rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> References<br><!-- End of picture text --> <!-- Start of picture text -->
[1] Acemoglu, Daron, Simon Johnson, and James Robinson (2001), “The<br>Colonial Origins of Comparative Development: An Empirical Investi-<br>gation,” The American Economic Review, 91 , 1369-1401.<br>(2) Acemoglu, Daron, and Thierry Verdier (2000), “The Choice between<br>Market Failures and Corruption”, The American Economic Review Pa-<br>pers and Proceedings, 90, 194-211<br>[3] Aghion, Philippe and Patrick Bolton (1997), “A Theory of Trickle-<br>Down Growth and Development,” The Review of Economic Studies,<br>64, 151-172.<br>[4] Alesina, Alberto and Dani Rodrik (1994), “Distributive Politics and<br>Economic Growth,” The Quarterly Journal of Economics, 109, 465-<br>490.<br><!-- End of picture text --> <!-- Start of picture text -->
[5] Banerjee, Abhijit, Gertler, Paul and Matreesh Ghatak (2002), “Empow-<br>erment and Efficiency: Tenancy Reform in West Bengal,” The Journal<br>of Political Economy, 110, 239-280.<br><!-- End of picture text -->

- [6] Banerjee, Abhijit and Lakshmi Iyer (2002), “History, Institutions and Economic Performance: The Legacy of Colonial Land Tenure Systems in India,” MIT Working Paper 02-27

- [7] Banerjee, Abhijit, Dilip Mookerjee, Kaivan Munshi, and Debraj Ray (2001), “Inequality, Control Rights, and Rent Seeking: Sugar Cooperatives in Maharashtra,” The Journal of Political Economy, 109, 138-190.

- [8] Banerjee, Abhijit and Andrew Newman (1993), “Occupational Choice and the Process of Development,” The Journal of Political Economy, 101, 274-298.

- [9] Becker, Gary (1983), “A Theory of Competition Among Pressure Groups for Political Influence,” The Quarterly Journal of Economics, 98, 371-400.

- [10] Bernheim, Douglas, and Michael Whinston (1986), “Menu Auctions, Resource Allocation, and Economic Influence,” The Quarterly Journal of Economics, 101, 1-32. 27
5

**Figure 1:** Malaysia’s Real GDP and Manufacturing Sector Growth, 1988–2016

<!-- Start of picture text --> 40 AFC Tech GFC<br>bubble<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>GDP Manufacturing Sector<br>Percent growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> Percent growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> Source: Haver Analytics.<br><!-- End of picture text --> Note: AFC = Asian Financial Crisis; GFC = global financial crisis.

The expansion of the manufacturing sector has created employment opportunities in the economy. It has become the second largest source of employment after the services sector (57%), accounting for 19% of total employment, on average, between 2000 and 2016. More importantly, it overtook the agriculture sector as the second largest source of employment in 1992, reflecting the transformation of Malaysia’s economic structure (figure 2).

**Figure 2:** Share of Employment in Major Sectors of the Malaysia Economy, 1982–2016

<!-- Start of picture text --> % share of total<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Manufacturing Agriculture Services<br>Percent of total (%)<br><!-- End of picture text --> Source: Department of Statistics Malaysia.

The Third Industrial Masterplan (IMP3) 1996–2020 highlights the importance of ensuring that Malaysia’s manufacturing sector remains competitive on the global market. While Malaysia has coped well with the progressive trade liberalization and opening of markets, the country is facing increasing competition from emerging markets such as China, India, Central European economies, and Latin America.
are observed in relation to the time spent using kerosene lanterns/lamps, kerosene-related and total expenditure, study time at home, and school attendance. Overall, the previous findings are robust in their consideration of multiplehypothesis testing.

# **S.4 Supplemental figures and tables**

Figures and tables provided in this supplemental appendix are as follows.

- Figure S.1: Product pictures.

- Figure S.2: Timeline of interventions and surveys.

- Figure S.3: Sample of a time diary.

- Table S.1: The number of students selected in each screening process.

- Table S.2: Summary statistics at baseline.

- Table S.3 and Table S.4: Balance tests across treatment conditions.

- Table S.5 to Table S.11: Heterogeneous treatment effects on children’s educational achievement.

- Table S.12: Impacts on households’ willingness to buy solar products.

- Table S.13: In this table, we disaggregated treatment effects on students’ time spent on “other activities” (that were examined in columns (f) and (l) in Table 5 in the article) into “sports,” “cooking and bathing,” “household chores,” and “other activities, including sleeping and rest.”

- Table S.14: By treatment arms, this table provides similar information to that reported in Table 1 in the article, collected during a follow-up household survey.

- Table S.15: Cost-effectiveness of solar lanterns on school attendance.

- Table S.16: Heterogeneous treatment effects on children’s educational inputs.

- Table S.17 to Table S.19: In these tables, a similar analysis to that conducted for students’ progression to the next grade in Table 7 in the article was performed to examine the relationship between educational inputs and GPA scores.

- Table S.20 to Table S.26: Complementarity between solar lamps and school inputs in explaining children’s educational achievement.

40
_p_ , _Khp,t−_ 1.<sup>32</sup> The tax terms ∆ _hpt_ , _Mht_ , and _Mpt_ are as defined in Section 2, now adding subscripts as appropriate. So, for instance, the critical tax differential term is The controls in (8) comprise time-varying macro variables in the host ( **_x_** _ht_ ) and parent countries ( **_z_** _pt_ ), along with host-parent time-invariant bilateral variables ( **_g_** _ph_ ). We include among these controls a set of host country-year ( _aht_ ) and parent country ( _cp_ ) fixed effects, except that when estimating the impact of the host country King-Fullerton marginal effective tax rate _Mht_ we replace _aht_ by year fixed effects ( _bt_ ). We cluster standard errors by host country to account for correlations within host countries over time.

The expected signs of the tax terms in (8) are implied by the analysis in Section 2. All else equal, an increase in the rate differential ∆ _hp_ —reflecting a reduction the statutory rate in the host country _h_ and/or an increase in the weighted average of statutory rates elsewhere<sup>33</sup> —is expected to increase FAI from _p_ into _h_ , so that _β_ ∆ _>_ 0. This effect is less, however, the higher is the initial differential, so that _β_ ∆2 is expected to be negative. A higher _KF_ - _METR_ in the host country, _Mh_ , on the other hand, is expected to reduce investment into _h_ ; so the expectation is that _βMH <_ 0. In contrast, the _KF_ - _METR_ in the parent country _Mp_ (indeed that in any country other than the host _h_ ) is predicted by the analysis above not to affect FAI into _h_ , so that _βMP_ = 0; but we have noted too that there may be circumstances in which there might be some (likely positive) effect. For the interaction term, the nonlinearity in (4) suggests that _βM_ ∆ _>_ 0: as noted earlier, a tax differential ∆ _ph_ that is more favorable to investment in _h_ dampens the adverse impact of a higher host marginal effective rate _Mh_ . Sufficiency of the _I_ - _METR_ in characterizing real investment decisions, as implied by Proposition 1, corresponds to the restrictions _β_ ∆ = _−βMH_ = _βM_ ∆ = _−β_ ∆2 and _βMP_ = 0.

> 32For this purpose, the bilateral stock of FAI is imputed using the perpetual inventory method, using an assumed depreciation rate of 0.195. The results this gives for the US are very close to the direct reports in the BEA data, giving some confidence that the estimates are reasonably accurate.

> 33In the presence of complementarities, corresponding to negative weights _δhjp_ , such an increase might arise from a reduction in statutory rates; in the empirics that follow, however, the weights will all be non-negative.

17
Table 5. The impact of homicide rates on enrollment rates using the ENOE

|**Dependent variable: enrollm**<br>Timeperiod: 2006-2010|**ent Rates**<br>Ages 12-14<br>(1)|Ages 15-17<br>(2)|Ages 18-20<br>(3)|Ages 21-24<br>(4)|Ages 12-14<br>(5)|Ages 15-17<br>(6)|Ages 18-20<br>(7)|Ages 21-24<br>(8)|Ages 12-14<br>(9)|Ages 15-17<br>(10)|Ages 18-20<br>(11)|Ages 21-24<br>(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Homicide Rate|0.000656|0.00451**|-0.000190|-8.60e-05|0.000820|0.00453*|-0.000316|-0.000121|||||
||(0.00174)|(0.00227)|(0.00225)|(0.000772)|(0.00177)|(0.00231)|(0.00231)|(0.000791)|||||
|Public Expenditure|||||-2.62e-10|0.0000|-1.55e-10|2.48e-10|||||
||||||(2.40e-10)|(3.78e-10)|(3.52e-10)|(2.26e-10)|||||
|Drug Related Homicide Rate|||||||||0.000282|0.00136*|-0.000552|-0.000366|
||||||||||(0.000580)|(0.000794)|(0.000722)|(0.000227)|
|Constant|0.821***|0.538***|0.263***|0.0958***|0.828***|0.540***|0.264***|0.0887***|0.822***|0.543***|0.265***|0.0973***|
||(0.00718)|(0.00923)|(0.00834)|(0.00478)|(0.0101)|(0.0134)|(0.0122)|(0.00764)|(0.00627)|(0.00827)|(0.00726)|(0.00459)|
|Observations|5,248|5,247|5,175|5,208|4,983|4,980|4,914|4,949|5,248|5,247|5,175|5,208|
|R-squared|0.416|0.477|0.476|0.438|0.425|0.478|0.477|0.435|0.416|0.477|0.477|0.438|
|Time-Varying Mun. Controls<br>**Notes.** Dependent variable is the the<br>Drug-related homicides are collected<br>2006 to 2010. Standard errors cluster|No<br>enrollment rate for m<br>by the National Coun<br>ed at the municipality|No<br>ales by age group, c<br>cil of Public Security <br>level in parentheses.|No<br>alculated from ENOE<br> (Consejo Nacional d<br>*** p<0.01, ** p<0.0|No<br>survey data. Homic<br>e Seguridad Pública)<br>5, * p<0.1|Yes<br>ide rates are normaliz<br> of the Federal Gove|Yes<br>ed according to the <br>rnment and include a|Yes<br> national homicide rat<br>ll deaths by "presum|Yes<br>e per 100,000 inhabi<br>ed delinquent rivalry|No<br>tants in 2007 (gene<br>".<br>All regressions i|No<br>ral homicide rate of 8<br>nclude municipality a|No<br>.3 and drug related <br>nd year fixed effects|No<br>homicide rate of 2.6).<br>. Data available from|

25
untreated manufacturers recover more slowly than untreated retailers? That is, is the behavior of the treated firms consistent with the data from the untreated sample?

Before looking at the microenterprise panel data, we note that the SME sample includes both retailers and manufacturers. There are relatively few retailers among the larger enterprises. The sample includes only 28 retailers (including hotels and restaurants) that were damaged by the tsunami. But these retailers had replaced 78 percent of their assets on average (85 percent at the median), compared with 64 percent (60 percent at the median) among the manufacturers. The difference in means is significant at the .05 level, in spite of the small sample size. On the other hand, we find no significant difference between retailers and manufacturers with respect to replacing household assets. By the summer of 2007, larger retailers had replaced 71 percent of the household assets damaged in the tsunami, compared with 67 percent for manufacturers. These data suggest that manufacturers had as many resources available to them as retailers for use in replacing household assets. These data are thus consistent with retailers having stronger incentives to replace or repair business assets.

We do not have good measures of inventories prior to the tsunami. This is a concern, because a larger share of retailers’ investment is made in inventories. Admittedly, even our measures of profits and sales are retrospective. However, leaving aside concerns about deliberate misreporting, we believe these are likely to more accurately reflect the pre-tsunami situation. Using the untreated part of the microenterprise sample, we now examine the characteristics of firms that recovered more quickly. We estimate the following random effects model for log real profits for untreated firm _i_ in time period _t=2,...,11_ : 26
# **What Happens When the Market Shifts to China?**

# **The Gabon Timber and Thai Cassava**

# **Value Chains**

_Raphael Kaplinsky Anne Terheggen Julia Tijaja_ Development Policy and Practice, The Open University, Milton Keynes, U.K.

## <u>R.Kaplinsky@open.ac.uk</u>

## **Keywords**

Global value chains, China, Thailand, Gabon, Cassava, Timber

## **JEL codes:**

F13, F14, F15, F16, F21, F23, L22, L23, L24, L70, L73, M11, M14, O13, O14
Table 13: Projected and actual wage increas for 700 industrial **rwms;** 1993.1/1992.1 <!-- Start of picture text --> Change in wage Projected -- A--Actual<br>(range) No. of firms Employment (%) No. of finns Employment(%)<br>constant 37 2.7 116 11.0<br>0.1-10% 121 11.7 119 28.4<br>10-15% 121 25.7 76 17.6<br>15-20% 148 30.4 84 11.2<br>20-30% 191 21.5 140 16.9<br>>30% 82 8.5 165 14.9<br>o/w 30-35% 30 3.6 41 5.3<br>olw >40% 27 1.9 88 6.4<br><!-- End of picture text --> Source: Ministry of Labor <!-- Start of picture text --> Table 14: Net Flows, 1990-1994<br>Based on Munkaeromerleg Based on LFS<br>1990 1993 Net change 1993 1994 Net change<br>Employed, above<br>working age 488 300 -188 175 138 -37<br>Working age<br>population 5957 6061 +104 6034 6014 -20<br>of which:<br>employed 4991 4188 -803 4046 3916 -130<br>unemployed 24 680 +656 498 476 -22<br>out of labor 942 1193 . +251 1490 1622 +132<br>force<br><!-- End of picture text --> Table 15: Pearson and partial correlation coefficients between regional umemployment and industial wage increase, 1992 (20 regions) <!-- Start of picture text --> dW92<br>Pearson Partial<br>(sign.)<br>Unemployment rate<br>1991.12. .275 .433<br>(.06)<br>Increase of the rate<br>1992.12. - 1991.12. -.099 -.360<br>(.13)<br><!-- End of picture text --> 31
work than others. On the other hand, if farmers have limited access to farm inputs, they might need to work more hours in agricultural sector.

Improved accessibility to jobs could help farmers to take non-agricultural work during the agricultural off-season. Commuting might be particularly hard not only due to long distance to urban areas but also due to poor road quality and poor transportation infrastructure. Improved roads and transportation infrastructure, therefore, are expected to increase the probability of household members engaging in work outside their village.

# **3 Background**

The paper focuses on Armenia to study the relationship between road quality and rural employment. Armenia is a lower-middle-income country (GDP per capita USD 3,917 in 2016 (constant 2010 US$)) with a population of 2.9 million people.<sup>4</sup> The country has quite a well-developed road network, Figure 1 shows that most of the settlements in the country are connected to roads. However, the quality of roads is still a matter of concern. The railroad network is not very well developed in the country mainly due to the rough terrain. Therefore, road network is vital for passenger and freight transportation (World Bank, 2017).

The classified road network of Armenia is 7,700 km long, from which around 1,400 km are interstate roads, 2,520 km are regional roads, and 3,780 km are local roads. Most of the roads were built in 1960s and 1970s, and have deteriorated since independence in 1991 due to poor maintenance (World Bank, 2017). Since the early 2000s the country has received funding from different international organizations, such as the Millennium Challenge Corporation (MCC), the World Bank (WB), and the Asian Development Bank (ADB) to rehabilitate the lifeline roads<sup>5</sup> in the country and expand the major interstate road into a highway, connecting the south to the north of the country. Lifeline roads network serves as a vital link in the country, connecting 960 communities and approximately 1.1 million rural residents - 37 percent of the total population - with one another and

> 4Source: Databank, the World Bank `https://data.worldbank.org/indicator` Accessed: June 2019.

> 5The roads connecting villages to major roads are often called “lifeline roads” in Armenia (World Bank, 2017).

7
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Clearly, these categorizations can overlap but they can provide a first cut at profiling poverty mobility for different groups, and we would expect an overlap between the synthetic panel estimates and the true rates for the groups whose heterogeneity mimics that of the whole country.

Not surprisingly, the 95 percent confidence intervals for synthetic panels estimates for both Peru and Vietnam are much smaller than those for the true rates with the gaps between the standard errors amplified roughly twice (i.e., multiplied by 1.96). Our estimates appear to be reasonably good, especially for those who are poor in either period or both periods. Except for a few cases (e.g., households where heads only achieve primary education or living in urban Selva in Figure 2, or households living in urban areas in Figure 7), there is much overlap between the true rates and our estimated rates.

# **VI.4. Poverty Mobility in Three Periods**

We turn next in Table 6 to examining our estimates on poverty mobility for three periods using data from all three survey rounds for the US in 2005-2007, and 2009, Vietnam in 2004, 2006, and 2008, and Peru in 2004, 2005, and 2006, where there are 8 possible poverty categories that each household can fall in in these three periods (for the unconditional probabilities).<sup>33</sup> As discussed above, we should expect estimates to be less accurate than those for two periods; however, our proposed method turns out to work quite well with more than two thirds (i.e., 17 out of 24) of all the point estimates being contained in the 95 percent confidence intervals around the true rates; the corresponding figure for the stricter test of one standard error is just one half (i.e., 12 out of 24). The efficiency test points to somewhat better

> 33 Estimation results for the trivariate normal probabilities in this table are calculated using the Stata algorithm by Cappellari and Jenkins (2006) with 100 Halton draws.

34
<!-- Start of picture text --> 14%<br>12%<br>:A<br>8% :: A\/ —RUII/ real GDP<br>: tet ++++ RUICOC/ real GDP<br>6% : y = -RUIC/real GDP<br>= Soy<br>4% SDVn<br>2% + > , ma,<br>we. wort tan YN o e-< -—_ -. 4 ---—-+ s Pom Lote,~~ eT.<br>0%<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text -->
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<!-- Start of picture text --> The Pollution Intensity of China's Overall Trade and Processing Trade<br>CoD $o2<br>t “NX v<br>a. eee a,<br>& a ae<br>££$££ssSssSs£££££sSsSessSs<br>—COD Intensityx —---—- CODIntensityPX — S02Intensityx —--+-—+ S02 Intensity PX<br>———-—s- COD IntensityM —+++==+ CODIntensityPM ———= SO2 IntensityM —«++===+ SO2 Intensity PM<br>Smoke Dust<br>8, g<br>g<br>€FSF SG EFF SS EE € FSF OG FF SES EE<br>———-s Smoke Intensity X —+++—ees Smoke Intensity PX ———_— Dust Intensity X —<++=s Dust IntensityPX<br>——_ Smoke Intensity M —---——= Smoke Intensity PM ———_ Dust Intensity M —---——= Dust IntensityPM<br><!-- End of picture text -->
Upgrading Investment Regulations in Second Pillar Pension Systems: A Proposal for Colombia Pablo Castañeda Heinz P. Rudolph<sup>1</sup> JEL classification: C13, D14, G11, G23 Keywords: Colombia, pension funds, lifecycle, strategic asset allocation

> 1 Pablo Castañeda is Assistant Professor at the School of Management, Universidad Adolfo Ibañez (Santiago, Chile) and Heinz P. Rudolph is a Senior Financial Sector Specialist at the World Bank. This paper is a revised version of a study presented to the Ministry of Finance of Colombia and the _Superintendencia Financiera de Colombia_ in 2010. The authors benefitted from valuable comments from Lily Chu, Gustavo Demarco, Gonzalo Reyes, Rogelio Marchetti, and Tony Randle.

<mark>2</mark>
the rainy season because the roads are seasonal ( **Error! Not a valid bookmark self-reference.** 4). The available means of transportation are also limited. Only 28% of communities have public transport services. Bicycles or pack animals are the main means of transportation for about 8% of rural households. As a consequence of poor road infrastructure, the time to travel to markets is high.

**Table 4: Road Types Within and Outside Communities, 2005**

||Within community|Outside community|
|---|---|---|
|Type of road|(%)|(%)|
|Mud|73|52|
|Concrete|19|30|
|Asphalt|3|13|
|Gravel|3|4|
|Other|2|2|

Source: 2005 Tanzania RICS Only 40% of communities are electrified. Not only do most of the surveyed communities lack access to electricity, but even in electrified communities most households do not have access to power. As few as 30% of households in electrified communities use electricity. In those communities that have access to electricity, responding community leaders report that getting a power connection for new businesses took more than 140 days (three times longer than in urban areas as measured by the urban ICA). The public electricity supply is not very reliable. It was interrupted on average 71 times during 2005. Most entrepreneurs do not have access to basic means of communication also. Only 13% of rural entrepreneurs own a fixed line or cell phone.

The evidence describing local governance is somewhat uneven. About two-thirds of the surveyed communities do not report conflicts with local authorities that negatively affect the business environment, but the other one-third does. A large majority of communities report confidence in 24