rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.7: Direct and indirect and foreign services VA in gross exports, by country, 2011**

<!-- Start of picture text --> Direct and Indirect services VA in gross exports Foreign services VA in gross exports<br>28.9 15.7<br>27.5 21.2<br>19.9 21.1<br>32.1 16.0<br>14.4 14.0 15.6<br>23.8<br>20.0 21.8 21.1<br>16.7 17.3 14.7 15.6 13.2 13.5 17.6 15.3 14.2<br>11.4 11.8 10.7 9.7<br>6.6<br>4.8<br>OECD IND BRA TUR POL RUS BGR CHN HUN CZE HUN CZE BGR POL CHN OECD TUR IND RUS BRA<br>Domestic direct Domestic indirect Foreign<br>50 50<br>40 40<br>30 30<br>20 20<br>Percentage of total VA Percentage of total VA<br>10 10<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations based on TiVA database, 2015.

Over time, the direct services value‐added share in gross exports has fallen (left‐hand panel of Figure 2.8), coming close to the OECD average. Turkey’s indirect share of services value‐added has increased somewhat, but has not significantly caught up with the OECD level. On the other hand, the foreign value‐ added share of services is increasing toward OECD levels (right‐hand panel of Figure 2.8).

**Figure 2.8: Direct, indirect and foreign domestic value‐added (VA) in gross exports (1995‐2011)**

<!-- Start of picture text --> Direct and Indirect services VA in gross exports Foreign services VA in gross exports<br>1995 2000 2005 2010 1995 2000 2005 2010<br>TUR Direct OECD Direct TUR Foreign<br>TUR Indirect OECD Indirect OECD Foreign<br>Source: OECD TiVA 2015; author’s calculations.<br>35 12<br>30 10<br>25 8<br>6<br>Percentage of total VA 20 Percentage of total VA<br>15 4<br><!-- End of picture text -->

**The manufacturing sectors that use and indirectly export the most services in Turkey are textiles, chemicals, non‐metallic minerals, and wood, but service usage is low compared to the OECD.** As shown in the left‐hand panel of Figure 2.9, most OECD industries have more than 30 percent indirect services value added as a share of gross exports. In Turkey, the average is closer to 20 percent (right‐hand panel of Figure 2.9). The sectors with higher services inputs are also somewhat different in the OECD.

11
hiring—such as Finland, Luxembourg, the Netherlands and Portugal—or to make them more flexible—such as Italy and Spain.<sup>8</sup> Besides Sweden, Denmark is the only other country in the EU15, G7 or Nordic region that has no statutory minimum wage. Among the other 18 economies the ratio of the minimum wage to the value added per worker ranges from 12 percent in Austria to 41 percent in Italy. There was no substantial change in the minimum wage ratio between 2008 and 2013 in these country groups except in Greece, where the ratio increased by 31 percent in 2011 before changing back to 34 percent in 2013.<sup>9</sup> Taken together, Sweden’s regulations on hiring and the minimum wage are not too restrictive and indeed are more flexible than those in many comparator economies. But the potential adverse consequences of fixed-term contracts of short duration, and the fact that Sweden’s labor law stipulates a shorter duration than the labor law of the majority of other economies in the sample, suggest room for policy action.

Table 2: Features of hiring regulations and the minimum wage in countries in the EU15, G7 and Nordic region, 2013

|Are fixed-term<br>contracts<br>prohibited for<br>permanent tasks?|Yes:**Finland**, France, Greece, Luxembourg,**Norway**, Portugal, Spain.<br>No: Austria, Belgium, Canada,**Denmark**, Germany,**Iceland**, Ireland, Italy, Japan,<br>Netherlands,**Sweden**, United Kingdom, United States.|
|---|---|
|What is the<br>maximum<br>cumulative<br>duration of fixed-<br>term contracts?|No limit: Austria, Belgium, Canada,**Denmark**, Greece, Ireland, Japan, United<br>Kingdom, United States.<br>50–60 months:**Finland**, Portugal.<br>40–49 months: –Italy,**Norway**.<br>30–39 months: Netherlands.<br>20–29 months: Germany,**Iceland**, Luxembourg,**Sweden**.<br>10–19months:France, Spain.|
|What is the<br>minimum wage<br>ratio?<sup>a</sup>|0:**Denmark, Sweden**.<br>10–19%: Austria, France, Netherlands, United States.<br>20–29%: Canada, Germany, Greece, Japan, Luxembourg, Portugal, Spain, United<br>Kingdom.<br>30–39%: Belgium,**Finland**,**Iceland, Ireland, Norway**. <br>40–49%: Italy.|

Source: World Bank Group, _Doing Business_ 2014. Note: The sample consists of all 20 countries in the EU15, G7 and Nordic region. a. Ratio of the minimum wage for a trainee or first-time employee to the average value added per worker.

> 8 For details on labor market reforms in all economies since 2004, see the _Doing Business_ website at http://www.do ingbusiness.org.

> 9 Other economies with a relatively large increase in the minimum wage ratio between 2008 and 2013 are Italy and Norway with 10 percent, Ireland with 15 percent and Japan with 17 percent.

13
# 7.4 Utilization

The evidence presented so far shows that treatment communities began to monitor the health unit more extensively in response to the intervention and that in light of better community monitoring, the health unit sta¤ responded by improving the provision of health services. We now turn to the question of whether increased community monitoring also resulted in improved quantity and quality of care.

Tables 7 and 8 report estimates of the treatment e¤ect on quantity. We collected detailed data from the health facilities on the number of out-patients, the number of deliveries, the number of antenatal care patients, and the number of people seeking family planning services.<sup>26</sup> Table 7 presents the results, for the four di¤erent utilization variables, from the estimations of equations (2) and (4). There are positive and signi...cant di¤erences between treatment and control facilities across all four services. One year into the program, utilization (for general outpatient services) is 16 percent higher in the treatment facilities. When controlling for district ...xed e¤ects, the point estimate is slightly larger and more precisely estimated (signi...cant at the 1 percent level). The di¤erence in the number of deliveries at the facility (albeit starting from a low level) is even larger (68 percent, regression 4) and fairly precisely estimated. There are also positive and signi...cant di¤erences in the number of patients seeking antenatal care (22 percent, regression 8) and family planning (60 percent, regression 10).

As a complement to the di¤erence approach, columns 3 and 6 present the results from the estimation of a value added speci...cation.<sup>27</sup> Di¤erence-in-di¤erences estimates, i.e., equation (4), are reported in table A.10 in the appendix.<sup>28</sup> The point estimates from both speci...cations are positive and highly signi...cant. The di¤erencein-di¤erences estimates also suggest a larger treatment e¤ect (28%) for outpatient services.

Table 8 reports changes in utilization patterns based on household data. We collected data on where each household member sought care during the last year in case of illness that required treatment. Apart from recording visits to the project facility (treatment or control facility), we recorded visits to private providers (for-pro...t and NGOs), traditional healers, self-treatment (i.e., purchases of medicine in drug shops), or other government facilities (i.e., not a project facility). Consistent with the ...ndings reported in table 7, we ...nd a positive and signi...cant di¤erence in the use of the project facility between treatment and control facilities (regression 1). The increase,

> 26As discussed in section 5, these data were assembled by counting the number of patients from daily patient records, maternity unit records, the antenatal care register, and the family planning register.

> 27Data on the number of antenatal care patients and the number of people seeking family planning services were not collected from medical records in the pre-treatment survey.

> 28The value added speci...cation is

> yjdP OST = �V A + �V ATjd + �yjdP RE + "jdP OST :

20
<!-- Start of picture text --> Policy options<br>—_—__<br>CAPEX _* Public <-——» Imported ~<br><< Se \<br>“TM Private <——* Domestic ~<br>| EPM OPEX <=_» Public =<»=< Imported HY+ MFMod<br>“a Private = * Domestic ————<br>Energy mix ae a<br>Emissions ~~<br><!-- End of picture text -->
# Land Fragmentation and Food Insecurity in

# Ethiopia<sup>_∗_</sup>

Erwin Knippenberg<sup>_†_</sup> & Dean Jolliffe<sup>_‡_</sup> & John Hoddinott<sup>_§_</sup> JEL: Q15 Keywords: Land Fragmentation, Food Security, Land reforms, Risk Mitigation, Weather Shocks, Ethiopia

> _∗_ The authors would like to thank Chris Barrett and Mark Constas, as-well as seminar participants at Cornell University and the World Bank for their valuable feedback. The authors are grateful to the UK Department for International Development Ethiopia and Tim Conway for generous funding assistance.

> _†_ Lead Economist, Cooper/Smith. Corresponding Author (erwin@coopersmith.org)

> _‡_ Lead Economist, Development Data Group, World Bank

> _§_ Babcock Professor of Food and Nutrition Economics and Policy, Cornell University
Table 6. System GMM estimation with bottom and top Ginis (dependent variable = per capita income growth at a given percentile of state income distribution; 1960-2010; five 10-year periods)

||dlny5|dlny10|dlny25|dlny50|dlny75|dlny90|dlny95|dlny99|
|---|---|---|---|---|---|---|---|---|
|gini_b40|-0.643|-0.0167|0.201|0.267**|0.258***|0.227**|0.188**|0.323**|
||(-1.60)|(-0.08)|(1.65)|(2.63)|(2.69)|(2.60)|(2.26)|(2.63)|
|gini_t40|-0.447*|-0.414*|-0.309*|-0.281**|-0.126|-0.0423|0.0831|-0.0440|
||(-1.70)|(-1.71)|(-1.83)|(-2.03)|(-0.99)|(-0.35)|(0.70)|(-0.22)|
|edu_ms_age2139|0.172*|0.0645|0.0327|0.0608|0.0531|0.0695|0.0978**|0.0274|
||(1.68)|(0.82)|(0.56)|(1.02)|(0.89)|(1.36)|(2.43)|(0.48)|
|edushort1518|-0.111***|-0.0920***|-0.0628***|-0.0588***|-0.0602***|-0.0530***|-0.0437**|-0.0581**|
||(-3.35)|(-3.32)|(-2.93)|(-2.96)|(-3.12)|(-2.84)|(-2.43)|(-2.50)|
|age015|-0.231|-0.0448|-0.0163|-0.00462|-0.0370|-0.102|-0.167*|0.00797|
||(-0.90)|(-0.23)|(-0.11)|(-0.04)|(-0.33)|(-1.15)|(-1.92)|(0.05)|
|age65|-0.119|0.0547|0.0808|0.0799|0.0298|-0.0151|-0.0473|0.0663|
||(-0.59)|(0.42)|(0.90)|(0.94)|(0.36)|(-0.21)|(-0.63)|(0.70)|
|olf_female|-0.115*|-0.148**|-0.140***|-0.117**|-0.108**|-0.0909**|-0.0727**|-0.119**|
||(-1.69)|(-2.35)|(-2.83)|(-2.61)|(-2.40)|(-2.36)|(-2.35)|(-2.36)|
|lnyxx|-0.141***|-0.0922***|-0.0621***|-0.0612***|-0.0552***|-0.0558***|-0.0543***|-0.0346**|
||(-4.37)|(-4.06)|(-3.62)|(-3.40)|(-3.05)|(-3.80)|(-4.75)|(-2.04)|
|constant|1.618***|1.055***|0.729***|0.703***|0.649***|0.674***|0.654***|0.446**|
||(4.33)|(4.07)|(3.82)|(3.67)|(3.37)|(4.35)|(5.21)|(2.34)|
|N|245|245|245|245|245|245|245|245|
|hansenp|0.355|0.302|0.127|0.137|0.190|0.104|0.303|0.338|
|ar1p|0.000299|0.0000480|0.0000644|0.00000838|0.0000141|0.0000575|0.0000126|0.000384|
|ar2p|0.867|0.108|0.0842|0.0488|0.0779|0.421|0.621|0.420|
|instruments|34|34|34|34|34|34|34|34|

Note: All right-hand side variables estimated at time=0. Income is household per capita. Gini calculated across individuals ranked by their household per capita income. _t_ statistics in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-period dummies are included but not reported.

26
10 with precision. The elasticity of tourism with respect to the island’s income is substantial in the FE model - a 10 percent increase in per capita income increase tourism arrivals by 14.57 percent.

It is likely that decisions about the amount of foreign lobbying expenditures are based on expectations about how they might impact tourism. Then, the variable LLOB is endogenous. In order to correct for possible endogeneity of LLOB, a two-stage procedure is used in which LLOB is predicted using an exogenous instrument in addition to the regressors in (1) in the first stage, and the predicted values are used in place of LLOB in the second stage. The instrument we use, denoted ASSET, is the external asset positions of reporting banks on the islands. Data on ASSET are from Bank of International Settlements. Its use is motivated by the fundamental instrumental variables (IV) idea of finding variables that are related with the lobbying variable LLOB but not the error term in the baseline regression.

Foreign lobbying is partly designed to attract U.S. institutions and individuals to use the services of Caribbean financial institutions that are less stringently regulated than their developed country counterparts. Since such lobbying spending is likely to be correlated with the size of external assets, the financial variable ASSET is correlated with LLOB. However, foreign lobbying directed at increasing participation by U.S. institutions and individuals in their financial sectors, for example offshore banking, is unrelated with the error term in the tourism regressions since shocks to the number of tourist arrivals (for example, due to inclement weather during a particular season) have little impact on financial sector participation. Further, there is no evidence in the literature of any significant participation of tourists in the Caribbean financial sector. To reduce any remaining doubt about its exogeneity, the two-period lagged value of ASSET is used to instrument LLOB.

The IV estimates reported in Table 2 indicate that correcting for the endogeneity of LLOB using the financial variable greatly increases the size of the quantitative association of foreign lobbying and U.S. tourism (Table A1 in the Appendix reports the first stage regressions). The elasticity of lobbying spending on tourism is estimated to be 0.296, indicating that doubling lobbying spending increases the number of tourist arrivals from the U.S. by approximately 15 percent. Doubling, or even quadrupling, lobbying spending is easily accomplished by countries that have a small or negligible political presence in the U.S.

The order of magnitude increase in the lobbying coefficient deserves further explanation. In (1) LLOB is negatively correlated with the error term (contemporaneous shocks) because
discussed so far, but generally smaller in magnitude and less statistically significant. We do, however, find that individuals living around 30 km from a gold mine are significantly more likely to work in agriculture and less likely to be employed in the non-primary sector. They are also less likely to be listed as unemployed or homemaker, and less likely to work in the public sector.

In summary, the tell-tale signs that suggest agglomeration effects facilitated by gold mines are, on the one hand, differences in the structure of the economy at the mining sites. A smaller share of individuals in these locations are engaged in agriculture and other nonwage labor, and more dedicate the majority of their time to providing services to the market; including as wage laborers in larger organisations and increasingly so in the formal private or the public sector. Similarly, more individuals learn a trade, specialise in home production, or are dedicated to looking for work opportunities on the market. On the other hand, the opposite effects can be detected (albeit with less precision and strength) at 20-30 km from mines. This suggests that non-rural activities coalesce where mines are.

# **5.1.1 Dynamic analysis**

Next we turn to a dynamic analysis of the census data. We first estimate model (8). Results, shown in Figures 9 and 10, are surprising: many estimates of _γk_ are not significant in the immediate vicinity of a mine, but when they are, they tend to have a sign opposite to that documented in Figures 5 to 8.

The explanation for these seemingly puzzling results comes to light when we estimate model (9) and interact distance to a gold mine with the sign of the change in mining output between the 1990s and 2000s. Estimates for _γ_<sup>+</sup> _k_<sup>areshowninFigures11and12whileestimatesfor</sup> _γ_<sup>_−_</sup> _k_<sup>areshowninFigures13and14.</sup> The _γ_<sup>+</sup> _k_<sup>reportedinFigures11and12representthe</sup> changes in economic activity associated with an expansion in formal gold mining, compared to locations with no change in gold production (typically, locations without gold mines). We see that the _γ_<sup>+</sup> _k_<sup>estimatesarequalitativelysimilartothosefoundinthecross-sectionanalysisfor:</sup> non-gold mining; food and accommodation; wage, non-wage, and apprenticeship sector; and private informal enterprise. Overall, this confirms that when we compare locations with and without a gold mine, the former display some tell-tale signs of proto-urbanization. There are some exceptions, however: coefficient estimates are reversed for trade and transport activities, 20
|P079165|2007|2008|4/11/2008|4 Closed|Peru|
|---|---|---|---|---|---|
|P079165|2007|2009|10/17/2008|5 Closed|Peru|
|P079165|2007|2009|5/26/2009|6 Closed|Peru|
|P079165|2007|2010|12/30/2009|7 Closed|Peru|
|P079165|2007|2010|6/16/2010|8 Closed|Peru|
|P079165|2007|2011|2/27/2011|9 Closed|Peru|
|P079165|2007|2012|8/12/2011|10 Closed|Peru|
|P079165|2007|2012|1/31/2012|11 Closed|Peru|
|P079165|2007|2013|9/26/2012|12 Closed|Peru|
|P079165|2007|2013|12/14/2012|13 Closed|Peru|
|P079165|2007|2014|7/9/2013|14 Closed|Peru|
|P079165|2007|2014|2/8/2014|15 Closed|Peru|
|P079165|2007|2015|10/11/2014|16 Closed|Peru|
|P079165|2007|2015|4/26/2015|17 Closed|Peru|
|P079165|2007|2016|11/14/2015|18 Closed|Peru|
|P079165|2007|2016|5/19/2016|19 Closed|Peru|
|P079165|2007|2017|12/21/2016|20 Closed|Peru|
|P079165|2007|2017|3/30/2017|21 Closed|Peru|
|P079165|2007|2017|6/27/2017|22 Closed|Peru|
|P079182|2006|2010|2/26/2010|1 Closed|Brazil|
|P079182|2006|2011|2/22/2011|2 Closed|Brazil|
|P079182|2006|2012|8/13/2011|3 Closed|Brazil|
|P079182|2006|2013|11/17/2012|4 Closed|Brazil|
|P079182|2006|2019|5/7/2019|5 Closed|Brazil|
|P079226|2005|2005||0 Closed|Bosnia and Herzegovin|
|P079226|2005|2006|1/31/2006|1 Closed|Bosnia and Herzegovin|
|P079226|2005|2006|6/7/2006|2 Closed|Bosnia and Herzegovin|
|P079226|2005|2007|12/4/2006|3 Closed|Bosnia and Herzegovin|
|P079226|2005|2007|6/7/2007|4 Closed|Bosnia and Herzegovin|
|P079226|2005|2008|2/14/2008|5 Closed|Bosnia and Herzegovin|
|P079226|2005|2008|4/1/2008|6 Closed|Bosnia and Herzegovin|
|P079226|2005|2009|4/1/2008|6 Closed|Bosnia and Herzegovin|
|P079226|2005|2020|6/11/2020|7 Closed|Bosnia and Herzegovin|
|P079259|2004|2004|6/2/2004|1 Closed|Kosovo|
|P079259|2004|2005|6/9/2005|3 Closed|Kosovo|
|P079259|2004|2006|3/3/2006|4 Closed|Kosovo|
|P079259|2004|2007|12/13/2006|5 Closed|Kosovo|
|P079259|2004|2007|5/9/2007|6 Closed|Kosovo|
|P079260|2003|2003||0 Closed|Kosovo|
|P079260|2003|2004|6/22/2004|3 Closed|Kosovo|
|P079260|2003|2005|5/16/2005|5 Closed|Kosovo|
|P079260|2003|2005|6/29/2005|6 Closed|Kosovo|
|P079260|2003|2006|1/25/2006|7 Closed|Kosovo|
|P079260|2003|2007|8/23/2006|8 Closed|Kosovo|
|P079260|2003|2007|3/19/2007|9 Closed|Kosovo|
|P079275|2006|2006||0 Closed|Ethiopia|
|P079275|2006|2007|10/10/2006|1 Closed|Ethiopia|
Table 1: Forward-Looking NKPC

|Method|Instruments|_λ_|_γf_|_JCHNSW_|
|---|---|---|---|---|
|_GMM_|_L_=4|0.24|0.883|17.09|
|||[0.20]|[0.19]||
||_L_=8|0,312|0.913|23.28|
|||[0.25]|[0.31]||
||_L_=20|0.37|0.950|36.21|
|||[0.28]|[0.32]||
|_TJIVE_|_L_=4|0.23|0.845|16.61|
|||[0.14]|[0.16]||
||_L_=8|0.27|0.734|20.50|
|||[0.12]|[0.09]||
||_L_=20|0.28|0.729|32.40|
|||[0.05]|[0.06]||

The p-values appear in brackets for the null hypothesis that the estimate is equal to zero.

14
110

# _&1.2 Non-nuclear Familes_

The average number of persons maintained at the worker's expense is even greater than the statistics concerning wives and children would suggest. Unemployed relatives enjoy the frequent hospitality of wage earners and often join their households. Nonnuclear families typically consist of elderly relatives and the children of brothers and sisters from the provinces.

The data on the number of people who are regularly fed (and sometimes clothed, in the case of the Pfeffermann data) are astounding by American and European standards. It should be noted that these figures do not include the numerous temporary guests who come to stay in town for periods ranging from a few days to several weeks. Again, if the two samples are representative and, moreover, if the question was indeeJ asked4in the same way, the figures indicate that the size of the extended non-nuclear family has increased since 1963. Pfeffermann found that the average worker in 1963 fed (and sometimes clothed) 9.6 people including himself. In 1986 the average worker fed about 11.9 people including himself (we did not include clothing in our study). Table 8.3 shows the frequency distribution of the workers by size of extended family. Note that 16 percent of the workers each supported 14 or more people in 1963; in 1986 the corresponding proportion rose to 35 percent.

In seeking to explain this pattern, we conjecture that increased size of the extended family is in part a result of the deterioration in Senegal's economic performance. In particular, with rising unemployment, stagnant industrial employment, and a decline in GNP per capita, holding a job has become more of a "privilege." Those who are now in this privileged minority are expected to shoulder heavier family responsibilities. The results of our study are consistent with this explanation and also supports Pfeffermann's finding (1968, p. 221) that there is a pattern between the size of the extended family and the age of the worker. Pfefferman noted that older workers tend to have larger extended families; our data reinforce this relationship. Table 8.4 presents the data from both surveys.