rafmacalaba/datause-encoder-data huggingface dataset:
nomic Journal 116, C34-45 Helliwell, J.F., Putnam R. (1995) Economic growth and social capital in Italy, Eastern Economic Journal 21, 295-307.

Helliwell, J.F., F., Barrington-Leigh, C.P., Harris, A., Huang, H. (2009). International evidence on the social context of well-being, NBER Working Paper No.14720.

Helliwell J.F., Wang, S. (2010) Trust and well-being, NBER working paper, No. 15911 Kafetsios, K., Nezlek, J.B. (2002) Attachment styles in everyday social interaction, European Journal of Social Psychology 32(5), 719-35 Kahneman, D., Krueger, A.B. (2006) Developments in the measurement of subjective well-being, Journal of Economic Perspectives 20(1), 3-24 Knack, S., Keefer, P. (1997) Does social capital have an economic pay-off?. Quarterly Journal of Economics 112(4), 1251-88 Knudsen, B., Florida, R., Rousseau, D. (2010) Bridging and bonding: a multidimensional approach to regional social capital, University of Toronto (mimeo) available at: http://www.creativeclass.com Konow, J., Earley, J. (2008) The hedonistic paradox, Journal of Public Economics 92(1-2), 1-33 Larsen, L., Harlan, S.L., Bolin, B., Hackett, E.J., Hope, D., Kirby, A., Nelson, A., Rex, T.R., Wolf, S. (2004) Bonding and bridging. Understanding the relationship between social capital and civic action, Journal of Planning Education and Research, 24, 64-77 Layard, R. (2005). Happiness: Lessons from a New Science. New York: Penguin Press Layard, R., Mayraz, G., Nickell, S. (2009). Does relative income matter? Are the critics right?. CEP Discussion Paper No. 918, March (in Diener, E., Helliwell, J.F., Kahneman, K. (Eds) International Differences in Well-Being, New York: Oxford University Press, forthcoming) Leung, A., Kier, C., Fung, T., Fung, L. and R. Sproule (2011), Searching for happiness: the importance of social capital, Journal of Happiness Studies, 12(3), 443-462 Loury, G. C. (1977) A dynamic theory of racial income differences. In: Wallace, P., LaMont, A. (Eds) Women, Minorities and Employment Discrimination,, Lexington: Lexington Books.

23
you seeking work elsewhere (in urban areas) at going wages there? If the answer were affirmative the person would be counted as national unemployed in the national, but not in the rural labor force. For rural and urban unemployed (labor force) to add up to national unemployed (labor force) we would then have to include rural residents not employed, not seeking rural employment but seeking urban employment, in the category of urban unemployed (urban labor force). From a demand-supply analytical point-of-view this would make sense since such persons would exert pressure on urban but not on rural wages.

Whether this interpretation of the Turkish LFS of 1985 holds water can only be settled through careful investigation into the questioning procedures applied by the LFSs. If corroborated, the interpretation makes the results of the 1985 LFSs compatible with the assumption of market clearing in rural labor markets and justifies the use of the national rate of unemployment in a Phillips relation for urban wage rates.

- 13 -
Policy Research Working Paper 9868

# **Abstract**

The informal sector is an important source of livelihoods and jobs for a vast majority of people in developing countries. However, there is concern that it may undermine growth and development of the formal sector. For instance, the growth literature indicates that research and development activity and innovation are a key driver of long-term growth. How does the competition that formal sector firms face from informal sector firms affect research and development activity by the formal firms? The present paper attempts to answer this question using firm-level survey data for small and medium-size enterprises in a large cross-section of mostly developing countries. The results show that higher informal competition leads to greater a likelihood of spending on research and development by formal firms. For the most conservative baseline specification, a one standard deviation increase in informal competition leads to an increase of 5.2 percentage points in the likelihood of spending on research and development by formal firms. This is a large increase given that less than 18 percent of the firms in the sample engage in research and development activity. Further, consistent with the “parasite” view of informality, the positive impact of informal competition on research and development activity is magnified when the business environment is less conducive to operating in the formal sector compared with informal sector due to factors such as higher corruption, weaker rule of law, more burdensome business regulations, and a higher tax rate on profits. As expected, there is no impact of informal competition on research and development activity among large firms. The main findings are robust to several controls, alternative specifications, and endogeneity checks.

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 author may be contacted at mamin@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
Following Schmidt and<sup>Sickles (1984) the model can be managedin the followingway.Since</sup> we know that E(uj) = 11 **>**<sup>**0,**wecan define:</sup> a _=a- p_ (4) u, = u,-P and consequently u*j is<sup>independentand identicallydistributed withE(u*i)= 0.Therefore,the</sup> model (1) can be expressed<sup>as follows:</sup> _K yi,=a +Zx,J,Jk+vi_-ui_ **_k=1_** Now, the two errors have<sup>mean zeroand therefore we can directlyapply all the results</sup> of panel data models. As<sup>aresult, wecanusethedifferent estimatorsproposedin the</sup> econometric literature of<sup>paneldata,thefixedeffectsmodelortherandomeffectsmodels.</sup> The choice between these<sup>twomodels,asiswell known, willdependon the possible</sup> correlation between the<sup>individualeffectsandtheobservable explanatory variables,inthis</sup> case, the inputs (xk).

If this correlation exists,<sup>the parametersof the model Equation5 can be estimatedwith</sup> the within groups (WG)<sup>estimators. Theindividualeffects canbe definedas</sup> as = a - ui = a - ui and<sup>their estimation will beobtainedfromthe withinestimatorsof the</sup> ^ _WG_ parameters of the model<sup>**_(Bk_**</sup> From this estimation of<sup>theNindependent terms</sup> _al,a2,...aN_ an estimation of the independent term and<sup>thelevelof(in)efficiency(ui)canbeobtained froma simple</sup> procedure: a = max(ai) _ui_ = a-a, (8) _ET7_ = _e-U'_ 9
# **_The role of income_**

The share of a country's population that resides in urban areas - that is, its level of urbanization - is highly correlated with its level of per capita income. The share of urban population increases rapidly at low levels of income (and of urbanization) to converge to an urbanization level of about 80% (Figure 3). This transformation is due to the structural changes that accompany development. The share of GDP derived from agriculture falls from 32% among low income countries to less than 3% among rich ones, and the share of employment accounted for by agriculture falls by even more: from about 66% to less than 6%.

Our basic model, therefore, is that urbanization is a function of income and income squared (to capture the non-linearity of the relationship),<sup>8</sup> and of the structure of the economy: U = E Y (YA/Y) (YM/Y) which in logs yields: In addition, we test the hypotheses mentioned above concerning rural-urban wage differentials, urban bias, rural "push" factors, civil disturbances and wars, and civil and political rights. Our data is organized as an unbalanced panel data set, with observations every five years from 1965 to 1995, for up to 100 developed and developing countries.<sup>9</sup> Regression 1 in Table 2 shows that income per capita, the share of GDP derived from agriculture and manufacturing, and a time trend ("year") explain 80% of cross country variations in levels of urbanization.<sup>10</sup> Because the variables are in logs, their coefficients are elasticities. Thus a 10% increase in the share of GDP derived from manufacturing occurs along with a 1.3% increase in the level of urbanization, while a 10% increase in the share derived from agriculture coincides with a -1.3% decrease in the level of urbanization.

# **_Rural-urban differences in earnings_**

The literature on rural-urban migration discussed in the preceding section emphasizes the importance of rural-urban income differentials in explaining decisions to migrate. No good measure of rural and urban wages was available, so we constructed one based on average returns to labor. As a proxy for rural wages we used the average product in agriculture, calculated simply as agricultural GDP (YA) divided by labor force

> 8 Since urbanization rates are bounded at 100%, the log-linear function is inadequate. We therefore use a polynomial to approximate the unknown function. Our database includes 1960, but because sectoral shares of GDP were not available for 1960, most regressions omit that year.

> '° Manufacturing was used rather than industry because industry includes mining - an activity that would not necessarily be correlated with the kinds of structural changes relevant to explaining urbanization.

11
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

individual reforms, they generally involve a combination of the following key elements (see e.g. IADB, 2001; Joskow, 1998; Newbery, 2002):

- i. Corporatisation of state-owned utilities;

- ii. Enactment of an electricity reform law;

- iii. Regulatory reform, including adoption of incentive regulation for the natural monopoly network activities;

- iv. Establishment of an independent regulator;

- v. Unbundling of vertically-integrated utilities into generation, transmission, distribution and supply activities, and where necessary horizontal splitting;

- vi. Provision of third party access to networks;

- vii. Establishment of a competitive wholesale generation market;

- viii. Liberalisation of the retail supply market;

- ix. Privatisation of electricity assets;

- x. Definition of rules concerning consumer protection, allocation of energy subsidies, and stranded costs.

A World Bank survey on the state of energy reform in developing countries focused on six key steps: (i) corporatisation or commercialisation of the core utility; (ii) enactment of an ‘Energy Law’; (iii) establishment of an independent regulatory authority; (iv) restructuring of the core utility; (v) private investment in greenfield sites; and (vi) privatisation (Bacon, 1999). The survey suggests a logical sequence of reform steps in which the most common (because logically the first) step is corporatisation and commercialisation of the publicly owned utility, and the least common (or logically final) step is privatisation. It should be noted that not all the above reform elements will be appropriate in all countries. For example, a particular issue that arises is whether smaller systems require vertical separation and third-party-access.

# **_Market structure_**

In our conceptual model of reform, the market structure component refers to the wider framework within which the interaction of supply and demand for electricity takes place. Market structure at the time of reform is the result of the historical development 11
and sequester more carbon. The ‘+’ in REDD+ stands for other co-benefits that have been added to the original REDD program (that was focused solely on carbon) to address potentially negative, unintended effects on non-carbon ecosystem services and to take account of effects on those who currently have claims to forests.

REDD+ is expected to create an opportunity to increase the investment in forest management that can bring a number of benefits, including achieving critical developmental goals, enhancing forest governance, bolstering global conservation efforts, reducing carbon emissions and deforestation, and contributing to poverty reduction particularly in the forest managing communities (Economist 2010a, Toni 2011, Wollenberg and Springate-Baginski 2010). From the community managed forestry perspective the effectiveness and the decision to adopt REDD+ contracts depend on incentives, benefit sharing arrangements, the opportunity costs of carbon sequestration, allocation of forest management decision making rights, and community interactions (McKinsey & Co, 2010; Gregorsen et al., 2011). At the same time there is limited evidence-based knowledge on the costs of carbon sequestration in developing countries, particularly in the context of community-managed forests and on community and household preferences for programs such as the REDD+ program.

In this paper we provide information to fill this knowledge gap by identifying the opportunity costs of carbon sequestration in communities with community managed forests. Specifically we use a choice experiment (CE) survey applied to rural Nepal communities to understand respondent’s preferences towards the institutional structure of REDD+ contracts and to calculate the opportunity cost of these contracts. In particular we focus on calculating the opportunity costs of reductions in firewood collection and restrictions on grazing.

Results show that respondents care about how REDD+ programs are structured with 3
Policy ReseaRch WoRking PaPeR 4754

# **Abstract**

This paper studies the decision of firms to extend trade credit to customers and its relation with their financing decisions. The authors use a novel firm-level database of Chinese SMEs with unique information on market power in both output and input markets and on the amount, terms, and payment history of trade credit simultaneously extended to customers (accounts receivable) and received from suppliers (accounts payable). The analysis shows that suppliers with relatively weaker market power are more likely to extend trade credit and have a larger share of goods sold on credit. Examination of the importance of financial constraints reveals that access to bank financing and profitability are not significantly related to trade credit supply. Rather, firms that receive trade credit from their own suppliers are more likely to extend trade credit to their customers, and to “match maturity” between the contract terms of payables and receivables. This matching practice is more likely used when firms face strong competition in the product market (relative to their customers), and enjoy strong market power in the input market (relative to their suppliers). These results highlight the importance of supply chain financing for market competition and risk management in credit constrained firms.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study SME finance. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at lklapper@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 13 OLS:  Are school/teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grades 5 and 4, KA</u>**

||**Kannada**<br>**Class 5**|**Math**<br>**Class 5**|**Kannada**<br>**Class 5**|**Math**<br>**Class 5**|**Kannada**<br>**Grade 4**|**Math**<br>**Grade 4**|**Kannada**<br>**Grade 4**|**Math**<br>**Grade 4**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**-0.03**|**0.12**|**-0.10**|**0.06**|**-0.18**|**0.48**|**-0.20**|**0.46**|
||**(0.93)**|**(0.75)**|**(0.82)**|**(0.87)**|**(0.71)**|**(0.27)**|**(0.68)**|**(0.30)**|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.00|-0.01|0.00|-0.01|
||(0.01)*|(0.46)|(0.03)*|(0.68)|(0.51)|(0.10)|(0.75)|(0.04)*|
|% non-sc teachers|-0.01|0.03|-0.01|0.04|0.01|0.01|0.02|0.02|
||(0.68)|(0.39)|(0.78)|(0.33)|(0.80)|(0.85)|(0.68)|(0.71)|
|% male teachers|0.03|0.07|0.01|0.06|-0.04|0.01|-0.05|-0.01|
||(0.46)|(0.06)|(0.73)|(0.11)|(0.29)|(0.85)|(0.15)|(0.77)|
|% teachers with college<br>deg|0.02|-0.02|0.02|-0.02|-0.06|-0.06|-0.06|-0.06|
||(0.67)|(0.42)|(0.57)|(0.50)|(0.14)|(0.13)|(0.15)|(0.14)|
|% teachers with<br>graduate deg|0.20|0.09|0.21|0.10|0.36|0.20|0.38|0.22|
||(0.07)|(0.43)|(0.05)*|(0.35)|(0.00)**|(0.07)|(0.00)**|(0.03)*|
|Av. Years of service|0.00|0.00|0.00|-0.004|0.00|0.00|0.00|0.00|
||(0.27)|(0.06)|(0.24)|(0.05)|(0.45)|(0.75)|(0.55)|(0.62)|
|Av. days in service in<br>lastyear|-0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.71)|(0.72)|(0.72)|(0.66)|(0.37)|(0.44)|(0.35)|(0.43)|
|% teachers with pre<br>service training|0.03|0.03|0.03|0.04|-0.05|0.02|-0.05|0.02|
||(0.57)|(0.38)|(0.51)|(0.33)|(0.35)|(0.75)|(0.32)|(0.77)|
|% teachers doing multi<br>grade|-0.05|-0.04|-0.05|-0.04|0.04|0.05|0.04|0.05|
||(0.31)|(0.27)|(0.32)|(0.29)|(0.41)|(0.34)|(0.41)|(0.35)|
|**% teachers actively**<br>**engaged inteaching**|**0.02**|**0.01**|**-0.12**|**-0.13**|**0.06**|**0.09**|**-0.08**|**-0.06**|
||**(0.77)**|**(0.89)**|**(0.16)**|**(0.10)**|**(0.39)**|**(0.18)**|**(0.34)**|**(0.49)**|
|**% teachers present in**<br>**school (mean over 4**<br>**visits)**|**-**|**-**|**0.26**|**0.27**|**-**|**-**|**0.28**|**0.31**|
||**-**|**-**|**(0.02)***|**(0.02)***|**-**|**-**|**(0.01)****|**(0.01)****|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2086|2086|2086|2086|2103|2103|2103|2103|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

28
East, for example, the rate of job creation in the private sector has failed to keep up with the rate at which new (young) workers have entered the job market, a fact that many have linked to the Arab Spring uprisings. Tepid job creation has also shown the limits of apparently significant market reforms that countries in the region adopted over the past ten years. One hypothesis that explains slow job creation, despite the relaxation of many formal, de jure regulatory and legal obstacles to private sector activity, is that close state-business relations have circumvented market reforms and continued to stifle competition, innovation, and job creation. We investigate this hypothesis with data from Egypt.

Additional work is needed to fully evaluate the macro-economic impact of political connections and to predict how the Egyptian economy would have performed in the absence of these close state-business relations. For instance, we would like to better evaluate the impact of political connections on unconnected sectors: did connected sectors weaken forward and backward linkages with unconnected firms, thereby exacerbating negative effects on aggregate growth (Jones, 2011)?

One implication of our results is to cast doubt on the feasibility of industrial policy under a closed political system. While this was successful in other parts of the world, it has not worked in Egypt nor in Tunisia (Rijkers et al, 2014). Industrial policies, which are seen by many analysts as an essential part of a successful development drive in the Middle East, in part offset the over-valuation of the exchange rates introduced by oil and remittances revenues, cannot work effectively in environments dominated by rent-seeking.

We have not focused in this paper on the broader political-economy of state-business relations – in particular, on the services that connected firms supplied to the regime in exchange for regulatory and fiscal privileges. This is also an important subject for future research. Similarly, we have not discussed the implications of political connections for income inequality, another important driver of the Arab Spring Uprisings – the regime of political connections must have led to the emergence of a very rich one percent. Moreover, the slow growth of the formal private sector must have exacerbated the prevalent labor market dualism in Egypt, increasing the inequality of opportunities in the labor markets (Assaad 2013).

33
# Exploring the Sources of the Agricultural Productivity Gender Gap: Evidence from Sri Lanka

Emiko Fukase,<sup>a</sup> Yeon Soo Kim,<sup>a*</sup> Cristina Chiarella<sup>b</sup> a World Bank b Université catholique de Louvain JEL classifications: J16, O13, Q12, Q18 Keywords: Agriculture, crop diversification, decomposition, export-orientation, gender gap, productivity, South Asia, Sri Lanka

> * Corresponding author. E-mail address: ykim8@worldbank.org.