rafmacalaba/datause-encoder-data huggingface dataset:
tenure, and education, especially on tertiary education. Finally, consistent with the LFWP literature, workers in larger firms have larger wage premiums.

Table 4: Innovation Premium, 2014-2017

||||Dependen|t variable: _ln_|(_wijkft_)|||
|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>Private|(3)<br>Public|(4)<br>All|(5)<br>Private|(6)<br>Public|(7)<br>Private|
|Innovation|1.254***<br>(0.107)|0.996***<br>(0.0496)|2.616***<br>(0.312)|0.189***<br>(0.0118)|0.178***<br>(0.0118)|0.366***<br>(0.0905)|0.0792***<br>(0.0246)|
|Age|0.303***<br>(0.0128)|0.262***<br>(0.00289)|0.365***<br>(0.0618)|||||
|Age Squared||||0.249***<br>(0.0176)|0.235***<br>(0.00403)|0.486***<br>(0.143)|0.224***<br>(0.00383)|
|Gender|-0.214***<br>(0.00352)|-0.198***<br>(0.00191)|-0.233***<br>(0.0114)|||||
|Primary school graduate|0.0386***<br>(0.00394)|0.0326***<br>(0.00269)|0.0457**<br>(0.0210)|||||
|Middle school graduate|0.0837***<br>(0.00939)|0.0730***<br>(0.00317)|0.0891*<br>(0.0542)|||||
|High school graduate|0.218***<br>(0.00774)|0.173***<br>(0.00391)|0.405***<br>(0.0342)|||||
|College graduate or higher|0.839***<br>(0.0181)|0.752***<br>(0.0116)|1.004***<br>(0.0466)|||||
|Tenure|0.0930***<br>(0.00254)|0.0736***<br>(0.000832)|0.176***<br>(0.0103)|0.0263***<br>(0.000579)|0.0292***<br>(0.000447)|0.00423<br>(0.00451)|0.0286***<br>(0.000540)|
|Temporary|-0.0325<br>(0.0216)|-0.0117<br>(0.0140)|-0.171***<br>(0.0598)|-0.0165***<br>(0.00588)|-0.0258***<br>(0.00506)|0.0895***<br>(0.0273)|0.0537***<br>(0.0102)|
|Public sector|0.0953***<br>(0.0193)|||0.0164*<br>(0.00891)||||
|Firm size|0.0509***<br>(0.00320)|0.0513***<br>(0.00207)|0.0521***<br>(0.0164)|0.0258***<br>(0.000914)|0.0264***<br>(0.000813)|0.00498<br>(0.00494)|0.0311***<br>(0.00113)|
|Geo-Occupation-Year|Yes|Yes|Yes|||||
|Individual-Year<br>Individual-Year-Firm||||Yes|Yes|Yes|Yes|
|Observations|172519611|136163626|36355985|157682620|122814877|33708892|122340110|
|R-squared|0.562|0.518|0.504|0.942|0.927|0.954|0.938|

Note: Innovative firms is measured by the share of PoTec workers in total employment for firm _f_ in year _t_ . Columns (7)-(9) exclude the public sector. Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, ** and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

The innovation premium in columns (1) and (3) could be explained by the lack of controls for workers sorting and employer heterogeneity. Abowd et al. (1999) show how worker effects can be very important in explaining wage differentials and the fact that more capable workers could sort into better and potentially innovative firms. Columns (4)-(6) estimate Equation 2 and control for worker and year effects; thus providing better control for workers’ characteristics. The new results show that the coefficients are reduced significantly to 0.18, 0.17, and 0.36, and the premium on workers earnings is thus reduced to 8%, 8%, and 17%, 15
Figure 4: The Rise in the Quantity of ICT <!-- Start of picture text --> A. 25-th Percentile B. Median Income Country C. 75-th Percentile<br>Avg. ICT Value/ NICT Value Avg. ICT Value/ NICT Value<br>Avg. Units of ICT per Unit of NICT Avg. Units of ICT per Unit of NICT<br>+/- 2 Std. Err. +/- 2 Std. Err.<br>Avg. ICT Value/ NICT Value<br>Avg. Units of ICT per Unit of NICT<br>+/- 2 Std. Err.<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>Year Year Year<br>10 10 10<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>Avg. Ratio at 25-th Income Percentile (%) Avg. Ratio at 50-th Income Percentile (%) Avg. Ratio at 75-th Income Percentile (%)<br>0 0 0<br><!-- End of picture text --> _Notes:_ The figure shows a comparison of the nominal and real share of ICT over time. The lines are plots of the time effects from regressions analogous to the ones in the first columns in panels A and B of Table 2. These time effects are normalized so that they reflect countries at the 25-th percentile (panel A), the median (panel B), and the 75-th percentile of log real GDP per capita in our sample. That is, for each panel we run a separate log linear regresssion in which we re-center log real GDP per capita such that it is zero at the respective percentile cutoff.

# (1).

Our resulting estimates of “real” ICT abundance are summarized in panel B of Figure 1 and Table 2, suggesting the following insights: first, the ratio of ICT capital goods to non-ICT capital goods is significantly higher in richer countries. Second, the difference in this measure between the poorest and the richest countries has increased significantly between 1993 - 2011. This is reflected both in the significantly steeper slope of our fitted regression line during the later periods in our sample (see panel B of Table 2) as well as the comparison of the maximum and minimum values in panel C of Table 1. These observations are also visually apparent in panel B of Figure 1 as well as Figure 4, which plots the time effects for three alternative versions of the full-sample regressions reported in Panels A and B of Table 2: ones in which income per capita is respectively normalized to be zero for the country at the 25-th, 50-th, and 75-th percentile of the real income distribution. This final figure particularly highlights that, while the nominal value of ICT as a fraction of NICT has remained roughly constant (or if anything declined mildly), the physical abundance of ICT has increased significantly around the world. Moreover, this pattern is relatively uniform across different levels of development.

In sum, these findings suggest that lower physical abundance of ICT capital in developing countries may partly be accounted for by its higher relative price. Similar to the argument laid 16
Instituto Nacional de Estadística, Geografía e Informática (INEGI) (2009). _Estados Unidos Mexicanos, Censo Agropecuario 2007, VIII Censo Agrícola, Ganadero y Forestal_ , Aguascalientes. INEGI, México.

Kimanzu, N., Schulte-Herbrüggen, B., Clendenning, J., Chiwona-Karltun, L., Krogseng, K., & Petrokofsky, G. (2021). What Is the Evidence Base Linking Gender with Access to Forests and Use of Forest Resources for Food Security in Low-and Middle-Income Countries? A Systematic Evidence Map. _Forests_ , _12_ (8), 1096.

Larson, A. M., Solis, D., Duchelle, A. E., Atmadja, S., Resosudarmo, I. A. P., Dokken, T., & Komalasari, M. (2018). Gender lessons for climate initiatives: A comparative study of REDD+ impacts on subjective wellbeing. _World Development_ , 108, 86-102.

Madrid, L., Núñez, J. M., Quiroz, G. and Rodríguez Y. (2009). La propiedad social forestal en México. _Investigación Ambiental, INE_ , 1 (2): 179-196 Marcos Morezuelas, P. (2021) _Género, bosques y cambio climático_ . Interamerican Development Bank. Washington DC, US.

Méndez-López, M.E., E. García-Frapolli, I. Ruiz-Mallén, L. Porter-Bolland, and V. Reyes-Garcia. (2015). From Paper to Forest: Local Motives for Participation in Different Conservation Initiatives. Case Studies in Southeastern Mexico. _Environmental Management_ 56: 695.

Sanders, M., Snijders, V., & Hallsworth, M. (2018). Behavioural science and policy: Where are we now and where are we going? _Behavioural Public Policy_ , 2(2), Shafir, E., ed. (2013) _The behavioral foundations of public policy._ Princeton University Press.

World Bank Group (2014). World Development Report 2015: Mind, Society, and Behavior. Washington DC: World Bank. doi: 10.1596/978-1-4648-0342-0 World Bank Group (2018). Closing the Gender Gap in Natural Resource Management Programs in Mexico. Washington DC, US: World Bank World Bank Group (2019). Behavioral Science Around the World: Profile of 10 Countries. Washington DC, US: World Bank 20
|Vietnam: Second Higher Education Project|Thanh Thi Mai|
|---|---|
|Vietnam: Second Higher Education Project|Thanh Thi Mai|
|Vietnam: Second Higher Education Project|Thanh Thi Mai|
|Vietnam: Second Higher Education Project|Thanh Thi Mai|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Karnataka Municipal Reform Project|Uri Raich|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
|Tamil Nadu Empowerment and PovertyReduction|Samik Sundar Das|
Policy Research Working Paper 11066

# **Abstract**

Fiscal rules have been shown to support fiscal discipline by improving government budget balances and restraining the growth of debt. However, questions remain about what enhances their effectiveness and how certain conditions help to build the credibility needed for their survival and success. Using data from 108 countries between 1984 and 2012, this paper studies the dynamic effects of fiscal rule adoption. It shows that although fiscal rules generally improve the primary balance, their effects depend on the time horizon under consideration and the context of adoption. In advanced economies and countries with strong political institutions, the effects strengthen over time. Conversely, in emerging markets and developing economies—especially those with weaker institutions—their impact tends to fade as time passes. The findings highlight the critical role of economic conditions and consensus building at the time of adoption. Specifically, fiscal rules introduced in times of economic hardship or under highly concentrated political power are often less effective in the medium term.

This paper is a product of the Prospects 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 antonio.fatas.@insead.edu; bgootjes@worldbank.org; jmawejje@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
Notes: (1) Panel Fixed Effects; All regressions include year fixed effects; (2) Anchored Relative Poverty line (60% of median at 2011); (3) Data Source: EU-SILC, Eurostat; (4) Symbols: ** P<0.01, * P<0.05, +P<0.1 Although diversifying the sources of income, between agricultural and non‐agricultural, may prove useful to alleviate poverty in households over time, further results suggest that households who receive agricultural income may be better off by specializing in a particular agricultural activity. Table 14 shows that an increase in the share of the area used in specialized crop production seems beneficial to poverty reduction, as regions with higher areas of specialization holdings show higher poverty reduction. However, an increase in the share of land used for specialized livestock shows no significant effect on poverty reduction. Hence, households who turn to agriculture as a source of income do better when they specialize, particularly in crops. Thus, for poverty alleviation, it is useful to have different sources of income. Nevertheless, households should not diversify across agricultural income sources.

**Table 14.** Regions with higher farm holdings specialization have higher poverty reduction

||(1)<br>Povertyrate|(2)<br>Povertyrate|(3)<br>Povertyrate|
|---|---|---|---|
|Agricultural area of specialized holdings share of total|-0.02|-0.02||
|Agricultural area of holdings specialized in crop share of total|||-0.05+|
|Agricultural area of holdings specialized in livestock share of total|||-0.02|
|Ratio of agricultural area to total||-0.03|-0.02|
|Agricultural employment share|0.22|0.22|0.22|
|GDP per inhabitant|-0.01**|-0.01**|-0.01**|
|Share of inhabitants with secondary education|0.28+|0.28+|0.30+|
|Share of inhabitants with tertiary education|0.16|0.16|0.17|
|Year fixed effects|Y|Y|Y|
|R-Squared:||||
|Within|0.42|0.42|0.43|
|Between|0.14|0.14|0.13|
|Overall|0.12|0.12|0.11|
|Observations|367|367|367|

Notes: (1) Panel Fixed Effects; All regressions include year fixed effects; (2) Anchored Relative Poverty line (60% of median at 2011); (3) Data Source: Farm Structure Survey, Eurostat; EU‐SILC, Eurostat ; (4) Symbols: ** P<0.01, * P<0.05, +P<0.1

# **5. Poverty, agriculture, and the CAP: A comprehensive analysis**

So far, we have documented how CAP funds can contribute towards inclusive growth by reducing poverty and inequality. With this message in mind, the specific areas for improvement will depend on a country’s level of structural transformation and on its current allocation of the CAP based on high poverty areas. The goal of this section is to integrate the parts of the past analyses; on one side we have the relationship between agriculture and poverty, which provides the state of a country 19
Ashraf, Nava. 2009. "Spousal Control and Intra-household Decision Making: An Experimental Study in the Philippines," _American Economic Review_ 99 (4): 1245-77.

Bankole, Akinrinola. 1995. “Desired Fertility and Fertility Behaviour among the Yoruba of Nigeria: A Study of Couple Preferences and Subsequent Fertility” _Population Studies_ 49 (2): 317-28.

Barrett, Christopher B. and Michael R. Carter. 2010. “The Power and Pitfalls of Experiments in Development Economics: Some Non-random Reflections,” _Applied Economic Perspectives and Policy_ 32 (4): 515–48.

Beegle, Kathleen., Frankenberg, Elizabeth and Thomas, Duncan. 2001. “Bargaining Power Within Couples and Use of Prenatal and Delivery Care in Indonesia”. _Studies in Family Planning_ 32 (2): 130–46.

Behrman, Jere R.and John Hoddinott. 2005. “Programme Evaluation with Unobserved Heterogeneity and Selective Implementation: The Mexican PROGRESA Impact on Child Nutrition.” _Oxford Bulletin of Economics and Statistics_ 67 (4): 547-69.

Bobonis, Gustavo J. 2009. “Is the Allocation of Resources within the Household Efficient? New Evidence from a Randomized Experiment.” _Journal of Political Economy_ 117 (3): 453-503.

Bomahangi, Allan, Cheryl Doss, and Ruth Meinzen-Dick. 2011. “Who Owns the Land? Perspectives from Rural Ugandans and Implications for Land Acquisitions.” IFPRI Discussion Paper 01136, Washington, DC.

Brown, Philip H. 2003. "Dowry and Intrahousehold Bargaining: Evidence from China," William Davidson Institute Working Papers Series 2003-608, William Davidson Institute, University of Michigan.

Chen, Joyce J. 2013. “Identifying Non-Cooperative Behavior Among Spouses: Child Outcomes in Migrant-Sending Households” _Journal of Development Economics_ 100(1): 1-18. Connelly, Rachel, Roberts, Kenneth and Zheng, Zhenzhen, 2010. "The Impact of Circular Migration on the Position of Married Women in Rural China." _Feminist Economics_ 16 (1): 3-41.

Datta, Namita. 2006. “Joint Titling – A Win-Win Policy? Gender and Property Rights in Urban Informal Settlements in India.” _Feminist Economics_ 12 (1 & 2): 271-298.

Deaton, Angus S. 1989. “Looking for Boy-Girl Discrimination in Household Expenditure Data.” _World Bank Economic Review_ 3 (1): 1-15.

36
Policy Research Working Paper 5447

# **Abstract**

As an instrument of trade and investment policy, special economic zones have played a catalytic role in processes of industrialization, diversification, and trade integration in many countries, particularly in East Asia. However, in the African context, anecdotal evidence suggests the experience has been disappointing on the whole. Among the reasons why many zones underperform may be that they fail to establish a high quality investment environment—this is, after all, one of the main promises that economic zones hold for investors. Drawing on original survey research, this paper presents a systematic analysis of the outcomes and the investment climate of economic zones programs in six African countries and four developing countries outside the region. The analysis finds that although performance across zones is mixed— with Ghana and Lesotho in particular performing well on some measures—African zones programs on the whole are underperforming in terms of attracting investment, facilitating exports, and creating jobs. Economic zones in Africa offer an improved business environment relative to what is available to firms based outside the zones; however, in comparison with the non-African countries in the survey, both absolute investment climate performance and relative improvements fall well short.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to study the role and performance of special economic zones and other instruments of trade and investment policy in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tfarole@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 1. Sample and region coverage

|Advanced Economies|Emerging Market and Developin<br>Economies|g|
|---|---|---|
|Austria|<br>East Asia and Pacific|Middle East and North Africa|
|Belgium|China|Bahrain|
|Canada|Indonesia|Egypt, Arab Rep.|
|Cyprus|Philippines|Iran, Islamic Rep.|
|Czech Republic|Thailand|Jordan|
|Denmark|Europe and Central Asia|Kuwait|
|Estonia|Bulgaria|Saudi Arabia|
|Finland|Croatia|Tunisia|
|France|Hungary|South Asia|
|Germany|Kazakhstan|India|
|Greece|Moldova|Sub-Saharan Africa|
|Hong Kong SAR, China|Poland|Benin|
|Iceland|Russia|Botswana|
|Ireland|Türkiye|Cameroon|
|Israel|Ukraine|Côte d'Ivoire|
|Italy|Latin America and the Caribbean|Kenya|
|Japan|Argentina|Lesotho|
|Korea, Rep.|Barbados|Mauritius|
|Latvia|Brazil|Mozambique|
|Luxembourg|Chile|Niger|
|Netherlands|Colombia|Rwanda|
|Norway|Costa Rica|Senegal|
|Portugal|Ecuador|South Africa|
|Slovak Republic|Guatemala|Swaziland|
|Slovenia|Honduras||
|Spain|Jamaica||
|Sweden|Mexico||
|Switzerland|Panama||
|United Kingdom|Paraguay||
|United States|Peru||
||Uruguay||

Source: World Bank.
2008); and leveraged social incentives among patients, for example, through social signaling (Karing 2019). Our paper is also related to a larger literature that examines various approaches to improving health outcomes in the developing world. A complete review of this literature can be found in Dupas (2011) and Dupas and Miguel (2017).

The rest of our paper is structured as follows. Section 2 describes the experimental design and details of the two interventions. Section 3 introduces our sampling procedure, the survey and Ebola case data, randomization and empirical strategy. Section 4 presents our medium-run findings and plausible mechanisms, longer run Ebola outcomes, and investigates alternative explanations. The final section concludes with a discussion of policy implications.

# **2. Healthcare in Sierra Leone**

## **2.1 Background**

According to the World Development Indicators, Sierra Leone had the second highest under-5 mortality rate in the world in 2011, with 149.8 deaths per 1,000 live births. In the same year, the country had the highest maternal mortality rate in the world. Figure A.2 displays Sierra Leone’s per capita health expenditure and under-5 mortality in 2010 relative to other countries that the World Bank classified as low income. Located in the upper-right quadrant, the country spent more and performed worse than countries at a comparable level of economic development.

In an effort to reduce infant and maternal mortality, the Government of Sierra Leone (GoSL) launched a free health care initiative in 2010, removing fees for pregnant women and children under 5 years old. The policy simultaneously increased pay for government healthcare workers; at the time 30–50 percent of staff did not receive a government wage and instead relied on charging illegal fees or inflated drug prices and accepting in-kind contributions from the communities they served. In addition to removing cost barriers and severe resource constraints, the GoSL saw a need to strengthen incentives for front-line healthcare workers. Absent incentives tied to service delivery, the government worried that nurses would miss work or continue to charge illegal fees — barriers to care that the free healthcare initiative was intended to eliminate.

Government-run clinics and hospitals are the primary providers of western-style medicine in Sierra Leone; private and NGO-sponsored facilities are scant (Denney and Mallett 2014). These facilities operate alongside traditional birth attendants and healers. Government clinics, referred to as peripheral health units (PHUs), come in three types: maternal and child health posts (MCHPs), community health posts (CHPs), 6