rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> TKM: Growth Orientation of Products MDA: Growth Orientation of Products<br>Top 10 Products Top 10 Products<br>Cotton Apples<br>Uncombed single cotton yarn Sunflower seeds<br>Walnuts w.o.shells<br>Unbleached plain cotton Footwear with rubberWine<br>Bars of coilSpirits<br>Polypropylene<br>Denim, with >=85% cotton, >200g/m2<br>Wine<br>Bars & rods coil<br>Bed linen (cotton)<br>Other grapes<br>T-shirts, singlets and other vests, of cottonPrinted bed-linen of cottonUncombed single cotton yarn, with >=85% cotto -40 -20 0 20<br>-10 0 10 20 WLD: CAGR of Exports 05-10(%)<br>WLD: CAGR of Exports 05-10(%)<br>UZB: Growth Orientation of Products UKR: Growth Orientation of Products<br>Top 10 Products Top 10 Products<br>Semi-fin. Products steel<br>Natural uranium<br>Agg. Iron or<br>Uncombed single cotton yarn<br>Automobiles<br>Copper cathodes Semi-fin. ProductsSemi-fin. Products iron<br>Tomatoes Flat rolled prod.coil<br>Urea Spelt, common wheat an<br>Cotton<br>Gold<br>AutomobilesFresh grapesPolyethylene having a specific gravity Flat rolled prod. Coil>600mmBars & rods coilAnhydrous ammounia<br>-10 0 10 20 30 40 -5 0 5 10 15<br>WLD: CAGR of Exports 05-10(%) WLD: CAGR of Exports 05-10(%)<br>Source: Authors’ calculations based on Comtrade data from WITS Source: Authors’ calculations based on Comtrade data from WITS<br>20<br>20<br>0<br>10<br>-20<br>0<br>MDA: CAGR of Exports 05-10(%)<br>TKM: CAGR of Exports 05-10(%) -40<br>-10<br>40 15<br>30<br>10<br>20<br>5<br>10<br>0<br>0<br>UZB: CAGR of Exports 05-10(%) UKR: CAGR of Exports 05-10(%)<br>-10 -5<br><!-- End of picture text --> The results for the ‘growth orientation of markets’ analysis are presented in Figures 16a and 16b for resource poor and resource rich countries, respectively. Take the case of Azerbaijan. Exports to its most important partner (Italy) have grown at an impressive annual rate of 40 percent, much higher than the 6 percent annual average rate of growth of world exports to Italy. This implies that over the period, Azerbaijan gained market share in Italy. However, this is purely explained by oil and gas exports. Excluding oil and gas, exports to Italy have contracted by about 20 percent per annum (and those to Germany have decreased in a similar manner). It was only within the region that Azerbaijan sustained positive non-oil and gas export growth in their most important markets. This was the case in Russia, Ukraine, Georgia, Iran and Turkey, with Azerbaijan increasing its global market shares in the last two destinations.

The Russian case is worth a mention; while this country lost market shares in all of its largest partners but one (Germany), it still increased non-oil and gas exports to most of them.

Most of the relatively smaller economies of the region, such as Kyrgyz Republic, Moldova, Tajikistan and Turkmenistan, exhibited an underwhelming export performance with their largest destinations, with 18
WPS3990

# **Does Aid Help Improve Economic Institutions?**<sup>**1**</sup>

## **Decio Coviello**

## **Roumeen Islam**

European University Institute The World Bank

### **Abstract**

Aid is expected to promote better living standards by raising investment and growth. But aid may also affect institutions directly. In theory, these effects may or may not work in the same direction as those on investment. This paper examines the effect of aid on economic institutions and finds that aid has neither a positive nor a negative impact on existing measures of economic institutions. These results are found using pooled data for non-overlapping five-year periods, confirmed by pooled annual regressions for a large panel of countries and by pure cross-section regressions. We explicitly allow for time invariant effects that are country specific and find our results to be robust to model specifications, estimation methods and different data sets.

### _JEL Classification:_ O11; O19; O16

### _Keywords:_ Aid; Economic Institutions; Dynamic Panel

### World Bank Policy Research Working Paper 3990, August 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This paper was written when Coviello worked in the World Bank Institute Poverty Reduction (WBIPR) of the World Bank. We thank Anindya Banerjee, Bill Easterly, Fabio Schiantarelli, Arvind Subramanian, Luca A. Ricci, Ariell Reshef, Jon Temple for helpful suggestions, comments and discussions. We also thank Fred Kilby for providing us with a version of the Fraser data. Authors’ contacts: rislam@worldbank.org, decio.coviello@iue.it.

1
change in prevalence in other parts of the country (China Tuberculosis Control Collaboration 2004). A national survey in Indonesia in 2004 found that the prevalence of smear-positive TB had fallen by a factor of three since a set of regional surveys were carried out between 1979 and 1982 (Aditama 1991; Soemantri, Senewe et al. in press). Most of this reduction may be due to drug treatment (especially the widespread availability of rifampicin since the early 1980s), though not to treatment administered by the recently expanded, higher-quality DOTS program.

Some investigations of the effect of DOTS programs have shown that after several years of implementation, TB incidence appears not to be falling as expected, as judged from nationally aggregated data. Vietnam apparently exceeded the targets for case detection and treatment success since 1997, and yet the case-notification rate remained approximately stable over that period (Huong, Duong et al. 2006). Closer inspection of surveillance data shows that while case-notification rates are falling among adults aged 35–64 years (especially women), they are increasing among 15–24 year-olds (especially men) (World Health Organization 2007). The program of drug treatment, therefore, does appear to be having the anticipated effect on transmission in one segment of the population─ middle-aged women. The increase in TB incidence among young adults is likely to be due, in part, to HIV co-infection.

The southern Indian states of Kerala and Tamil Nadu, among others, showed an increase in the average age of TB cases over the past decade, which may reflect falling transmission, corresponding with the expansion of the revised national TB control program (RNTCP). The RNTCP has yet to demonstrate that TB incidence is falling on a large geographical scale (e.g., across a whole state) as a result of its activities, though transmission and prevalence have been reduced in the model DOTS project in the Tiruvallur District in Tamil Nadu (Gopi, Subramani et al. 2006; Subramani, Santha et al. 2006).

Although it is not straightforward to evaluate the effect of DOTS on transmission because large-scale public health programs are not carried out as controlled experiments and because major changes in TB incidence happen over decades, it is widely believed that high-quality drug treatment, properly administered under DOTS, can markedly 6
**Table 6. Logit models on the probability of being classified as urban**

||T|raditional d|ata|||Modern dat|a||Benchmark|Extended|Quadratic|Full|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Population|0.101***||0.0916***||||||0.105***|0.106***|0.0994***|0.163***|
||(0.00715)||(0.00720)||||||(0.00848)|(0.00858)|(0.00984)|(0.0431)|
|Population density||0.346***|0.127***||||||0.0146|0.0171|0.144***|0.461***|
|||(0.0291)|(0.0243)||||||(0.0139)|(0.0150)|(0.0447)|(0.142)|
|Built-up share||||0.134***||||0.110***|0.0837***|0.0841***|0.168***|0.247***|
|||||(0.00925)||||(0.00940)|(0.00877)|(0.00883)|(0.0163)|(0.0490)|
|Lit-up share|||||0.0234***|||0.0107***|0.0137***|0.0140***|0.0199*|0.0558***|
||||||(0.00148)|||(0.00177)|(0.00215)|(0.00218)|(0.0120)|(0.0179)|
|NDVI||||||-2.816***||||0.612|-3.820|2.389|
|||||||(0.575)||||(1.154)|(5.164)|(8.896)|
|NDWI|||||||-0.415|||-1.964|6.444**|7.253|
||||||||(0.639)|||(1.217)|(2.906)|(7.259)|
|Population squared|||||||||||-7.95e-06***|-9.92e-06|
||||||||||||(1.38e-06)|(7.15e-06)|
|Population density squared|||||||||||-0.00250***|-0.00596***|
||||||||||||(0.000833)|(0.00164)|
|Built-up share squared|||||||||||-0.00147***|-0.00216***|
||||||||||||(0.000196)|(0.000258)|
|Lit-up share squared|||||||||||-9.32e-05|-0.000215*|
||||||||||||(0.000117)|(0.000126)|
|NDVI squared|||||||||||6.534|4.058|
||||||||||||(8.223)|(14.25)|
|NDWI squared|||||||||||-38.36***|-27.90*|
||||||||||||(10.68)|(16.44)|
|Constant|-1.562***|-1.235***|-1.794***|-1.519***|-1.603***|0.550***|-0.233***|-1.919***|-3.330***|-3.361***|-3.276***|-6.541***|
||(0.0977)|(0.0887)|(0.109)|(0.0929)|(0.112)|(0.176)|(0.0817)|(0.122)|(0.201)|(0.370)|(0.768)|(1.584)|
|Interaction terms|No|No|No|No|No|No|No|No|No|No|No|Yes|
|Observations|1,276|1,276|1,276|1,276|1,276|1,276|1,276|1,276|1,276|1,276|1,276|1,276|
|Pseudo R2|0.282|0.148|0.306|0.317|0.170|0.0142|0.000242|0.338|0.506|0.507|0.553|0.597|
|Log likelihood|-626.5|-744|-606|-596.2|-724.3|-860.4|-872.6|-578.1|-431.4|-429.9|-390.5|-351.8|
|BIC|1267|1502|1233|1207|1463|1735|1760|1178|898.6|909.9|874|903.8|

_Note:_ The dependent variable is the urban status of the place according to the pooled classification. _Bechmark_ refers to the four key indicators; _Extended_ denotes the inclusion of NDVI and NDWI; _Quadratic_ adds quadratic terms of individual indicators; and _Full_ indicates that both quadratic terms and terms interacting two indicators at a time are included. Standard errors are reported in parentheses, and statistical significance is reported by *** if p < 0.01, ** if p < 0.05, and * if p < 0.1.

20
# Acknowledgements

This study was commissioned by the World Bank on behalf of the Stop TB Partnership and financed by the Bill & Melinda Gates Foundation. The working group included Olusoji Adeyi, Chris Dye, Marcos Espinal, Katherine Floyd, Mario Raviglione and Peter Small. Katherine Floyd, Andrea Pantoja and Catherine Watt provided mortality projections and costs from the Global Plan to Stop TB (2006-2015). Joe Aldy, Jishnu Das, David Dunlop, Marcos Espinal, Mead Over, Phil Musgrove, Andrea Pantoja, Mario Raviglione, Adedoyin Soyibo, Carlos Tan, and Virginia Wiseman provided helpful feedback on the methodology underlying this analysis and the paper. Two anonymous reviewers from the World Bank’s Development Economics Group provided additional input. The authors alone are responsible for the contents of the paper, its methods, and conclusions.
|**Country**|**Intervention**|**Effect**<br>**Sizes**|**Country**<br>**GNI Per**<br>**Capita,**<br>**2015 PPP**|**Labor**<br>**Share of**<br>**Income,**<br>**latest**<br>**available**<br>**year**|**Average**<br>**income,**<br>**2015**<br>**PPP**|**Return to**<br>**literacy**|**Percentage**<br>**change in**<br>**earnings**|**NPV$**|
|---|---|---|---|---|---|---|---|---|
|South Africa|English and Operacy<br>programme (EOP)|0.6|12,830|0.46|5,850|0.18|0.11|11,069|
|Liberia|EGRA Plus: teacher training,<br>assessment and supervision,|0.81|720|0.25|181|0.59|0.48|1,395|
||provision of materials<br>hld||Compute|r Assisted Lea|rning||||
|Peru|One Laptop Per Ci +<br>applications|‐0.36|11,960|0.23|2,736|0.72|‐0.26|‐12,635|
|India|Curriculum substitution|‐0.11|6,020|0.29|1,769|0.51|‐0.06|‐1,609|
|Nepal|One Laptop Per Child|‐0.11|2,500|0.41|1,025|0.51|‐0.06|‐932|
|Peru|One Laptop Per Child|‐0.02|11,960|0.23|2,736|0.72|‐0.01|‐642|
|Uruguay|One Laptop Per Child +<br>Internet Access|0|20,360|0.34|6,997|0.72|‐|‐|
|India|Shared computer time during<br>and after class|0|6,020|0.29|1,769|0.51|‐|‐|
|China|CAL remedial sessions|0.01|14,160|0.49|6,941|0.24|0.00|274|
|Colombia|ICT in pedagogy|0.03|13,520|0.33|4,431|0.72|0.02|1,756|

47
# **<mark>Figures</mark>**

|FIGURE1: FRAMEWORK FORANALYZINGIMPLEMENTATIONEFFICIENCY.................................................................... 17|
|---|
|FIGURE2: EVOLUTION OFHIV PREVALENCE FOR THEGENERALPOPULATION INCOLOMBIA, 1991-2015 ........................ 20|
|FIGURE3: ADULTHIV PREVALENCE INSELECTLATINAMERICAN ANDCARIBBEANCOUNTRIES, 2001-2009 .................... 21|
|FIGURE4: REPORTEDHIV/AIDS CASES. COLOMBIA, 1985-2011 ........................................................................... 22|
|FIGURE5: HIV/AIDS REPORTEDCASES BYDISEASESTAGE, COLOMBIA, 1983-2011 .................................................. 23|
|FIGURE6: MORTALITYRATES BYAIDSPER100,000 POPULATION. COLOMBIA, 1991-2009 ....................................... 23|
|FIGURE7: MAINRELATIONSHIPS WITHIN THEHEALTHINSURANCESYSTEM OFCOLOMBIA............................................. 25|
|FIGURE8: EVOLUTION OFTUTELAGEACTIONS RELATED TOHIV/AIDS, 2003-2010 ................................................... 44|
|FIGURE9: RANKING IN THEQUALITY OFHIV/AIDS CARE INCOLOMBIAACCORDING TO THEINSURANCEMODALITIES OF|
|PATIENTS ANDAREA OFRESIDENCE........................................................................................................... 46|

# **<mark>Tables</mark>**

|TABLE1: POPULATION OFCOLOMBIA, 2009 ........................................................................................................ 19|
|---|
|TABLE2: PEN 2008-2011 ESTIMATEDCOSTS AS PERAXIS.................................................................................... 39|
|TABLE3: PEN 2008-2011 ESTIMATEDCOSTS AS PERACTIVITYGROUPS.................................................................. 39|
|TABLE4: HIV/AIDS EXPENDITURES IN2008AND2009AS PERCATEGORIES............................................................. 41|
|TABLE5: HIV/AIDS EXPENDITURES IN2008AND2009AS PERFUNDINGSOURCES.................................................... 42|
|TABLE6: PERCENTAGE OFADULTS ANDCHILDRENLIVING WITHHIV RECEIVINGART, 2007, 2008AND2009 ................ 48|

# **<mark>Boxes</mark>**

|BOX1: FINDINGS FOR THEPROGRAMMATICDIMENSION OFIMPLEMENTATIONEFFICIENCY............................................. 8|
|---|
|BOX2: FINDINGS FOR THEBUDGETARYDIMENSION OFIMPLEMENTATIONEFFICIENCY.................................................... 9|
|BOX3: FINDINGS FOR THESERVICE DELIVERYDIMENSION OFIMPLEMENTATIONEFFICIENCY............................................ 9|
|BOX4: PREVENTIVE, DIAGNOSTICS ANDTREATMENTSERVICES FORPEOPLE WITHHIV/AIDSINCOLOMBIA..................... 32|
|BOX5: SUMMARY OFFINDINGS OF THEPROGRAMMATICDIMENSION....................................................................... 36|
|BOX6: SUMMARY OFFINDINGS OF THEBUDGETARYDIMENSION............................................................................. 43|
|BOX7: SUMMARY OFFINDINGS OF THESERVICEDELIVERYDIMENSION..................................................................... 52|
year raises the social status by only 0.02) pales relative to that for indicators to access to power (i.e., 0.82 for Congress or Consultative Conference memberships), indicating a higher return (in terms of raising political and social statuses) to political capital (and potentially rent seeking) than that to human capital investment, and that there is likely persistent discrimination against non-elite firms. Interestingly, the current implicit encouragement to attain political office for elites reflects the millennium-long tradition of the paramount importance of power in traditional Chinese societies.

_Evolution of firm characteristics_ **.** Based on the private enterprise surveys, the annual surveys of industrial firms, and the CSMAR database on listed firms, we obtain three findings. First, as shown in panels A (for private firms) and B (for listed firms) of Table 6, Chinese private firms have expanded rapidly in size whether as measured by firm assets or by the number of employees. The average number of employees from the private enterprise surveys rises from 61 in 1993 to 163 in 2012, an increase of 167 percentage points. Moreover, the capital intensity of private firms has quadrupled on average, indicating rapid technological upgrading.

Second, whether measured by input (R&D input) or output (number of patents, and sales of new products), Chinese private firms have become increasingly innovative.<sup>13</sup> They invest more in R&D and training, and have more patents and self-designed products over time (panel B in Table 6). Moreover, the intensity of firm innovation displays substantial variations across regions and firm size, with larger firms and coastal firms being significantly more innovative (panel B in Table 6 and Figure 4). Furthermore, private firms invest much larger proportions of their revenue in R&D than state-owned enterprises: the relative R&D intensity (i.e., R&D expenditure over sales) of private firms is seven-fold that of SOEs (panel B in Table 6).

Table 7 presents the regression results on the determinants of innovation intensity in private firms. The innovation intensity is higher in private firms that are larger and located in regions with more FDI and is lower for firms with strong political connections (i.e., with the firm head being Congress member or Consultative Conference member). This finding is consistent with findings elsewhere in the world. For instance, Akcigit et al. (2017) find that

> 13 See Wei et al. (2017) for further summary of evidence on innovation in China.

22
travel is assumed to take place by foot — a reasonable assumption for Nepal given the nature of the terrain. The median distance from surveyed wards to nearest town is about 2 hours and 21 minutes. The mean distance is however much higher; about 4 hours and 26 minutes, because a number of villages are located far o ff from nearest towns (Figure 2a). Indeed, about 14% of villages are located at least 10 hours or more from the nearest town, the farthest one being about 29.5 hours away. Such wide variations in access to urban markets are outcomes of striking geographical disparities in Nepal, a country which extends from the relative plains of Terai to high Himalayas.

Data on city/town population are available from the Population Census of 1991. The population density of towns/cities in Nepal displays wide variations; the smallest town had barely 10,000 people residing in it. The largest city, Kathmandu, on the other hand had a population of 421,000 in 1991. We estimated per capita consumption expenditure for urban residents from the NLSS data. Total urban income is derived by multiplying per capita income by urban population. In the cases of smaller towns, per capita expenditure data are not always available. In those cases, average per capita consumption expenditure in the district where town is located is used to compute total urban income. The median income in the nearest town/city is about Rs. 302 million, and mean about Rs. 1.2 billion. Urban income displays high degree of variations (Figure 2b). Using the formula in equation (4) and assuming K = 34 (total number of towns/cities in Nepal in 1991), we de fi ne the e ff ective market size Mi<sup>eA</sup> .<sup>11</sup> The large variations in travel times and in urban income resulted in even larger variation in the measure of the extent of market at the village level (SD= 573 and mean=105). These variations help us explore to the relationships between di ff erent measures of agricultural specialization and the extent of the market.

> 11 As we discuss later, our results are not sensitive to alternative de fi nitions of market size corresponding to the di erent values of K chosen.

12
WPS3952

# **Can migration reduce educational attainment?**

# **Evidence from Mexico**<sup>*****</sup>

**David McKenzie**<sup>**a**</sup> **and Hillel Rapoport**<sup>**b**</sup>

**a Development Research Group, World Bank**

> **b Department of Economics, Bar-Ilan University, CADRE, University of Lille II,**

### **and Stanford Center for International Development**

### **Abstract:**

This paper examines the impact of migration on educational attainment in rural Mexico. Using historical migration rates by state to instrument for current migration, we find evidence of a significant negative effect of migration on schooling attendance and attainment of 12 to 18 year-old boys and 16 to 18 year-old girls. IV-Censored Ordered Probit results show that living in a migrant household lowers the chances of boys completing junior high school and of boys and girls completing high school. The negative effect of migration on schooling is somewhat mitigated for younger girls with low educated mothers, which is consistent with remittances relaxing credit constraints on education investment for the very poor. However, for the majority of rural Mexican children, family migration depresses educational attainment. Comparison of the marginal effects of migration on school attendance and on participation in other activities shows that the observed decrease in schooling of 16 to 18 year-olds is accounted for by the current migration of boys and increased housework for girls.

Keywords: Migration, migrant networks, education attainments, Mexico JEL codes: O15, J61, D31 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

## World Bank Policy Research Working Paper 3952, June 2006

> * Corresponding author: David McKenzie, MSN MC3-300, The World Bank, 1818 H Street N.W., Washington D.C., USA 20433. Email: dmckenzie@worldbank.org. We thank Thomas Bauer, Gordon Hanson, Frédéric Jouneau, Omar Licandro, Ernesto Lopez-Cordoba, François-Charles Wolff, and participants at the Inter-American Development Bank's "Economic Integration, Remittances and Development" conference in Washington D.C., February 2005, at the meeting of the European Society for Population Economics, Paris, June 2005, and at the Development Economics Workshop, Louvainla-Neuve, September 2005, for useful comments on an earlier draft.

1