rafmacalaba/datause-encoder-data huggingface dataset:
25 distorted by the subsidies provided for retail fuel prices, which were eliminated in 2014 and replaced by a targeted cash transfer program.<sup>25</sup> The average standard deviation (SD) of the output wedge for the F&B (0.98), metal (0.86), and petrochemical (0.82) industries are higher than the average for the manufacturing sector (0.58) (table 3). This shows that the allocation of resources in these industries is more affected by the distortions in the output market compared to other industries. As for distortions in the capital market, the SD in capital wedge for the textile industry (1.90) is higher than the manufacturing sector average (1.64). Identifying the distortionary factors would be useful for policy, but requires further investigation. At this point, anecdotal evidence associates price subsidies with distortions in the output market and misallocation of resources, particularly in the F&B industry.

**Figure 18:** Average Productivity Gains for Selected Industries in Malaysia during the Census 2000, 2005, and 2010

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>Average TFP gains<br>F&B Petchem Metal<br>M&E&C Wood Transport a<br>Textile Manufacturing<br>Percent productivity gains<br><!-- End of picture text --> <!-- Start of picture text --> Percent productivity gains<br><!-- End of picture text --> Source: Authors’ calculations.

Note: F&B includes the food and beverage industries. Textile includes the textile and apparel industries. Wood includes the wood, paper products and furniture industries. Petchem includes refined petroleum, basic chemicals, and other allied chemical products industries. Metal includes non-metallic mineral, basic metal, and fabricated metal industries. M&E&C includes machinery, equipment, electronics, and electrical industries.

a.The productivity gains estimated for the transport industry is derived from 499 firms, while the other industries are represented by more than 1500 firms in the three census periods.

> 25 The blanket fuel subsidies mainly benefited the 20% richest households, which received 42% of the subsidies, while the bottom 20% of the households received only 4% of the fuel subsidies in 2009 (Bank Negara Annual Report 2014).
# _Evaluation and Policy-Relevant Questions_

This study represents the first systematic effort to evaluate the impact of the female stipend component of PERSP. There are several policy-relevant research questions regarding this stipend program<sup>8</sup> : 1) What is the impact of the stipend component on female enrollment in public middle/secondary schools?

2) What is the impact on overall female enrollment, public and private schools? (Despite the fact that there are fewer private middle schools in the province, there might have been some switching from private to public school.) 3) What is the impact on enrollments across household welfare quintiles? And was the stipend sufficient to attract a female child at the margin (indifferent between attending and not attending)?

4) Did it have any (negative or positive) externality effect on male schooling (via changes in the explicit/implicit cost of male and female schooling within the household)?

5) What is the impact on quality of learning?

6) Does the stipend program have impacts outside of schooling outcomes such as that on health, in particular, on marriage, fertility and malnutrition?<sup>9</sup> 7) Could the money currently being spent on the stipend component be rather spent on other types of interventions with greater impact?

This study focuses on addressing the first question and most direct impact of the intended intervention – did it increase female enrollment in public schools? To address this question, we draw upon data from the provincial Education Management Information Systems (EMIS) school censuses 2003 and 2005. We estimate the _net_ growth in female enrollments in grade 6-8 in stipend eligible schools. Impact evaluation based on counterfactual analysis using methods including difference-and-difference (DD), triple differencing (DDD) and regression-discontinuity design (RDD), indicate modest but statistically significant impact of the intervention. The preferred estimator derived from a combination of DDD and RDD empirical strategies suggests

> 8 This is not meant to be an exhaustive set of policy relevant questions.

> 9 A recent study from Bangladesh indicates that the female secondary education may have had significant positive effects on fertility choices and their children’s malnutrition (A paper presented at a conference on “Towards a Strategy for Achieving the MDG outcomes in Bangladesh” in June 2006 by Qaiser Khan, Syed Rashed Al Zayed, and Yaniv Stopniztky).

6
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Columns 3 and 4 of Table 1 report the aggregate ratios of external market capitalization to sales and total debt to sales at the country level and then aggregated across legal origin. Consistent with LLSV (1997), these estimates suggest that the better the legal environment, (i.e., the higher the level of the legality index), the greater the relative value of external equity capital. The ratio of market-cap to sales increases from 0.34 for firms from countries with a French legal origin to 0.75 for firms from countries with an English legal origin. However, there is considerable variation in the measures at the country and firm level which is partly attributable to differences in industry mix, firm size, and other factors (as we show subsequently).

The total debt to sales ratios reveal a different story. Debt ratios across the legal origins are _inversely_ related to the legality index so that firms in French legal origin countries tend to have the greater values of debt . This result is in contrast with LLSV (1997) who find that legal origin is unrelated to the level of debt capital. Our later and narrower sample suggests a more extreme result that firms residing in countries with low legality measures have _more_ debt capital than firms residing in countries with high legality measures.<sup>7</sup> However, this result is consistent with the findings of Demirguc-Kunt and Maximovic (1999) and Fan, Titman, and Twite (2003).

We further examine the impact of legality on the structure of debt looking at short-term and long-term debt separately (columns 5 and 6 of Table 1). The alternative Hypothesis 1A predicts that debt maturity is positively related to legal quality hence firms in poor legality countries will tend to rely more on short-term debt. Consistent with this hypothesis, we find that firms residing in countries with lower legality measures tend to rely more on short-term debt than firms residing in countries with high legality measures. The ratio of short-term debt to sales decreases from 0.30 to 0.15 as we move from low legality (French origin) to high legality

> 7 Note that the negative relationship between legality and debt capital is not a “mechanical” relation expected as a result of our finding that legality is positively linked to equity. The ratio that we use reflects the magnitude of external capital in each country not relative levels of firm capital (such as a debt-equity ratio).

11
5. Conclusions This paper has aimed to shed light on the following questions: (i) What was the impact of COVID-19 on household income and income poverty in Egypt? and (ii) What was the role of key fiscal measures, in particular targeted cash transfers, in minimizing the immediate negative income consequences of the crisis?

The results confirm existing evidence of a large income shock due to the pandemic, particularly for urban households and those engaged in the informal sector. The analysis points to an average contraction in household income per capita of about 1.7 percent, driven by the poorest 50 percent of urban households. It further suggests an increase of 2.2 percentage points in income poverty, pushing around 2.2 million more people into poverty, compared to a non-COVID-19 scenario for the same year, using the international poverty line of $3.65 a day (2017 purchasing power parity). Two key features are correlated with Egyptians’ likelihood of becoming poor due to COVID-19: living in metropolitan areas and being an informal worker.

Fiscal policy is a key instrument to protect households and firms from the negative impacts of economic crises and downturns, and to rekindle economic growth. When the COVID-19 pandemic hit, most countries implemented fiscal measures to minimize the immediate consequences of the economic shock, protect families from falling into poverty, and allow firms to weather the crisis. The GoE was no exception, implementing a rapid set of monetary and fiscal measures estimated to cost about 1.8 percent of GDP.

Our findings highlight the salience of cash transfers, especially via the Takaful and Karama programs, in partly compensating for household income losses and limiting poverty increases in rural areas, at least at the onset of the crisis. The importance of the Takaful and Karama transfers as a compensatory measure resulted from the size of the transfer compared to other measures, but also reflects the programs’ targeting rules, which effectively target relatively poorer households.<sup>15</sup> The observed patterns have also pointed to a challenge for the traditional targeting approach in periods of crisis or shocks. As the Takaful and Karama targeting rules focus on the poorest regions (more rural) and adopt a proxy-means-testing model to better proxy chronic poverty, the expansion of Takaful and Karama during COVID-19 preferentially benefited Egypt’s rural households. This highlights the importance going forward of devising mechanisms to better identify households impacted by shocks that might not be eligible for support under traditional targeting algorithms.

> 15 Recent evidence shows that, notwithstanding strong targeting performance, exclusion errors remain due to the size of the program relative to the poverty rate, and because of errors inherent to the use of PMT in targeting practices. See footnote 5, above, and World Bank (2022a).

15
## **Chapter 3**

# **Methodology**

We now turn to the discussion of methods which can help estimate the actual impact of interventions on development outcomes.

The CPIA ratings can be used to empirically assess the effect of particular changes in economic policy or governance regimes on development outcomes of interest. The methodological problems are however significant, as policy outcomes get determined in an environment embedded by social, political institutions and governance quality. Research can thus pursue two separate methodological avenues, which we describe and assess.

### **3.1 Regression Methods with Dynamic Panel Data Models**

The first method uses classical regressions with dynamic panel data models which can estimate the average effect of particular interventions on development outcomes. Given that OLS and fixed effects estimators are biased due to unobserved time-invariant country-specific effects, and the availability of external instruments that meet exclusion restrictions is weak, these estimators have become central to the empirical growth literature. Bond et al. (2001) use the system GMM estimators to account for the endogeneity of variables such as investment, and to remove time invariant There are some critical problems associated with the use of these methods in our context. Firstly, there is no convergence in the literature on the functional form for capturing differential effects of particular policy interventions. Neither the augmented Solow/Ramsey nor the endogenous growth models that are workhorses of modern macroeconomics really have institutional structures as endogenous parameters in the models. Till date, the role of institutions and even most policy interventions is mostly seen in improving allocative efficiency, and hence increasing total factor productivity, which is generally calculated as the residual from the regressions that are based on the workhorse models. This creates a problem, as empirical papers generally use reduced form equations and lump various variables of policy and institutional quality on the right hand side of the regression equations. Bhagwati and Srinivasan (1999) argue that cross-country regressions are hence not the correct way to understand the impact of reforms (such as trade liberalization) on growth outcomes, because they are not based on any strong theoretical models, and have no description of the functional forms of the relationships between the dependent and independent variables. Using more advanced methods such as system GMM estimators cannot solve these problems.

Also significantly, these models use cross-country variation in growth outcomes as predicted by policy changes to estimate the average effect of a particular policy/governance change on growth outcomes. This, however, assumes that the relative importance of particular policy/governance inputs is similar across the diversity of countries in the sample group. Here, outliers become 9
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_ ln _MRERt_ = α + β ln _CPRICEt_ + ε _t t_ = 1,...., _T_ We then test for the stationarity of the residual (Engle-Granger Test), which turned out to be _I(0)_ so that the equation above is indeed a cointegrating relation.

**Cocoa prices move Cote d’Ivoire’s real exchange rate, and are a source of volatility** . Refer to the FMLS model results reported in Table 1.  The real world price of cocoa is negatively associated with Cote d’Ivoire’s real exchange rate. The parameter point estimate for β is of the correct sign, plausible in magnitude, and statistically significant. The evidence here supports the assertion that the Cote d’Ivoire CFA is indeed a “cocoa currency” in Cashin’s sense. One single commodity price is an important factor in the country’s competitive equilibrium, a consequence of cocoa specialization. By this best estimate, a one percent increase in cocoa prices results in eleven-one-hundredths of one percent (0.108) appreciation in Cote d’Ivoire’s multi-lateral real effective exchange rate.<sup>13</sup> This evidence supports the assertion that world cocoa prices are a source of volatility to the competitive equilibrium of the economy of Cote d’Ivoire.

Even though we expect that the competitiveness of Cote d’Ivoire has experienced a major structural change, we are unwilling here to assert the timing of such event. We resorted to the Hansen-Phillips (1996) test for structural break in the presence of _I(1)_ processes. In other words, we applied a test of parameter stability once we had established that there is at least one long-run equilibrium relationship between cocoa prices and MREER in Cote d’Ivoire.

The Hansen-Phillips test is based on estimating the following set of regressions: where lnMRER is the (log of) the multilateral real exchange rate, lnCprice is the log of the real price of cocoa and φ is a dummy variable constructed for every candidate break year _(b)._ We then ran an exhaustive block of equations with level dummies in the year, one for each ‘candidate’ breakpoint. We then computed the _ADF_ statistics for _each_ of the residual series (in our case, 42 regressions) and chose our statistic as the largest negative number of these (i.e. _ADF*=min{ ADF1,...., ADF43}_ ) where we test the null of no level shift in the cointegrating vector of prices.

**The statistical evidence: 1994 was a break-point for international competitiveness** . This unsurprising and plausible result came from the data alone, and does not arise from a prior hypothesis. Hansen and Phillips (1996) present the asymptotic critical values for the _ADF_ * statistic as defined above for our level-shift model. The data strongly suggests that

> 13 Whether this rate of adjustment is rapid enough or large enough is yet another question, not addressed here.

13
CLEMENS & TIONGSON Table 1: **The Korean Language Test**

||||⩽5 pts from|
|---|---|---|---|
|Batch|Date|Total #|cutoff score|
|1|Sep 2005|411|56|
|2|Nov 2005|2,811|435|
|3|Jun 2006|6,110|1,045|
|4|Oct 2006|7,586|1,291|
|5|May 2007|8,402|589|
|Total||25,320|3,416|

32
definitions of the middle class—as suggested by Birdsall (2007), Ravallion (2010), Chun (2010), and Ferreira et al. (2013)—to household data for Egypt and the MENA region as a whole. They show that the different definitions lead to dramatically different middle-class sizes in low-income and middle-income regions. For example, the estimated size of the MENA middle class is either implausibly small, representing under 5 percent of the population with the definitions proposed by Birdsall (2007) and Ferreira et al. (2013), or implausibly large, accounting for more than threequarters of the population with the definitions in Chun (2010) and Ravallion (2010). The other paper by Ali (2011) uses the national poverty line converted to PPP dollars and $13 per day as the lower bound and the upper bound for the middle class, respectively, but also appears to overestimate the size of the middle class as vulnerable households with consumption levels just above the poverty line are considered part of the middle class.

Several limitations exist, however, with respect to the definitions that rely on fixed thresholds. Absolute thresholds will have to differ across countries and time in order to be useful for making inferences about the size of the middle class. The MENA region is comprised of low-income, lowmiddle-income, high-middle-income, and high-income economies, and the heterogeneous income distributions of these economies may not even overlap. Furthermore, fixed thresholds cannot capture the differential price effects within countries and other important welfare effects that are incorporated in the estimation of national poverty lines. An alternative is to define income groups by asking people if they considered themselves to be poor, middle class, or rich and cross check the outcomes with those obtained using objective definitions based either on absolute or relative threshold measures (Ferreira et al., 2013).

We follow two approaches in setting the welfare thresholds (lines) that define the different income groups (i.e., the low-income, middle-income, and top-income groups). Our first approach builds on the literature discussed above, but adopts a hybrid combination of both the absolute and 8
**Figure 9. Overall Affordability**

<!-- Start of picture text --> Sweden<br>Finland<br>Netherlands<br>Ireland<br>Belgium (Flemish)<br>Belgium (French)<br>Austria<br>Germany<br>France<br>High Income<br>Italy<br>Canada<br>Mexico<br>Australia<br>United States<br>Average<br>United Kingdom<br>New Zealand<br>Japan<br>Colombia<br>LAC Average<br>Brazil<br>Peru<br>Overall Affordability (level)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries. Note: The graph displays the level of affordability of tertiary education compared to other countries. Findings for Brazil and Peru are based upon tuition fees of a relatively small sample of higher education institutions. The findings for these countries should therefore be considered preliminary.

Tertiary education in the selected four LAC countries is less affordable than in the highincome countries. The previous analysis suggests three mean reasons for this finding: (i) a relatively larger share of non-governmental education; (ii) a relatively lower GDP per capita, and (iii) a low level of student assistance.

# **3.2. Accessibility Findings**

This section presents at the findings on accessibility of tertiary education in LAC compared to accessibility of tertiary education in high-income countries. As described earlier, the accessibility indicators attempt to capture the accessibility of tertiary education in terms of two broad concepts: first, how many people get to participate and attain tertiary education (quantity), and second, who gets to access tertiary education (composition).<sup>21</sup> <u>Participation Rates for the selected LAC and high-income countries are presented in</u> Figure 10 with participation rates on the horizontal axis and the countries on vertical axis in order of decreasing prevalence.

> 21 As reported by Usher and Cervenan, 2005, data was unavailable for Japan on the participation indicator, while data was unavailable for New Zealand on the Educational Equity Index. In contrast to the affordability section where data for Belgium was presented separately for two linguistic communities, accessibility indicators for Belgium are not divided into communities, but presented on a national basis.

21
# **Chart 1**

<!-- Start of picture text --> House Prices and Mortgage Originations<br>4,500<br>190<br>Total Mortgage Originations<br>4,000<br>(Right Hand Scale)<br>170<br>3,500<br>150 3,000<br>2,500<br>130<br>2,000<br>110 House Prices<br>(Left Hand Scale)<br>1,500<br>90<br>Subprime<br>1,000<br>70 FHA/VA<br>500<br>50 0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Sources: S&P Case-Shiller, Inside Mortgage Finance Year<br>Dollars in Millions<br>S&P/Case-Shiller National House Price Index<br><!-- End of picture text -->

**Subprime lending drove house price increases in some areas.** Banks and investors act procyclically, extending credit more aggressively and competing for market share as collateral prices rise, when lending decisions seem less risky. Real estate cycles are lengthened by investor optimism during a boom and pessimism following its exhaustion. The current credit crunch is emblematic, with liquidity drying up and spreads widening dramatically on high quality prime mortgage-backed paper even though it continues to perform well. Booming real estate lending carries accelerator effects, and its cessation in a bust contributes to a more rapid slow down. Mian and Sufi (2008) show that mortgage credit underwriting standards were relaxed from 2001 to 2005 in zip codes with large numbers of high risk borrowers and negative relative income and employment growth. Relaxed standards were associated with increased mortgage lending, rising house prices, and a subsequent increase in defaults.

**Disaster myopia in sub-prime lending manifested itself in weakened underwriting standards, aggressive investor bids for above-market security yields, and an absence**

3