rafmacalaba/datause-encoder-data huggingface dataset:
On the basis of the findings in this paper, it would appear that continued policy vigilance is required, in order to allow the Syrian economy to successfully transition toward an even more diversified economy with a strong industrial base. Such policies go beyond trade liberalization. Horizontal measures strengthening both institutions and the business environment—across all sectors—will be essential. In practice, this includes measures that would shrink the size of Syria’s large shadow economy, provide finance to small and medium enterprises, reduce barriers to entry for small firms, eliminating privileges for large and long-standing private sector firms, as well as privatizing some of their state-controlled counterparts. We regard this as a first-best policy, since such reforms would raise total factor productivity and, in turn, raise the embedded productivity of its (currently low value) export basket.

Activist industrial policy, on the other hand, would likely not be the appropriate choice at this stage. First, vertical policy intervention may simply be infeasible and potentially counterproductive in the presence of weak institutions and a business environment severely weakened by corruption and nepotism. Second, while the recognition and support for certain broad _classes_ of exports—such as manufactures with high values of equation (6)—may lead to improved export and output performance (Hausmann _et al._ 2007), a focus on one or two narrowly-defined _products_ can easily lead to resource misallocations and export disappointments (Easterly, Reshef & Schwenkenberg 2009). Finally, activist industrial policy may send the wrong signal in a country with a traditionally large state sector and a command economy.

# **References**

Amurgo-Pacheco, Alberto & Martha Denisse Pierola (2008). “Patterns of Export Diversification in Developing Countries: Intensive and Extensive Margins”. _Policy Research Working Paper_ 4473, Washington, DC: The World Bank

- Atiya, Basima (2008). “Comparative Advantages of Selected Commodities”. _Technical report_ , Damascus, Syria: National Agricultural Policy Center

- Balassa, Bela (1965). “Trade Liberalisation and ‘Revealed’ Comparative Advantage”. _The Manchester School_ 33(2) (May): 99–123

- Barseghyan, Levon (2008). “Entry Costs and Cross-Country Differences in Productivity and Output”. _Journal of Economic Growth_ 13(2) (June): 145–167

> Bernatonyte, Dalia (2011). “Export Productivity and Specialization Patterns of Lithuania”. _Ekonomika ir Vadyba_ 16(16): 109–115

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**Table 3. COVID-19 impacts on participations in the labor force among the population**

||Employed|Unemployed|Discouraged|Preparing for<br>business or<br>have future jobs<br>arranged|Full-time<br>school|Outside of LF<br>(Otherwise)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Female**|||||||
|COVID|0.013***|0.002|0.003***|0.001***|-0.011***|-0.009**|
||(0.004)|(0.001)|(0.000)|(0.000)|(0.001)|(0.003)|
||[0.005]|[0.123]|[0.000]|[0.000]|[0.000]|[0.015]|
|Pre-pandemic average|0.541|0.020|0.003|0.002|0.093|0.341|
|**Panel B: Male**|||||||
|COVID|-0.024***|0.009***|0.003***|0.001***|-0.012***|0.023***|
||(0.004)|(0.001)|(0.000)|(0.000)|(0.002)|(0.003)|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Pre-pandemic average|0.810|0.031|0.005|0.002|0.093|0.058|
|Test:βm= βf(p-value)|0.000|0.000|0. 799|0.825|0.238|0.000|

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Parentheses indicate standard errors, while square brackets indicate the False Discovery Rates (FDR). Data are taken from Sakernas 2016–2020 (both February and August rounds). The main variable of interest in the above results is the COVID dummy, which takes on the value of 1 if the time period is August 2020. Pre-pandemic averages are population averages of the employment outcomes throughout 2016–2019 (both February and August rounds). Regressions are of the 15–64-year-old population, consisting of 1,614,217 male and 1,640,149 female individuals. Control variables include age, squared age term, marital status, household size, education level dummies, training experience dummy, and urban/rural dummy. All specifications include year, province and month fixed effects.

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# **Figures**

Figure 1: SNAP benefits per population by state, 2006-2015 <!-- Start of picture text --> Farm ARRA<br>Bill ARRA Ends<br>2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Year-month<br>Minimum 1st quartile<br>Median 3rd quartile<br>Maximum<br>40<br>30<br>20<br>Benefits per population 10<br>0<br><!-- End of picture text --> Notes: This figure plots out the SNAP benefits per population for five states from 2006 to 2015, ranking states by their average SNAP benefits per population from 2006 to 2015 and picking the states at the minimum, 1st quartile, median, 3rd quartile, and maximum.

Figure 2: SNAP benefits per recipient and recipients per population by state, 2006-2015 <!-- Start of picture text --> (a) Benefits per recipient (b) Recipients per population<br>Farm ARRA Farm ARRA<br>Bill ARRA Ends Bill ARRA Ends<br>2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Year-month Year-month<br>Minimum 1st quartile Minimum 1st quartile<br>Median 3rd quartile Median 3rd quartile<br>Maximum Maximum<br>180 0.25<br>160<br>0.20<br>140<br>0.15<br>120<br>Benefits per recipient 0.10<br>100 Recipients per population<br>80 0.05<br><!-- End of picture text --> Notes: This figure plots out the SNAP benefits per recipient and recipients per population, each for five states from 2006 to 2015, ranking states by their average SNAP benefits per recipient or recipients per population from 2006 to 2015 and picking the states at the minimum, 1st quartile, median, 3rd quartile, and maximum.

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<!-- Start of picture text --> Exchange rate Output growth<br>34 10<br>33<br>8<br>32<br>6<br>31<br>30 4<br>29<br>2<br>28<br>0<br>27<br>26 -2<br>2001 2002 2003 2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 2006 2007 2008<br>Inflation Interest rate<br>14 10<br>12<br>8<br>10<br>6<br>8<br>4<br>6<br>2<br>4<br>2 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

# **3.2 Unit root tests**

The assumption of stationarity must be tested in the data. Traditional tests, however, based on Dickey-Fuller and Phillips-Perron, are criticized for suffering from lower power and size distortions. As stressed by Ng and Perron (2001), statistical power is lower for highly persistent time series, while size problems are determined by the presence of a strong negative MA component in the series representation. Improvements in the test procedure have been proposed by Perron and Ng (1996), Elliott, Rothenberg and Stock (1996), and Ng and Perron (2001). Essentially, the modifications evolve the combined use of GLS detrended data and the modified Akaike information criterion to choose the optimal truncation lag for the augmented term of the test equation. Asymptotic critical values for both tests, labeled MADF<sup>GLS</sup> and MPP<sup>GLS</sup> , are reported in Ng and Perron (2001).

The presence of structural breaks in the time series might severely bias the previous tests. To account for such breaks, it is applied tests proposed by Perron (1997) and Lee and Strazicich (2003), which endogenously select the time of the breaks. Perron (1997) proposes a test that
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# **VIII. Lessons for GCC Countries**

This paper assesses the impact of foreign labor management policies on labor outcomes in Singapore and Malaysia, two countries characterized by robust economic growth and significant foreign labor. Are there lessons applicable to GCC countries, where foreign labor force shares ranging from 50 to 90 percent are much higher than in Singapore and Malaysia? Several conclusions emerge from this analysis. The first involves the fact that managing foreign labor not only requires immigration controls but also a conducive macroeconomic environment and mutually reinforcing labor market institutions regarding wage and employment policy, as well as a capacity to enforce immigration rules. As is demonstrated by Kuwait's recent experience, it is not enough to establish a stable macroeconomic climate with favorable investment incentives (necessary but not sufficient conditions).

Singapore and Malaysia benefit from more diversified economic structures relative to the oildependent GCC economies. Moreover, the,East Asian economies' private sectors have been integral to overall economic growth, especially with respect to manufactured exports that are competitive on international markets. Without comprehensive policies that address these related issues, measures to control foreign labor flows are likely to be ineffective. Policy changes must be considered within the context of the theory of second best; i.e. correcting one distortion will not necessarily lead to a welfare improvement if other distortions remain. In the Bahrain context of distorted public employment and pay policies, for example, it may take a large increase in the price of work permits to induce a significant shift in private employment toward Bahraini nationals. The solution to stem the influx of foreigners into GCC countries is complex; firm- or micro-level data on employment and wages would be useful in estimating labor demand elasticity in order to quantify the likely employment response to various policy measures.

It is notable that both Singapore and Malaysia face difficulties in setting the price and quantity of work permits in their highly regulated immigration systems where permit types vary according to nationality, skill level, and sector of employment, enforced with the help of high quality administrative capacity but also rather draconian measures that sometimes violate individuals' rights. Despite labor legislation promoting equitable conditions of employment, earnings evidence from Malaysia indicates that ethnic discrimination among citizens persists. Moreover, the proliferation of large informal sectors consisting primarily of foreign workers suggests imperfect enforcement. In view of these challenges, it may be more efficient to rely on market signals to address labor misallocations, for example through tradable work permits.
# **1. Introduction**

Millions of small businesses are started every year in developing countries. However, more than a quarter of these die within their first year (McKenzie and Paffhausen 2017), while only a small subset of firms grows rapidly, creating disproportionate value in terms of employment and incomes (Olafsen and Cook 2016). The ability to identify ex ante which firms will succeed is of key interest to investors seeking to maximize returns, determining the extent to which capital can be allocated to the highest return projects. Being able to identify these high growth potential firms is also important for governments seeking to target programs to these firms (OECD 2010), and is of interest to researchers seeking to characterize what makes a successful entrepreneur. Moreover, if the characteristics that are predictive of high growth are malleable, this can also spur policy efforts to attempt to change these in individuals without these attributes.

Business plan competitions are increasingly used in both developed and developing countries to attempt to spur high growth entrepreneurship. These competitions typically attract contestants with growth prospects that are much higher than the average firm in the economy. But then the key question is whether one can predict which firms from among these applicants have better growth prospects. We use data from applicants to Nigeria’s YouWiN! program, the world’s largest business plan competition (Kopf 2015), to answer this question. We compare the relative performance of three different methods for identifying which firms will be most successful: the standard approach of relying on expert judges, simple ad hoc prediction models used by researchers that employ a small number of variables collected in a baseline survey, and modern machine learning (ML) methods that consider 393 possible predictors along with potential interactions among these.

Business plan competitions typically rely on expert judges to score proposals, with higher scores given to those businesses judges view as having higher likelihoods of success. This approach has the advantage of using context-specific knowledge and enabling holistic judgement of business plans. However, using judges to score proposals can be costly and time-consuming, and there are also concerns that human evaluations can sometimes lead to discriminatory decisions (Blanchflower et al. 2003), or be driven by overconfidence (Zacharakis and Shepherd 2001). One alternative may then be to attempt to predict which businesses will succeed based on simple regression models that include survey variables like gender, age, and ability suggested by theory and researcher judgement (Fafchamps and Woodruff 2017; McKenzie 2015). However, the selection of which variables to include in these models is ad hoc and, since it reflects researcher opinion, may omit important determinants of future success.

Machine learning techniques offer an alternative approach that is less reliant on human judgement, and are designed specifically for out-of-sample prediction (Kleinberg et al. 2015). They offer the potential for better 2
provide the first meta-analysis of financial incentive impacts on RMCH service coverage across PBF, voucher, and CCT programs, and estimate mean effect sizes for each intervention type separately. Moreover, while subgroup comparisons remain challenging given statistical power limitations, we also undertake a first quantitative analysis of possible sources of effect size heterogeneity, investigating, for instance, the role of baseline service coverage levels as well as whether programs that combine supply- with demand-side incentives are associated with larger effects on RMCH service coverage.

# **Methods**

# **_Search strategy and selection criteria_**

We conduct a systematic review and meta-analysis of the impacts of health financing interventions on RMCH service utilization in low- and middle-income countries (LMICs). A study protocol was published on the PROSPERO website in November 2016 <u>(https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=49272).</u> We use the following criteria to identify relevant studies. In terms of _publication format_ , we include studies in English that were published either in peer-reviewed scientific journals, as part of a working papers series, in books (with ISBN numbers), as doctoral dissertations, or official research or project reports. This excludes unofficial draft reports, conference abstracts, comments, op-eds, media briefings, and bachelor’s or master’s theses.

Regarding _interventions_ , we include those taking place in countries classified as low- or middleincome by the World Bank in at least one year in the 1987-2019 period and falling into one of three categories of financial incentivization: PBF of providers of primary or secondary care that reward RMCH service provision, vouchers which give beneficiaries free or subsidized access to RMCH services for which providers are reimbursed on a fee-for-service basis, and CCTs designed to financially reward enrollees for compliance with RMCH service use conditions. Because they are based on a different theory of change, this review will not consider interventions which affect the monetary price of providing or using MCH services only indirectly or implicitly. On the supply-side, omitted interventions include health worker training, provider performance tournaments, and the introduction of mobile health units or health worker home visits. On the demand side, we exclude interventions like information campaigns, 2
**Decoupling and recoupling**

# **Emerging economies in the 2000s: Real decoupling and financial recoupling**

Eduardo Levy Yeyati<sup>1</sup> Universidad Torcuato Di Tella Tomas Williams Barcelona GSE

**Abstract**

The paper documents an intriguing development in the emerging world in the 2000s: a decoupling from the business cycle of advanced countries, combined with the strengthening of the co-movements in the main emerging market assets that predates the synchronized sell-off during the crisis. In addition, the paper tests the hypothesis that financial globalization, to the extent that it creates a common, global investor base for emerging markets, could lead to a tighter asset correlation despite the weaker economic ties. While an examination of the impact of alternative financial globalization proxies does not yield conclusive results, a closer look at global emerging market equity and bond funds shows that the latter indeed foster financial recoupling during downturns, reflecting the fact that they trade near their respective benchmarks and respond to withdrawals by liquidating holdings across the board.

> 1 We would like to thank Mariana Barrera for outstanding research assistance, and seminar participants at the World Bank, Brookings Institution, the Geneva Graduate Institute and Universidad de San Andres for their useful comments. Tatiana Didier kindly provided the data used in some of the tests in the paper. We would also like to thank The World Bank ́s CEO for financial support for this research agenda, as part of the 2011 flagship report on Latin American financial markets. The usual disclaimers apply.

<u><mark>1</mark></u>
ters, as before. However, we can also see that the Beta distribution approach overestimates systematically (except for capitalists and rentiers) changes in the FGT indicators. Figure 5 shows again the relative differences with respect to the micro-accounting framework combined with reweighting techniques. For workers in the rural sector, the discrepancy fluctuates and is between plus-minus 50 percent, but lower in the beginning and at the end of the adjustment period. For workers in the urban informal sector, the indicated change for P1 lies systematically between 25 to 30 percent above, whereas for unskilled workers the difference is between 100 to 250 percent for P0 and nearly 100 percent for P1 above. For skilled workers in the urban sector, the deviations are equivalent to up to 6 times the change indicated by the micro-accounting plus reweighting method, but they are small for P0 in the beginning, in the middle and at the end of the adjustment period.

In addition, the Beta distribution approach appears to induce a bias concerning poverty differentials between groups. Skilled workers seem to have almost the same headcount ratio and poverty gap as unskilled workers, which is not the case if we use the micro-accounting approach. For stronger or other shocks, the Beta distribution approach may even induce a different ranking of household groups.

With the Beta distribution approach, as noted earlier, we can only measure changes in between-group inequality. Between-group inequality amounts in our artificial survey to approximately 25 percent of total inequality, but given that we expand incomes for each household with group-specific growth rates, total within-group inequality changes only slightly and insofar as we change the weights given to each within-group distribution. The change computed by the Beta distribution method is slightly higher than that computed by the micro-accounting method, but both are consistent when it comes to the direction of the change.

# 5.2 Increase in Employment Subsidies

The simulation results associated with a permanent, doubling of the nominal employment subsidy on unskilled labor (that is, an increase in the subsidy rate from 5 to 10 percentage points of the nominal minimum wage) are illustrated in Tables 3 and 4 for the first 10 periods after the shock, as before. This subsidy is paid on a per worker basis. We assume that the government changes nothing else in the tax and transfer system, implying therefore an offsetting adjustment in the investment of private firms, in order to main26
intensity in 2003 (0.16 and 0.20 TSC latrines per capita, or about one per household); including or excluding these outlier districts (the “restricted sample”) has no impact on the results.

Further tests, not reported in the table, confirm these results. First, much policy-making – – in India in education, sanitation, and other sectors is done at the state level. However, heterogeneity in state experiences cannot account for this result: adding state-specific linear time trends does not change the estimate of the sanitation-achievement gradient, although it does reduce precision. For example, with these trends the coefficient predicting recognizing simple numbers changes from 0.754 to 0.893, with a standard error of 0.536 and a two-sided _p_ -value of 0.097.

Second, any convergence or divergence of test scores that is correlated with baseline levels of education or sanitation would limit the credibility of our causal identification. To account for this possibility, we replicate the main specification of column 1 with the addition of interactions of year indictors with baseline sanitation and literacy, both from the 2001 census. If districts that started out disadvantaged on one of these dimensions saw faster (or slower) subsequent growth in test scores, then this would change our results. However, the results are unchanged: compared with the estimated effects of 0.752 for recognizing letters and 0.754 for recognizing numbers, the corresponding effects and (standard errors) are 0.775 (0.339) and 0.781 (0.359) with year indicators interacted with pre-program sanitation, 0.846 (0.343) and 0.830 (0.360) with year indicators interacted with pre-program literacy, and 0.864 (0.344) and 0.848 (0.359) with both sets of controls.

Third, differencing out fixed effects produces consistent estimates because linearity is assumed. When a quadratic polynomial term in TSC intensity is introduced, it is not statistically significant for any of the three dependent variables (the _t_ statistics are 0.13, 0.81, and 1.47). _F_ -statistics jointly testing the addition of a quadratic term and a cubic term are not statistically significant (the test statistics are 0.01, 0.48, and 1.70). Figure 4 plots local polynomial regressions of cognitive achievement on TSC intensity and finds 19
implementing officials are both key elements of project success: both of these facets of governance could suggest a mechanism for a causal link between civil liberties and performance. Second, we show that the data between manifestations of civil strife and project success show a positive effect that is best understood as a mediating mechanism.

# 1) Other literature civil liberties and performnance

Experience in developing counties illuminates two aspects of governance related to civil liberties that affect project performance.

_Beneficiary involvement and participation._ There is increasing consensus among aid agencies that input from and involvement of potential beneficiaries in government investment projects is key to their success (e.g. World Bank 1995). Myriad case studies (e.g., Korten and Siy 1988) illuminate the mechanisms between beneficiary participation and performance. Isham, Narayan, and Pritchett (1995) use data from 121 water projects to show that greater participation by potential beneficiaries directly caused better project performance. It is likely that beneficiary involvement in projects is greater in countries which score higher on a ranking of civil liberties. At least six of the 14 elements of the Freedom House civil rights index, for example, are directly compatible with beneficiary participation in projects: media free of censorship, open public discussion, freedom of assembly and demonstration, free trade unions, peasant organizations, businesses or cooperatives, free professional or other private organizations, and freedom from gross government indifference or corruption.

_Public sector accountability._ A related dimension of the performance of government projects is the degree to which the public sector officials are held accountable for their

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