rafmacalaba/datause-encoder-data huggingface dataset:
- <u>Figure 5 Cross Sectoral Flows (U O) (Trends and Cycle): Mexico an Brazil</u>

<!-- Start of picture text --> Mexico Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>O-U U-O O-U U-O<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>O-U (HP) U-O (HP) O-U (HP) U-O (HP)<br>.1 .9 .05 .5<br>.08 .8 .04<br>.45<br>O-U U-O O-U U-O<br>.06 .7 .03<br>.4<br>.04 .6<br>.02<br>.4<br>.2<br>.1<br>.2<br>0<br>0 -.1<br>-.2<br>-.2<br><!-- End of picture text --> Notes: The graph shows the transition rates between inactivity (O=Out of the Labor Force) and unemployment (U=Unemployed). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

39
Table 5: Selection into GVC participation

||(1)|(2)<br>Demeaned<br>(Mundlak- Chamberlain|(3)|(4)|(5)<br>Demeaned<br>(Mundlak-<br>Chamberlain|(6)|
|---|---|---|---|---|---|---|
|VARIABLES||correcton)|Diferenced||correcton)|Diferenced|
|TFP|0.2030***|0.0774***|0.0128|0.1503***|0.0065|0.0139|
||(0.0240)|(0.0228)|(0.0173)|(0.0243)|(0.0209)|(0.0172)|
|Size|0.5443***|0.4659***|0.1167***|0.6176***|0.4650***|0.1201***|
||(0.0219)|(0.0284)|(0.0291)|(0.0227)|(0.0294)|(0.0292)|
|Wage Per Value Added|0.1591***|0.1224***|0.2068***|0.0282|0.1111**|0.2991***|
||(0.0435)|(0.0470)|(0.0693)|(0.0451)|(0.0484)|(0.0693)|
|Financial Leverage|0.0093|0.0345*|0.0744***|0.0067|0.0663***|0.0739***|
||(0.0191)|(0.0199)|(0.0216)|(0.0192)|(0.0233)|(0.0216)|
|Age|0.0027***|0.0713***|0.0135***|0.0031***|0.0745***|0.0135***|
||(0.0009)|(0.0045)|(0.0009)|(0.0009)|(0.0046)|(0.0009)|
|Service Input Intensity|0.6054***|0.1243***|0.0786***|0.0942***|0.1169***|0.0806***|
||(0.0229)|(0.0215)|(0.0183)|(0.0230)|(0.0231)|(0.0186)|
|Complex Service Intensity||||0.0776***|0.0810***|0.0015|
|||||(0.0178)|(0.0166)|(0.0137)|
|IT Service Intensity||||0.2238***|0.0480***|0.0172**|
|||||(0.0138)|(0.0126)|(0.009)|
|Observatons|106,102|106,102|84,135|106,012|106,012|84,053|
|R-squared|0.1323|0.1407|0.0363|0.1368|0.1458|0.0364|
|Number of company_code|16,417|16,417|13,695|16,114|16,114|13,952|

Note: The dependent variable is the depth of GVC integration. All variables are lagged by one year. The size of a firm is measured by log annual sales. Complex services intensity is measured as log share of expenditures of complex services on total service expenditures. Industry and year fixed effects are included in all specifications. The sample includes all manufacturing and service firms and spans the years from 1995 to 2016. In column (2) all variables are demeaned at firm level to take out firm heterogeneity, column (3) accounts for firm heterogeneity by differencing the variables. Standard errors clustered at firm level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

16
formance brought about by increases in secondary school quality. One might reasonably expect the effect of attending _any_ secondary school to differ from the effect of increased school quality. The rise in primary school completion associated with the achievement of the Millennium Development Goals means that a large cohort is about to reach the age of secondary schooling, which until now has been rationed in much of Sub-Saharan Africa. Despite the policy urgency of this issue, few studies to date have identified the effect of relaxing this constraint: the impact of secondary schooling on the marginal student in low-income country contexts.<sup>3</sup> A rare exception, Filmer and Schady (2014), found that scholarship-driven attendance of lower secondary school in Cambodia had no effect on either test scores or fertility choices.

In this paper, I use a regression discontinuity approach to estimate the impacts of secondary schooling in Kenya. The discontinuity I use is based on a standardized 8th grade test, the Kenya Certificate of Primary Education (KCPE). Probability of admission to government secondary school rises sharply at a cutoff score close to the national mean on the examination. I collect an administrative KCPE dataset, and combine it with a recent, detailed survey of young adults in Kenya that includes educational attainment, along with a number of other outcomes. With these two datasets, I use a technique from time series econometrics to identify the structural breaks in patterns of secondary school completion, thereby locating the test score cutoffs in Kenya’s secondary school admission policy. I am able to confirm that the

> 3Lucas and Mbiti (2012) find that the increased school participation in Kenya brought about by the abolition of _primary_ school fees actually reduces average test scores, with composition effects explaining less than half the decline. This, however, is a very different population from those who are on the margin of attending secondary school.

3
counterfactual reduction in poverty is again larger than the actually observed although the difference is less than we found for counterfactual (1) (comparing Figures 7 and 8).

Finally, Figure 9 shows the comparison of the actual and the counterfactual poverty rate under the third scenario of common sectoral growth across all the provinces. In this case, and opposite to the finding of the previous scenarios, actual and counterfactual poverty are quite close over the sample period.

Thus it is clear from Figures 7-9 that it is the sectoral unevenness in the growth process, rather than its geographic unevenness, that led to lower poverty reduction. Without the sectoral unevenness in growth rates (but maintaining the geographic structure) the poverty rate would have been less than half its actual value by the end of the period.

We checked the effect of producing the counterfactuals using the fixed effects estimator instead of the first differences estimator. The process is the same as described above, but the estimated parameters correspond to this alternative estimation method. Notice that the estimation does not have to deliver the same results since the estimators are different and the sample is smaller than before (the calculation of first differences eliminates many observations for the need to find consecutive non missing data). The results in Figures 7-9 were very similar using this estimation method.

# **6. Conclusions**

A long-standing development policy debate has concerned the priority to be given to agriculture versus industrialization or an expanding services sector as the routes out of poverty. We have studied the experience of the country that has almost certainly had the greatest success in reducing poverty in modern times, China. A newly constructed subnational panel data set offers a powerful lens on the role played by the geographic and sectoral pattern of growth in China’s progress against poverty.

We find that the primary sector was the real driving force in China’s remarkable success against absolute poverty, rather than the secondary (manufacturing) or tertiary (services) sectors, and that the unevenness of the growth process across sectors greatly attenuated the overall pace of poverty reduction. Yes, China has had great success in reducing poverty through economic growth, but this happened despite the unevenness in its sectoral pattern of growth. The idea of a trade-off between these sectors in terms of overall progress against poverty in China turns out to be a moot point, given how little evidence we find of any poverty impact of non-primary sector growth, controlling for primary-sector growth. We 16
# **1 Introduction**

Labor-intensive public works programs (PWPs) are common and costly social protection tools in low-income settings (Grosh et al. 2008). These programs require beneficiaries to work in order to receive a cash payment or in-kind transfer. They have been widely used to protect poor households in the face of large macro-economic or agro-climatic shocks, due to their relatively rapid rollout (Ravallion 1999). In addition, PWPs are widely used as a building block of national social protection portfolios. Well-known examples include the Employment Guarantee Scheme in Maharashtra (Ravallion, Datt & Chaudhuri 1993) and the National Rural Employment Guarantee Act (NREGA) in India (Dutta et al. 2014), and the Productive Safety Net Project in Ethiopia (Hoddinott et al. 2012). Such programs are widespread – albeit not on as large a scale – in sub-Saharan Africa, where 39 of 48 subSaharan African countries have government-supported PWPs (World Bank 2015).<sup>1</sup> Much of the literature on cash-for-work programs focuses on estimating the potential crowding out effect of the program on labor market outcomes or the extent of self-targeting for a given wage rate or participation requirement (Alatas et al. 2013). Despite the pervasiveness of cash-for-work programs and their considerable costs, there is limited evidence about their effectiveness at increasing or smoothing consumption for beneficiaries. This paper estimates the effect of Malawi’s large-scale public works program, which operates under the Malawi Social Action Fund (MASAF). The stated objectives of the program are to improve lean season food security and increase use of fertilizer and other agricultural inputs. Though the PWP relaxed budget constraints by offering beneficiaries the opportunity to earn up to $US 44 in a country with a per capita GNI of only $US 320, we find no indication that the program achieved either objective.

Malawi’s PWP has been operational since the mid-1990s and provides short-term, laborintensive employment opportunities to poor, able-bodied households for the purpose of enhancing their food security. The implementation of the program is decentralized, with funding allocated to each of Malawi’s 31 districts based on population and poverty estimates. The food security objective is addressed through a combination of support for short-term consumption as well as investments in fertilizer, which is intended to increase yield in the subsequent season. Since 2004, the program has been designed to complement Malawi’s large-scale fertilizer input subsidy program by synchronizing the availability of public works employment with the availability of fertilizer coupons, at planting season. Malawi’s PWP ranks fourth in population covered among all such programs in low- and middle-income

> 1Of the 19 sub-Saharan African countries for which the World Bank’s ASPIRE database has cost information, spending on PWPs totals more than one-tenth of a percent of GDP in nine, and accounts for more than 10 percent of the country’s social protection budget in eight.

2
# **Table A7. Correlates of fertilizer use**

# **<u>Used any inorganic fertilizer</u>**

||(1)<br>Ethiopia|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Malawi|(5)<br>Nigeria|(6)<br>Nigeria|(7)<br>Burkina<br>Faso|(8)<br>Uganda|
|---|---|---|---|---|---|---|---|---|
|Wealth quintile|0.0204*|0.0481***|0.0600***|0.0619***|-0.0250***|-0.0252***|0.0728***<br>|0.0138**<br>|
||(0.0120)|(0.0142)|(0.00808)|(0.00794)|(0.00904)|(0.00899)|(0.0101)|(0.00551)|
|Distance to nearest market|-0.00141***||-0.000967||-6.55e-05||||
||(0.000395)||(0.000975)||(0.000288)||||
|Distance to nearest road|0.000820||0.000785||-0.00346**||||
||(0.00102)||(0.00137)||(0.00148)||||
|Distance to nearest|||||||||
|population center|-0.00232***||-0.000171||-0.000992||||
||(0.000755)||(0.00115)||(0.000678)||||
|Constant|0.754***|0.531***|0.613***|0.586***|0.547***|0.508***|0.349***|0.00946|
||(0.0463)|(0.0480)|(0.0397)|(0.0332)|(0.0394)|(0.0335)|(0.0361)|(0.0154)|
|Observations|2,786|2,786|4,507|4,511|3,064|3,064|3,757|4,027|
|R-squared|0.105|0.017|0.081|0.080|0.069|0.062|0.042|0.019|
|Survey Wave FE|YES|YES|YES|YES|YES|YES|YES|YES|

Note: Results from OLS regression of extensive margin fertilizer use on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

28
Figure B.4: Missing payment identifiers <!-- Start of picture text --> CE MG<br>8<br>0.04<br>6 0.02<br>4 0.00<br>0.02<br>2<br>0.04<br>0<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>0.25<br>0.04<br>0.20<br>0.02<br>0.15<br>0.00<br>0.10<br>0.02<br>0.05<br>0.04<br>0.00<br>2009 2011 2013 2015 2017 2019 2014 2016 2018 2020 2022<br>RS SP<br>10 50<br>8<br>40<br>6<br>30<br>4<br>20<br>2<br>0 10<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Null Payments (%) Null Total Paid (%)<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of observations by year for which we were unable to build a unique identifier (id_pagamento_bd) in the _payment_ table.

51
## **III Using Aggregate Balance Sheet Data to Assess the Insulating Potential of the Banking System**

If a banking system acts to stabilize or destabilize macroeconomic aggregates, this should become evident in the way in which the size and composition of its balance sheet evolves in response to shocks. In this section we look at the short-term dynamics of banking-system balance sheets as they change from quarter-to-quarter. In contrast to previous work examining the probability of crises -- relatively rare events occurring **nprhanoe nnp nr turiwn** in a nmlqrtPr-. **nt-lirv** -- ofur ga!n is **_tn_** examine the ability nf hanking systLewLs to iis-ulalte .iU-L1qur.LLy **ULUU** baUnce.

T lhis goal requires linking two distinct sources of data, namely quarterly baiance sheet aggregates and information on structural characteristics of the banking systems. For the latter, we use the database of Barth, Caprio and Levine (2001a), which defines the outer margins of our sample of countries. For the former, we turn to _International Financial Statistics (IFS)._

# **_IIII. Simplired Banking Svstem Balance Sheets_**

**TI.xrn upnonn** what is in **p%r.inrpleP** a icnmmnn e-t **nf** defintions IS c-o.ntsine monetary survey data on well over a h-ulndred wuliulueL, **Una4UUU1 Vhl.UUtL t11** of **Ut** countries for which we have banking quality data. Our focus is on the component data for deposit-money banks, not including the monetary authorities. But the balance sheet classifications of the monetary survey are too numerous to allow for a cross-country study without considerable consolidation and rationalization. A total of 44 distinct balance sheet category codes are included in the monetary survey, though any given country only has entries against a subset of these, typically fewer than twenty. Even 20
are important, some results are sensitive to specific countries. Specifically, no P individual country is highly influential for 0 or 0 except for Algeria FFLLNNAAFF GG (discussed further below). 1 is fairly robust to the exclusions of individual ββ ββ countries, though estimates ofFFLLNNAAFF1P are sensitive to Qatar: excluding this country ββ makes 1P negative, although still insignificant.GG 56 ββ GG Given that Appendix Figure 7 identifies Algeria and Qatar as potentially ββ influential, in Panel F of Table 4 we re-estimate excluding these two countries.<sup>57</sup> The coefficients 0 and 1 are mostly unchanged (including 1 > 0, as in in the model’s second prediction). But the standard deviation of FFLLNNAAFF FFLLNNAAFF FFLLNNAAFF0P and ββ ββ ββ 1P are much higher relative to our main results. Even though the point estimates GG ββ still suggest GG 0 0P and 1 1P , we now cannot reject 0P = ββ PFFLLNNAAFF GG FFLLNNAAFF GG GG 0 or 1 1 at conventional levels. ββ > ββ ββ > ββ ββ FFLLNNAAFF GG FFLLNNAAFF A final concern is on the quality of the commodity revenue data. Appendix ββ ββ = ββ Figure 9 shows that there is some disagreement with respect to the size of resource revenues for some countries across the GRD and WCE datasets. This disagreement raises the concern that our main results may be sensitive to measurement error. To test that possibility, panel G reports the estimates dropping all observations if the reported change in resource revenue from GRD differs from the WCE by more than 3% of MAGDP (in absolute value). The results are very similar to those in panel B, except that 1P is higher relative to benchmark – although still insignificant – GG P and we fail to reject 1 1 . ββ GG FFLLNNAAFF Our final robustness test is of the 2SLS specification itself. As the coefficient ββ = ββ on the first-stage regression is close to one, an alternative specification is a reduced-

> 56 Although Botswana, Kuwait and Trinidad and Tobago are influential for 1 , the coefficient remains positive and statistically significant at 5% or 10% when dropping either of these countries. FFLLNNAAFF Dropping Botswana from the sample increases significantly the standard deviation of ββ 1 . The precision of the estimation falls because Botswana is a large diamond exporter with the lowest FFLLNNAAFF ββ persistence in the sample. By dropping that country, the variance of the half-life across countries with floating ERs reduces significantly, increasing the standard deviation of 1 . 57 Recall there is some disagreement in the literature regarding the type of ER regime in Algeria. FFLLNNAAFF ββ̂ We depart from Ilzetzki et al. (2019) and classify Algeria as having a fixed ER in 2000–2015. This adjustment is based on Achouche and Allaoua (2018) who identify a “fear of floating” in Algeria during this period.

44
<!-- Start of picture text --> 1 Response to a U.S. shock 0.03 Response to a U.K. shock<br>0.9<br>0.8 0.025<br>0.7<br>0.02<br>0.6<br>0.5 0.015<br>0.4<br>a4 0.01<br>0.2 0.005<br>0.10 (] EL b ] 0 l |<br>ARG BRA -DEU- = MEX._—s ZAF-_—s—s«STUR-—sSCGBR ARG BRA DEU MEX ZAF TUR USA<br>0.25 Response to a German shock noe Response to a Turkish shock<br>0.2 0.02<br>0.15 0.015<br>0.1 0.01<br>0.050 | | 0.0050 L |<br>ARG BRA MEX ZAF TUR GBR USA ARG BRA DEU MEX ZAF GBR USA<br>0.16 Response to a Mexican shock 0.3 Response to a Brazilian shock<br>0.14<br>0.25<br>0.12<br>0.2<br>0.1<br>0.08 0.15<br>0.06<br>0.1<br>0.04<br>0.05<br>0.02<br>0 | a a 0 oa hoeee kL<br>ARG BRA DEU ZAF TUR GBR USA ARG DEU MEX ZAF TUR GBR USA<br><!-- End of picture text -->
at the local level, and improving the regulatory framework for medicines for human use.

Boxes 1, 2 and 3 below summarize the findings and recommendations for each of the three dimensions of implementation efficiency:

# **Box 1: Findings for the Programmatic Dimension of Implementation Efficiency**

|**STRENGTHS**|**CHALLENGES**|**RECOMMENDATIONS**|
|---|---|---|
||Programmatic Dimension||
|Colombia has increased the<br>understanding of the HIV/AIDS<br>epidemic and its impact on the<br>most vulnerable groups.|Studies on the HIV epidemic have<br>been produced only at the national<br>level—not at the departmental or<br>district level.|Strengthen the programmatic and<br>coordinating leadership in HIV/AIDS<br>at the national and local (DTS)<br>levels, trough:|
|The existing legal framework is<br>sufficiently developed so as to<br>sustain a sound and stable<br>programmatic response.<br>The HIV/AIDS response is supported<br>at the national level by a number of<br>key policies, including the PNSP and<br>the PEN.|The HIV/AIDS response strategic<br>decision-making is not always<br>supported by evidence.<br>The PEN and the coordinating<br>bodies have had limited<br>effectiveness in designing a<br>centralized HIV/AIDS response_in_<br>_practice_.<br>There has been a lack of leadership<br>at the local level in regard to making<br>use of the entire institutional<br>arsenal (Territorial Public Health<br>Plans, Departmental HIV/AIDS<br>Committees, etc.)|Align the programmatic<br>objectives, resources, services<br>and actions that form the<br>response to the HIV/AIDS<br>epidemic with the SRH policy.<br>Ensure a sense of ownership of<br>and commitment to the PEN<br>among all stakeholders, by<br>facilitating an open, inclusive and<br>transparent process of<br>formulation and implementation.<br>Grant the new PEN a binding<br>status for all DTSs, EPSs and IPSs,<br>by detailing responsibilities,<br>goals, information requirements<br>and potential sanctions.<br>Strengthen the programmatic<br>leadership of the local entities by<br>enhancing their monitoring and<br>inspection capacity over EPSs and<br>IPSs. Support local entities in<br>improving their knowledge of the<br>HIV/AIDS epidemic and its impact<br>on the people within their<br>territories, in order for this<br>evidence to inform intervention<br>plans and strategies.|

8
WPS 3246

**The Quality of the Legal System, Firm Ownership, and Firm Size**

Luc Laeven and Christopher Woodruff<sup>*</sup> World Bank Policy Research Working Paper 3246, March 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Laeven is at the World Bank and CEPR, and Woodruff is at the Graduate School of International Relations and Pacific Studies, UCSD. The authors thank Franklin Allen, Thorsten Beck, Luis Cabral, Simon Johnson, Ross Levine, Inessa Love, John McMillan, Bernard Yeung and seminar participants at the AEA 2004 annual meetings, Carnegie Mellon University, New York University, Pittsburgh University, Centro de Investigación y Docencia Económicas (CIDE), and the University of California at San Diego for helpful comments, Gerardo Leyva and Benito Arciniega for providing the data from the Mexican economic census, and Guillermo Noguera and Augusto Nieto for research assistance.