rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.4. Evolution of services from traditional to modern**

The 3T‘s have changed the very nature of services from traditional to modern. These modern services (such as financial services, information processing services etc.), which are tradable and impersonal, take advantage of ICT, globalization, and scale of economies; and benefit from higher productivity growth. Also, there is ample scope for traditional service (such as tourism, education, musicians, entertainment production media etc. which require face to face contact) to absorb productivity gains, knowledge spillovers, tradability and fragmentation from ICT. In the absence of disaggregated national accounts data on services, we use the IMF Balance of Payments service exports data to classify service exports into modern and traditional service exports. Figure 7 uses available categories in Balance of Payments and shows that modern services are growing faster after the turn of the century. Figure A2 confirms that measurable modern services are growing faster than traditional service exports only after 2000.<sup>6</sup>

**Figure 7. Average annual growth in Modern and Traditional Services, World**

<!-- Start of picture text --> 20<br>18<br>1990-99 2000-08<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Modern Traditional<br><!-- End of picture text --> _Source_ : Balance of Payments, IMF, 2010 and authors‘ calculations.

Notes: Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational services.

> 6 Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational
). The representativeness of the ES will be further validated once the Establishment census data recently collected by the Ghana Statistical Service will be available.

Two broad metrics of private sector employment are informative.<sup>12</sup> The first is the static stock of employment, which is an indicator of distribution of jobs in the formal private sector. That is, what is the share of private sector employment accounted for by certain types of firms? A second broad metric is the rate of job growth (overall). That is, what is the rate at which the stock of jobs is growing, and which types of firms are responsible for that job growth? The latter metric can be decomposed as well into firms that increased their employment levels between 2010 and 2012 (job growth) and firms that shed jobs (job contractors).<sup>13</sup>

> 12 For a full discussion of results and metrics using similar data, see Aga et al. (2015).

> 13 A well-documented issue with periodic growth data on firm growth and employment is a so-called “reversion-tomean” effect, where growth measures of firms are upwardly biased when the base of the growth rate is the first period considered, as in a common arithmetic growth rate. The source of this bias is that firms that have shed jobs are then the likeliest to quickly add those jobs in times of recovery; conversely, using the end-point of measurement (in this case 2012), has an equivalently downward bias. To ameliorate this problem, the two-period average of full-time employment is used to classify firms, ��,��� � 0.5 ���,���� + ��,����), where _E_ is the full-time employment of firm _i_ in fiscal year 2010 and 2012 respectively (As all figures are based on survey estimates, employment figures are considered as survey-weighted).

9
Buller et al. (2016) conducted a mixed methods study in Ecuador, combining secondary analysis from a field experiment on the impact of a transfer program on IPV with in-depth interviews and focus group discussions with male and female beneficiaries. The qualitative component aimed to better understand the mechanisms underlying the quantitative results, which showed substantial reductions in physical and sexual violence among beneficiaries of the cash and in-kind food transfer program. These qualitative interviews revealed a similar finding to Arugay et al. ( _forthcoming_ ). Specifically, a core feature of women’s reported joint decision making was asking their spouse or partner for input into a decision, so that they would not be blamed if something went wrong.

Similarly, an analysis of qualitative data from eight projects in Africa and Asia focused on understanding women’s empowerment finds that joint decision making can be empowering for women. In particular, in focus groups in Ghana, women stressed the importance of family harmony and in the individual interviews, “women indicated that they want more input on decisions, but do not want full responsibility for decisions in case they go wrong (Meinzen-Dick et al, 2019, p. 20).

The qualitative evidence provides one interpretation of our empirical results. Couples who agree that they jointly make decisions may also be sharing responsibility for these decisions. And less IPV results when the responsibility is shared. These results are in line with the literature from management and political science on shared decision making and conflict.

# **6. Conclusion**

26
_(Table A1 continued)_

||Inside H<br>|omeland<br>|Outside H<br>|omeland<br>|
|---|---|---|---|---|
||1996|2011|1996|2011|
|Share Male|0.459|0.470|0.491|0.495|
||[0.034]|[0.032]|[0.055]|[0.040]|
|Share Connected to electricity grid|0.325|0.800|0.690|0.839|
||[0.347]|[0.271]|[0.325]|[0.210]|
|Share No refuse|0.176|0.098|0.050|0.045|
||[0.232]|[0.149]|[0.100]|[0.074]|
|Share Piped water|0.599|0.624|0.899|0.808|
||[0.399]|[0.347]|[0.201]|[0.291]|
|Total population|1,614.7|2,140.1|2,879.0|4,989.4|
||[2,759.5]|[3,761.2]|[4,944.0]|[6,885.0]|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Sample of communities within 30 kilometers of the former homeland borders (on both sides). Income expressed in December 2012 rand. Due to data availability, education variables are for total population and not household head in 2011. Employment by sector not available for 2011. The definition for
of the state value added tax (lCMS) to limit its potential for state industrial policy (see Afonso and Lobo, 1996).

# _Outward Orientation_

Economic liberalization is now commonly accepted as a cornerstone of good economic management. World Bank has recently ranked countries on the openness of their economies taking into account factors such as GNP originating from trade, manufacturing exports, foreign direct investment as a share of GDP, commercial credit rating and manufacturing content of exports. This index of economic liberalization is positively related to the index of fiscal decentralization.

# _Institutional Environmentfor Economic Management_

Huther and Shah (1998) combine the three aspects of economic management considered above in a quality index of economic management, the resulting index shows a positive association with the degree of fiscal decentralization. This is to be expected as the decentralized systems are more transparent in defining the role of various public agents and place a greater premium on accountability for results.

# _Quality of Governance and Decentralization_

Huther and Shah (1998) further combine indices on institutional environment for economic management, social development, government orientation and citizen participation to derive an overall index of governance quality. This index is then related to the degree of fiscal decentralization. Given the positive correlation between all of the governance quality component indices and the composition of government expenditures, the positive relationship between fiscal decentralization and governance quality is to be expected yet it is surprising to discover the strength of this positive association (as indicated by correlation coefficient of 0.62) and further that about 38% of the variance in governance quality is explained by fiscal decentralization alone.

_Decentralization and Regional Equity_ 28
currencies, recorded by the IMF’s COFER database, stood at US$294 billion as of Q1-2012. This is an order of magnitude higher than the US$22 billion recorded in 1999, but still represents just 5% of the total allocated amount (Figure 7). Total global reserve holdings, including unallocated reserves not included in the COFER data were approximately US$10.4 trillion as of Q1-2012.

- ii. Sovereign wealth funds (SWFs). SWFs have also grown rapidly and had assets of around $4.6 trillion as of Q1-2012.<sup>10</sup>

Given their size, allocations by central banks and SWFs to local currency EM bonds need increase only very slightly to boost demand significantly. EM central banks may themselves drive this trend, as EM reserve assets continue rising and “south-south” trade linkages grow.

A second additional investor category is absolute return investors: global hedge fund AUM stood at about US$2 trillion at the end of 2011, following net inflows for the year of US$70 billion.<sup>11</sup> Hedge funds’ aggregate allocation to EM debt is hard to gauge but significant. As of Q2 2012, one estimate placed EMdedicated hedge fund AUM at US$203 billion (though this includes allocations to EM asset classes other than bonds, notably equities); macro hedge fund AUM was estimated at US$173 billion (a portion of which is allocated to EM debt).<sup>12</sup> Increases in liquidity as debt markets continue to develop may attract more trading-oriented, leveraged investors.

A third additional investor category is benchmarked funds which have not traditionally focused on EM debt, but may increasingly begin allocating at least some assets to this area. The drivers of such fresh allocations include:

- i. Adjustment of funds’ investment mandates to incorporate benchmarked EM debt exposure;

- ii. Funds making permissible off-index asset allocations to gain exposure that is determined to be

> 10 Source:
processing exports are greater for destinations that are more populated and have a higher GDP per capita. Furthermore, we find a general trend for processing exports to become more sensitive to internal and import distances over time.

The results in columns 5 and 6 of Table 7 provide supporting evidence for Hypothesis 4. In column 5, the coefficient on ln ���� �ln ���� is negative, which suggests that a rise in oil prices reduces the share of China’s processing exports in total exports especially for far away destinations. Conversely, the coefficient on ln ���� �ln ��� is not significant.

The coefficients on the other control variables in columns 4, 5 and 6 suggest that the share of processing exports in total exports is larger for more populated provinces, closer to the coast with lower wages. The share is also larger when destined for richer, less populated countries that are further away. Finally, we find an upward trend for the share of processing exports in total exports, but this trend is smaller for internal provinces and faraway destinations.

In conclusion, we find evidence that China’s processing exports become more sensitive to both import and export distance in times of rising oil prices, but not more sensitive to internal distance. We also find supporting evidence that processing exports are more sensitive to changes in trade costs related to oil price movements than non-processing exports. Specifically, an increase in oil prices tends to reduce the share of processing exports in total exports, and especially when destined for far away countries.

# 6. **Intra-GPN Trade and the Great Recession**

Finally, we can use the processing trade data to examine the impact of the Great Recession on intra-GPN trade flows. This analysis has become particularly relevant in light of the collapse in trade during the crisis, which was significantly larger than the drop in world GDP. A number of scholars have attributed the disproportionate trade collapse to vertical specialization. Barry Eichengreen, for example, stated that “
expectancy coefficient from specification IV in Table 2. The standard errors for the Wald estimates are obtained from 500 bootstrap replications with the corresponding sample. In the pooled sample, the Wald estimates are 16% lower compared with the Mincerian estimates (7.90% vs. 9.40%). The Wald estimate is slightly higher for urban residents (10.21% vs. 9.50%), but it is lower for males (9.90% vs. 10.60%), females (4.30% vs. 7.30%), and rural residents (2.90% vs. 8.8%). For both Mincerian and Wald estimates, the return to schooling is always higher for males than females, and higher for urban than rural residents.

# **6.4 Robustness checks**

We reexamine our findings using several estimation methods and samples. We reestimated the models (i) without weights, (ii) using a different sample consisting only of data from one survey per country, (iii) using an alternative definition of parents’ life expectancy,<sup>19</sup> (iv) including cohort fixed effect with an alternative definition of cohort,<sup>20</sup> (v) using a sample consisting only of young age groups, and (vi) using a higher-order age variable to wipe out all age effects while estimating the life expectancy effects on earnings.<sup>21</sup> These robustness checks generate similar estimates of life expectancy effects on schooling and earnings. In most cases, the sign and statistical significance of the life expectancy coefficient is positive and statistically significant. The results for all robustness checks are briefly discussed and reported in Appendix C _._

> 19 Previously, parents’ life expectancy was constructed by taking a 25-year lag of life expectancy at birth. The youngest cohort in our survey was born in 1987. In the 1980s, in many regions, especially
: evidence from northern Mali_ (IWMI Working Papers No. H043847). International Water Management Institute. Retrieved from https://ideas.repec.org/p/iwt/worppr/h043847.html

- Dillon, A. (2011). The Effect of Irrigation on Poverty Reduction, Asset Accumulation, and Informal Insurance: Evidence from Northern Mali. _World Development_ , _39_ (12), 2165-2175. Retrieved from https://ideas.repec.org/a/eee/wdevel/ v39y2011i12p2165-2175.html doi: 10.1016/j.worlddev.2011.0

- Duflo, E., & Pande, R. (2007). Dams. _The Quarterly Journal of Economics_ , _122_ (2), 601646. Retrieved from https://EconPapers.repec.org/RePEc:oup:qjecon: v:122:y:2007:i:2:p:601-646.

- Dyer, J., & Shapiro, J. (2023). Pumps, prosperity and household power: Experimental evidence on irrigation pumps and smallholder farmers in kenya. _Journal of Development Economics_ , _163_ , 103034. Retrieved from https://www.sciencedirect.com/ science/article/pii/S0304387822001766 doi: https://doi.org/10.1016/ j.jdeveco.2022.103034

- Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C., & Taneja, J. (2021). Annual time series of global viirs nighttime lights derived from monthly averages: 2012 to 2019. _Remote Sensing_ , _13_ (5). Retrieved from https://www.mdpi.com/2072-4292/13/5/922 doi: 10.3390/rs13050922

- Ersado, L. (2005, January). _Small-scale irrigation dams, agricultural production, and health - theory and evidence from Ethiopia_ (Policy Research Working Paper Series No. 3494). The World
The effect of foreign aid on economic growth, poverty, and developmental outcomes may depend heavily on the fiscal response of recipient governments. One aspect of this fiscal response is the possibility that aid may be fungible (i.e., the net effect of earmarked aid differs from the intended effect).

This paper endeavors to determine the extent to which earmarked education and health aid are fungible. Many studies of foreign aid fungibility are hampered by a lack of comprehensive data pertaining to the intended purpose of aid. I use the OECD’s Creditor Reporting System (CRS), which disaggregates aid by sector, to overcome this problem. To cope with the incompleteness of the CRS data, I propose a novel data construction method that begins with the CRS and adds information from other OECD aid databases to provide more complete measures of education and health aid disbursements.

These data also enable me to divide education and health aid into on- and off-budget components. I demonstrate how a failure to adequately deal with off-budget aid (aid that is not recorded in a recipient government’s budget) may have biased previous estimates of fungibility. When donor-based measures of aid are employed, a potentially large fraction of this aid is off-budget aid. Hence, even if aid is used in the targeted sector, some of it may not be recorded as the sectoral expenditures of a recipient government. This failure to record some aid reduces the estimated marginal effect of total sectoral aid on government sectoral expenditures and thus leads to an overestimation of the extent of fungibility. Other papers employ aid data that are reported by recipient governments. In this case, the effect of on-budget aid on government expenditures is estimated, and off-budget aid acts as an omitted variable. Hence, the first problem is that we cannot estimate the degree of fungibility of off-budget aid. Moreover, because off- and on-budget aid are likely correlated, the estimated effect of on-budget aid is biased unless the marginal effect of off-budget aid on government spending is zero.

I use sector program (
insights in a number of respects. Mean age for the 868 estates with data is 19 years with largest estates the oldest (table 4). Most (68%) are owned by natural persons of Malawian nationality, 16% by ‘others’ -most likely legal persons- and 10% by expatriates. Expatriates’ ownership share peaks at the 100-500 ha size while that of government and ‘others’ peaks in the greater than 500 category. In contrast to smallholders, almost all estates (87%) use fertilizer and close to two-thirds (61%) purchase pesticide or seeds, a practice more prevalent for larger sizes in both cases. With about a third of estates having tenants; the share of estates with tenants peaks at close to 50% in the 10-100 size category. Compared to large farms in other countries that produce bulk commodities and often generate little employment (Ali _et al._ 2015), many of Malawi’s estates are quite labor intensive. Permanent or temporary male (female) labor is hired by 60% (25%) and 66% (52%) of estates respectively. For these, demand for permanent labor per ha cultivated is almost equal to the amount of labor spent by smallholders based on the 2010/11 LSMS-ISA survey (Deininger _et al._ 2015). It increases with size to about 0.9 males and 0.6 females in the largest category though the pattern for temporary labor is more volatile.

Claims about un- or underused estate land recurred in Malawi’s policy debate (Holden _et al._ 2006). While our sample was not designed to provide conclusive evidence on this, data suggest that for surveyed estates, 8
A related though different question to that of long-term impact is how efficient CCTs are in comparison with other unconditional programs. While this area has yet to be more profoundly studied, recent empirical research has started to shed some light. Baird et al. (2013) analyze data from 35 studies in 75 reports to compare the effectiveness of CCTs relative to UCTs in terms of schooling outcomes. Their review suggests that both CCTs and unconditional cash transfers (UCTs) increase the probability of enrollment and attendance, though the effects on test scores are moderate at best. Differentiating between intensity of conditioning, their analysis indicates that interventions that are explicitly conditional and monitor and penalize noncompliance have a larger impact than programs with no conditions or that have some conditions with minimum enforcement and monitoring. Özler, Baird, and McIntosh (2016) look at the sustained (medium-term) effects of a two-year CCT program compared with a UCT program among young women and adolescent girls in Malawi.<sup>3</sup> The study finds declines in HIV prevalence, teenage pregnancy and early marriage among the UCT group, unlike the CCT group, however these effects dissipated after the cessation of the unconditional grants. Conditionality, they conclude, could be limiting the social protection aspect of CCTs, even if it appears to be more effective in achieving behavioral change. The study also finds that children born to UCT beneficiaries

> 3 While the conditional transfer is based on attendance, recipients only need to show up to obtain the unconditional one.

3
is available through the gravity portal of the US ITC.<sup>6</sup> For further robustness checks, we also use WIOD.

While Release 2 of the ITPDE includes trade data for manufacturing and agriculture since 1989, information on services is only available starting in 2000. Hence, our sample spans the years 2000 to 2015, a time crucial for the development of RTAs: According to the WTO, the number of RTAs in force more than tripled in this period.<sup>7</sup> For computational ease we restrict the sample to the 120 largest countries measured in terms of GDP in 2019 taken from the World Bank’s World Development Indicators.<sup>8</sup> To estimate Equation 4, we further need information on the presence of RTAs and their type. For partial scope agreements, free trade agreements, and customs unions, we draw information from Mario Larch’s RTA database (Egger and Larch 2008). For non-reciprocal arrangements the database on Economic Integration Agreements maintained by Jeffrey Bergstrand and Scott Baier is the main source (Bergstrand et al. 2015), we update it to 2014 ourselves using information from the WTO’s PTA database.<sup>9</sup> With these two data sets at hand, we know whether there was any RTA in force in the first place and we can distinguish between agreements that were notified under the Enabling Clause, i.e. non-reciprocal trade arrangements and partial scope agreements, and those that were notified under Article XXIV, i.e., free trade agreements and customs unions.

Our analysis builds on the World Bank’s Deep Trade Agreement Dataset (DTA data) provided by Hofmann et al. (2019) to determine which Article XXIV agreements can be considered to be “deep”. The data set on the content of RTAs maps 52 provisions in 279 PTAs notified to the WTO and signed between 1958 and 2015. It also includes information about the legal enforceability of each provision
0.001)|(0.001)|(0.001)||
|||[1.000]|[0.462]|[1.000]||
|(3) Illness/injury in HH|0.579|_−_0.003|_−_0.009|0.004|5053|
||(0.494)|(0.016)|(0.018)|(0.019)||
|||[1.000]|[0.462]|[1.000]||
|(4) Child weight for length|0.546|0.133|0.156|0.109|1991|
||(1.682)|(0.081)|(0.093)<sup>_∗_</sup>|(0.093)||
|||[0.544]|[0.252]|[1.000]||
|(5) Vaccine completion index (Under 2)|3.085|0.032|0.303|_−_0.209|1457|
||(2.560)|(0.152)|(0.184)|(0.163)||
|||[1.000]|[0.252]|[1.000]||
|(6) Child birth complication index|0.000|_−_0.026|_−_0.126|0.061|856|
||(1.000)|(0.077)|(0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|0.002|0.024|
**Figure B.6: Effects on different components of women’s and girls’ assignable expenditures**

<!-- Start of picture text --> Expenditures for adult women<br>0.02<br>Cosmetics<br>1.06<br>Clothing<br>0.54<br>Accessories<br>1.10<br>Health<br>Expenditures for girls<br>0.27<br>Clothing<br>0.94<br>Education<br>-1.00 0.00 1.00 2.00 3.00<br>selected coefficient with 90 % confidence interval<br>Note: All expenditures expressed as % in household income<br><!-- End of picture text -->

**Figure B.7: Cumulative distribution functions of women’s assignable goods expenditures for nominated and selected women**

<!-- Start of picture text --> 0 2 4 6 8 0 5 10 15<br>Cosmetics (% in HH income) Clothing (% in HH income)<br>0 5 10 15 20 25 0 50 100 150<br>Accessories (% in HH income) Health (% in HH income)<br>Nominated Selected<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>Cumulative distribution function Cumulative distribution function<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>Cumulative distribution function Cumulative distribution function<br><!-- End of picture text --> Note: Both waves of household surveys are pooled for this graph.

50