rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Table A.2: Surveys

|Country|Survey name|Survey<br>type|Period|# house-<br>holds|Geo-<br>ref.?|
|---|---|---|---|---|---|
|1986–8||||||
|Cˆote d’Ivoire|CILSS2-4|IS|Feb.86-May.89|4,814|Yes|
|Ghana|GLSS1-2|IS|Mar.87-Apr.89|6,336|Yes|
|1992–4||||||
|Cˆote d’Ivoire|ENV1|IS|Apr.92-Oct.93|9,598|Yes|
|Cˆote d’Ivoire|DHS 1994|DHS|Jun.94-Nov.94|5,914|Yes|
|Ghana|DHS 1993|DHS|Sep.93-Feb.94|5,804|Yes|
|Ghana|GLSS3|IS|Sep.91-Sep.92|6,336|No|
|Mali|EMCES|IS|Mar.94-June94|9,554|Yes|
|Guinea|EIBC|IS|Jan.94-Feb.95|4,417|Yes|
|1998||||||
|Cˆote d’Ivoire|ENV2|IS|Sep.98-Dec.98|4,178|No|
|Cˆote d’Ivoire|DHS 1998-99|DHS|Sep.98-Mar.99|2,115|Yes|
|Ghana|GLSS4|IS|Apr.98-Mar.99|6,016|Yes|
|Ghana|DHS 1998|DHS|Nov.98-Feb.99|5,983|Yes|

_Source_ : Household survey documents. _Notes_ : The three first Ivorian (CILSS 2 to 4) and the two first Ghanaian (GLSS 1 and 2) surveys are stacked to obtain large samples covering the 1986–8 period. IS = Income Survey, LSMS-type. DHS= Demographic and Health Survey.

38
**Table 1: Descriptive statistics**

||**Entire**||**By Re**|**gions**||
|---|---|---|---|---|---|
||**Sample**|**N&NE**|**Coastal**|**Centre**|**SW**|
|**Household characteristics & rental**<br>**participation**||||||
|Household size|3.94|3.89|3.69|4.14|4.03|
|Members aged 15-60 years|2.99|2.97|2.88|3.15|2.93|
|Head’s age|46.56|46.85|46.34|46.59|46.25|
|Head with secondary education (%)|52.12|58.98|55.31|45.12|45.84|
|Head with high school education (%)|18.30|21.02|23.74|17.62|9.41|
|Households renting in (%)|13.49|10.72|8.43|20.50|14.55|
|Households renting out (%)|9.81|6.15|10.76|13.68|10.53|
|**Assets and income**||||||
|Total owned land (mu)|6.24|8.41|5.29|5.51|4.31|
|Owned land per capita (mu)|1.68|2.30|1.49|1.42|1.13|
|Value of total assets (Y)|27166|29992|36183|24244|17404|
|Per capita income|2983|3022|4184|2677|2158|
|of which from agri. (%)|58.38|60.91|49.42|57.59|63.43|
|of which from wage (%)|15.00|18.32|22.88|7.96|10.43|
|of which from migration (%)|11.21|6.42|9.98|16.77|13.85|
|of which from non-farm self emp.(%)|10.92|10.8|13.11|12.03|7.73|
|of which from transfer (%)|4.49|3.55|4.61|5.65|4.57|
|**Renting in**||||||
|Renting in from relative (%)|39.32|58.59|25.86|35.08|54.43|
|Share with contract, incl. oral ones (%)|59.44|37.84|57.08|66.50|55.70|
|Share of contracts, written|7.78|18.46|24.44|1.67|3.41|
|Share with fixed term (%)|24.15|30.61|29.20|21.08|22.78|
|if yes, length of term (years)|2.71|3.82|3.80|2.11|1.57|
|**Renting out**||||||
|Renting out to relative (%)|31.04|55.81|24.29|23.18|64.58|
|Share with contract, incl. oral ones (%)|59.29|22.73|50.00|75.00|47.92|
|Share of contracts, written|8.63|0.00|13.89|6.55|13.04|
|Share with fixed term (%)|26.61|34.09|22.14|28.77|22.92|
|if yes, length of term (years)|2.92|1.60|3.06|3.19|2.73|
|**Village characteristics & land policy**||||||
|Village per capita land endow. (mu)|1.33|1.84|1.30|1.02|0.88|
|Villager per capita income (Y)|2256.26|2306.15|3229.46|1930.47|1649.44|
|<br>Share of members migrating (%)|17.83|10.05|17.07|24.85|22.03|
|Of which, % migrating out of province|39.61|27.27|17.60|61.08|51.03|
|Have land certificate (%)|81.16|73.92|83.05|81.33|92.02|
|Renting to outsiders not allowed (%)|13.53|16.60|25.74|6.90|4.83|
|Uncultivated land taken away (%)|9.74|9.08|15.72|10.22|4.09|
|No of observations|19570|6622|3533|5187|4228|

_Source:_ Own computation from NBS/World Bank land market survey 17
in these factors, beyond their direct effects for Indian businesses, may also provide beneficial effects from an urbanization and spatial allocation perspective.<sup>13</sup> Our findings suggest that policies that take an inclusionary approach to the urban informal economy may be more successful in promoting local development and managing its strains than those focused only on the formal sector. It is very important for Indian policy makers to recognize that much of the urbanization that is occurring is in the unorganized sector. Moreover, education and infrastructure investments, regardless of original motivation, are primarily operating through the unorganized sector. Going forward, adequate provision of infrastructure is necessary for the informal sector to develop. The more Indian cities recognize this influx and design appropriate policies and investments to support it, the more effective the policy interventions will be. Examples of inclusionary policies are mechanisms to ensure that urban informal livelihoods are integrated into urban plans, land allocation, and zoning regulations; that the urban informal workforce gains access to markets and to basic urban infrastructure services; and that organizations of informal workers participate in government procurement schemes and policy-making processes.

It is something of a paradox that India, among the most densely populated countries in the world, is also among the least urbanized. An important aspect for India’s continued growth is better and deeper urbanization over the next two decades than it has achieved over the past two decades.

# **References**

Amin, Mohammad and Aaditya Mattoo, “Human Capital and the Changing Structure of the Indian Economy,” World Bank Policy Research Working Paper 4576 (2008).

- Baily, Martin, Charles Hulten and David Campbell, “Productivity Dynamics in Manufacturing Plants,” _Brookings Papers on Economic Activity: Microeconomics_ (1992): 187-249.

- Balakrishnan, Pulapre, K. Pushpangadan and M. Suresh Babu, “Trade Liberalisation and Productivity Growth in Manufacturing: Evidence from Firm-level Panel Data,” _Economic and Political Weekly_ , 35(41), (2000): 3679-3682.

> 13 Evaluating the impact of infrastructure investment on Russia’s peripheral regions, Brown et al. (2008) find that the firm-level industrial data for 1989 and 2004 highlights a ‘death of distance.’ Particularly, regional infrastructure is found to be a critical driver of productivity for new and for privately-owned firms. Further, simulations of their model show that the benefits of infrastructure improvements are highest in regions where economic activity is already concentrated.

20
- 29 -

# (Gev"-erg and Swoboda 1987, Gupta and Islam 1983, Moran 1983). 7/

The reasons for tS.e discrepancy between the apparent quality of the results obtained using the ordinary least-squares method and those obtained using a structural model do not appear to have been explained clearly. One reason is perhaps the following: if the sample of countries is heterogeneous for certain relationships, there is a good chance that the estimation of the simultaneous model will detect it and that the quality of the adjustment will be affected thereby; in contrast, in the reduced form equation the effects of the heterogeneity of the sample on various underlying relationships may tend to compensate each other. Moreover, the fact that cross-sectional series analyses probably give rise to special heteroscedasticity problems (cf. supra) raises the question of whether the resulting loss of efficiency of the estimation is not greater in the double least-squares than the ordinary least-squares method.

# Conclusions

Four principal conclusions can be drawn from the foregoing analysis of the main methodological problems that arise in a crosssectional study of factors affecting growth.

The quality of the available statistical data appears to have a greater impact on the results when it affects the independent variables than when it affects the growth rate itself. The studies examined clearly brought out the need for better statistical knowledge of developing economies. This need is particularly important in the case of the economic policy indicators.

The results obtained appear to be very sensitive to the choice of periods and samples. However, the choice of periods seems to open the door less to arbitrary subdivision. Interpretation of the results of a cross-sectional study of factors affecting growth should be based on an analytical description of the composition of the sample (taking into account not only the number of countries but also their geographic location, development level, political system, etc.). The hypothesis of sample homogeneity must be suitably tested (coefficient stability and homoscedasticity tests).

Interpretation of the results must, moreover, be accompanied by close attention to the way in which the variables are combined in the equation, which reflects the structure of the underlying explanatory model. This attention is crucial to the validity of the economic policy conclusions that can be drawn from examination of the regression coefficients. It is therefore appropriate to explain the theoretical model used, particularly since a number of different growth models can be conceived. The use of a reduced form equation -- currently the commonest practice - in preference to a structural model involves an
This study used Babajob data to identify the skills demanded by employers in two ways. The first method was to review employer qualification screening questions, and the second was to apply text analysis to capture the demand for skills as expressed in the text of job descriptions. Figure 4 presents the most commonly asked screening questions by occupation level.<sup>9</sup> Four broad categories of screening questions were identified: (i) skills; (ii) personal characteristics; (iii) work conditions and benefits; and (iv) other. The skills category includes overall education level and academic credentials, as well as computer skills, language skills and relevant occupational experience. The work conditions and benefits category includes location or distance to work, salary, and the proposed terms of employment. The “other” category includes uncommon and job-specific questions.

The data reveal that skills-related questions are the most common category of screening filter, though the skills requested differ by occupation level. Skills-related questions appear in about 50 percent of qualifications screenings for most occupation levels, including managerial (50.7 percent) and professional positions (47.9 percent). However, while employers searching for professionals and associate professionals tended to ask about education levels and specific technical skills, occupational experience appears to be more relevant for clerical staff, service and sales workers, and construction and craft workers. Language skills, most often English and Hindi, came up more frequently for managerial and sales positions.

The relatively high frequency of personal information questions demonstrates the importance employers place on collecting basic information, especially for service-related hires in the informal sector, such as maids, cooks and gardeners. Personal information inquiries include questions on the applicant’s gender, age, police verification certificates, as well as requests for resumes and photos. The applicant’s acceptance of the stated employment terms, including the proposed salary and work hours, is another important filter for recruitment decisions.

> 9 Occupational levels are defined according to the ISCO classification. Employers can either use standard questions or manually enter their own questions.

13
who are not (Rs.224 versus Rs.180 per month). To the extent that parental perceptions of intelligence can be viewed as “objective”, this implies that even at the young ages of 5-15, parents start supporting children who are doing better with more investments, and cutting back on the children they feel are not performing.

The second main pattern in the data, which we see in both the decision to attend a school and the choice of the school to attend, is the impact of distance. Across boys and girls, enrollment declines by 8- 10 percentage points for every 500 meters that a school is further from the house, and the relationship is as strong moving from 0 to 100 meters as from 800 to 900 meters. As expected, the relationship is (much) stronger for girls than for boys; in fact, the drop in enrollment as the distance to the closest eligible school increases is 3-4 times as much as that for boys—4 percentage points for every 500 meters for boys and 11-16 percentage points for girls. In a multivariate regression context that controls for age, household expenditures (a measure of income), education and the intelligence of the child, the “distance penalty” for boys is further reduced to 1.5-3 percentage points; for girls, the effect of distance is still large at 9-11 percentage-points for every 500 meters. While household and child characteristics, such as household expenditures and the child’s age, all have independent effects on enrollment they do not alter the size of the basic gender gap of 15-16 percentage points in enrollment in the LEAPS data. In sharp contrast, allowing for the distance-enrollment relationship to differ across boys and girls reduces the pure effect of gender on enrollment to 5 percentage points—a dramatic decline of 60 percent.

27
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2227 TheSri Lankan Unemployment<br>Problem Revisited|Martin Rama|November 1999|S. Fallon<br>38009|
|WPS2228 Fiscal Contingency Planning for<br>Banking Crises|Patrick Honohan|November 1999|A. Yaptenco<br>38526|
|WPS2229 Do School Facilities Matter? The Case<br>of the Peruvian Social Fund<br>(FONCODES)|Christina Paxson<br>Norbert Schady|November 1999|N. Schady<br>88247|
|WPS2230 Bankruptcy Organization through<br>Markets: Auction-Based Creditor<br>Ordering by Reducing Debts<br>(ACCORD)|David Hausch<br>S. Ramachandran|November 1999|L. Tsang<br>80516|
|WPS2231 What's Behind Mercosur's Common<br>External Tariff|Marcelo Olarreaga<br>Isidro Soloaga<br>L. Alan Winters|November 1999|L. Tabada<br>35555|
|WPS2232 Market Access Advances and<br>Retreats: The Uruguay Round and<br>Beyond|J. Michael Finger<br>Ludger Schuknecht|November 1999|L. Tabada<br>35555|
|WPS2233 User's Guide to an Early Warning<br>System for Macroeconomic<br>Vulnerability in Latin American Countr|Santiago Herrera<br>Conrado Garcia<br>ies|November 1999|C. Garcia<br>87969|
|WPS2234 The Green Revolution and the<br>Productivity Paradox: Evidence from<br>the Indian Punjab|Rinku Murgai<br>|November 1999|M. Fernandez<br>33766|
|WPS2235 Beyond Capital Ideals: Restoring<br>Banking Stability|Gerard Caprio Jr.<br>Patrick Honohan|November 1999|A Yaptenco<br>38526|
|WPS2236 Valuing Water for Chinese Industries<br>A Marginal Productivity Assessment|: Hua Wang<br>Somik Lall|November 1999|R. Yazigi<br>37176|
|WPS2237 Reforming Tax Systems: The World<br>Bank Record in the 1990s|Luca Barbone<br>Arindam Das-Gupta<br>Luc De Wulf<br>Anna Hansson|November 1999|L. Barbone<br>32556|
|WPS2238 A Review of the Role and Impact of<br>Export Processing Zones|Dorsati Madani|November 1999|L. Tabada<br>36896|
|WPS2239 The EU Factor in the Trade Policies<br>of Central European Countries|Bartlomiej Kaminski|November 1999|L. Tabada<br>36896|
**Figure 6: Annual growth in modern and traditional services, 2000-08.**

_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.

# **2.5 Europe’s comparative advantage in service exports**

Next we study the revealed comparative advantage (RCA), as defined by the share of a particular export in a country‘s export basket compared to the share of that export in the global export basket, in the three European groups.<sup>4</sup> EU-15 has RCA in services and the gap between goods and services is increasing. NMS has also a comparative advantage in services (see Figure 7). The gap to goods was large in the 1990s but has now decreased to the same level as in EU15. ACC did have a comparative advantage in services until early 2000s when it dropped and has since had a comparative disadvantage in services.

**Figure 7: Revealed comparative advantage in services and goods, for EU-15, NMS and ACC, 1990-007.**

<!-- Start of picture text --> 2.5 2.5 2.5<br>2 2 2<br>1.5 1.5 1.5<br>1<br>1 1<br>0.5<br>0.5 0.5<br>0<br>0<br>0<br>Goods Services Goods Services Goods Services<br><!-- End of picture text --> _Source_ : Authors‘ calculation using Balance of Payments, IMF. 2011.

> 4 Formally, the RCA index for country _C_ and product _J_ is defined as: _RCA_   _ECJ_ / _ECT_  / _EWJ_ / _EWT_  , where _E_ denotes exports, _T_ denotes all products, and _W_ denotes the world.

9
integrated information system to track clients, it is impossible to learn the degree of overlap in the outreach of the various programs.

**Table 4.3   Credit Programs in Private Sector Development**

|**Credit Programs**|**Percentage**|
|---|---|
|Reprogramación de deudas PYME, CORFO|62.3%|
|FOGAPE, Banco Estado|19.1%|
|Financiamiento de Inversiones de las medianas y pequeñas empresas (línea de crédito<br>B.11), CORFO|5.6%|
|Crédito corto plazo individuales y organizacionales, INDAP|3.6%|
|Financiamiento al comprador extranjero de Bienes Durables y Servicios de Ingeniería<br>chilenos. (Línea de crédito B.21), CORFO|2.2%|
|Crédito a largo plazo individuales y organizacionales, INDAP|2.1%|
|Construcción y Mejoramiento Obras de Riego (Ley de Riego). Crédito de Enlace,<br>INDAP|0.8%|
|Crédito de reprogramación de pasivos de pequeñas empresas (línea de Crédito B.13),<br>CORFO|0.8%|
|Others|3.5%|
|Total|100.00|

Source: Almeyda and de la Barra (2002). See Annex B.

Also in 2001, the credit programs provided an average of UF 9,171 UF (US$ 22,900) per borrower. Table 4.3 provides a breakdown of the credit programs.The 22 loan and guarantee programs represented an investment of 25,741.183 UF (US$643.5 million in late 2001).

This menu of grant and credit programs is the result of years of uncoordinated planning and has lead to much duplication and an overabundance of low-performing programs. For example, the following list of four irrigation programs illustrates the redundancies of Chile’s grant and credit programs.

- 1) Law No 18.450 Fomento al Riego y Drenaje provides for a subsidy for irrigation works, managed by the National Irrigation Commission. Since it is legislated, it is difficult to make adjustments to the product. It is designed on the assumption that small farmers have the cashflow necessary to build the required structures—and paid retroactively.

- 2) Preinversión Riego (Pre-investment in irrigation) finances feasibility studies for irrigation, and is managed by the Production Development Corporation. Again, the reimbursement mechanism makes it difficult for small farmers to finance such studies.

- 3) Crédito de Enlace para Construcción y Mejoramiento Obras de Riego (Loans for Irrigation Construction and Improvement) is a “bridge” loan that provides prompt financing for irrigation works which would eventually be covered by the Production Development Corporation subsidy under Ley No 18.450.

- 4) Programa Riego, managed by Agricultural Development Institute, subsidizes those projects not covered by Ley No 18.450.

To eliminate this redundancy, small adjustments in Law No 18.450 and changing the timing of the subsidy for irrigation, would result in eliminating three of the programs—or merging them into one. Thus, the proposed reform of SME support programs, described in the following section, seeks to eliminate these redundancies and reduce administrative expenses as it meets requirements for legally mandated programs. The reform removes those programs with very limited outreach and those that do not target formally registered micros or SMEs. Further it provides for a review of new programs and encourages innovation.

33
_11_ _Ways Out of Poverty - Diffysinj_<sup>_Best Practices and Creating Capabilities_</sup> restraining institutions, policies,<sup>and vestedinterests-turnedaround within a</sup> decade, particularly China and Central<sup>Europe.</sup>

**Arrested development.** Precisely<sup>because many countrieshave becomeso much richer,</sup> we now see that some may run<sup>into "traps" at higher incomes.Argentina, once among the</sup> most advanced in the world,<sup>has stagnatedrelativeto others since the1930s.Stagnation</sup> could also befall Japan. The communist<sup>countriesthat initially grew in the early phases of</sup> industrialization are the most<sup>dramatic illustrationof the phenomenon. We do not know</sup> how to predict such cases, but<sup>it seems clear that, after initial success,development may</sup> be arrested for some time.

Overall, while many countries have<sup>emerged from traps, many othershave not or are</sup> getting trapped again. The upshot<sup>appearsto be that thereare indeeda variety of factors</sup> that make it hard for a country<sup>to develop,but they can in principle be overcome-often</sup> in fairly short time. What it<sup>takesto do so is the issue.</sup>

# C. **The key to development: Capable**<sup>**institutions**</sup>

**Countries.** One way to reduce<sup>poverty is to allow poor people to move to richer</sup> countries. Immigrants in the<sup>UnitedStates of America producemore than 4.5 timesmore</sup> there than in their country of<sup>origin.Their skill level is the same. They benefitfrom extra</sup> and more advanced physical<sup>capitalin the UnitedStates.But that explainsonly abouta</sup> third of the difference (figure<sup>5). The main explanationis the capability of institutions-</sup> be they firms or governments-that<sup>allowindividuals to performat higher levels of</sup> productivity.

**Figure 5: Factor accumulation**<sup>**accountsforonly**</sup> **a fraction of income differences**<sup>**(in percent)**</sup>

<!-- Start of picture text --> Otilput pa Output paworka Diffarcein Diffaercein<br>iningrarn worka in honrcroutry outpLAdueto outpLAdueto<br>in US Ngna physical Inlstutiorna<br>capital in US environmret<br>Source: Calculatons based on Hendricks (2002).<br><!-- End of picture text --> Looking at output in 127 countries,<sup>Hall andJones (1998) find that humanand physical</sup> capital differences account<sup>for only a modest amount of the difference(figure 6).The</sup>