rafmacalaba/datause-encoder-data huggingface dataset:
Table A.12: Robustness

|**Dependent variable:**|**Househ**<br>**(fnancial inst**|**old per capita s**<br>**itutions + cash**|**aving in 2007**<br> **+ interest earned)**|
|---|---|---|---|
||Caste-religion<br>(1)|Around YOB<br>(2)|Non-missing obs.<br>(3)|
|_Firstborn girl_ * _Expected dowry_|623.55**|368.73*|637.40*|
||[220.87]|[200.02]|[354.23]|
|_Firstborn girl_|-3228.32|-826.20|-3635.87**|
||[2032.56]|[2359.00]|[1669.78]|
|N|2,836|2,837|2,610|

NOTES: This table reports the coefficients corresponding to specification (2) estimated for households where all children have the same mother. Each column is a separate regression. The dependent variable is the sum total of per capita savings in financial institutions, cash savings, and interest earned in 2007. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average dowry paid (received) by brides (grooms) from the same social group and state as the child and who married during the year of the child’s birth or the prior four years. In column (1), we construct seven social groups based on the caste and religion. Specifically, we split Hindus by caste and use other religions as it is (i.e., Hindu SCs, Hindu STs, Hindu OBCs, Hindu OCs, Muslims, Sikhs, Other religions). In column (2), expected dowry is defined using marriages around the year of birth (YOB) of the child (i.e., during YOB + 2, YOB + 1, YOB, YOB - 1, YOB - 2). In column (3), we construct expected dowry only using marriages where both gifts are non-missing. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.

55
7 to the year 2000 during the 6-year period, 1995 to 2001. Countries which did household surveys before 1995 are not included in the data set.

With respect to the skill composition of migrants, few (if any) of the developing countries in the world publish accurate records on the number of international migrants that they produce. It is therefore necessary to estimate migration information by using data collected in the main labor-receiving countries of the world. For the purposes of this paper, the main labor-receiving countries (regions) include two: OECD (America), including United States and Canada; and OECD (Europe), excluding America and Asia.<sup>7</sup> Unfortunately, no data are available on the level of worker migration to the third most important labor-receiving region in the world, the Persian Gulf. This is unfortunate because large numbers of workers from the Middle East and South Asia go to work in the Persian Gulf.

In the past there has been a lack of disaggregated data on the skill composition of migrant stocks and flows. However, a new data set compiled by Docquier and Marfouk (2005) classifies international migrants from each country according to their level of educational attainment: low-skilled (less than 8 years of schooling); medium-skilled (9 to 12 years of schooling); and high-skilled (13 years or more of schooling). This data set, which is based on population census and register data from nearly every OECD country, counts as international migrants all those who are “foreign born” and of working age (25 years and over), who are living in either the OECD (America) or OECD (Europe).

This paper uses the Docquier and Marfouk (2005) data to analyze how the skill (educational) level of migrants from different developing countries affects the flow of international remittances to these countries. However, like all data sets on international
**Table A4: Investment financial performance by decade of first cash flow.** The total column refers to the performance measure when pooling cash flows from all investments in the relevant vintage years. The IRR is undefined for 138 investments that were written off and have zero or trivial positive cash flows. For the purposes of this table the IRR is coded as -100 in these cases.

|||P<br>|ME vs. S<br>|&P500<br>||||IR<br>|R<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|Vintage<br>years|Total|Median|Average|Standard<br>error|Standard<br>deviation|Total|Median|Average|Standard<br>error|Standard<br>deviation|
|Panel A) D|irect in|vestments|||||||||
|1961-69|1.706|1.016|1.442|(0.182)|1.627|0.139|6.993|6.711|(5.844)|52.267|
|1970-79|1.090|0.509|0.908|(0.118)|1.309|0.124|4.061|-5.816|(3.191)|35.536|
|1980-89|0.687|0.376|1.395|(0.560)|8.512|0.113|3.371|-5.828|(8.628)|131.140|
|1990-99|1.694|0.635|2.594|(0.500)|11.358|0.134|3.032|170.954|(134.189)|3,048.181|
|2000-09|0.954|0.917|3.226|(1.012)|22.643|0.104|5.196|4,717.448|(4,693.084)|105,045.400|
|2010-19|0.702|0.715|0.835|(0.051)|1.220|0.075|-0.000|4,290.112|(3,693.514)|88,721.250|
|ALL|1.185|0.707|1.965|(0.289)|12.999|0.136|3.106|2,427.559|(1,564.048)|70,451.660|
|Panel B) F|und inve|stments|||||||||
|1978-89|1.392|0.679|4.519|(3.462)|15.864|0.214|8.044|6.568|(6.413)|29.390|
|1990-99|1.389|0.652|0.778|(0.052)|0.529|0.109|3.494|-0.341|(1.932)|19.699|
|2000-09|0.990|0.751|1.109|(0.135)|1.532|0.106|3.016|5.919|(5.047)|57.319|
|2010-19|0.642|0.769|0.767|(0.021)|0.320|0.031|-0.000|-4.112|(1.696)|25.499|
|ALL|0.956|0.757|1.025|(0.157)|3.439|0.091|1.631|-0.132|(1.660)|36.362|
Policy Research Working Paper 8704

# **Abstract**

Estimates of total factor productivity growth, a measure of increases in the efficiency of production, have traditionally been based on a two-factor model of labor and fixed capital. Because profits are measured residually in the System of National Accounts, they implicitly include rents on natural resource exploitation, with the result that the contribution of fixed capital to growth in the inputs to gross domestic product is misstated, particularly in resource dependent developing countries. This leads to incorrect measures of total factor productivity growth. Using data on natural resources from the World Bank’s Wealth of Nations database and methods combining the Solow growth accounting model with recent work at the Organisation for Economic Co-operation and Development, this paper makes new estimates of total factor productivity growth for 74 developing countries over 1996–2014. In the aggregate, including natural resources as a factor of production increases estimated total factor productivity growth across all country income classes and regions of the world when compared with the traditional two-factor approach. In addition, the estimated total factor productivity growth including natural resources is less volatile over time in the great majority of countries compared with the traditional approach. The availability of World Bank data on natural resource quantities and rents for a wide range of countries suggests that natural resources should be included in total factor productivity growth estimation going forward. Further research could focus on the distinctive roles played by different natural resource endowments.

This paper is a product of the Environment and Natural Resources Global Practice . 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/research. The authors may be contacted at glange1@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
Table B4: First stage regression of program participation on treatment status

||Dwelling|sprayed|Share of dwell|ings sprayed|
|---|---|---|---|---|
||(1)<br>Probit|(2)<br>Probit|(3)<br>OLS|(4)<br>OLS|
|Treatment|0.762***|0.782***|0.758***|0.758***|
||(0.0325)|(0.0334)|(0.0346)|(0.0355)|
|Controls|No|Yes|No|Yes|
|Observations|1456|1364|1617|1503|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. One observation per household. In Columns (1) and (2) the dependent variable is a dummy variable equal to 1 if the household reported that their dwelling has been sprayed with IRS in the last 5 months and zero otherwise. In Columns (3) and (4) the dependent variable is the share of households in the village who reported their dwelling has been sprayed. Independent variable is equal to one if the household is in the treatment group or zero otherwise. Columns (1) and (2) report marginal effects. Standard errors clustered at village level are reported in parentheses. Controls include gender, age, education and marriage status of the respondent, household size, tribe and religion, information about access to water, dwelling characteristics, sub-zone dummies and village characteristics (size and share of women living in the village).

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# **An Evaluation of the Contributing Factors of Water Scheme Failures in Nigeria**<sup>**1**</sup>

## **Luis Andres, Gnanaraj Chellaraj, Basab Das Gupta, Jonathan Grabinsky, and George Joseph**

**JEL Classification: D02, O18, Q25**

**Key words: Water schemes, Nigeria, Functionality**

> 1 Luis A. Andres: World Bank Water Global Practice, landres@worldbank.org [Corresponding author]. Gnararaj Chellaraj: World Bank Water Global Practice, gchellaraj@worldbank.org . Basab Dasgupta: Social Impact, Impact Evaluation Division, bdasgupta@socialimpact.com . Jonathan Grabinsky Zabludovsky: World Bank Water Global Practice, jonathan.grabinsky@gmail.com . George Joseph: World Bank Water Global Practice, gjoseph@worldbank.org. Senior authorship is not assigned.

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. The findings, interpretations, and any remaining errors in this paper are entirely those of the authors.
# TABLE 2 Methods to estimate potential growth

|**Methodology**|**Advantages**|**Disadvantages**|
|---|---|---|
|Production<br>function approach|Produces estimates that help explain the<br>movement of potential output in terms of<br>its inputs.<br>Low correlation with actual output growth.|Relies on proxies for potential productivity<br>and labor supply growth and capital<br>accumulation that could be subject to<br>measurement errors. Relies on assumption<br>of specific functional form.|
|Time-series filters|Univariate filters are straightforward to<br>implement,<br>even<br>in<br>data-poor<br>environments.|“End-point” problems can lead to large<br>revisions as new data become available.<sup>37</sup>|
||Multivariate filters produce output gaps<br>that are consistent with indicators of<br>domestic demand pressures (inflation,<br>unemployment, current account deficits,<br>capacity utilization).|Strong correlation with actual output<br>growth, which could reflect short-term<br>shocks to potential growth or, alternatively,<br>are associated with cyclical movements.|
|Long-term growth<br>expectations|In principle, incorporate judgment and,<br>thus, capture factors that cannot be<br>modelled during periods of high volatility.|In practice, tend to be sticky and, at times,<br>in ways that are challenging to interpret.|

Source: World Bank.

45
**Multiple jobs.** In labor force surveys, workers are typically classified by their primary sector of employment. A potential concern with this classification is for individuals classified as “agricultural” but who work a substantial fraction of their hours in nonagricultural activities (Haggblade, Hazell, and Reardon 2007), as this would lead to an underestimate of labor productivity in agriculture. Gollin, Lagakos, and Waugh (2014) use Living Standards Measurement Study data for several developing countries to estimate labor productivity using hours worked; Adeyinka, Salau, and Vollrath do the same in Chapter 6 of this book on Nigeria. They find that the overwhelming majority of individuals classified as working in agriculture

do in fact allocate almost all of their time to agriculture. Gollin, Lagakos, and Waugh (2014) also find that a significant portion of individuals in rural households is classified as working in nonagricultural activities.

**Accounting for human capital.** If human capital differs significantly across sectors, using the number of workers unadjusted for differences in human capital can be misleading. For example, if most of the labor in agriculture is unskilled and most of the labor in services is skilled, simple measures of productivity will understate labor productivity in agriculture and overstate labor productivity in services. One way to account for this is to adjust employment numbers for educational attainment, which is what Gollin, Lagakos, and Waugh (2014) do for their sample of countries (in the poorest countries, human capital is on average 1.4 times higher in the nonagriculture sector than in the agriculture sector). However, even after making this adjustment, they still arrive at the conclusion that average labor productivity in agriculture is significantly lower than average labor productivity in other economic sectors.

**Average versus marginal productivity.** The country authors of this book compare gaps in sectoral productivities using measures of average labor productivity, as is done in McMillan and Rodrik (2011) and Gollin, Lagakos, and Waugh (2014). It is well known that efficiency in well-functioning markets is characterized by an equalization of productivities at the margin. Under a Cobb-Douglas production function specification, the marginal productivity of labor is the average productivity of labor multiplied by the labor share. Thus, if labor shares differ greatly across sectors, comparing average labor productivities can be misleading. However, the existing evidence suggests that labor shares do not vary widely across sectors, except in a few activities (like public utilities) that typically do not absorb lots of labor (Mundlak, Butzer, and Larson 2012; Gollin, Lagakos, and Waugh 2014).

**Quality of African statistics.** Recently, concerns about the quality of Africa’s national accounts data have been raised by a number of researchers, including

13
# **DRIVERS OF CONVERGENCE IN ELEVEN EASTERN EUROPEAN COUNTRIES**

By Jesus Crespo Cuaresma, Harald Oberhofer, Karlis Smits, and Gallina A Vincelette<sup>1</sup> JEL: O47;O52; P20; O10 Sector Board: Economic Policy (EPOL) 1 Jesus Crespo Cuaresma is at the Department of Economics, Vienna University of Economics and Business (WU); World Population Program, International Institute of Applied Systems Analysis (IIASA); Wittgenstein Centre for Demography and Global Human Capital (WIC) and Austrian Institute for Economic Research (WIFO). Address: Augasse 2-6, 1090 Vienna (Austria). Email: jcrespo@wu.ac.at.

Harald Oberhofer is at the Department of Economics and Social Science and Salzburg Centre of European Union Studies (SCEUS), University of Salzburg. Address: Residenzplatz 9, 5010 Salzburg (Austria). Email: <u>Harald.Oberhofer@sbg.ac.at</u> Karlis Smits is a Senior Economist at the Poverty Reduction and Economic Management Department, Europe and Central Asia Region, the World Bank. Address: 1818 H Street, NW. Washington DC 20433. Email: ksmits@worldbank.org Gallina A Vincelette is a Senior Economist at the Poverty Reduction and Economic Management Department, Europe and Central Asia Region, the World Bank. Address: 1818 H Street, NW. Washington DC 20433. Email: gvincelette@worldbank.org
# **D. Hybrid Maize Seed**

46. The input analysis in Malawi also included an examination of farm and assembly-level costs of hybrid seed production. This work was undertaken to derive the conversion factors required by the spreadsheet methodology for the analysis of smallholder hybrid maize and is based on information supplied by one commercial seed company only. Given this limitation, the results should not be interpreted as a definitive picture of the costs and returns to seed multiplication, but only as a snapshot view of what one company is reporting.

47. Seed multiplication requires strict adherence to management guidelines and is therefore mainly undertaken by large and medium-scale estates with a commercial outlook rather than by smallholders. The company modeled here uses foundation seed imported from South Africa which is then multiplied on contract by farmers with the capacity to cultivate a minimum of 10 hectares. There is no provision for loans and most growers are within 200km of Lilongwe.

48. **Farm level analysis.** Farm-level data for the hybrid seed multiplication based on a yield of 7MT/ha and price to the farmer of MWK 49,350 (USD 352.50) per MT are set out below. As shown, the enterprise can be quite profitable, but also requires a large cash commitment. Over the 10ha minimum plot size, the grower’s total variable costs work out to nearly USD 16,000 (MWK 2.24 million).

**Table 6: Farm-level analysis of hybrid maize seed multiplication**

|**FARM PRODUCTION**|**Per He**|**ctare**|**Per T**|**on**|
|---|---|---|---|---|
|**LCF**<br>**Hybrid Seed**|||||
||**MWK**|**USD**|**MWK**|**USD**|
|**Gross revenue (yield* price)**|**345,450**|**2,467.50**|**49,350**|**352.50**|
|**Production costs**|||||
|Variable costs|223,250|1,594.64|31,893|227.81|
|Investment costs|41,258|294.70|5,894|42.10|
|**Total costs**|**264,508**|**1,889.34**|**37,787**|**269.91**|
|**Farmer income**|||||
|**Gross margin (revenue - var costs)**|**122,200**|**872.86**|**17,457**|**124.69**|
|**Netprofit(gross margin - invest costs)**|**80,942**|**578.16**|**11,563**|**82.59**|

49. The charts in Figure 11 show the cost composition of farm-level costs for hybrid seed from the financial and value chain perspectives. As shown, imported seed and fertilizer account for a combined 56% of total costs. Once other foreign components are taken into account, around 2/3 of the farm gate value of domestically multiplied seed is foreign. As a mechanized operation, hired labor only accounts for an estimated 5% of farm-level SV.

**Figure 11: Build-up of financial costs for maize seed multiplication**

<!-- Start of picture text --> Build-up of Farmer's Financial Costs Composition of Shipment Value<br>Credit & land Depreciation Farm Gate Product<br>rent/tax 16% Seed33% Official duties Additional<br>Overheads & 0%<br>and tax expenses<br>management 9% 0%<br>6%<br>Hired labor<br>Domestic<br>5%<br>costs & mark-<br>ups<br>Marketing 25%<br>costs<br>7% Spraying,<br>irrigation & machinery Chemicals4% Fertilizer23% Foreign costs66%<br>6%<br><!-- End of picture text --> 19
_Ways Out of Povertv - Dzffusine Best Practices and Creatingz Capabilities_ _24_

# **Box 4: Markets, innovation and the state**

In the 1920s and 30s, around 100,000 different varieties of airplane were flown. All over the world, enthusiastic inventors were selling airplanes to intrepid pilots and to fledging airlines. Many of the pilots crashed, and many of the airlines became bankrupt. Of 100,000 types of airplane, about 100 survived to form the basis of modem aviation. The evolution of airplane was a strictly Darwinian process in which almost all the varieties of airplane failed, just as almost all species became extinct. Because of the rigorous selection, the few surviving airplanes are reliable, economical, and safe.

## **Source: Dyson (1998).**

**The symbiosis of large and small firms.** All sectors in all market economies have a mix of many small, fewer medium-size, and a few large companies. The average size varies with economies of scale between sectors, but within sectors there are firns of many different sizes - not like in the textbooks that suggest all firms in an industry should look similar, reflecting relevant economies of scale. Small, medium, and large firms do not just exist next to each other in markets. They also interact through buyer-seller relationships, through various co-operative arrangements, through credit relations. Many times intricate networks of firms exist, within large companies, between large and smaller ones and in clusters of various types.

The way that a firm enters, grows, and exits varies from country to country, an area little studied so far. A recent OECD study shows that the average new entrant in the United States is less efficient than existing firms. But entry is valuable because it allows new ideas to be tried out and the companies that turn out to be good can easily grow to become larger. Most productivity growth in the United States is thus due to the internal growth of firms and not to the entry of more efficient firms. But in several European countries, the entry of better firms accounts for a large part of productivity growth, and internal growth appears less effective. This may reflect banriers to growth in those countries, such as labor regulations for firms above small sizes (figure 14). In developing countries very small informal enterprises proliferate, preferring to remain informal because of the burden of regulations and taxes on formal firms.
- Grosh, M., Leite, P., & Wai-Poi, M. (in press). _A New Look at Old Dilemmas: Revisiting Targeting in Social Assistance_ . The World Bank.

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- Jerven, P. (2013). _Poor numbers_ . Cornell University Press.

- Karlan, D., Lowe, M., Osei, R., Osei-Akoto, I., Roth, B., & Udry, C. (2021). _Cash transfers as covid-19 relief: Evidence from ghana._ `https://www.theigc.org/blog/cash-transfers -as-covid-19-relief-evidence-from-ghana/` .

- Karlan, D., & Thuysbaert, B. (2019). Targeting ultra-poor households in Honduras and Peru. _The World Bank Economic Review_ , _33_ (1), 63-94.

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