rafmacalaba/datause-encoder-data huggingface dataset:
To see the effectiveness of the CLI in predicting the cycles of the Lithuanian economy it is useful to make a backcast estimation, where the forecasting ability of the composite index is tested against cycles that are known to have happened. This exercise is displayed in Figure 1.2. In March 1999 the composite index had already predicted the downturn in industrial production that would occur one month hence.

**Figure 1.2 Composite Leading Indicator and IIP, March 1999**

<!-- Start of picture text -->
1.4<br>1.3<br>1.2<br>1.0<br>0.8<br>0.7<br>-Q  - t*  - e  e  @<br><!-- End of picture text -->

CLI---IIP

# **THE EXECUTION AND UPDATE** OF **THE** CLI PROGRAM

The main steps involved in the execution and update of the CLI program are sketched below: Step 1. It is necessary to update the monthly database in the Excel file LITUDATA.XLS. The updating process consists of adding the most recent value of each variable to the file. Some variables, such as the Real Effective Exchange Rate, are subject to revisions and corrections, therefore updating then requires the replacement of the whole series.

After the database is updated, the EViews _program finalprog.prg_ must be run.

8
# **References**

Adelman, Melissa and Miguel Székely. 2016. “School Dropout in Central America: An Overview of Trends, Causes, Consequences, and Promising Interventions.” World Bank Policy Research Working Paper 7561. Washington, D.C.: World Bank.

Allensworth, E. M., & J.Q. Easton. 2007. _What Matters for Staying On-track and Graduating in Chicago Public High Schools: A Close Look at Course Grades, Failures, and Attendance in the Freshman Year_ . Chicago: Consortium on Chicago School Research.

Avitabile, Ciro and Rafael de Hoyos. 2014. “Heterogeneous Effects of Information about the Returns to Schooling on Student Learning: Evidence from a Randomized Control Trial in Mexico.” World Bank Working Paper. Washington, D.C.: World Bank.

Balfanz, Robert, Liza Herzog, and Douglas MacIver. 2007. “Preventing Student Disengagement and Keeping Students on the Graduation Path in Urban Middle-Grades Schools: Early Identification and Effective Interventions.” _Educational Psychologist_ 42(4): 223-235.

Barrera-Osorio, Felipe, Marianne Bertrand, Leigh Linden, and Francisco Perez-Calle. 2011. “Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia.” _American Economic Journal: Applied Economics_ 3(2): 167-195.

Bassi, Marina, Matias Busso, and Juan Muñoz. 2016. “Is the Glass Half Empty or Half Full? School Enrollment, Graduation, and Dropout Rates in Latin America.” _Economia_ (forthcoming).

Becker, Gary. _Human Capital_ . New York: Columbia University Press, 1964.

Behrman, Jere, Rafael de Hoyos, and Miguel Székely. 2015. “Out of School and Out of Work: A Conceptual Framework for Investigating “Ninis” in Latin America and the Caribbean.” Washington, DC: World Bank. Background paper for the “Out of School, Out of Work” study.

Bentaouet-Kattan, R., and Székely, M. 2015. “Patterns, Consequences and Possible Causes of Dropout in Upper Secondary Education in Mexico.” _Education Research International_ , Volume 2015, Article ID 676472.

Borra, Simone and Agostino Di Ciaccio. 2010. “Measuring the Prediction Error: A Comparison of Cross-Validation, Bootstrap and Covariance Penalty Methods.” _Computational Statistics & Data Analysis_ 54(12): 2976-2989.

Bowers, Alex. 2010. “Grades and Graduation: A Longitudinal Risk Perspective to Identify Student Dropouts.” _Journal of Educational Research_ 103(3): 191–207.

Bowers, Alex and Ryan Sprott. 2012. “Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis.” _The Journal of Education for Students Placed at Risk (JESPAR)_ 17(3): 129-148.

18
Policy Research Working Paper 9491

# **Abstract**

Total factor productivity is a key element of economic growth and an important performance metric for policy makers. This note describes the methodology for measuring firm-level total factor productivity using the World Bank’s Enterprise Surveys cross-country data. It also presents some estimates recovered from the production function. Two versions of the production function are estimated: one Cobb-Douglas, the other a more flexible translog specification. Both estimations are at the two-digit industry level pooling all the Enterprise Surveys data across economies. Evidence is found against using a Cobb-Douglas specification, which is more parsimonious, and in favor of using the flexible translog specification. The resulting firm-level estimates are all published in the Enterprise Surveys database with a unique firm identifier to link to the rest of the Enterprise Surveys data; because the estimates are reliant on new data, they are updated periodically as new Enterprise Surveys data become available. The results show that: (i) median firms operate close to constant returns to scale; (ii) gross-output and value-added production functions provide similar ranking of sectors in terms of output elasticities, capital intensity, and returns to scale; (iii) there is large, firm-level heterogeneity in output elasticities; and (iv) gross-output-based total factor productivity measures are less dispersed than the value-added ones.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hmaemir@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Conflicts by Region<br>6050 | "=<br>40<br>g TM Americas<br>=& 30 mAfrica<br>BAsia<br>TM Middle East<br>20 m Europe<br>10<br>0<br>LEP PP FEF EP EF EP ELS SSE IP EEE IS ELIS SSF SS<br>©PRIO 2009<br><!-- End of picture text -->
One of the striking features of the smallholder sector is the amount and diversity of off-farm activity. For many smallholder families, off-farm earnings are an important factor in alleviating -overty. Few rural households can subsist without some form of off-farm income (11.O, 1986). Approximately onethird of smallholder family incomes, on average, are derived from off-farm sources, with an additional 10 percent coming from remittances. Lower-income smallholders, waho comprise 40 percent of all smallholder families, are significantly more dependent on off-farm income for their survival (GOK and UNICEF, 1984). One study suggests that in the mid-1970s, 77 percent of family income of these lower income smallholders came from off-farm sources (Wisner, 1986). These smallholders have less access to services and resources as well as to training and educational opportunities. Households with little or no education have a greater dependence on unremunerative off-farm income derived from trading and casual labor (ILO, 1986). Middle-income smallholders have larger landholdings and are more dependent on agriculture for their livelihood.

|Relati|onship<br>Between<br>Educati|Table 4<br>Sources of Off-Farm Income<br>and<br>onal<br>Attainment|
|---|---|---|
||Educati|onal attainment|
||None<br>1-4|vrs,<br>5-7 yrs.<br>8+ yrs.|
|Net off-farm income<br>(Kshs)|1,494<br>1,8|84<br>3,610<br>5,570|
|Sources of off-farm<br>income|--|in percent--|
|Regular employment|<br>23<br>22|<br>51<br>79|
|Casual employment|15<br>14|<br>6<br>6|
|Remittances|23<br>16|<br>9<br>4|
|Gifts|4<br>5|<br>2<br>1|
|Trade/crafts|30<br>35|<br>27<br>5|
|Miscellaneous|9<br>8|<br>4<br>5|

Source: ILO, 1986-based on IRS 1, 1974-75 The relationship between off-farm activities and the farm is important. On the one hand, off-farm earnings tend to reflect marginal earning capacity from the holding. Members of a household (generally men) seek off-farm employment because income derived from the farm is not sufficient to support the 9
Is South Asia an outlier on remittances? Table 6 shows that South Asian countries were significant and positive outliers when we compare their remittances as a ratio of GDP with the rest of the world, and control for stage of development, country size and nonlinearities in development. India, Bangladesh, Pakistan, and Sri Lanka were all significant positive outliers compared to the norm in 1982. China was not a significant outlier compared to the norm. Thus remittances have been an important source of foreign exchange for South Asia. However, its importance as a source of foreign exchange earning has declined over time in India and Pakistan. Bangladesh, Sri Lanka and Nepal, however, remained significant and positive outliers in 2005, when we compare their share of remittances in GDP with more than 100 countries. These results hold up when we compare their remittances as a ratio of imports.

**<u>Figure 15: Coefficient of Variation of Remittances in South Asia</u>**

_Sources_ : Ratha, Mohapatra and Xu (2008).

Considering that remittances are the major form of capital for the region the next question is how stable are remittances compared to other forms of capital inflows? Remittances are distinctive by their stability, persistence, and reliability. In order to examine the resilience of remittances to South Asia, coefficient of variation of remittance flows is constructed relative to three other categories of external financial flows – foreign direct investment (FDI), private debt and portfolio equity; and official development assistance (ODA) for 1998 to 2007. These findings are presented in Figure 15. Private debt and portfolio equity flows to South Asia were the most volatile, with a coefficient of variation of 134 percent, followed by FDI with the coefficient of variation of 89 percent. Migrant remittances were significantly less volatile than both categories of private flows, with variation coefficient of 40 percent during 1998 to 2007. The volatility of migrant remittances was slightly higher than that of official aid during this period. Migrant remittances received by South Asia were therefore more stable than foreign direct investment and other private capital flows and behaved similarly to official aid between 1998 and 2007.

28
We find some indication that growth spillovers are indeed heterogeneous and are more pronounced for neighborhood specifications based on RTA membership than on simple contiguity. Spillovers are most pronounced in OECD countries but appear basically absent in SSA. In particular, landlocked countries experienced little benefit from their coastal neighbors, and this can be attributed in large part to poor transportation and communications infrastructure. We illustrate the implications of these findings in a hypothetical simulation exercise designed to illustrate the potential welfare losses associated with the lack of integration. If Switzerland, during the period 1970 to 2000, had been exposed to the growth spillovers experienced by the equally landlocked Central African Republic, it would have forgone more than $330 billion (2000 international dollars) of GDP, all else being equal.

The layout of the remainder of the paper is as follows. Section 2 reviews related empirical literature estimating the strength of cross-country growth spillovers. Following this, section 3 sets out the different methods of defining a country's neighbors used in this paper, while section 4 presents the results of exploratory spatial analysis of our 19702000 panel data. This provides evidence on the extent of clustering of growth rates across countries and is, therefore, suggestive of the possible presence of heterogeneous spillover effects. Section 5 then presents results on the strength of localized cross-country spillovers using both the full sample of countries for the period 1970-2000 and various geographically and income based sub-samples. This is followed, in section 6, by the presentation of results relating to the interactions between landlockedness, infrastructure 5
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent of GDP. Furthermore, investment occupies a higher share of total infrastructure spending in Liberia than in all other countries considered in the benchmarking exercise (figure 14).

# **Table 14. Financial flows to Liberian infrastructure, average 2004 to 2008**

<u>US$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|C<br>ODA|apital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|Information and communication technologies|2|0|0|1|17|18|19|
|Power|7|1|13|1|0|15|23|
|Transport|7|6|26|1|0|33|39|
|Water supply and sanitation|2|2|5|0|0|7|8|
|Total|16|9|44|2|17|72|90|

Source: Derived from Foster and Briceño-Garmendia 2009.

O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

**Figure 14. Spending allocated to address infrastructure needs**

<!-- Start of picture text --> capital expenditure % of GDP operations and maintenance<br><!-- End of picture text --> Source: Foster and Briceño-Garmendia 2009.

28
5, Issue 2.

Lay, C. J. 2009. “Race, Retrospective Voting, and Disasters: The Re-Election of C. Ray Nagin after Hurricane Katrina”, _Urban Affairs Review_ **44** (5): 645–662.

Litschig, S. and K. Morrison. 2012. “Government Spending and Re-election: QuasiExperimental Evidence from Brazilian Municipalities”.

Manacorda, M., E. Miguel and A. Vigorito. 2011. “Government Transfers and Political Support”. American Economic Journal: Applied Economics, 3(3): 1-28 Pop-Eleches, C. and G. Pop-Eleches. 2009. “Government Spending and Pocketbook Voting: Quasi-Experimental Evidence from Romania”.

Rodriguez-Chamussy, L. 2014. “Local Electoral Rewards from Centralized Social Programs: Are Mayors Getting the Credit?” Sobel, R. and P. Leeson. 2006. “Government’s Response to Hurricane Katrina: A Public Choice Analysis”, _Public Choice_ **127** (1): 55–73.

Page **18** of **29**
**Table 2 Uruguay: Selected Indicators, 1986-1991**

||**1986**|**1987**|**1988**|**1989**|**1990**|**1991**|
|---|---|---|---|---|---|---|
||**------**|**------**|**------**|**-**<br>**-**<br>**-**|**-**<br>**-**<br>**-**|**-**<br>**-**<br>**-**|
|GNP Per Capita (US$)|1,750|2,020|2,280|2,510|2,620|2,860|
|GNP Per Capita (Latin America, US$)|1,710|1,720|1,780|1,920|2,130|2,370|
|Overall Public Sector Deficit (% GDP)|5.2|4.8|_5.0_|7.8|3.6|1.1|
|National Accounts (Percentage Growth)|||||||
|GDP Market Prices|8.7|8.0|-0.01|1.3|0.9|2.9|
|GDP Deflator|71.5|72.8|63.9|75.5|98.8|94.0|
|Private Consumption|10.5|15.0|-0.8|0.0|-5.3|3.6|
|Government Consumption|8.9|5.4|-2.3|1.6|4.2|4.1|
|Gross Domestic Investment|17.1|18.4|-7.2|-9.2|5.6|18.2|
|Exports of G&NFS|11.5|-8.5|9.1|10.3|10.5|-1.0|
|Imports of G&NFS|29.4|15.8|0.0|1.4|-2.7|16.9|
|Foreign Sector|||||||
|Terms of Trade Change<br>(deterioration -)|12.5|-0.5|1.9|7.8|-5.5|N.A.|
|Devaluation Rate (NU$/US$)|49.9|49.1|58.5|68.5|93.4|72.4|
|Real Effective Exchange Rate<br>(Index 1985 = 100)|98.6|96.3|90.7|94.8|85.5|97.3|
|Total Foreign Debt (% of GDP)|70.3|60.6|52.5|_55.0_|54.5|45.3|
|Debt Service Ratio (% of XGS)|32.0|36.4|38.3|30.1|42.5|38.2|
|Foreign Reserves (Months of MGS)|11.4|11.7|10.2|9.0|8.1|5.9|

Source: World Bank Tables and Interinational Financial Statistics.

and 1987 (see Table 2). The recovery was mainly the result of the acceleration of growth in Argentina and Brazil, following the Austral Plan (June 1985), and the Cruzado Plan (February 1986). respectively. In 1987, real GDP just exceeded 1981 real GDP, although real growth has slowed since then. The drop in output can be explained in part because of inadequate gross fixed investment, and in part because of a severe drought experienced in 1989-90. In 1992, preliminary figures indicate that growth exceeded 3 percentage.

Investment activity in Uruguay has gone througn three phases in the last twenty years, which helps to explain the observed fluctuation in income. From 1970 to 1976, gross investment averaged 11.5 percentage of GDP, 19.5 percentage of GDP during the period 1977 to 1982, and about 11 percentage of GDP from 1983 to 1990. The surge in investment during the period 1977 to 1982, when several hydro-electrical plants were built, was mainly financed by foreign savings. An overvalued exchange rate during the period favored investment by providing incentives to imports in general and capital-goods imports in particular. The level of imports of goods and non-factor services grew from an average 23.7 **share** of GDP in 1970-76, to 27.6 in 1977-82. As a result, the current account deteriorated substantially, 24
PROVISION OF WATER TO THE POOR IN AFRICA

**Figure 3.3   Costs and informal prices of household water resellers and standpipes / kiosks**

<!-- Start of picture text --> 6,00<br>5,00<br>4,00<br>Cost of water tanker<br>3,00<br>2,00<br>1,00<br>0,00<br>Addis Cotonou Accra Abidjan Maputo Blantyre Kampala Kigali Dar es<br>Ababa Salaam<br>HH reseller cost of water standpipe/kiosk informal price HH reseller informal price<br>$/m3<br><!-- End of picture text --> Source: AICD WSS database 2007.

The average household water reseller sells water at three times its cost (table 3.4). This generally covers the cost of water sold, plus their own consumption, and in some cases provides a small amount of extra income. Household water resellers face an increasing block tariff (IBT) in most of the countries studied, and can be charged a high tariff even if the final costumers are low-income households (Whittington 1992; Collignon and Vézina 2000). It is not clear whether increasing block tariffs (IBT) are hurting the poor by increasing water costs of household resellers. In many countries, the IBT has defined large “social” blocks in terms of consumption, and the difference between the prices paid by the different blocks is small (Komives and others 2005; Banerjee and others 2007).<sup>22</sup> As some utilities have not kept pace with metered consumption, but rather charge based on flat or estimated rates, the volume of water sold through household resale can represent a significant loss of income for the utility (as the case in Mozambique illustrated). In these cases (as in Maputo), the household reseller can make a substantial profit.

Payment mechanisms to household resellers include monthly payment schemes, as well as payment by bucket. Case studies point to the importance of personal relations and mutual trust in determining which scheme is used, but also in determining prices. In Maputo, Mozambique, 40 percent of household resellers had monthly payment agreements; standpipe managers started to offer such schemes to customers also in order to compete with household resale (Thompson, SAL consultants, 2007).

> 22 In the case of Côte d’Ivoire, the resale of water by household consumers is a regulated activity, and therefore there is a record of the average amount sold per reseller, which is 40m3/month (Kariuki and others 2003). This level of consumption, which represents 5 people per household consuming 40 l/c/d (6.7 households/household reseller), is the one used for estimation of the water costs of the household reseller.

28
Rank each topic on a scale of 0 to 5.

VXAMPLK

|Tooic<br>Fre<br>Diarrhoea<br>in children|ouency<br>5|x Severitv<br>4|Ability to<br>x Interveng<br>2|<br>Need for info<br>x to iustify<br>**oro**<br>2|- Tota<br>80|
|---|---|---|---|---|---|
|Brain cancer|1|5|0|5|0|
|Anaemia|3|2|3|0|0|
|Infant formula<br>use|3|3|4|4|144|

Questions on cause-specific morbidity will typically follow questions on general morbidity. For certain diseases, questions on symptom/sign complexes have high sensitivity and specificity in most settings. These diseases tend to be easily recognised by the population and have clear symptoms eg. measles and diarrhoea. Kroeger (in WHO et al 1986) presents groups of diseases which can be elicited by means of interviews directly, indirectly or after having received a medical diagnosis. It is particularly important that questions on specific diseases are pre-tested for cultural appropriateness. In Bangladesh for example, it was discovered that an evaluation of an ORS programme was using a word for diarrhoea that meant cholera in certain parts of the country (Chowdhury 1986).

A problem that many developing countries are particularly interested in is long term, or chronic, disability. However, measuring disability through health interview surveys is fraught with difficulties. The main problems arise from the fact that both the I= and severitv of disability need to be defined and measured. In defining 'disability' much confusion arises from the casual use of different terms such as impairments, disabilities and handicaps. Previous surveys have been incomparable since different definitions were used. The International Classification of Impairments, Disabilities and Handicaps (ICIDH) goes some way to clarifying and standardising definitions and its use is to be encouraged in any survey tackling the topic of disability (WHO 1980). When it comes to severity of disability it is advisable to develop tests which lay 29
Table 3 Government Size and External Risk: OLS and Instrumental Variable Regressionsa (Dependent Variable: Government Consumption/GDP in 1985 International Prices)

|Panel A<br>Independent Variables|OLS|OLS|IVb|IV|
|---|---|---|---|---|
|Openness|0.152***|0.044|0.157***|0.049|
||(0.033)|(0.049)|(0.037)|(0.051)|
|Volatility in Terms of Trade|-0.050|0.539|-0.204|0.438|
||(.249)|(0.345)|(.249)|(0.368)|
|Openness*STOT||0.973***||1.017***|
|||(0.344)||(0.389)|
|Nunber of Observations|348|348|336|336|
|R-squared'|0.5614|0.5696|0.5308|0.5388|

|Panel B|||||
|---|---|---|---|---|
|Independent Variables|OLS|OLS|IV|IV|
|Openness|0.135***|0.077*|0.132***|0.068|
||(0.031)|(0.042)|(0.036)|(0.045)|
|Terns of Trade Increases (TOTI)|0.234|-1.788**|0.246|-2.068**|
||(.580)|(0.896)|_(.615)_|(1.013)|
|Terns of Trade Decreases|-2.537*|-2.469|-2.472*|-2.471|
||(1.380)|(2.247)|(1.483)|(2.839)|
|Openness & TOTI||-3.141***<br>(0.939)||-3.280***<br>(1.038)|
|Openness & TOTD||0.103<br>(2.202)||-0.243<br>(2.738)|
|Nunber of Observations|357|357|345|345|
|R-squared|0.5716|0.5831|0.5389|0.5525|

****** significant at 1% level significant at 5% level ***** significant at 10% level

- ' Standard errors, corrected for heteroscedasticity, are in parentheses. Coefficients on the other regressors are not shown. Other regressors are the same as those listed in Table 2.

- **b** For all IV regressions in Table 3, the instruments for the following variables are the previous five year lag of itself and regional and decade dummy variables: per capita GDP; relative price of government consumption; openness; and political freedom. All other variables in the regression were treated as exogenous.

'The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

22