rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. Poverty among Coastal Populations, 2000**

||**Popula**<br>**Area**<br>**High**<br>**Mor**<br>**(mill**<br>**Total**|**tion in**<br>**s with**<br>**Infant**<br>**tality**<br>**ions)**<br>**Rural**|**Rural**<br>**Share**<br>**(%)**|**Popula**<br>**Areas**<br>**Malnou**<br>**Chil**<br>**(milli**<br>**Total**|**tion in**<br>**with**<br>**rished**<br>**dren**<br>**ons)**<br>**Rural**|**Rural**<br>**Share**<br>**(%)**|**Share (%) of**<br>**Rural Coastal**<br>**Population in**<br>**Areas with**<br>**High Infant**<br>**Mortality**|<br> <br> <br>**Share (%) of**<br>**Rural Coastal**<br>**Population in**<br>**Areas with**<br>**Malnourished**<br>**Children**|
|---|---|---|---|---|---|---|---|---|
|**Developing Country**|**408.5**|**235.5**|**57.6%**|**339.9**|**205.6**|**60.5%**|**28.4%**|**24.8%**|
|_By Income:_|||||||||
|Low Income|117.7|80.3|68.2%|62.5|45.1|72.1%|66.5%|37.4%|
|Lower Middle Income|249.2|136.5|54.8%|243.3|147.2|60.5%|28.1%|30.3%|
|Upper Middle Income|41.6|18.7|44.9%|34.0|13.3|39.0%|8.4%|6.0%|
|_By Region:_|||||||||
|East Asia & Pacific|39.3|29.5|75.1%|123.3|79.8|64.7%|7.9%|21.4%|
|Europe & Central Asia|0.0|0.0|--|3.2|1.4|43.1%|0.0%|5.3%|
|Latin America & Caribbean|24.3|14.5|59.7%|17.4|7.6|43.5%|17.7%|9.2%|
|Middle East & North Africa|22.3|14.7|66.0%|9.8|5.7|58.6%|25.0%|9.7%|
|South Asia|204.0|113.3|55.5%|158.2|95.2|60.2%|50.4%|42.4%|
|Sub-Saharan Africa|118.5|63.4|53.5%|28.0|15.8|56.7%|97.4%|24.3%|
|**Developed Country**|**0.3**|**0.3**|**76.9%**|**3.4**|**0.7**|**20.9%**|**0.2%**|**0.6%**|
|**World**|**408.9**|**235.7**|**57.7%**|**343.3**|**206.3**|**60.1%**|**24.9%**|**21.8%**|

Coastal populations are those living within 100 kilometers (km) of the coast, and excluding countries without territory within 100 km of a coastline measured using an Azimuthal Equidistant (world) projection.

High infant mortality is 50 or more deaths within the first year of life per 1,000 births (or 500 deaths/10,000 live births).

Child malnutrition counts are the product of malnutrition rates and population counts. Malnutrition rates are based on the percentage of children with a weight‐for‐age z‐score more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population.

See the appendix for details on the spatial methods in calculating these coastal population distributions.

Low‐income economies are those in which 2013 per capita income was $1,045 or less, lower‐middle‐income economies are those in which 2013 per capita income was between $1,046 and $4,125, upper‐middle‐income economies are those in which 2013 per capita income was between $4,126 and $12,745, and developed countries are those in which 2013 per capita income was more than $12,745 , as defined by the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> 12
# **6. References**

- Black S. and L. Lynch (1997) _How to compete: the impact of workplace practices and information technology on productivity_ , NBER WP 6120.

- Bils, M., (1987) _The cyclical behavior of marginal cost and price_ , The American Economic Review, Vol 77 No. 5.

- Caballero, R., and E. Engel (1993) Microeconomic adjustment hazard and aggregate dynamics, _Quarterly Journal of Economics_ 108 (2) : 359-383.

- Caballero, R., E. Engel and J. Haltiwanger (1995) Plant-Level Adjustment and Aggregate Investment Dynamics, _Brookings Papers on Economic Activity_ 2: 1-54.

- Caballero, R., E. Engel and J. Haltiwanger (1997) Aggregate employment dynamics: building from microeconomic evidence _, American Economic Review_ 87 (1): 115-137.

- Cassoni, A, G. Fachola and G. Labadie (2001 _) The Economic Effects of Unions in Latin America: Their Impact on Wages and the Economic Performance of Firms in Uruguay,_ Research Department WP R466, Inter American Development Bank.

- Casacuberta, C., G. Fachola, and N. Gandelman, (2004) _The Impact of Trade Liberalization on Employment, Capital and Productivity Dynamics: Evidence from the Uruguayan Manufacturing Sector_ , Journal of Policy Reform, Vol 7 (4)

- Cooper, R., and J. Willis, (2004) _The economics of Labor adjustment: mind the gap_ , The American Economic Review, Vol 94 No. 4

- Eslava, M.,  J. Haltiwanger, and M. Kugler,  (2005) _Heterogeneous adjustments of employment and capital after factor market deregulation._ Mimeo.

- Davis, S., and J. Haltiwanger,  (1992) _Gross job creation gross job destruction and employment reallocation_ , Quarterly Journal of Economics, 107(3)

Davis, S., J. Haltiwanger, and S. Schuh, (1996) _Job creation and destruction_ , MA: MIT Press

- Levinsohn, J., and A. Petrin, (2003) _Estimating production functions using inputs to control for unobservables_ , Review of economic studies, Vol 70 (2).

- Levinsohn, J., A. Petrin, and B. Poi (2003) _Production function estimation in Stata  using inputs to control for unobservables_ , Stata Journal, 4 (2).

- Vaillant, M., (2000) _Limits to trade liberalization: a political economy approach_ , Ph.D. dissertation, Universiteit Antwerpen UFSIA.

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data set. Section V analyzes how these relationships might depend on the prevailing degrees of poverty and/or inequality, and gauges the direct and indirect effects of inequality on growth. Finally, Section VI concludes.

# **II. An illustrative model**

To illustrate the effects of poverty and inequality on growth, we sketch a model in the spirit of Aghion et al. (1999), who introduce learning-by-doing and knowledge spillovers in a simple overlapping-generations framework. We modify their basic setup by adding a minimum consumption requirement in the model. Poor consumers are defined as those whose initial endowment is below the minimum consumption level. In the absence of capital markets, they cannot invest, and do not contribute to the economy’s aggregate growth.<sup>8</sup> There is a continuum of non-altruistic overlapping-generations individuals, indexed by _i_  [0,1], who live for at most two periods. Individuals born at time _t_ have a random endowment _wit_ . Survival into the second period entails a minimum consumption requirement _~~c~~_ (possibly reflecting nutritional needs), which can exceed the original endowment. We denote by  the share of population with initial endowment below survival needs, to whom we shall refer as the poor (i.e.,  is the headcount poverty rate), where _f_ (.) is the probability density function of individual endowments _wit_ , with mean _~~w~~_ and standard deviation  .

The utility of the _i-th_ individual of generation _t_ is given by:

> 8 We do not need to rule out capital markets altogether. It would suffice to assume that lenders impose on borrowers a collateral requirement, which individuals below the minimum consumption level would be unable to meet.

5
**Table 8. Impact of the performance-based grant on examination scores in primary and** **<u>junior secondary schools in Jakarta</u>**

||Jakar<br>|**Primary**<br>ta governm<br>governme<br>|**Schools**<br>ent v. Jakart<br>nt schools<br>|a non-<br>|**Ju**<br>Jakart<br>|**nior Secon**<br>a governme<br>governme<br>|**dary Scho**<br>nt v. Jakart<br>nt schools<br>|**ols**<br>a non-<br>|
|---|---|---|---|---|---|---|---|---|
||Bottom<br>quartile<br>(1)|2nd<br>quartile<br>(2)|3rd<br>quartile<br>(3)|Top<br>quartile<br>(4)|Bottom<br>quartile<br>(5)|2nd<br>quartile<br>(6)|3rd<br>quartile<br>(7)|Top<br>quartile<br>(8)|
|**JakartaGov*Year2015**<br>**JakartaGov*Year2016**|1.88***<br>(0.34)<br>1.61***<br>(0.40)|0.06<br>(0.33)<br>-1.26***<br>(0.40)|-1.20***<br>(0.32)<br>-2.76***<br>(0.39)|-2.15***<br>(0.31)<br>-2.93***<br>(0.38)|1.85***<br>(0.33)<br>2.16***<br>(0.48)|3.01***<br>(0.30)<br>3.81***<br>(0.41)|3.35***<br>(0.26)<br>5.20***<br>(0.53)|2.22***<br>(0.24)<br>6.93***<br>(0.48)|
|Observations|3,303|3,294|3,300|3,297|2,049|2,055|2,043|2,049|
|R-squared|0.034|0.064|0.116|0.130|0.801|0.800|0.795|0.792|
|No. of Jakarta gov.|||||||||
|schools|396|393|395|394|70|72|68|70|
|No. of comparison|||||||||
|schools<br>|705|705|705|705|613|613|613|613|
|S.D. of 2014 UN score<br>in comparison schools|11|11|11|11|7.2|7.2|7.2|7.2|
|P-value of difference in<br>impact between 2016<br>and 2015|0.45|0.0002|0|0.02|0.45|0.05|0.0002|0|
|Years included<br>Controls for students per|2014-16|2014-16|2014-16|2014-16|2014-16|2014-16|2014-16|2014-16|
|classroom, teacher<br>education and<br>graduating students|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time dummies and<br>school fixed effects|Yes|Yes|Yes|Yes<br>|<br>Yes|Yes|Yes|Yes|

Notes: Dependent variable is national examination scores expressed as a percentage. Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Comparison schools are non-government schools in Jakarta. Full results, including the coefficient estimates for the control variables, are reported in Annex Table A1.9.

# **_What impact did the additional funds have on student learning in schools that received the performance component?_**

In order to look at whether the additional resources that schools were awarded from the performance component helped to improve learning outcomes, the paper compares schools that did not receive the performance component but were very close to doing so with schools that just managed to improve their performance enough to get the performance award. Table 9 shows the impact estimates from the regression discontinuity design in 2015 and 2016 for schools on either side of the cut-off for awarding of the performance grant. One way of thinking of the RD estimate is as a local average treatment effect – the effect on those induced to comply as their scores cross the threshold of eligibility. Given the narrow focus on schools around the threshold of eligibility, any differences in test scores have to be sufficiently large to be statistically distinguishable from zero.

19
<!-- Start of picture text --> Klinger, Bailey, and Daniel Lederman, 2006, "Diversification, Innovation and Imitation Inside<br>the Global Technological Frontier", Policy Research Working Paper No. 3872, The World Bank<br>Laborde, David, Houssein Boumelassa, and Maria C. Mitaritonna, Forthcoming, "A Consistent<br>Picture of the Protection Across the World in 2004: MAcMap HS6 V2", Working Paper, CEPII.<br>Mayer, Thierry, and Soledad Zignago, 2006, "Notes on CEPII’s Distance Measures", Working<br>Paper, CEPI.<br>Melitz, Marc J., 2003, "The Impact of Trade on Intra-Industry Reallocations and Aggregate<br>Industry Productivity", Econometrica, 71(6), 1695-1725.<br>Ozden, Caglar, and Eric Reinhardt, 2005, "The Perversity of Preferences: GSP and Developing<br>Country Trade Policies, 1976-2000", Journal ofDevelopment Economics, 78(1), 1-21.<br>Prebisch, Raul, 1959, "Commercial Policy in the Underdeveloped Countries", The American<br>Economic Review, 49(2), 251-273.<br>Rodrik, Dani, 2004, "Industrial Policy for the Twenty-First Century", Mimeo, http://ksghome.harvard.<br>edu/~drodrik/UNIDOSep.pdf.<br>Santos Silva, J.M.C. and Silvana Tenreyro, 2006, "The Log of Gravity", Review ofEconomics<br>and Statistics, 88(4), 641-658.<br>UNCTAD, 2006, UNCTAD Handbook ofStatistics, Geneva: United Nations.<br>Wilson, John S., Catherine L. Mann, and Tsunehiro Otsuki, 2005, "Assessing the Benefits of<br>Trade Facilitation: A Global Perspective", The World Economy, 28(6), 841-871.<br>Winkelmann, Rainer, 2000, Econometric Analysis of Count Data, 3rd edition, Berlin: Springer.<br>Wooldridge, Jeffrey M., 1997, "Quasi-Likelihood Methods for Count Data" in M.H. Pesaran<br>and P. Schmidt (eds.), Handbook ofApplied Econometrics, Vol. 2, Oxford: Blackwell.<br>Wooldridge, Jeffrey M., 2002, Econometric Analysis of Cross Section and Panel Data, Cam-<br>bridge, MA: MIT Press.<br>32<br><!-- End of picture text -->
# **1 Introduction**

The robust positive relationship between economic performance and trade openness is a result that has, literally, been demonstrated millions of times (Sala-i-Martin 1997). Countries that have more open borders are also countries that tend to grow quickly (Frankel & Romer 1999), and many economies that expanded their trade through the second half of the 20th century have also enjoyed growth takeoffs (Jones & Olken 2008). This has led policymakers in many transition economies, eager for growth performance, to pursue strategies of trade liberalization.

Such trade liberalization policy is often accompanied by conscious industrial policy that seeks to diversify the economic base of the liberalizing economy, and thus fostering diversification in its exports. However, export diversification is itself not a monolithic strategy, and _how_ a country’s export structure evolves can be important. The extensive margin of export diversification can occur along several dimensions, involving expansions into new products (Hummels & Klenow 2005), new markets (Brenton & Newfarmer 2009), or up the quality ladder (Schott 2004).<sup>1</sup> A question that has been less frequently considered is _what_ gives rise to a greater degree of export diversification. Other than the level of economic development (Carr`ere, Strauss-Kahn & Cadot 2007; De Benedictis, Gallegati & Tamberi 2009), there are reasons to believe that the extent of export diversification is also influenced by trade policy (Edwards & Lawrence 2008; Estevadeordal & Martincus 2006), proximity to major markets for the export in question (Parteka & Tamberi 2008), and foreign direct investment (Gourdon & Nassif 2009). Despite these recent efforts at understanding the determinants of export diversification, much of the empirical literature has concentrated on factors that apply at the cross-country level. What is far less understood are the factors that matter at the within-country sectoral or firm level, and especially the contribution of sector-specific inputs and policies related to trade. To our knowledge, only a few papers directly address the issue of micro-level determinants of export diversification. Goldberg, Khandelwal, Pavcnik & Topalova (2008) use firm-level data from India and find that access to new imported inputs—measured by tariff declines and the subsequent prices of intermediates—can account for an expansion of domestic firm product scope. Hausmann & Klinger (2008) apply a product-space methodology to argue that the specialization patterns of South African export sectors do not bode well for future exporting efforts.

> 1There is evidence that the first-order driver of export growth is the intensive rather than extensive margin (Besede ̆s & Prusa 2007), although this may vary according to income, with poorer countries more likely to diversify along the extensive margin (Cadot, Carr`ere & Strauss-Kahn 2007). Exports of existing products into new markets also accounts for a greater share of export growth than that of new products alone (Brenton & Newfarmer 2009).

2
incomes. However, it is important to keep in mind that rural nonfarm households, which are less affected, are higher in number and have higher initial poverty levels (Appendix, Table A.1).

Given this increase in poverty across all population groups and considering the experience from other countries, the question arises as to how much compensation would be needed for the poorest of the poor. Model results show that this compensation in Reform 1 and Reform 2 will require direct transfer payments of about 19,700 YER and 13,800 YER, respectively, per household and per year for the poorest 30 percent on average (Figure 2 and Table 7). The total annual cost of compensating this group during the period of 2010 to 2015 is estimated at about 17.6 billion YER under the accelerated reform and 12.3 billion YER with under the gradual reform (Table 7). In the accelerated reform, losses in real incomes peak in the second year and steadily decline over time; while in the gradual scenario, real incomes decline in the fourth year before picking up from the fifth year on. However, even several years after reform, real incomes are likely to remain below their baseline levels, indicating that direct transfers are not generating sufficient growth for sustainable fiscal and economic development.

Figure 2. Compensation Required under Accelerated and Gradual Scenario <!-- Start of picture text --> 25,000<br>Compensation needed for the poorest one-third of households<br>20,000<br>15,000<br>10,000 Reform 1 (accelerated)<br>Reform 2 (gradual)<br>5,000<br>,0<br>2010 2011 2012 2013 2014 2015<br>Annual per household (YER)<br><!-- End of picture text --> Source: DCGE model results.

# **Using the Savings for Budget Consolidation, Direct Transfers, and Investment**

Fuel subsidy reform provides the Yemeni government with the financial resources to reduce the budget deficit; to provide transfers; and to invest in generating new growth, employment, and income opportunities. To illustrate this point and to assess the impacts of such measures, the reform scenarios 1B–2B and 1C–2C investigate the trade-offs and synergies between “transfers” and “investments”. Given the urgent need for fiscal deficit reduction, we consider parts of the savings from reform to be used for deficit reduction consistent with Table 5 in both sets of simulations.

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|Table 8: OLS: Effects of refugee presence on household welfare di<br>with analysis at cluster level and the distance weighted refugee inde|saggregated by time<br>x at 50km from the<br>Log of welfare (<br>adu<br>|-varying main so<br>clusters (Panel 2<br>consumption agg<br>lt equivalent)<br>|urce of income,<br>009-2012)<br>regate per<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|0.0219*|0.0219*|0.0263*|
||(0.0114)|(0.0114)|(0.0136)|
|Log of refugees(50km)#Commercial farming|0.0515*|0.0515*|0.0770***|
||(0.0280)|(0.0279)|(0.0276)|
|Log of refugees(50km)#Wage employment|0.0143|0.0141|0.0032|
||(0.0121)|(0.0121)|(0.0129)|
|Log of refugees(50km)#Non-Agric self-employment|-0.0136|-0.0136|-0.0010|
||(0.0127)|(0.0127)|(0.0115)|
|Observations|5,453|5,450|4,105|
|Root MSE|0.3966|0.3967|0.3911|
|**_Included controls_**||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education, occupation,<br>other income sources, land ownership)|_No_|_No_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|
|Household fixed effects|_No_|_No_|_No_|

For our second investigation, we correlate welfare and refugee presence interacted with an indicator equal to one if the household moves to a particular main source of income. The results then suggest that households which changed from their initial main source of income to commercial farming and wage employment likely benefit more from the influx of refugees (Table 9).<sup>22</sup> Overall, these results suggest that households respond to the market dynamics created by the influx of refugees by switching to commercial farming in order to increase their welfare. Given our descriptive statistics (Table 1), we should nonetheless acknowledge than less than 1 percent of the households in refugeehosting areas generate their income mainly from commercial farming. While such a coping strategy certainly offers large pay-offs, it remains limited in scope.

Table 9: OLS: Effects of refugee presence on household welfare disaggregated by change in main source of income, with - <u>analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of welfare (<br>|consumption aggregate<br>equivalent)<br>|per adult<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|0.0183|0.0183|0.0175|
||(0.0132)|(0.0131)|(0.0188)|
|Log of refugees(50km)#To Commercial farming|0.0772**|0.0772**|0.0731**|
||(0.0313)|(0.0313)|(0.0306)|
|Log of refugees(50km)#To Wage employment|0.0206|0.0206|0.0400**|
||(0.0166)|(0.0167)|(0.0172)|

22 Details in Appendix 4; Table A30.

29
TABLE V Intent-To-Treat Effects on Student Test Scores

||(1)<br>_Y_2|(2)<br>_Y_3|
|---|---|---|
|Treatment efect|-0.005|0.010|
||(0.024)|(0.026)|
|Observations|279,066|274,993|
|_R_<sup>2</sup>|0.28|0.24|

_∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. The table reports intent-to-treat effects on student-level test scores. Estimates are reported separately for _Y_ 2 and _Y_ 3 data. Test score data are constructed by standardizing by subjectgrade-year (so that mean and variance in the control group are 0 and 1 respectively), then stacked so that the unit of observation is student-subject-year. These test scores are then regressed on a dummy variable indicating a treatment school. The estimated parameter on the treatment indicator is reported in column (1) and (2). The regression model further includes district-triplet fixed effects (the strata used for randomization), baseline standardized student-level test scores, baseline standardized averaged school-level test scores. For observations for which baseline test scores are not observed, the baseline values are set to zero. Two dummy variables indicating observations for which individual baseline test scores or school averaged baseline scores are not observed, are also included in the regression model. Weights are applied to scale the student-subject level data back to the level of the student. School-level clustered standard errors reported in parentheses.
|**Third country**|**_competition shockijkt_**|||-0.089|0.335*|
|---|---|---|---|---|---|
|**supply shock**||||(0.173)|(0.181)|
||**_upstream shockilt_**|||**2.082****|**2.172****|
|||||(0.890)|(0.891)|
||**_global outputmt_**|**1.102*****|**0.839*****|**1.054*****|**0.753*****|
|||(0.151)|(0.158)|(0.162)|(0.166)|
||**Constant**|**1.013*****|**0.732*****|**0.947*****|**1.243*****|
|||(0.053)|(0.018)|(0.082)|(0.103)|
||Observations|496,295|496,295|496,295|496,295|
||R-squared|0.424|0.424|0.424|0.424|
||Exporter-Time FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|
||Sector-Time FE|Yes|Yes|Yes|Yes|
||Cluster|Exporter-Partner|<br>Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

The interaction between GVC participation in partner countries and the variable retail mobility ( _retail mobilityjt*gvc partnerjl_ ) shows that the negative effect on bilateral exports of a shock in the exporters’ partner countries is more pronounced in sectors that have a larger share of imported inputs. Specifically, the impact of the demand shock for sector-countries with low shares of imported inputs (1<sup>st</sup> quartile of the distribution) is almost 12 percentage points smaller than for sector-countries with high shares of imported inputs (3<sup>rd</sup> quartile of the distribution).<sup>26</sup> These results suggest that buyer firms in partner country-sectors that are hit by the Covid-19 shock import fewer inputs from their suppliers. This result can also be explained by the fact that disruptions in production caused by the pandemic prevent firms in partner countries from satisfying consumer demand for final goods and this translates into higher shares of imports of final goods relative to intermediate inputs from foreign suppliers.

Finally, column (3) presents the impact of a supply shock in third countries on bilateral export growth through the competition and GVC channels. The coefficient capturing the former ( _competition shockijkt_ ) has the expected negative sign but is not statistically significant. A possible explanation for the lack of significance is that the Covid-19 shocks impacted the set of 28 exporters in our sample simultaneously, <mark>restricting bilateral trade growth to adjust through the competition channel. We come back to this issue</mark>

> 26 W e compare the differential impact at the first and third quartile of the distribution in the partner’s GVC participation ( _ß4_ * _gvc partneril_ ) . From Table 1, column (4) we obtain _ß4_ = 0.234 which is multiplied with GVC participation at the first quartile of the distribution (Q1=7.4%) and again with GVC participation at the third quartile of the distribution (Q3=57.7%). Taking the interquartile difference yields 0.234 *(57.7%-7.4%) = 11.8%-pts.

18