rafmacalaba/datause-encoder-data huggingface dataset:
implausible jump for 1980. The Cohen and Soto (2002) estimate provides the closest approximation. Additionally, not shown by Figure 2, the average years of schooling estimates here come close to values obtained from census and survey data. For example, Psacharopoulos and Arriagada (1986) report that mean education in the labor force was in the order of 1.7 for 1964. Edwards (2002) reports a value of 4.3 years for 2000. According to the estimate here, average years of schooling was in the order of 1.9 years in 1964 and 4.4 years in 2000.

A closer look at Figure 2 yields two important descriptive outcomes. First, the data suggest that mean education evident in the labor force slightly declined during the early 1990s. This outcome is associated with the disastrous effect of the civil war on the country’s human capital base. Those disadvantaged cohorts from the 1980s entered later into the labor force. Second, there has been substantial increase in the average years of schooling within the economically active population since 1998. This can be attributed to improvements within the education system and increased attention to education after the signing of the 1996 Peace Accords.

Even so, as it can be appreciated from Figure 2, this increased attention to education only has compensated for the loss of educational capital caused by civil strife. Consequently, recent educational progress does not represent a major improvement regarding the long-run growth of the country’s human capital base. In this context, it is worth recalling that educational attainment in Guatemala remains lowest compared to other Latin American countries.

# **4.2 Labor Force**

The measure of labor quantity here is the economically active population. For Guatemala there are several estimates. The National Statistic Institute (INE) provides calculations different from those of the Ministry of Work, both of which date back to 1980. Based on census and survey data, estimates for selected years have also been provided by the United Nations Development Programme (UNDP) for Guatemala. The labor force is usually defined as the working and job-seeking population, but the different calculations do not always reveal what underlies the specific assumptions and age definitions used for calculations. To develop a consistent time series of the economically active population, the International Labor Organisation (ILO) has used information on age specific labor force participation rates and population statistics. Unfortunately, for the reasons clarified below, these estimates are unreliable.

_(1) Data discrepancies._ First, there is no agreement either on the level or on the growth rates of the labor force. Virtually all data is different from each other. For example, UNDP (2003a) reports a total labor force estimate of about 2.84 million for 1989, as compared to 2.54 million from INE or 2.95 million from ILO. Second, as typical for estimates in other countries, labor force data should show some cyclical fluctuations as labor responds to higher output growth. Official estimates for Guatemala, however, are remarkably free of _any_ fluctuations and follow a

-21-
Our study is based on a firm-level data set collected annually by the National Bureau of Statistics of China. This data set covers about 300,000 firms in 1998-2007, but the length is only 10 years and only a subset of approximately 32,000 firms are available for the entire 10 year period. Thus we adapt their approach and use two methods to measure productivity volatility.

For the firms that survived the entire 10 year period (1998-2007), we define the productivity volatility as the standard deviation of the annual growth rate of total output per worker during 1999-2007: where γ _i_ , _k_ is the annual growth rate of output per worker for firm _i_ at year _k_ , and<sup>~~γ~~</sup> _i_<sup>the average</sup> growth rate between 1999 and 2007.

However, this measure only allows us to perform cross-section analysis. To study the evolution of volatility over time, we use an alternative method, shortening the time span to five years, and focus on the firms that survived at least 5 years. We compute the productivity volatility as the standard deviation of the annual growth rate of total output per worker during each 5 consecutive years: where γ _i_ , _k_ is the annual growth rate at year _k_ for firm _i_ , and<sup>~~γ~~</sup> _i_<sup>the average growth rate between</sup> _t_ − 2 and _t_ + 2 ; _t_ varies from 2001 to 2005. This measure is similar to that of Comin and Philippon (2005). But it allows us to expand the sample to a panel and analyze the change of productivity volatility over time.

Figure 2 presents the relationship between the level and volatility of productivity for the firms that survived the entire 10 year period 1998-2007. It shows that, as the level of productivity increases, volatility tends to decline. In other words, overall, there is some negative association between the level of productivity and the volatility of productivity.

7
|**Table 6B: Estimates of elastici**|**ty of firms with respect top**<br>**R&D intensity**|**rice for Ke**|**nya by sector an**|**d by Kenyan trading partner region**|
|---|---|---|---|---|
||R&D expenditures divided by||**Elasticity Estim**|**ates**|
||sales(times 1000)for the US*|**Africa**|**EU**|**ROW**|
|**SERVICES**|||||
|telecommunications|52-high|2.5|13.4|20|
|banking|4-low|3.3|3.3|10|
|insurance|4-low|3.3|3.3|10|
|professional services|116-high|2.5|13.4|20|
|air transport**|medium|1.9|10|15|
|road transport|low|3.3|3.3|10|
|rail transport**|medium|1.9|10|15|
|water transport**|medium|1.9|10|15|
|**MANUFACTURING**|||||
|beverages and tabacco|14-low|3.3|3.3|10|
|grain milling***|7-low|3.3|3.3|10|
|sugar&bakery&confectioners***|7-low|3.3|3.3|10|
|petroleum|2-low|3.3|3.3|10|
|chemicals|34-medium|1.9|10|15|
|metals and machines***|33-medium|1.9|10|15|
|non-metallicproducts***|0-17-low|3.3|3.3|10|
|*Based on average R&D expenditures for t<br>**We evaluate transportation as a medium<br>These are transportation, chemiicals and co<br>multinationals operatingi in the US was per<br>TechnologyLinkages," at 'http://www.nsf|heyears 2004 and 2005. The average<br>R&D sector since three sectrors dom<br>mputers and electronics. Moreover, a<br>formed in the same three sectors. See<br>.gov/statistics/seind04/c4/c4s5.htm.|for all US indu<br>inate R&D exp<br>bout two-third<br>"U.S. and Inte|stries was 36.<br>enditures of US multina<br>s of all R&D expenditu<br>rnational Research and|tionals operating abroad.<br>res of foreign<br>Development: Funds and|
|~~SOU C :~~<br>~~&~~<br>~~a d sa es data~~<br>~~o~~<br>~~Nat o~~<br>***Food is the proxy for grain mlling and s<br>we usedplastics,rubber,mineral and wood|~~a Sc e ce~~<br>~~ou dat o ,~~<br>~~v s o o S ~~<br>ugar, bakery and confectioners; mach<br> products.|~~c e ce~~<br>~~esou c ~~<br>inery is used fo|~~es Stat st cs,~~ _~~Su vey of ~~_<br>r metals and machines;|_~~ndust ial~~_<br>_~~esea ch and~~_<br>for non-metallic products,|
|_Development: 2005, Data Tables_. Availab<br>details of the calculations.|le at: http://www.nsf.gov/statistics/n|sf10319/conten|t.cfm?pub_id=3750&i|d=3. See appendix E for|

50
Basu, K. and Morita, H. (2006), “International Credit and Welfare: A Paradoxical Theorem and Its Policy Implications,” **European Economic Review** , vol.50. Reprinted in Kaushik Basu, **Collected Papers in Theoretical Economics, Volume 3** , Oxford University Press.

Bolton, P. and Jeanne, O. (2011), “Sovereign Default Risk and Bank Fragility in Financially Integrated Economies,” **IMF Economic Review** , Vol 59(2), 162-194.

Claessens, S., Mody, A., and Vallee, S. (2012), “Paths to Eurobonds,” IMF Working Paper 12/172.

Cruces, J. and Trebesch. C. (2011), “Sovereign Defaults: The Price of Haircuts,” Working Paper No. 3604, CESifo.

Darity, W. and Horn, B. (1986), **The Loan Pushers: The Role of Commercial Banks in the International Debt Crisis** , Ballinger, Cambridge, MA.

De Grauwe, P. (2011), “The Governance of a Fragile Eurozone,” Working Document No. 346, CEPS.

De Witte, Bruno (2011), “The European Treaty Amendment for the Creation of the Financial Stability Mechanism,” Stockholm: SIEPS.

Farrell, J. and Maskin, E. (1989), “Renegotiation in Repeated Games,” **Games and Economic Behavior** 1(4), 327-360.

Ghatak, M. and Guinnane, T. (1999), “The Economics of Lending with Joint Liability: Theory and Practice,” **Journal of Development Economics** , vol. 60.

Gibbons, R. (1992), **Game Theory for Applied Economists** , Princeton University Press, Princeton.

Greenwald, B. and Stiglitz, J. (1986), “Externalities in Economies with Imperfect Information and Incomplete Markets,” **Quarterly Journal of Economics** , vol. 101, No. 2.

27
_50_ correlated with inflation. The coefficients ot, **et.,** and **_a 3_** can be estimated and the hypothesis that these coefficients are significantly not different from one can be tested empirically.4z/ Instead of estimating the model above, we will focus on the causal relationships between these variables and nominal devaluations. The idea is to determine the direction of causality and what are the policy implications of such findings. For example, if money growth causes inflation then the policy lesson is clear; a tighter monetary policy may be required. The interesting question is about the causality direction between devaluations-inflation and devaluations-money growth.

Mhe analysis is based on Granger's (1969) causal model and Alvi and Islam (1989) who implement the test for time series-cross sectional data.

**_k k k k AFr=ui.2; a1 4Pe.1_**<sup>**_+._**</sup><sup>_b_</sup> _1_<sup>_AM_.L+ 2c_</sup> _4_<sup>_A. .Y_</sup> _1_<sup>_+f2 d_</sup> _1_<sup>_ANS_</sup> **+**<sup>**6**</sup> **,** Y **-i f-i i** 1-i **I-1 i** **_"v,_** . **_k k k k_** AMwhee **_aE_** al **_-jAMtbl_** a _fAP,nau+_ **_glA_** _Ythrbte_ o **_-AN, ite_** _k k_ ~~~k **kIu."(0 i-I J-a - i-a** It **_k kr_** _k_ **_A&NDWf=yo+_** S **_n1 &Nv,,,. + So 1AP,-1 + Sp 1 AMt.1+ESqA&Yc.-ite- 4_** - **_i-2 J-a._ J-3. i-a.**

where all variables are in natural logarithms, therefore, rate of growth of pric es (inflation), rate of growth of money stock, rate of growth of output, and rate of growth of nominal exchange rate (devaluations), which is defined as the percentage change of the nominal exchange rate. All variables in the system are stationary time series. The error terms are uncorrelated. The idea is to conduct a series of F tests on all the coefficients in the Vector Auto-Regressive (VAR) system to test the nuill hypotheses that the sum of each of the coefficients is equal to zero. For example, if the sum of the coefficients b is statistically different from zero, then we say money growth Granger-causes inflation. It is important to carefully determine the number of lags, k.43/ The number of lags here is set equal to four.

**42/** The other model for inflation is the structurlist model. This model emerged in **Latin** America in the 1950s as an alternative to the monetary model above. The model emphasizes bottlenecks that accompany development processes as determinants of inflation. Thus, inflation is a finiction of sectoral impasses or constraints.

43/ We **use** the Akaike (1974) Information Criterion (AIC) and Hall's (1992) general-to-specific method to determine the optimal lag length. We fit a general lag structure (4 lags) and use an F-test to test **backward** and eliminate all unnecessary lags.
COUNTRIES, ECONOMIES, AND TERRrIORIES 115 Sumnary Projection for 25-Year Periods NICARAGUA

|AGE GROUP|1985|2000|2025|2050|2075|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|TOTAL N+F|3272|5198|8680|<br>11553|13219|13682|13898|14000|
|MALES|||||||||
|0-4|308|410|405|418|420|<br>419|418|418|
|5-9|254|381|424|423|420|418|418|418|
|10-14|216|339|431|426|419|417|417|417|
|15-19|180|296|424|420|415|416|417|417|
|20-24|148|246|417|405|413|416|417|417|
|25-29|121|207|397|400|414|417|416|416|
|30-34|99|171|370|418|419|416|415|414|
|35-39|73|139|327|423|420|414|413|413|
|40-44|57|113|283|413|412|408|410|412|
|45-49|48|92|232|404|394|404|408|410|
|50-54|39|66|192|379|385|401|405|406|
|55-59|32|50|153|343|394|399|400|399|
|60-64|24|39|118|292|<br>387|391|389<br>|389<br>|
|65-69|17|29|87|237|362|369|371|375<br>|
|70-74|11|20|60|175|327|332|348|355|
|75+|9|19|58|224|574|731|797|824|
|TOTAL|1635|2616|4379|5799|6575|6769|6858|6900|
|FEMALES|||||||||
|0-4|297|395|386|398|400|399|399|398|
|5-9|246|368|406|403|401|399|398|398|
|10-14|210|329|414|406|399|398|398|398|
|15-19|178|287|409|400|396|397|398<br>|398<br>|
|20-24|149|239|404|386|394|397|398|398|
|25-29|123|201|386|384|396|399|398|398|
|30-34|102|168|360|403|401|399|398|397|
|35-39|75|140|320|410|403|397|397|397|
|40-44|59|115|276|403|397|394|395|397|
|45-49|49|95|228|396|382|391|395|396|
|50-54|42|69|189|376|377|391|395|396<br>|
|55-59|35|53|155|346|392|394|394|394|
|60-64|27|42|125|300|393|392|390|390|
|65-69|19|33|96|250|379|381|382|386|
|70-74|13|24|71|194|359|358|374|381|
|75+|13|24|77|299|776|1027|1130|1177|
|TOTAL|1637|2582|4302|5754|6644|6913|7040|7100|
|BIRTH RATE||37.9|24.4|16.2|13.1|12.1|11.9|11.7|
|DEATH RATE||6.3|4.2|4.9|7.8|10.8|11.2|11.4|
|NET MIGRATI|ON<br>RATE|-1.0|-. 3|**.O**|.0|.0|.0|.0|
|GROW4TH<br>RATE||3.09|2.05|1.14|.54|.14|.06|.03|
|TOTAL FERTI|LITY|5.050<br>|2.906<br>|2.078<br>|2.067<br>|2.062<br>|2.059<br>|2.058<br>|
|etO)**-** BOTH S|EXES|65.92|72.99|78.48|81.75|83.59|84.99|85.54|
|INR**-** BOTH SE|XES|50.7|22.9|5.7|3.4|2.7|2.2|2.1|
Erkens, D., M. Hung, and P. Matos, 2012, Corporate governance in the 2007-2008 financial crisis: Evidence from financial institutions Worldwide, _Journal of Corporate Finance_ 18, 389-411.

Fahlenbrach, R., and R. Stulz, 2011, Bank CEO incentives and the credit crisis, _Journal of Financial Economics_ 99, 11-26.

Hillegeist, S., E. Keating, D. Cram, and K. Lundstedt, 2004, Assessing the probability of bankruptcy, _Review of Accounting Studies_ 9, 5–34.

Hovakimian, A., E. Kane, and L. Laeven, 2003, How country and safety-net characteristics affect bank risk-shifting, _Journal of Financial Services Research_ 23, 177– 204.

John, K., L. Litov, and B. Yeung, 2008, Corporate governance and risk-taking, _Journal of Finance_ 63, 1679–1728.

Laeven, L., 2013, Corporate governance: What’s special about banks?, _Annual Review of Financial Economics_ 5, 63-92.

Laeven, L. and R. Levine, 2009, Bank governance, regulation and risk taking, _Journal of Financial Economics_ 93, 259–275.

Mehran, H., A. Morrison, and J. Shapiro, 2011, Corporate governance and banks: What have we learned from the financial crisis?, Federal Reserve Bank of New York Staff Report no. 502.

Merton, R., 1974, On the pricing of corporate debt: the risk structure of interest rates, _Journal of Finance_ 29, 449–70.

Pathan, S., 2009, Strong boards, CEO power and bank risk-taking, _Journal of Banking and Finance_ 33, 1340–1350.

25
# World Production TrnUds

World production of cashew nuts grew rapidly during the 1950s and 1960s, reaching a peak of 510,000 tons (of raw nuts) in 1974.7 Three countries--India, Mozainbique, and Tanzania-accounted for the bulk of such production, although smaller industries had alco developed in Brazil, Kenya, and several other African countries. Beginning in 1975, however, and continuing through the mid-1980s, there was a sharp fall in world production, contributing to a very tight international market. While Indian production was stagnant, the global fall can pi'marily be attributed to the huge decline of production in Mozambique and Tanzania. There bas been some recovery in world production since the mid-1980s, due primarily to a significant expansion in Brazilian production. Table 1 summarizes major patterns in world production over the past two decades.

**Table 1: World** and Country Cashew Nut Production, 1969/71 to 1989/91

|Country/Region<br>l________|<br>Avera|ge Annual Pr<br>(000 Tons|oduction<br>)|Sha|re of World Pr<br>(%)|oduction|
|---|---|---|---|---|---|---|
||1969-71|1979-81|1989-91|1969-71|l 1979-81|1989-91|
|India|175.7|_159.0_|140.0|33.8|36.2|28.1|
|Brazil|24.1|71.3|_145.5_|_4.6_|16.2|29.2|
|Africa|311.1|174.9|140.4|59.9|39.8|28.2|
|Mozambique<br>Tanzania<br>Kenya<br>Nigeria<br>Guinea Bissau<br>Others*<br>l____________|180.0<br>108.4<br>16.2<br>N.A.<br>2.4<br>_5.0_|69.4<br>54.2<br>15.8<br>25.0<br>3.8<br>6.7|44.7<br>27.0<br>_11.5_<br>25.0<br>19.0<br>13.2|||_ _|
|World|_519.5_|439.7|497.9|100.0|100.0|100.0|

*Including Cote Ivoire, Madagascar, Angola, Benin, Togo, Mali, and Zambia

# Source: FAO Production Yearbooks

The table indicates that the share of sub-Saharan Africa in world production decreased from nearly 60% in 1969-71 to only 28% in 1989-91. A small recent expansion in West African production has not compensated for the extreme decline in Mozambique's and Tanzania's production levels. While in 1969-71 Brazil's production was only 8% that of sub-Saharan Africa, by 1989-91 Brazil's output exceeded than of the African continent.

> 7 Gill and Duffus Edible Nut Market Reports, various years.

6
||||(0.005)***|(0.004)***|(0.005)|(0.005)|(0.003)|
|---|---|---|---|---|---|---|---|
|Travel time (minutes)|||-0.017|-0.005|0.003|-0.016|0.006|
||||(0.004)***|(0.003)**|(0.003)|(0.004)***|(0.005)|
|Cost of travel (real yuan)|||0.122|0.166|0.158|0.133|0.161|
||||(0.107)|(0.083)**|(0.122)|(0.133)|(0.088)*|
|Waiting time (minutes)|||0.007|0.006|0.002|0.006|0.003|
||||(0.002)***|(0.001)***|(0.002)|(0.002)***|(0.002)|
|Medicine regularly available|||-0.032|0.032|0.064|-0.018|0.055|
||||(0.052)|(0.036)|(0.042)|(0.053)|(0.039)|
|Constant|-11.816|-11.329|-10.513|-9.533|-8.771|-12.895|-9.519|
||(0.779)***|(0.739)***|(0.692)***|(0.436)***|(0.607)***|(0.796)***|(0.707)***|
|Observations|6249|6249|6199|6199|6199|6199|6199|
|Robust standard errors,clustered at the c|ommunitylevel,inp|arentheses;1|76 community|fixed effects||||

Source. China Health and Nutrition Survey, 1991-2000.

Note. * significant at 10%; ** significant at 5%; *** significant at 1%.

12
_Africa’s Macroeconomic Story_ _Source:_ World Bank _International Debt Statistics_ and April 2013 IMF _World Economic Outlook_ database. _Note:_ Estimates for 2012; projections for 2013.

The analysis above focuses on external debt (obligations incurred to non-residents) and hence does not include domestic debt (public sector obligations to domestic residents).<sup>12</sup> Data limitations prevent us from examining the evolution of public debt (public sector obligations to domestic and foreign residents) over the past few decades.<sup>13</sup> Data available from 2000 indicate that public debt burdens have declined significantly throughout much of the region. The median level fell from 100 percent of GDP in 2000 to under 30 percent in (Figure 13). Public debt is now below 50 percent of GDP in three of four countries (the upper quartile).

**Figure 13: General Government Gross Debt in Africa**

# _Percent of GDP_

<!-- Start of picture text --> 125<br>100<br>75<br>50<br>25<br>0<br>2000 2003 2006 2009 2012<br>Africa region Median Upper quartile<br><!-- End of picture text --> Source: April 2013 IMF _World Economic Outlook_ database. _Note:_ Estimates for 2012; projections for 2013.

As mentioned earlier, the dramatic decline in debt levels beginning in the early 2000s was largely due to the HPIC and MDRI debt relief initiatives.<sup>14</sup> In most cases, debt burdens have not 12 External debt also includes obligations incurred by the private sector to nonresidents. However, the amount of private external debt has not significant for most countries in the region, except South Africa. 13 Only five countries in the region provide data on government debt from 1980, 18 from 1990 and 40 from 2000.

14 28 of the 44 countries in our sample benefited from HIPC and MDRI debt relief.

22
# **REFERENCES**

- Adhvaryu, Achyuta, Prashant Bharadwaj, James Fenske, Anant Nyshadham, and Richard Stanley. 2016. Dust and Death: Evidence from the West African Harmattan. _CSAE Working Paper WPS/2016-03._ Centre for the Study of African Economies, University of Oxford.

- Agüero, Jorge, Michael Carter, and Ingrid Woolard. 2006. The Impact of Unconditional Cash Transfers on Nutrition: The South African Child Support Grant. SALDRU Working Paper Series No. 06/08.

- Aizer, Anna, Shari Eli, Joseph Ferrie, and Adriana Lleras-Muney. 2016. The Long-Run Impact of Cash Transfers to Poor Families. _Am Econ Rev_ 106 (4): 935-971.

- Alderman, Harold, Jere Behrman, and Afia Tasneem. 2015. The contribution of increased equity to the estimated social benefits from a transfer program: An illustration from PROGRESA. IFPRI Discussion Paper 1475.

- Almond, D. 2006. Is the 1918 influenza pandemic over? Long-term effects of _In Utero_ Influenza Exposure in the Post-1940 U.S. Population. _Journal of Political Economy_ 114 (4): 672712.

- Almond, Douglas and Bhashkar A Mazumder. 2011. Health capital and the prenatal environment: the effect of Ramadan observance during pregnancy. _American Economic Journal: Applied Economics_ 3 (4): 56-85.

- Anderson, Michael L. 2008. Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. _Journal of the American Statistical Association_ 103 (484): 1481-1495.

- Anderson, Siwan and Chris Bidner. 2015. Property Rights over Marital Transfers. _Q J Econ_ 130 (3): 1421-1484.

- Araujo, M Caridad, Mariano Bosch, and Norbert Schady. 2016. Can Cash Transfers Help Households Escape an Inter-Generational Poverty Trap?. _NBER Working Paper 22670._ National Bureau of Economic Research.

- Ashraf, Nava, Natalie Bau, Nathan Nunn, and Alessandra Voena. 2015. Bride Price and Female Education. _Unpublished manuscript_ :

http://scholar.harvard.edu/files/nbau/files/paper_draft_indonesia_zambia_20140821.pdf.

- Baez, Javier Eduardo and Adriana Camacho. 2011. Assessing the long-term effects of conditional cash transfers on human capital: evidence from Colombia. _IZA Discussion Paper No. 5751_ .

- Baird, Sarah, Craig McIntosh, and Berk Özler. 2011. Cash or Condition? Evidence from a Cash Transfer Experiment. _Q J Econ_ 126 (4): 1709-1753.

- Baird, Sarah, Francisco HG Ferreira, Berk Özler, and Michael Woolcock. 2013. Relative effectiveness of conditional and unconditional cash transfers for schooling outcomes in developing countries: a systematic review. _Campbell Systematic Reviews_ 9 (8).

- Baird, Sarah, Jacobus De Hoop, and Berk Özler. 2013. Income shocks and adolescent mental

37