rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Robustness checks**

# **4.1. Sensitivity to lining up methods**

The results reported above are based on the selected line-up method (Method 1), which adjusts the population using the UNDESA projections and redraws the poverty line in each survey to match the World Bank’s published poverty estimates for 2013, as described in section 2.2 above. This method aims to reduce biases that arise from pooling data collected in different years and ensures consistency with the existing published estimates. However, the method relies on a line-up procedure that imposes strong assumptions. These assumptions have not to our knowledge been systematically validated across several countries, and there is no hard evidence that applying the procedure provides a more accurate poverty profile for 2013 than simply pooling data for multiple years. Therefore, as an initial robustness check, we report selected key results under two other lineup methods: one that adjusts both survey weights and the welfare aggregate as described in section 2.2 above, and uses the international poverty lines $1.90 and $3.10 (Method 2); and another that adjusts only the sample survey weights to match the UNDESA population projections (Method 3).

In general, the choice of method makes little difference to the estimates. The key results, with respect to the share of the extreme poor belonging to different groups, changes by at most one to two a percentage points (Table 8). The results that are mildly sensitive to the choice of lineup method are the share of the poor working in agriculture and the share of the poor with no education, but even in these cases the changes barely exceed 2 percentage points and do not alter the qualitative nature of the results. This comparison does, however, illustrate that replicating the lineup procedure used to generate the published estimates is not necessarily straightforward, and that the nature of the line-up method usually has small but noticeable impacts on the profile.

**Table 8: Sensitivity to line‐up method**

||**Method 1 (Use published**<br>**estimates)**|**Method 2 (Adjust welfare**<br>**andpopulation)**|**Method 3 (Adjust**<br>**population only)**|
|---|---|---|---|
|Percent of extreme poor in<br>rural areas|80.1|80.0|80.3|
|Percent of poor working<br>adults in agriculture|64.6|62.7|63.1|
|Percent of extreme poor<br>being0‐14years old|44.2|43.0|42.5|
|Percent of poor adults with no<br>education|39.1|37.4|37.7|
|**Methodological details**||||
|Year|2013|2013|2009‐2014 mixed|
|Poverty lines|Derived from PovcalNet<br>povertyrates|$1.90 and $3.10|$1.90 and $3.10|
|Welfare aggregate<br>adjustment|Adjusted using national<br>account data on real<br>consumption or GDP<br>growth|Adjusted using national<br>account data on real<br>consumption or GDP growth|None|
|Population||||

26
percent of Mexico's cultivated land required regular irrigation. Because of the high cost of irrigation, the government has emphasized expanding production on existing farmland rather than expanding the area under irrigation. Although corn is grown on almost half of Mexico's cropland, the country became a net importer of grain during the 1970s.

Agricultural practices in Mexico range from traditional techniques, such as the slash-and-burn cultivation of indigenous plants for family subsistence, to the use of advanced technology and marketing expertise in large-scale, capital-intensive export agriculture. Government extension programs have fostered the wider use of machinery, fertilizers, and soil conservation techniques. These diverse agricultural practices call for a diverse rural labor market.

# **The Labor Force**

The analysis now turns to an examination of data on economic activity and occupation in order to obtain a “snapshot” overview of the farm and nonfarm sector in rural Mexico during the 1990s and early 2000s. The analyses are based on ENE survey data.

The share of the workforce in the formal sector experienced a large long-run decline from the late 1980s to the mid-1990s, followed by a partial recovery in the late 1990s, while the 1994/95 crisis and the 2000/02 period of stagnation tended to increase both unemployment and informality. Since 1995, Mexico's rural workforce has decreased by 0.5 million reaching around 9.3 million in 2003; of these 9.2 were employed, down by 0.3 million since 1995 (see Appendix A). The main explanation is migration of the younger age cohorts to urban areas or abroad. This is causing an increase in the average age of the work force (Table 2.2).<sup>3</sup> Women, according to data, comprise a relatively small part of Mexico's rural workforce; only 27.0 percent are women (calculations based on ENE 2003 survey). However, this estimation is likely to under-represent the female share of the work force. The way the question is phrased may lead some women to say that they are not part of the work force when they actually are.

Mexico’s seven regions each followed their own individual rural labor force development path (see Figure 2.1 and Appendix A). The Pacific and Center-North regions experienced a rapid increased in job creation since 1995. During 1995-2003, the labor force decreased in the Center, Gulf, North, and South regions and in the latter by far the most (around 400,000 workers). The Center-North and Pacific regions experienced their work forces increase by around 200,000 and 400,000 workers, respectively. The increase in the production of export crops in these regions has increased the demand for labor and workers from other parts of Mexico and workers have migrated to grasp the increased opportunity to improve their livelihood.

> 3 The rural Mexican labor force is defined as people above 12 years of age and living in areas with less than 2,500 inhabitants.

4
**Table 3: The “Cost” of Alternative DAU 2006 Allocation**

||**DAU 2006 Actual Allocation**<br> <br>|
|---|---|
||% of districts under resourced<br>% of Total DAU misallocated|
|#1 : Pure Formula 2006|51%<br>12%|
|#2 : Pure Formula 2005|55%<br>9%|
|#3 : Multiplicative Cost|31%<br>22%|
|#4 : Poverty|48%<br>16%|
|#5 : Wage Distribution|47%<br>4%|
|#6: Pure Formula 2006 + Hold Harmless|50%<br>10%|
|#7: Population|29%<br>30%|

The costs of mis-targeting of intergovernmental transfers can be measured by the extent to which local governments are under-funded relative to those that are overfunded. We can capture the trade-off by moving from one allocation mechanism to the other based on overall level of misallocated transfers (Table 3) and statistics that summarizes the individual discrepancies, notably for the under-resourced (Figure 2 & 3, Table 4).

Table 3 summarizes the share of districts under-resourced and the amount to which they are under-resourced as a share of the overall DAU comparing the actual 2006 DAU to alternative specifications.<sup>20</sup> By definition, the amount that certain districts are under-resourced is the amount that others are over-resourced (given the absolute overall vertical pool). Mis-targeting losses are significant, ranging from nine to 30 percent of the DAU or from USD 1.2 billion to almost 4 billion. For example, if one believes that poverty should be the main indicator for expenditure needs, this implies that poor localities are under-resourced by Rps. 21 trillion (16 % of DAU share for districts or estimated USD two billion) relative to the prevailing DAU allocation for 2006..

> 20 As similar measure could be constructed for aggregate resourcing of individual districts (i.e., predicted OSR, all revenue sharing, DAU, and other transfers), although our chosen measure purposively focuses on the DAU misallocations.

19
Table 7: General Equilibrium Effects, improving connectivity and deepen integration of the African Region

|||Dee<br><br>Wi<br>|p PTAs plu<br>and ports i<br>thout<br>|s roads, b<br>mproveme<br>W<br>|orders<br>nts<br>ith<br>|
|---|---|---|---|---|---|
|||Mariti|me Pref.|Mariti|me Pref.|
|Country|Baseline|∆_Xi_|∆_GDPi_|∆_Xi_|∆_GDPi_|
|||(1)|(2)|(3)|(4)|
|Algeria|2018|9.12|1.40|14.42|2.22|
|Botswana|2018|4.79|21.45|4.70|19.56|
|Burkina Faso|2014|56.63|3.09|56.00|3.02|
|Burundi|2018|59.20|1.83|66.42|1.89|
|Cabo Verde|2014|4.23|0.28|6.86|0.46|
|Cameroon|2014|19.96|3.01|11.63|2.14|
|Congo, Rep.|2014|9.57|8.28|12.91|10.12|
|Cˆote d’Ivoire|2018|11.57|1.85|18.74|2.58|
|Egypt, Arab Rep.|2018|12.00|1.50|13.30|1.67|
|Ethiopia<br>|2014|16.51|0.67|10.64|0.43|
|Ghana|2018|8.15|1.39|7.90|1.27|
|Kenya|2018|22.93|1.43|38.71|2.21|
|Lesotho|2018|7.50|9.10|7.09|8.75|
|Madagascar|2014|3.75|0.47|4.60|0.57|
|Malawi|2014|41.00|3.43|31.95|2.68|
|Mauritius|2018|3.67|1.07|3.70|1.07|
|Morocco|2018|3.97|1.13|3.97|1.15|
|Mozambique|2018|8.47|3.20|8.01|3.03|
|Namibia|2018|6.24|16.86|6.55|15.96|
|Niger|2018|9.15|4.45|9.51|4.73|
|Nigeria|2018|9.55|0.81|17.65|1.49|
|Rwanda|2018|60.82|2.88|61.22|2.73|
|Senegal|2018|18.08|1.78|24.73|2.26|
|South Africa|2018|9.34|1.36|9.37|1.35|
|Eswatini|2018|13.77|10.30|15.15|11.01|
|Tunisia|2018|5.59|2.47|5.91|2.66|
|Uganda|2018|25.47|2.65|32.16|3.29|
|Tanzania|2018|23.83|2.77|33.30|3.65|
|Zambia|2014|27.90|10.53|22.33|8.43|
|Zimbabwe|2014|17.83|32.58|16.08|28.97|
|Overall||11.49|2.00|14.80|2.27|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries. We assume _σ_ = 6. The reference country for the normalization is New Zealand.

28
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" significant penetration in this group of households. Nearly 32 percent of these very poor households had electricity in their homes. Very few had in-house water taps (6 percent), sewer connections (3 percent), or telephones (3 percent).

Telephone coverage remains at 3 or 4 percent among households in the first five 5-plercent quantiles (i.e., the lowest 25 percent of the sample households). Only when the median income proxy reaches US$120 per household per month does telephone coverage begin to rise. Coverage of electricity and of in-house water taps, on the other hand, begin to rise immediately and increases sharply from 5-percent quantile to 5-percent quantile. By the tenth 5-percent quantile (i.e., rnedian income proxy = US$225 per household per month), 66 percent of the sample households had electricity, and 33 percent had in-house water taps. Above US$225 per household per month, use of electricity and in-house taps continues to rise, but at a slower rate (Figure 1). Nearly all of the households in the wealthiest 5-percent quantile (US$1300 per household per month) had electricity, 88 percent had in-house water taps, and 72 percent had telephones.

Electricity was the most widespread of these three services at all income levels, and telephone service was the least common. In Figure 1 the coverage lines for these three sectors never cross, and the slope of the three lines is remarkably similar among households with incomes (as approximated by the consumption aggregate) above US$250 per month.

Figure I does show one puzzling result. One would generally expect more households (and particularly more poor households) to have modem water services than advanced sanitation solutions, but coverage of in-house water taps and sewer connections appear to be virtually identical up to US$300 per household per month. In fact, there are two shortcomings in the LSMS data used for this analysis that cause this result in Figure 1.

First, _in-house_ water taps are just one form of private household water connection. In many types of dwellings, in-house taps might not be feasible or desirable to install, or households may not initially want to invest in indoor plumbing facilities. In these cases households could choose to install 7
Control Effect size Weight Study Timing Mean with 95% CI (%) Short-run effects Comoros -4m 95.85 -1.19[ -3.61, 1.23] 13.13 Djibouti Om = 72.34 -3.27[ -4.25, -2.29] 13.39 Heterogeneity: t? = 1.27, I? = 58.84%, H? = 2.43 4 -2.54[ -4.48, -0.59] Test of 6, = 0; Q(1) = 2.43, p= 0.12 Test of8 = 0: z=-2.55, p=0.01 Medium-run effects Sierra Leone Im 89.48 741 [ -0.31, 15.13] 10.80 Egypt Infrastructure 4m 267.98 -14.21 [-32.56, 4.14] 5.65 Djibouti om 45 0.47[ -0.12, 1.06] 13.43 Tunisia 12m 37.54 so 8.20[ 2.76, 13.64] 11.99 Rural DRC 12m 36.08 Hs 18.36[ 14.22, 22.50] 12.56 Urban DRC 25m 94.97 -2.18[ -8.57, 4.21] 11.52 Tunisia 66m 59.78 ——— 15.09[ 1.24, 28.94] 7.53 Heterogeneity: t? = 70.21, ? = 92.52%, H? = 13.37 5.72 [ -1.24, 12.69] Test of 0, = 0;; Q(6) = 87.71, p = 0.00 Test of 6=0: z= 1.61, p=0.11

# Overall

3.64 [ -2.08, 9.37] Heterogeneity: t? = 63.46, I? = 98.39%, H? = 62.30 Test of 0; = 8; Q(8) = 140.89, p = 0.00 Test of 6=0: z= 1.25, p=0.21 Test of group differences: Q,(1) = 5.01, p = 0.03

-30 -20-10 0 10 20 30

Random-effects REML model
12 presented below are derived from a generalized least-squares process correcting for country-specific heteroskedasticity and autocorrelation of errors. Right-hand side variables are YASHR, TOTB, RR, LTDPC, STDPC, countryspecific dummy variables, year-specific d=mmy variables and IAL. I report here the coefficients of the external environment variables and IAL. The results of the country-specific effects are of independent interest and are examined in Conway (1990).

The coefficients of IAL in Table 1 represent the amount by which the dependent variable rises on average during years when a SAL or SECAL is in force. Adjustment lending programs are correlated with an increase of real GDP growth of 1.98 percent per year and with an increase in the annual inflation rate of 10 percentage points. Countries with adjustment lending programs had current-account deficit ratios to GNP that were roughly 2.1 percentage points more positive than those without. The investment ratio is positively but insignificantly correlated with participation in an adjustment lending program: there is a rise in the ratio of on average .56 percentage point annually for those economies participating ia the programs. All of these correlations are significantly different from zero at the 95-percent level of confidence except for that of the investment ratio.
# Table A5: EYOS of all learning interventions

|**Country**|**Intervention**|**Test**|**Evaluation**<br>|**Effect Size**|**EYOS_STEP**|**Reference**|
|---|---|---|---|---|---|---|
||h d|**Structured pedago**<br>lh d|**Method**<br>**gy**|||d|
|Uganda|Nortern Ugana Literacy Project (NULP)<br>NGO treatment arm|Engis wor<br>recognition|RCT|‐0.14|‐0.91|Kerwin an<br>Thornton (2015)|
|Uganda|Northern Uganda Literacy Project (NULP):<br>Gov treatment arm|English word<br>recognition|Multivariate<br>analysis|‐0.1|‐0.65|Kerwin and<br>Thornton (2015)|
|Chile|The Collaborative Language and Literacy<br>Instruction Project (CLLIP)|Vocabulary|RCT|‐0.1|‐0.65|Pallante 2013|
|Uganda|Northern Uganda Literacy Project (NULP):<br>Gov treatment arm|Writing test score|Multivariate<br>analysis|‐0.09|‐0.59|Kerwin and<br>Thornton (2015)|
|Chile|The Collaborative Language and Literacy<br>Instruction Project (CLLIP)|Reading<br>comprehension|RCT|‐0.06|‐0.39|Pallante 2013|
|Kenya|Reading to Learn (RtL)|Written literacy<br>exam|RCT|0.02|0.13|Lucas et al. , 2014|
|Philippines|Reading materials, teacher training to<br>incorporate reading into curriculum|Reading|RCT|0.06|0.39|Abeberese,<br>Kumler and<br>Linden, 2011|
|India|PicTalk: Year 2, Machines and activities||RCT|0.06|0.39|He, Linden and<br>Macleod, 2007|
|India|PicTalk : Year 2, Activities only|English test|RCT|0.06|0.39|He, Linden and<br>Macleod, 2007|

36
<mark>Gonzalez-Uribe, Juanita, and Michael Leatherbee (2018) " The Effects of Business Accelerators on Venture Performance: Evidence from Start-Up Chile"</mark> _<mark>, Review of Financial Studies</mark>_ <mark>31(4): 1566-1603.</mark> <mark>Hallen, Benjamin, Christopher Bingham, and Susan Cohen (2014) “Do Accelerators Accelerate? A Study of Venture Accelerators as a Path to Success?”,</mark> _<mark>Academy of Management Proceedings</mark>_ <mark>12955.</mark> <mark>Howell, Sabrina (2017) “Financing Innovation: Evidence from R&D Grants”,</mark> _<mark>American Economic Review</mark>_ <mark>107(4): 1136-64.</mark> <mark>Howell, Sabrina (2018) “Learning from Feedback: Evidence from New Ventures”, Mimeo. NYU. Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto (2011) “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.”</mark> _<mark>American Political Science Review</mark>_ <mark>105 (4): 765–89.</mark> <mark>Kaplan, Steven and Per Strömberg (2004) “Characteristics, Contracts, and Actions: Evidence from Venture Capitalist Analyses”,</mark> _<mark>Journal of Finance</mark>_ <mark>59(5): 2177-2210.</mark> <mark>Karajkov, Risto (2009)</mark> _<mark>Bridging the Gap: Access to Risk Finance for Early-Stage SMEs in Southeast Europe.</mark>_ _<u><mark>http://www.incubat.ro/uploads/Publicatii_Investment_SMS_report.pdf</mark></u>_ <mark>Kerr, William R., Josh Lerner, and Antoinette Schoar (2014) "The consequences of entrepreneurial finance: Evidence from angel financings."</mark> _<mark>Review of Financial Studies</mark>_ <mark>27.1 (2014): 20-55.</mark> Lee, David (2009) “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects", _Review of Economic Studies_ 76(3): 1071-1102.

Mason, Colin and Richard Harrison (2001) “Investment Readiness': A Critique of Government Proposals to Increase the Demand for Venture Capital”, _Regional Studies_ , 35(7): 663-668 Mason, Colin and Richard Harrison (2003) “Auditioning for money: what do technology investors look for at the initial screening stage?” _Journal of Private Equity_ 6(2): 29-42.

Mason, Colin and Jennifer Kwok (2010) “Investment Readiness Programmes and Access to Finance: A Critical Review of Design Issues”, _Local Economy_ 25(4): 269-92.

30
Table A.6: Attrition rates of included studies

|Study|Time since<br>program end|Attrition rate|
|---|---|---|
|Blattman et al.(2014);<br>|2 years|15%|
|Blattman et al.(2019a)|4 years<br>9 years|18%<br>14%|
|Blattman et al.(2016)|16 months|3.7%|
|Brudevold-Newman|7-10 months|6%|
|et al.(2017)|14-22 months|7.5%|
|DeMel et al.(2008);|8 quarters|9.6%|
|DeMel et al.(2012)|5 years|6% (a)|
|DeMel et al.(2014)|4 months|6.4%|
||8 months|6.5%|
||16 months|10.8%|
||25 months|11.5% (a)|
||6 years|14.6% (a)|
|Fafchamps et al.(2014)|1 to 4 quarters<br>3 years|16.6% (b)<br>14% (a)|
|McKenzie(2017)|2 years|24.4%|
||3 years|11.1%|
||5 years|16.9%|

(a) Refers to rate of firms interviewed at follow-up. Survivorship and location information could have then been collected on a larger fraction of firms from other sources. (b) Did not answer survey in any of the waves 4 to 6.

46
In thc following, I evaluate the role of regionalism at three levels. First, **I** examine closely the only serious attempts at preferential trading, the ASEAN which has recently announced plans to form the ASEAN Free Trade Area (AFTA). I suggest that the costs of such sub-regional schemes far outweigh their expected benefits. Second, I evaluate the case for a formal East Asian bloc along the lines of the EC or North America and conclude that though the threat of such a bloc may serve some purpose, its actual execution is a highly risky proposition. Finally, I evaluate the case for simultaneous, MFN style, nondiscriminatory liberalization on a region-wide basis. I argue that a case for such a regional approach "may" exist, though it is far from airtight.

# **3.1 Sub-Regional Groupings: ASEAN and AFIA**

I divide the discussion in this sub-section into three parts. Sections 3.1.1 and 3.1.2 desgribe the evolution of ASEAN in trade area and plans for the AFTA, respectively. Section 3.1.3 evaluates the case for promoting formal regional integration in the sub-region.

# **3.1.1 ASEAN: The Disappointing Past**<sup>**17**</sup>

Major developments in ASEAN are associated with four Summits attended by the heads of states of the member nations. The First Summit, held in August 1967, created the association through the ASEAN Declaration. The Second Summit produced the ASEAN Concord of February 24, 1976 which paved the way for economic cooperation among member nations. The Third Summit attempted to strengthen the cooperation through Manila Declaration of December 15, 1987. Finally, the Fourth Summit concluded with the Singapore Declaration of January 28, 17For details on ASEAN until 1987, see Sopiee, See and Jin (1987). For more recent developments, see Pangestu, Soesastro and Ahrned (1992) and Ariff and Chye (1992). In the following, I draw freely on Pangestu et al.

20
# **<u>Figure 1.0 Informal Institutions, Policies, and Formal Institutions</u>**

<!-- Start of picture text --> Level<br>Informal institutions, e.g. signing petitions,<br>customs, traditions, forming voluntary 1<br>norms associations, peaceful<br>protest<br>Institutional 2<br>e.g. policies governing<br>Environment: formal<br>petitions requirements to<br>rules (polity, judiciary,<br>effect referenda, civil<br>bureaucracy)<br>rights, taxation of NGOs<br>3<br>e.g. formal institutional<br>Governance – ruling<br>norms such as holding<br>administration<br>elections and ceding<br>power to the winner<br><!-- End of picture text --> Source: adapted from Williamson (2000), “The New Institutional Economics”.

We can consider a wide range of reasons why formal institutions, or quality of governance, ought to matter for the capacity of states to respond to environmental hazard events. For example, Sen and Dreze (1989, 1995) have argued that a major contributor to population vulnerability with respect to famine is the degree of transparency in governance and freedom of the press. In countries where the media has been able to report the onset of famine without restrictions and where politicians have electoral incentives to prevent widespread hunger, potential famines are identified early and public interventions are made to support precarious groups and communities. Democratic government and civic rights should therefore facilitate adaptation to climate-induced risks. We should find this effect to be particularly strong in the presence and organization of local and international NGOs, as such organizations provide an important informational role by monitoring and reporting on the degree of risk, as well as disbursing aid and assistance to affected groups (Martin and Taher 2001).

5