rafmacalaba/datause-encoder-data huggingface dataset:
# **Table of Contents**

|1.|Introduction ............................................................................................................................. 1|
|---|---|
|2.|Analytical Framework ............................................................................................................. 3|
||Development Trajectories ........................................................................................................... 3|
||Resource Endowments and Dependence in MENA ................................................................... 4|
||Autocracy to Democracy? ........................................................................................................... 5|
||Political Typologies and Prospective Transitions ....................................................................... 8|
|3.|Violent Conflict and Oil .......................................................................................................... 1|
|4.|Violent Conflict Trends in the MENA Region, 1960-2009 .................................................... 9|
|5.|Oil, Political Stability, and Conflict: MENA Case Study Evidence ..................................... 12|
|6.|Political Transitions and PSM and PSD Priorities in MENA ............................................... 15|
||Fiscal Management for Sustainability and Conflict Mitigation ................................................ 17|
||Promoting Economic Diversification and Private Sector Development................................... 22|
||Promoting Female Labor Force Participation ........................................................................... 24|
|7.|Conclusions ........................................................................................................................... 25|
|Re|ferences ..................................................................................................................................... 28|

ii
find a strongly significant negative effect of migration. Children born in households with a migrant member are estimated to be 3 percent (IVprobit) to 4.5 percent (2SLS) less likely to die in their first year than children born in households without a migrant member. Controlling for state-level health infrastructure, state historic infant mortality rates, and state GDP per capita reduces the estimated effect slightly to 3.7 percent. Likewise children in households with a higher migration prevalence are found to be statistically less likely to die as infants. A one standard deviation increase in household migration prevalence is estimated to result in a 1.8 percent lower infant mortality rate, which is approximately three times the size of the reduction associated with a one standard deviation increase in the schooling of the mother.

# **5.2 Impact on Birthweight**

Birthweight is an important early indicator of child health. Infant mortality risk declines steeply with birthweight (Wolpin, 1997) and it is claimed that low birthweight can result in cognitive and neurological impairment that limits the returns to human capital investment later in life. Using data on twins, Behrman and Rosenzweig (2003) find significant positive effects of higher birthweight on schooling attainment, adult height, and labor-market payoffs. Any impact of migration on birthweight is therefore likely to have important short- and long-term effects.

Table 6 estimates the effect of migration status on birthweight in Columns 1 to 7, and on the probability of being underweight (less than 2.5 Kilograms). The OLS results in Column 1 show a significant but small (69 gram) increase in birthweight being associated with having a migrant in the household. As discussed in Section 2, birthweight is only reported for 73 percent of the babies born after January 1, 1994, with non-reporting more prevalent among mothers with lower schooling and slightly more prevalent in non-migrant households. We use maximum-likelihood estimation of Heckman’s (1979) sample selection model to examine whether differences in reporting between migrant and non-migrant households bias our estimates.<sup>11</sup> Column 2 reveals no significant change in the migration coefficient, suggesting that non-reporting should not bias our results.

> 11 We do not have variables which can be plausibly assumed to impact on the likelihood of non-reporting of birthweight but not on birthweight itself, so the selection correction relies on the standard functional form assumptions here.

- 12 -
# **4 Model and Estimation Results**

In this Section, we provide some basic intuition for the empirical question we are examining in this paper. We then present our empirical model and estimation results. We keep the theoretical presentation highly stylized in order to make the basic mechanisms as clear as possible.

Starting from a benchmark of free and standard-less trade, the introduction of foreign product standards imposes two sorts of costs on exporters. On the one hand, there is a fixed cost of product adaptation to meet the foreign standard. In addition, there is the marginal cost of demonstrating conformity, in addition to any higher per unit production costs the standard itself may imply. Tables 8-10 present some firm-level evidence on the extent of these effects, taken from the World Bank’s Technical Barriers to Trade Database. Design costs and testing/certification costs play a significant role in firm decisions whether or not to export, and how much. Technical regulations are an important factor in expanding exports for most surveyed firms.

For simplicity, we assume that the costs of compliance are uniform across countries.<sup>11</sup> Intuitively, there are two main channels through which standards can affect trade flows.<sup>12</sup> On the one hand, higher variable costs mean that exporting firms tend to export less due to the presence of increased trade frictions. But in addition, higher fixed costs make it harder for producers to export at all, since the hurdle they must jump in order to gain access to a foreign market is

> 11 Relaxing this assumption could be a promising avenue for future research. Intuitively, differential compliance costs would provide an additional mechanism whereby the effects on standards “insiders” would differ from those on “outsiders”—including developing countries.

> 12 For simplicity, we leave to one side the kinds of information benefits posited by Swann et al. (1996), and Moenius (2004, 2006). Due to data constraints in the empirical part of the paper, we also exclude the possibility of cumulative effects, whereby standards relating to intermediate inputs indirectly impact the fixed and variable production costs of final products.

- 13 -
**Table 1. The economy is growing ...**

**Figure 3. ... but investment is declining**

||**2004**|**2005**|**2006**|**2002-**<br>**06**|
|---|---|---|---|---|
|Emerging East Asia|8.0|7.5|7.8|7.4|
|Develop. E. Asia|9.1|9.0|9.2|8.8|
|S.E. Asia|6.0|5.1|5.2|5.3|
|Indonesia|5.1|5.6|5.5|5.2|
|Malaysia|7.2|5.2|5.5|5.5|
|**Philippines**|**6.2**|**5.0**|**5.4**|**5.3**|
|Thailand|6.2|4.5|4.5|5.3|
|Transition Econ.|||||
|China|10.1|10.2|10.4|9.9|
|Vietnam|7.8|8.4|8.0|7.7|
|Small Economies|6.6|7.6|6.0|5.7|
|Newly Ind. Econ.|6.0|4.7|5.1|4.8|
|Chinese Taipei|6.1|4.0|4.0|4.3|
|Hong Kong, China|<br>8.6|7.3|5.9|4.7|
|Korea|4.7|4.0|5.1|4.7|
|Singapore|8.7|6.4|7.4|5.7|

<!-- Start of picture text --> Gross Fixed Investment as % of GDP (Nominal)<br>44%<br>39%<br>34% China<br>Korea<br>Indonesia<br>29%<br>Malaysia<br>Thailand<br>24%<br>19% Philippines<br>14%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ World Bank, 2006a.

_Source:_ World Bank East Asia Update, November 2006.

**As of the late 1990s, investment has been on a declining path.** In almost all post-crisis economies of East Asia investment is far from pre-crisis levels<sup>5</sup> . However, since 1997, gross fixed investment in the Philippines has been stagnant in real terms and has declined as a share of GDP, thinning out to less than 15 percent (Figure 1 and Figure 3).

# **1a. What’s falling is domestic investment**

**The financial crisis took a toll on both public and private balance sheets.** After 1997, revenue collection weakened<sup>6</sup> , the fiscal deficit increased sharply, and the central government debt rose to over 65 percent of GDP. Limited by these fiscal constraints and by a poor investment environment, gross fixed capital formation declined as a share of GDP to the lowest level in 20 years. Public capital spending moved in a pro-cyclical rather than a counter-cyclical direction, falling to an estimated 2.3 percent of GDP in 2004-05 (Figure 4). The cuts in public investment led to under-investment in infrastructure and public education; the resulting deficiencies added to the ongoing decline in private investment.

> 5 Investment does not necessarily need to return to pre-crisis levels. In the early 1990s it may have been excessive, with resources allocated to lower return or unproductive projects, leading to a buildup of financial vulnerabilities.

> 6 The Government provided tax incentives and loosened tax administration.

9
**Table 10. Baseline and Expected Outcomes of the Project on Mombasa-Kigali**

|Link|Obser<br>2005 (<br>Avg.<br>delay|ved<br>days)<br>Std<br>dev|East Africa Trade and Transport<br>Facilitation Project Measures|Expected<br>Project (d<br>Avg.<br>delay|from<br>ays)<br>Std<br>dev|
|---|---|---|---|---|---|
|Port|13|9.5|Single window system<br>Customs automation and risk|7|5|
|Transit Kenya (t)|4|2.5|management|3|1.5|
|Border (t)|1|1.5|Joint border post|0.3|0.8|
|Transit<br>Uganda|||Customs automation and risk|||
|(t)|2|1.5|management<br>Customs automation, single window|1.5|1|
|Final Rwanda|5|3|system and risk management|3|1.5|
|Total|25|10.5||14.8|5.6|
|Technical||||||
|Minimum|5|||5||
|Average Excess|20|||9.8||
|σlog normal||0.493|||0.530|

(t) = link with truck transportation.

On the next page, Figure 10 summarizes the probability of a shipment from the ship in Mombasa to a consignee in Rwanda exceeding a given delay (in days), based on the lognormal shape (semi-log scale).

53
sub-panels. The countries which were most affected by the crisis included Benin, Cameroon, Guinea-Bissau, Kenya, Nigeria, Senegal and Swaziland.<sup>20</sup> In most of these countries, banks witnessed a dramatic erosion of their equity and capital. For instance, at the end of 1996, these loans accounted for over 30 percent of total loans in Cameroon; in Guinea-Bissau, they accounted for over 45 percent of commercial banks’ total loans portfolios in 1995; in Kenya, financial institutions accounting for over 30 percent of total assets faced solvency problems between 1993 and 1995.<sup>21</sup> Interesting enough, the countries most affected by the crisis also exhibited the lowest ratio of equity to liquid assets, an indication of a fragility of banks, which often resort to short-term financing. In particular, Swaziland and Benin have the lowest ratio; about 14% and 20%, respectively (see Table 1 in the Annex).

The financial cost of the crisis was variable across countries. For instance, in Kenya, the 16 distressed financial institutions closed or taken over by the Central Bank of Kenya in the early 90s had assets and deposits of about US$370 million and total losses of about US$158 millions.<sup>22</sup> In Senegal and Côte d’Ivoire where the magnitude of the crisis was also significant, the estimated losses were much smaller in absolute terms, yet much higher when expressed in terms of GDP, reflecting the size of these economies. They accounted for over 17 percent of GDP in Senegal and about 25 percent of GDP in Côte d’Ivoire in the late 80s and early 90s, respectively [Caprio and Klingebiel (2002)].<sup>23</sup>

**Figure 2: Non-Performing Loans (in Millions of US dollar) in Sub-Saharan Africa: Contrast between Sub-panels of CFA and non-CFA countries**

<!-- Start of picture text --> 7000 300<br>6000<br>250<br>5000<br>200<br>4000<br>150<br>3000<br>100<br>2000<br>50<br>1000 Non-CFA CFA (right scale)<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> 20 The magnitude and length of the crisis was particularly significant in countries which experienced systemic financial crises in the 1980s and 1990s (Cameroon, Kenya and Senegal) and during most of the 1990s (DRC and Nigeria). 21 In Senegal, about 50 percent of banking system loans was nonperforming in the late 80s and early 90s [Husain and Faruqee (1994)].

22 For further details, see Central Bank of Kenya Monthly Economic Review (1995).

23 Although most countries in Sub-Saharan Africa were affected in the 90s, the financial crisis broke out much earlier in a number of countries, including Benin (1988), Equatorial Guinea (1983), Tanzania (1987), and Senegal (1988). The crisis had a devastating effect in Senegal where six commercial banks and one development bank accounting for over 30% of financial system assets were forced to close.

7
Variable Name and Description in STARS Page (s) in Annex

|11S15-64/65|Historical: ratio pop. 15-64 to pop. over 65|1|
|---|---|---|
|HIS20-59/60|<br>Historical: ratio pop. 20-59 to pop. over 60|1|
|HISOVER60|Historical: percentage of population over 60|1|
|l-SUNDER15|Historical: percentage of population under 15|1|
|HISW60+M60|+Historical: ratio women over 60 to men over 60|1|
|PRO20-64/65|Projections: ratio pop. 20-64 to pop. over 65|2|
|PRO20-59/60|Projections: ratio pop. 20-59 to pop. over 60|2|
|PRO15-64/65|Projections: ratio pop. 15-64 to pop. over 65|2|
|PRO65/15-64|Projections: ratio pop. over 65 to pop. 15-64(%)|2|
|PRO65/20-64|Projections: ratio pop. over 65 to pop. 20-64 (%)|2|
|PRO60/20-59|Projections: ratio pop. over 60 to pop. 20-59(%)|2|
|POPOVER60|Projections: percentage population over 60|2|
|POPOVER65|Projections: percentage population over 65|2|
|POPOVER75|Projections: percentage population over 75|2|
|COVERAGE|Labor force covered by public pension schemes|28-40|
|SYSDEP|System dependency ratio. (Pensioners/Contributors)|40|
|RETIREMENT|M Retirement age of males|41|
|RETIREMENT|F Retirement age of females|41|
|EXPDURETM|Expected duration of retirement of males|57|
|EXPDURETF|Expected duration of retirement of females|57|
|LFPM65PLUS|Labor force participation rate of males over 65|56|
|LFPM55/64|Labor force participation rate of males 55-64|56|
|PENTAXEMP|R Payroll tax for pensions from employer|41-47|
|PENTAXWOR|K Payroll tax for pensions from employee|41-47|
|PENTAXTOT|AL Total payroll tax for pensions|41-47|
|SYSTAXEMP|R Employer payroll tax, all "social security" programs|n.a.|
|SYSTAXWOR|K Worker payroll tax, all "social security" programs|n.a.|
|SYSTAXTOT|AL Combined payroll tax, all "social security" programs|n.a.|
|PENSGDP|Public Pension spending as % of GDP|3-17|
|PENSGOV|Public pension spending / government spending|3-17|
|PENSREVGD|P Receipts of pension schemes as % of GDP|3-17|
|REVPAYTAX|Share of pension revenues from payroll taxes|3-17|
|REVINVINC|Share of pension revenues from income from capital|3-17|
|REVGENREV|Share of pension revenues from general revenues|3-17|
|PUBRESERVE|S Public pension reserves, as % of GDP|18-23|
|PRIRESERVE|S Private pension reserves, as % of GDP|49-53|
|DEFICITBEN|Deficit / pension spending|3-17|
|ADMINCOST|Per member administrative cost / income per capita (%)|23-26|
|PUBINVRETS|Public pension fund investment retums|18-23|
|REPRIVRET|Real private pension fund returns|49-53|
|PRIRESEQ|Share of portfolio invested in equities|49-53|
|PRIRESBOND|Share of portfolio invested in bonds|49-53|
|PRIRESLOAN|Share of portfolio invested in loans|49-53|
|PRIRESOTHE|R Share of portfolio invested in others|49-53|

123
# XVi INTRODUCnON

Figure 3 Assumed Medium Trend and Aftemative Slow and Rapid Trends in Total Fertility During the Fertility Transition <!-- Start of picture text --> 5.0-<br>4.5-<br>4.0-<br>3.0-<br>2.5-<br>192 5-90 1595-2000 2005-10 2015-20 2025-30 2035-O0 204-;-50<br><!-- End of picture text --> Figure 4 Assumed Trends in Total Fertility at the End of the Fertility Transition <!-- Start of picture text --> 2.9-<br>2.8-<br>2. 7<br>2.0- . w<br>2 5-<br>2.4-<br>2. 3-<br>2.2-<br>2.0-<br>1.9<br>*1.9<br>1.5<br>930-8s5 1990-95 2000-05 2012-1S 2020-25 2030-35<br><!-- End of picture text --> Figure 5 Percentage of Total Fertility Assigned to Different Ages, by Level of Total Fertility <!-- Start of picture text --> 26-<br>24-<br>20-<br>0-2<br>14-<br>12-<br>10-<br>4-<br>2-<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49<br><!-- End of picture text -->
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72
landholdings. The average eligible household owns 0.8 hectares corresponding to the 28<sup>th</sup> percentile while the average non-eligible household owns 1.3 hectares which corresponds to the 69<sup>th</sup> percentile of the distribution of landholdings.

Figure 3 reports the targeting by landholdings. In particular, it reports the percentage of beneficiaries in each landholding quintile from the villager survey, for the start of the program in 2008/09 as well as the year of the intervention 2010/11. At the start of the NAIVS program, households with more land were also more likely to become beneficiaries. Participation in NAIVS among farmers in the lowest landholding quintile was only 5 percent and it increased to 10 percent among farmers in the highest quintile. This finding is consistent with results from Zambia and Malawi (Jayne et al., 2016; Goyal and Nash, 2017). By the year of the intervention, however, the probability of participation was roughly constant at around 15 to 20 percent across landholding quintiles.

Table 2 complements Figure 3 as it also reports improvements in the targeting of NAIVS between the start of the program in 2008/09 and the year of the intervention 2010/11. Panel A of Table 2 reports data from all villages, Panel B from study villages in Arusha, and Panel C and Panel D from non-study villages in Kilimanjaro and Morogoro, respectively. We focus on three of the eligibility criteria that can proxy for liquidity constraints, namely, whether the beneficiary had used hybrid seeds or chemical fertilizer prior to the start of the NAIVS program and whether they cultivated less than 1 hectare. Recall that eligible individuals should not have used improved inputs prior to becoming a beneficiary since purchasing inputs at market prices would signal that they are not credit constrained and thus may not have the highest marginal returns. Based on these three eligibility criteria, Table 2 reports the percentage of beneficiaries that meet all three criteria. In all panels, the targeting of NAIVS improves over time as the percentage of new beneficiaries who are eligible increases from the first year of NAIVS, 2008/09 to the 2010/11 season. In study villages, the percentage of beneficiaries who are eligible increases from 24 percent in 2008/09 to 46 percent in 2010/11. Following Galasso and Ravallion (2005) and Pan and Christiansen (2012), we define the targeting differential Tv in village _v_ as: where BEv are the number of eligible beneficiaries in village _v_ , Ev is the number of eligible individuals in village _v_ , BNEv is the number of beneficiaries that are not eligible while NEv is the number of non13
|**Table 7.**<br>**US Agricultural Import**|**s: Value of Preferences by Co**<br>**Incorporating Actua**<br>**_Fresh & Proc._**|**untry, 2003**<br>**l Utilization**|
|---|---|---|
||**Value**<br>**_Fruits & Veg._**|**_Sugar_**<br>**_Other_**|
|**_Beneficiaries of:_**<br>**CBERA-only & GSP**|_(% of Total Dutiable Agricultu_|_ral Exports to US)_|
|**_Netherlands Ant._**|5.7<br>0.0|0.0<br>5.7|
|**CBTPA & GSP**|||
|**_Barbados_**|22.5<br>0.0|0.0<br>22.5|
|**_Belize_**|20.6<br>19.2|1.3<br>0.0|
|**_Guatemala_**|11.0<br>8.7|1.3<br>1.0|
|**_Honduras_**|9.2<br>7.3|0.4<br>1.5|
|**_Costa Rica_**|8.3<br>7.3|0.1<br>0.8|
|**_Trinidad. & Tobago_**|5.4<br>0.0|0.0<br>5.4|
|**ATPA & GSP**|||
|**_Peru_**|11.0<br>10.2|0.3<br>0.5|
|**_Colombia_**|6.7<br>0.3|0.6<br>5.8|
|**_Ecuador_**|6.1<br>2.2|0.2<br>3.8|
|**AGOA & GSP**|||
|_Senegal_|6.8<br>2.8|0.0<br>4.0|
|**AGOA-LDC & GSP**|||
|**_Malawi_**|8.6<br>0.0|0.3<br>8.3|
|**_Guinea_**|5.6<br>2.5|0.0<br>3.1|
|**GSP-only**|||
|**_Tonga_**|7.5<br>7.5|0.0<br>0.0|
|**_Lebanon_**|6.8<br>5.3|0.4<br>1.1|
|**_Heard/McDonald Is._**|6.4<br>0.0|0.0<br>6.4|
|**_Paraguay_**|6.4<br>0.0|6.3<br>0.1|
|**_Anguilla_**|6.0<br>0.0|0.0<br>6.0|
|**_Pitcairn Is._**|5.6<br>0.0|0.0<br>5.6|
|**_Bosnia-Hercegovina_**|5.5<br>0.0|0.0<br>5.5|
|**GSP-LDC-only**|||
|**_Cambodia_**|9.9<br>9.9|0.0<br>0.0|
|**_Bangladesh_**|6.9<br>0.4|0.0<br>6.5|
|**_Togo _**|5.7<br>0.0|0.0<br>5.7|

38