rafmacalaba/datause-encoder-data huggingface dataset:
component’s growth rate. Those components that experienced higher growth rates will have higher levels of force at the 92<sup>nd</sup> percentile. In comparison, a component that is more stable, only growing moderately in any one year will have a lower value. For example, social benefits tend to have a large share of expenditure across countries as well as a large deviation of force results. As such, it has a relatively higher benchmark compared to consumption of fixed capital, which has a smaller share and is more stable.

Examining the results in the 3<sup>rd</sup> and 4<sup>th</sup> columns we see that the benchmarks for advanced economies were lower than those of emerging economies. In general this is because emerging budgets are growing at faster nominal rates than advanced economies. As such, when we are examining thresholds for force at the 92<sup>nd</sup> percentile, they will tend to be higher for emerging economies. The results in Table 6 represent the median estimate across country groupings. For any particular country, the threshold could be lower or higher than those presented in the table.

# **g. Country Example**

So far we have focused on the predictive qualities of force at an aggregate level. We now show an example of how force could work through a country example, namely Bahrain in the early 2000s. As we can see from Table 7 Bahrain’s expenditure-GDP ratio increased sharply in 2001 and 2002, rising from 22.1 percent of GDP to 31.9 percent. This was not primarily due to a large increase in aggregate revenues, with the revenue-GDP ratio declining slightly over the same period.

|**Table 7: Indicative Benchmark**<br>(percent)|**s for 2-Year Force**|||||||
|---|---|---|---|---|---|---|---|
||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|
|**Revenue***|21.1|23.2|30.7|28.4|28.5|27.5|26.3|
|**Expenditure***|24.6|28.2|22.1|27.7|31.9|29.3|26.1|
|* Percent of GDP||||||||

Source: IMF WEO April 2014 We can examine this same period using momentum and force. In Figure 10 we plot momentum (blue bars) and force (red bars) as well as the force threshold at the 92<sup>nd</sup> percentile (black line). In the period 19982004 there were two periods when force was near or over the threshold (2000 and 2002). The second occurrence, 2002, was above the threshold for force increases, therefore a signal was issued. In line with our analysis, two years after the signal, there was a sharp reduction in the expenditure-GDP ratio, which fell from 31.9 to 26.1 percent of GDP in 2004.
**Graph 4. Relationship between the highest PAA achievers and student economic background (2000)**

<!-- Start of picture text --> 40%<br>35% Total 100%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>(lowest) (highest)<br>Income quintile<br>Source: Larrañaga (2001)<br>highest PAA achievers<br>Distribution of the 20 percent<br><!-- End of picture text --> Indirect public funding has been successful in providing tertiary institutions – public and private – a strong incentive to respond to student needs and provide learning opportunities of high quality and relevance. As such, the AFI has been an important example of performance funding in Chilean tertiary education. Nonetheless, potential exists for improving the mechanism, particularly in regard to equity. As Graph 4 shows, PAA achievement – and hence AFI allocations – is closely linked to the ability to pay for quality basic education (Bernasconi and Rojas 2004). An alternative approach to AFI would be to tie the capitation grant to the best students from the lowest three income quintiles.

## **Funds for institutional development**

In 1998 a decision was taken to gradually phase out the Institutional Development Fund due to criticism that political criteria dominated the allocation of funds. These shortcomings were challenged by the creation of a competitive fund for quality improvement (MECESUP) under which all tertiary institutions are eligible for support.

Designed to accelerate processes of institutional modernization, the MECESUP fund supports projects developed and proposed by tertiary institutions. Committees of peers review and select proposals according to transparent procedures and criteria. Thus far, supported areas include: (i) technical training programs; (ii) undergraduate programs in the hard sciences; (iii) graduate programs in all areas; and (iv) curricula redesign (World Bank 1998).

## **Setting the stage for reform**

With only five percent of AFD allocated on a true performance basis, significant potential exists for increasing transparency and tying incentives to key sector priorities by rescaling and reforming existing public funding structures. Despite good intentions and a decade long debate, no action has so far been taken to supplement demand side financing with changes to the public funding regime for tertiary education established in the early 1980s. This may be for good reason. Results-based funding systems are difficult to design and may be met with considerable resistance from key stakeholders. It is against this background that the following sections discuss preconditions, design options, tradeoffs and strategies to be considered when tying funding to results.

# **2. FUNDING HIGHER EDUCATION INSTITUTIONS BASED ON RESULTS**

Results-based funding can be defined as the allocation of resources contingent on achieved rather than promised results. Tying funding to results departs from traditional considerations in tertiary education of line item expenditures and inflationary increases. These constitute

- 6 -
- 32 -

**Figure 9.**

<!-- Start of picture text --> Share of Lending and Contribution to GDP by Sector<br>June 1996<br>30<br>20 - ---<br>* BaniG<br>15 a ECs<br>1~~~~~~~~~~~~~~~~~~~~~o B cS<br>10<br><!-- End of picture text --> <!-- Start of picture text --> Source: Bank of Thailand.<br><!-- End of picture text -->

**_Competition and the Customer Base._** Under the BIBF umbrella, 19 new licenses were granted to 19 foreign banks in 1993. The increasing possibilities for AAA companies to borrow off-shore and the establishment of BIBFs increased the competitive pressure in the Thai financial marketplace, especially in the prime borrower and multinationals segments. As a result, finance companies and commercial banks (although to a lesser extent) lost their "good" customers as these now had more funding alternatives. Nevertheless due to their aggressive lending practices, finance companies managed to increase their overall market-share quite substantially from 12.7 percent to 21.8 percent of total financial system assets over 1988 to 1996.

**_Increase in Market Risk for Finance Companies._** Most commercial banks limited their interest rate risk exposure by extending floating rate loans to their customers, hence transforming interest rate risk into credit risk. Finance companies, typically extended fixed rate loans to their custorners and increased the maturity mismatch between their assets and liabilities considerably after 1992. Although the bulk of loans extended by finance companies were short-term (less than one year), the average maturity of their liabilities was much shorter than that of their assets (about 56 percent of their borrowings had a maturity of less than three months in 1996, up from 30 percent prior to 1992).
# **Overview**

The impact of structural reforms on growth has generated new interest. In an era of secular stagnation, it has become a major concern in Europe and Central Asia (ECA), where growth has been stagnating, mainly because growth in advanced economies has been subdued (Figure 1). As a result of weaker than expected activity in the euro area, the pace of growth in the region’s developing countries has also decelerated and so has not yet fully recovered from the slowdown after the global financial crisis, hence showing the weakness of the underlying structural fundamentals. Also, the forecast is subject to further deceleration, more pronounced for Central Europe and the Baltics, due not only to the tight connections with euro area economies but also to the region’s own worsening demographic trends and minimal growth in productivity and investments (World Bank 2019a).

For the last decade labor productivity in ECA has stagnated, and since 2006, its pattern has been significantly diverging from the rest of the world (Figure 2). In the past, productivity in ECA mirrored the world pattern, but it has taken a different path since 2006 and especially since 2009. Growth of labor productivity has been falling in both industrial and developing countries for decades.<sup>2</sup> The slowdown is driven not only by stagnation in the EU but also by the deceleration of growth in lowincome countries. In fact, since 2009 labor productivity growth in ECA (high-income countries excluded) has been considerably less than in the first decade of the 21<sup>st</sup> century.

**Figure 1. Real GDP Growth**

<!-- Start of picture text -->
1.8<br>0.8<br>-0.2<br>-1.2<br>ECA<br>World<br>ECA (excluding high income)<br>(1991=100)<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>GDP (Constant 2010 US$)<br><!-- End of picture text -->

**Figure 2. Labor Productivity Growth**

<!-- Start of picture text --> 14<br>9<br>4<br>-1<br>-6<br>ECA<br>World<br>ECA (excluding high income)<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>Labor Productivity (1991=100)<br><!-- End of picture text --> _Source_ : World Development Indicators (WDI).

_Source_ : WDI.

This paper attempts to identify the structural determinants of growth in ECA. The analysis builds on the approach introduced by Bouis and Duval (2011), Barnes et al. (2013), and Johannson et al. (2013) and elaborated by Egert and Gal (2016). The methodology allows to identify the main structural determinants of potential GDP growth by studying separately capital, labor, and productivity—the channels through which those determinants may work. As in Lusinyan (2018), productivity here is 2 See Cusolito and Maloney (2018), Arteta and Kasyanenko (2019) and World Bank (forthcoming) for a recent analysis of productivity trends in ECA.

2
**Galal &** Nauriyal

22

**Table 9. Real Changes in Consumer Surplus**

(average annual changes in consumer surplus relative to sector's average annual revenues)

|Country|Pre-reform|Period|Post-reform|<br>Period|
|---|---|---|---|---|
|Argentina|-7.1|1981-90|-4.7|1991-92|
|Chile|0.4|1981-86|0.5|1987-92|
|Jamaica|-13.4|1981-87|8.1|1988-92|
|Malaysia|4.2|1982-86|1.8|1987-92|
|Mexico|1.1|1982-89|-13.2|1990-92|
|Philippines|0.0|1980-85|_-5.1_|1986-91|
|Venezuela|10.4|1986-89|-7.8|1991-93|

Source: Calculated from data from Intemational Telecommunications Union (various editions).

# **IV.3 Adding it all up**

We focused so far on linking the changes in outcomes with the success or failure in resolving the three regulatory problems identified at the outset. The question remains, however, as to whether the net welfare effect of reforms has been positive or negative. The answer obviously depends on whether the changes in producer and consumer surplus move in the same or opposite directions. In cases where the producers and consumers were both better off after reform, it is possible to declare the regulatory reforms and privatization a success. This conclusion holds for Chile, Argentina and Jamaica. The same conclusion can not be drawn, however, in cases where the producers were better off but not the consumers, as in Malaysia, Mexico, Venezuela and the Philippines. The net effect of reform in these cases depends on the magnitude of the gains and losses and the weights attached to each. It also depends on whether the reform led to more investment, higher efficiency and better pricing, or not.

With respect to Mexico, a comprehensive evaluation of the welfare effects _of privatizing TELMEX is available (Galal et al., 1994). The conclusion of this evaluation is that the privatization of the company was welfare improving, thanks to improved productivity and more efficient pricing policy. While we cannot draw such a conclusion for Malaysia and Venezuela, it seems reasonable to conclude that the reform in the Philippines has not been welfare improving, given that investment declined and productivity remained
Industrial Organization Implications of QR Trade Regimes: Evidence and Welfare Costs* by Timothy Condon and Jaime de Melo

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Profitability<br>and Firm Behavior in Manufacturing<br>Under<br>Dif. rent Trade Regimes:<br>Evidence and Modelling Issues|2|
|3.|A Stylized CGE Model with QR and Industrial Organization<br>Focus|7|
|4.|Illustrative<br>Simulations of the Welfare Costs of<br>Protection Under QR Trade Regimes|11|
|5.|Conclusions|18|
|Note|s|20|
|Bibl|iography|21|
|Appe|ndix|23|

* This paper, forthcoming in _Empirical Economics,_ is part of research project "Industrial Competition, Productive Efficiency, and the Relation to Trade Regimes (RPO 674-46)," funded by the World Bank. An earlier version of this paper was presented at :he meeting of the Applied Econometric Association in Istanbul in December 1986. We thank conference participants and a referee for comments or an earlier draft.
(SESNP), for which the World Food Program is both a recipient of the grant and an implementing agency, involves two components: i) Safety Net Transfers to provide unconditional cash transfers, and ii) Delivery Systems, Monitoring and Evaluation, and Learning and Project Management. The estimated total beneficiaries are expected to be about two million people in Sudan (which is around 4.6 percent of the total population).<sup>6</sup> Under the first component, the project will provide cash or food transfers. The cash transfers of US$7 per person per month will be provided for a period of four months to targeted vulnerable households in localities with functioning food markets. The food transfers equivalent to US$7 per person per month will be provided to households for a period of four months in localities with poorly functioning food markets. Given the limited number of beneficiaries, in this context, an efficient targeting tool will be crucial for the selection of beneficiaries of the program.

The objective of this study is to support the efforts of Sudan in the targeting the Sudan Emergency Safety Nets Project (SESNP) to those most in need and to improve the implementation of the SESNP to reach its intended beneficiaries. To meet this objective, we highlight the feasibility of using a Proxy Means Test (PMT) for targeting social programs in Sudan by developing a PMT and presenting how a PMT can be used to improve program targeting to reach those most as need.

A PMT is a regression-based index of observable and verifiable household characteristics that serves as a proxy for household welfare, such as consumption expenditure or income. The resulting PMT score is commonly used to determine household eligibility for public assistance programs.

We focus solely on analyzing the PMT method. We note that a number of alternative targeting approaches exist, such as targeting based on self-selection (Coady and Parker 2009), nutritional risk (Reber et al. 2019), social worker evaluation (Rossell et al. 2018), multidimensional poverty (Alkire and Seth 2013), and Poverty Scorecards (Skoufias et al. 2020), as well as community-based targeting (Alatas et al. 2012; Sabates‐ Wheeler et al. 2015) and geographical targeting (Van Domelen 2007), to name a few. However, PMTs have been widely used as a targeting system in several countries, due to their efficacy at accurately targeting the poor, as well as their relatively low cost and ease of implementation compared to alternative targeting methods.<sup>7</sup> Furthermore, a PMT can be combined with other targeting methods based on program goals, for example nutritional risks, environmental or hazard risks, or educational risk, to identify individuals who

> 6 As of 2020, Sudan’s population is estimated to be 43.8 million according to statistics from the World Development Indicators (WDI). Link here.

> 7 We direct the reader to Grosh (1994), Coady, Grosh, and Hoddinott (2004), Grosh et al. (2008), Kidd and Wylde (2011), and del Ninno and Mills (2015) for additional reviews of various program targeting approaches and the pros and cons of the PMT.

4
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Table (D9) Impacts of the Program on COVID-19 Credit-Related Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Loans taken pre-frst-lockdown (=1)|-0.20***|(0.07)|[0.01]|0.61|(0.49)|1,608|
|Log loans taken pre-frst-lockdown|-0.39**|(0.16)|[0.04]|5.48|(0.85)|923|
|Loans taken during frst lockdown (=1)|-0.34***|(0.07)|[0.00]|0.78|(0.41)|1,608|
|Log loans taken during frst lockdown|-0.28*|(0.16)|[0.19]|5.55|(0.77)|1,167|
|Loans taken after frst lockdown (=1)|-0.33***|(0.07)|[0.00]|0.42|(0.49)|1,607|
|Log loans taken after frst lockdown|-0.80***|(0.19)|[0.00]|5.08|(0.86)|586|
|Loans given pre-frst-lockdown (=1)|0.02*|(0.01)|[0.21]|0.00|(0.06)|1,610|
|Loans given during frst lockdown (=1)|0.00|(0.00)|[0.40]|0.00|(0.00)|1,610|
|Loans given after frst lockdown (=1)|0.00|(0.00)|[0.77]|0.00|(0.04)|1,610|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The periods referred to in the tables are before lockdown: (January 15 - March 15, 2020); during lockdown: (March 15 - May 15, 2020); and after lockdown: (May 15 - interview date). Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 14 in the pre-analysis plan.

11
To obtain the index ln _AitR_ itself we estimated (a log transformation of ) equation (1) for each of the industries covered by the India sample, but on observations pooled from World Bank Investment Climate Survey data for seven countries including India.<sup>20</sup> Details of the estimates are given in Table 3. The first two columns of the table present _R_ OLS estimates which are, however, bound to biased. The reason is that TFP term, _ait_ , is a state variable that influences the choice of inputs. As a result it must be correlated with all other right hand side variable of the equation. There are at least three recent techniques of achieving identification in this setting. These are Olley and Pakes (1996), Blundell and Bond (2000), and Levinshon and Petrin (2003). We have used the Levinshon-Petrin estimator because of the relative ease of its application given our data. We have too few observations on investment to use the Olley-Pakes estimator, and our panel of observations over time is too short for effective use of the Blundell-Bond approach.<sup>21</sup> In the estimation of production functions labor input is often measured as the number of employees, _N it_ , of the producer. This, however, would entail ignoring the possible heterogeneity of the workforce in terms of both of skills and effort. One way of avoiding this is to take into account differences in labor productivity between producers by using the average plant-level wage rate, _wit_ , as a conversion or weight factor to define labor input as the wage bill at constant prices, that is, to set _Lit_ = _wit N it_ .

# **III. Econometric issues**

Let us assume, for a moment, that the population of manufacturing establishments in the 40 cities and the 11 industries from which our sample is drawn is all we care about when we model the relationship between establishment level productivity, ln _A_<sup>ˆ</sup> _ijtR_ , on the one hand, and business environment, _xijt_ , and economic geography, _g ijt_ , on the other,

> 20 The countries are Bangladesh, Brazil, China, Honduras, India, Morocco, Nicaragua, Pakistan, and Peru. The only criteria for the selection of countries was the availability of data on the industries of interest. The ideas was to form as large a sample size as possible on which to base the estimation of each industry’s average production function.

> 21 See Appendix B for a description of  the Levinshon-Petrin estimator.

17
introduction 6 and their magnitude, distribution, and urgency depends on the socioeconomic status of affected households.

Asset losses measure a dimension of disaster costs that accrues mostly to the wealthy. By definition, wealthy individuals have the most assets to lose. Therefore, asset losses often define–and accurately summarize–their experience of a shock. Conversely, the poor definitionally have few assets to lose, so damage to their collective property is a small fraction of aggregate asset losses. However, the poor also lack the resources and instruments to smooth income shocks while maintaining their consumption, or to recover their asset stock. Therefore, their experience of shocks is better described by other loss metrics. After most shocks, the poor are more likely than the wealthy to forego consumption of food, health, or education, and to take longer to recover.

Disaster risk management strategies that seek to maximize impact in terms of avoided asset losses will almost inevitably favor central business districts and other areas with high concentrations of valuable assets, leaving the poor to resort to humanitarian assistance when disasters inevitably occur. By highlighting these areas, asset losses disincentivize investments in slums and informal settlements, even when small investments could significantly reduce disease transmission, absenteeism from work and school, transportation costs, lost wages, or other types of disaster costs.

Finally, asset losses obscure the relationship between disasters and development in Sri Lanka. The long-term impacts of disasters on communities’ wellbeing and prospects depends not only on direct or immediate impacts (asset losses), but also on the duration of the recovery and reconstruction period and on the tools affected populations have to cope with and recover from the shock, such as social transfers, formal and informal post-disaster support, savings, insurance, and access to credit. Households that lack access to these tools will struggle to cope with shocks, and some could fall into chronic poverty as a result.

To provide a more comprehensive measure of disaster impacts, the _U_ nbreakable report introduced the concept of wellbeing losses. Wellbeing losses incorporate people’s socio-economic resilience, including (1) their ability to maintain their consumption for the duration of their recovery, (2) their ability to save or borrow to rebuild their asset
<!-- Start of picture text --> weae TAN “<br>= ae 4<br>= ao pe bee ee 5<br>a st —s an 7 a .<br>ny iu oa aa .<br>1940 1970 1980 1400<br>BirthYear Cohort<br>————- ReceivedIDs Before 1988 ——_ ReceivedIDs in 1988<br>vooocce Received[Ds After 1988<br><!-- End of picture text -->