 food items (10 of which meet the criteria for inclusion in this study), enumerators visited the village market and recorded the most common units in which each item was sold. They precisely measured the unit in kilograms or liters, and noted the price. Unit prices were

> 7Helpfully, 98.7% of purchases recorded in units other than kilograms or liters were recorded as integer values. Most of the decimal entries in kilograms or liters stem from unit conversions, not from respondents being forced to convert non-standard units into kilograms or liters on the spot.

> 8Additional descriptive statistics for the survey households are provided in Appendix S1.

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bad times. For example, insuring oneself by selling risk to others (through, say, credit default swaps) can actually raise systemic fragility through interconnected risk and lead to contagion and accentuated downward spirals in bad times, when default risk becomes highly correlated and the value of collateral collapses. Or else rising participation along the intensive dimension (the same intermediaries engaging in more transactions and becoming financial giants) can boost social moral hazard by vastly increasing the social costs of individual institution failures through the too-big-to-fail or too-interconnected-to-fail syndromes.

The dark side has yet a third mode in which the successful reduction of agency or participation frictions leads to problems of collective cognition. The bonanza associated with FD feeds a collective mood of optimism that puts the system on a disequilibrium path. This is typically amplified by financial innovations (for example, securitization) that unleash bouts of exuberance, even if the full implications of the innovations are not well understood. The mood swings first accentuate the upswing; once the market sentiment switches and euphoria turns into despair, they worsen the collapse.<sup>15</sup>

# **3.         The empirics of financial development**

# **_a. Data and methodology_**

We now proceed to conduct a simple empirical analysis of aggregate FD indicators. Our aim is to illustrate the above conceptual framework by showing that the order of appearance of various financial activities and the path they follow as they develop validate, at least broadly, the linkages and dynamics described in the previous section. We measure domestic financial development based on a battery of indicators that include:<sup>16</sup>

- Key components of commercial banks’ operations (deposit and non-deposit funding, credit to the private sector, claims on other domestic financial institutions);

- Insurance companies premia (life and non-life);

- Mutual funds and pension funds assets;

- Public and private debt securities (domestic and external capitalization);

- Domestic equity market capitalization.

- Domestic equity market turnover

- The share of fee-based income in total banking income

- Banks’ net interest margin;

To reflect cross-border finance, we use two financial globalization indicators:

- Gross country external debt (claims and liabilities);

> 15 The importance of mood swings for financial bubbles and panics finds its roots in Keynes’s animal spirits and Hyman Minsky’s writings on financial crises (see Minsky 1975). More recently, it was popularized by Kindleberger (1989) and Shiller (2006).

> 16 The data are from FinStats 2009, a world-wide financial database put together by the World Bank, which covers 40 key financial indicators for the period 1980-2008 (coverage quality varies between variables). The data come from a variety of sources including IFS, BIS, WDI, S&P, Bankscope, Axco, and national sources.

8
45

# **Table 3**

# **Weighted Actual and Target SME Lending**

|**Country**|**Target for**<br>**SME**<br>**Lending**|**No. of Banks**|**Actual SME**<br>**Lending**|**No. of Banks**|
|---|---|---|---|---|
|**Bahrain**|5.88|3|1.14|6|
|**Egypt**|24.72|7|5.22|9|
|**Jordan**|25.276|12|10.40|13|
|**Kuwait**|9.22|6|2.45|4|
|**Lebanon**|30.64|8|16.14|8|
|**Morocco**|29.4|3|23.79|5|
|**Oman**|11.39|5|2.48|4|
|**Palestine**|27.84|4|6.20|4|
|**Qatar**|15.14|7|0.49|5|
|**Saudi Arabia**|8.90|9|1.70|7|
|**Syria**|13.90|9|4.12|10|
|**Tunisia**|15.66|4|15.34|4|
|**United Arab Emirates**|24.26|8|3.85|7|
|**Yemen**|22.71|4|20.25|2|
|**GCC**|12.47|38|2.02|33|
|**Non-GCC**|26.71|61|13.16|65|
|**MENA**|19.59|99|7.59|98|

(*) Non-GCC and MENA averages and totals include Iraq, but it is not reported in the table above as its coverage is less than 30%. Iraq is however included in the remaining analysis. Libyan banks are not included in the average and quantitative data analysis as their figures could not be verified during the follow-up process. Libya is however included in qualitative analysis.

# **Table 4**

# **Average share of SME Loans in total Bank Loans**

|**Source**|**Developed**<br>OECD<br>(2010)<sup>1</sup>|**Countries**<br>World Bank<br>(2008)<sup>2</sup>|**De**<br>World Bank<br>(2008)<sup>3</sup>|**veloping Countr**<br>World Bank/|**ies**<br>UAB (2010)|
|---|---|---|---|---|---|
|**Number of countries**|5 countries|7 countries|38 countries|15 Middle<br>East countries|9 Non-GCC<br>countries|
|**SME Loans/Total Loans(%)**|26.8|22.1|16.2|7.59|13.16|

1 Canada, France, Korea, Sweden, and the US.

- 2, 3 Beck, Demirguc-Kunt, Peria (2008).