rafmacalaba/data-use-mentions-extended huggingface dataset:
older, non-household head women — also compared to high reported/economic land ownership for women heads — indicating that this group may be particularly vulnerable.

**Figure 3.  Non-Household Head Women vs. Men Overall: Reversal/Narrowing of Gender Gaps in Exclusive Land Ownership/Rights under Individual Interviews (T5/IHPS)**

<!-- Start of picture text --> (a) Reported ownership - exclusive<br><!-- End of picture text --> <!-- Start of picture text --> (b) Economic ownership - exclusive<br><!-- End of picture text -->

# **(c) Right to sell - exclusive**

- **(d) Right to bequeath - exclusive**

## <u>Notes:</u>

- (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

- (2) The rights related variables are defined irrespective of the reported need to obtain consent/permission from anyone – a topic that the IHPS collected additional information on.

- (3) The graphs are near-identical if the sample is limited only to men in male-headed households.

16
businesses and operations, and general uncertainty about the costs of regulation.

Entrepreneurs in South Asia and Southeast Asia ranked the top obstacles to doing business as high taxes and tax regulations, inadequate infrastructure, inflation, labor regulations, and regulations for starting new businesses and operations.

* In the Middle East and North Africa, entrepreneurs considered lack of infrastructure the chief obstacle to doing business, followed by corruption, high taxes and tax regulations, and financing.

* In Central and Eastern Europe, high taxes and tax regulations were the only regulation-related obstacle ranked high, followed by financing, corruption, and inflation.

* The worst obstacles in Latin America were considered to be corruption and inadequate infrastructure, followed by crime and theft, problems with finance, and high taxes and tax regulation.

* In Sub-Saharan Africa the biggest problems were corruption, tax regulations and high taxes, inadequate infrastructure, inflation, crime and theft, and financing.

- This paper a product of the Office of the Chief Economist and Senior Vice President, Development Economics- was produced as a background paper for _World DevelopmentReport 1997_ on the role of the state in a changingworld. The study was funded in part by the Research Support Budget under the research projects "Cross-Country Indicators of Institutional Uncertainty" (RPO 680-51), and "Indicators of Government Quality as Perceived by the Private Sector" (RPO 681-52). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Michael Geller, room N7-078, telephone 202-473-1393, fax 202-522-0056, Internet address wdr@worldbank.org. April 1997. (75 pages)

_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
that liberalization of agricultural products marketing induced entry in subgroups of the food marketing channel, but not in groups where entry barriers were high. Vakis et al. (2003) measured the transaction costs occurring when a farmer sells his crops on the market. They showed that the level of transaction costs will determine which market (farmgate, local or distant market) farmers enter.

The literature’s relative disregard of entry costs in and out of commercial agriculture is understandable given that those costs can be expected to be small in magnitude, as poor farmers are unlikely to switch altogether to sophisticated input-intensive farming. However what matters is not the absolute magnitude of entry costs but their magnitude relative to the income of subsistence farmers, in particular when credit markets are deficient so that sunk costs must be self-financed. Moreover, populations on the fringe of markets are likely to be vulnerable ones, and even well-wishing reforms can have the unwanted effect of destroying fragile markets, as changes in relative prices can be too small to cover switching costs or make intermediation profitable. When middlemen’s trucks no longer come, rural roads are left to decay and entire regions can shrink into subsistence. A report by Oxfam (Oxfam-IDS 1999) argued that this is precisely what happened in Zambia after the maize reform (cited in Winters 2000).

This paper is an attempt to bridge the literature’s gap. We propose a method to evaluate entry costs based on a simple asset-return model of farm specialization, the gist of which is as follows. Suppose that being in subsistence or in the market depends on a given household characteristic _z_ . High- _z_ farmers are in the market whereas low- _z_ are in subsistence, with cutoff _z_<sup>+</sup> . Each sector of activity can be thought of as an asset whose dividend is the net value of production (evaluated at market prices for subsistence farmers, leaving aside for the moment the issues involved in that valuation). The asset’s price is the present discounted value
averages were close to zero (which masked substantial heterogeneity). The overall pattern is once again similar when using median regression.

The results are also similar when restricting the analysis to primary school teachers or to public school teachers. Public sector teachers tend to have either no premium or a positive hourly earnings premium when compared to other public sector employees (13 of the 14 countries for which this could be estimated). Only in Tanzania do they have a deficit relative to comparable civil servant employees. Last, the hourly earnings premiums are typically also found for secondary school teachers (i.e., these are now more in line with teachers overall/primary school teachers).

Across contract types, earnings premia are higher (or less negative) for teachers with permanent contracts than for teachers with fixed-term contracts. For both monthly and hourly wages, there are more statistically significant premia for permanent contract teachers than for fixed-term contract teachers (Table 10).

# **<u>3.5. Correlations</u>**

In this section, we explore the associations between teacher pay and student performance as well as various broader aspects of the economy and the labor market. None of these associations should be assumed to be causal. Other variables, not observed in this analysis, may affect both teacher pay and other aspects of the economy. However, understanding how teacher pay differs across different types of economies both provides a clearer picture of where we observe higher teacher salaries and yields avenues for further exploration.

# **<u>3.5.1. Teacher pay and student performance</u>**

We examine the cross-country association between teacher earnings premiums and student achievement using two measures: first, the harmonized test scores from the Human Capital Index database, recently developed and then updated by the World Bank (2020); second, the rate of “learning poverty” defined as the share of children who are unable to read and understand a simple text by age 10 (World
# **References**

Aedo, Cristian, and Ian Walker. 2011. “Education and Skills for the 21<sup>st</sup> Century in LCR.” Mimeo, LCR Regional Study, Office of the Chief Economist for LCR, World Bank, Washington, DC.

Aghion, Philipe, Nicholas Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt. 2005. “Competition and Innovation: An Inverted-U Relationship.” _Quarterly Journal of Economics_ 120(2): 701-28.

Amiti, Mary, and Jozef Konings. 2007. “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia.” _American Economic Review_ 97(5): 1611-38.

Artuc, Erhan, Shubham Chaudhuri and John McLaren. 2010. "Trade Shocks and Labor Adjustment: A Structural Empirical Approach." _American Economic Review_ 100(3): 1008-1045.

Artuc, Erhan, and John McLaren. 2010. "A Structural Empirical Approach to Trade Shocks and Labor Adjustment: An Application to Turkey." Chapter 2 in _Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses_ , edited by G. Porto and B. Hoekman. Washington, DC: CEPR for the World Bank.

Brambilla, Irene, Rafael Dix-Carneiro, Daniel Lederman, and Guido Porto. 2011 (forthcoming). “Exports, Skills and the Wages of Seven Million Latin American Workers.” _World Bank Economic Review._ Brambilla, Irene, Daniel Lederman and Guido Porto. 2010. “Exports, Export Destinations, and Skills.” NBER Working Paper #15995, Cambridge, Massachusetts.

Broda, Christian, and John Romalis. 2009. “The Welfare Implications of Rising Price Dispersion.” Mimeo, Booth School of Business, University of Chicago, Illinois.

http://faculty.chicagobooth.edu/john.romalis/Research/Draft_v7.pdf Deaton, Angus. 1997. _The Analysis of Household Surveys: A Microeconometric Approach to Development Policy_ . The Johns Hopkins University Press for the World Bank
the **IHS4** , an adult household member is tagged as a reported owner if he/she is listed by the most <u>knowledgeable household member(s) as a reported owner for at least 1 agricultural parcel. All</u> indicators of interest are dichotomous in nature, and separate versions capturing exclusive versus joint ownership/rights are too part of our analysis, as detailed below. And although the IHS4 and the IHPS included parcel-level questions on documented ownership, only 1 percent of men and women across both surveys responded that they were documented owners of any agricultural parcel.<sup>21</sup> Furthermore, different combinations of ownership and rights are possible, although Figure 1 shows that individuals are either likely to have both reported and economic ownership over any parcel, or neither — as opposed to having reported (but not economic) ownership or vice-versa. Similarly, individuals with reported/economic ownership for the most part either had rights to both sell and bequeath land, or rights to neither, although about 10-12 percent of men and women reported rights to bequeath, but not sell a parcel, across both survey approaches. In separate tabulations, less than 1 percent of individuals were tagged as having the right to sell/bequeath in either survey if they were not either reported or economic owners of any parcel. Thus, even though the business-asusual approach in the IHS4, as discussed earlier, allowed for reporting of rights independent of ownership, this difference across surveys does not appear to matter. In separate results, the share of men and women with all ownership and rights — reported and economic, as well as rights to bequeath and sell — was actually quite similar across the survey approaches. For T1/IHS4, 19 percent of men and women had all types of ownership/rights; this figure was 23 percent for men and women in T5/IHPS.

Table 2 presents summary statistics on variables capturing exclusive versus joint ownership and rights, along with a dichotomous variable,
an estimation stage and a prediction stage. For the machine-learning methods, we perform this procedure at two different levels of analysis, namely the PSU and municipality levels. Our focus is nevertheless on obtaining estimates of poverty rates at the municipality level, meaning that when we conduct estimation and prediction at the PSU level, we further aggregate the predictions to the municipality level using population-weighted averaging. Further, recall that we have two alternative sets of covariates available, including geo-referenced and census-based variables. Given that the geo-referenced data is only available at the municipality level, we conduct estimation and prediction at the municipality level for any implementation that uses these covariates. When using census-based covariates, however, we consider implementations at both the PSU and municipality levels.<sup>13</sup>

# **3.2 Traditional small-area methods**

We consider three alternative traditional methods for obtaining small area estimates of poverty as a way to benchmark the performance of the non-parametric machine-learning approach.<sup>14</sup> Specifically, we consider area-level models, unit-level models, and a method that attempts to combine unit- and area-level models. First proposed by Fay and Herriot (1979), area-level models conduct estimation and prediction using data aggregated to the geographic area of interest (e.g., the PSU or municipality level).

The area-level model consists of two basic parts. The first part of the model links the actual or true poverty rates _yi_<sup>_a_forthe</sup><sup>_ith_entitytoavectorofarea-levelcovariates</sup><sup>_xi_asfollows:</sup> where _β_ is a vector of parameters to be estimated and _εi_ represents random area-level effects that are assumed to be mean zero with a constant variance _σε_<sup>2.Sincewelackinformationonthetrue</sup> poverty rates, this model cannot be immediately fit.

Instead, direct estimates of poverty rates from survey data _y_ ˆ _i_<sup>_d_areusedastheoutcomevariable.</sup> These estimates are nevertheless subject to sampling error because they are based on
performing relatively well, except for those in the low-income category. All income groups show a basic or evolving level of development for the regulatory framework related to cross-border data flows and private intent data. On average, only 17 percent of regulatory best practices are adopted under the private intent data dimension, ranging from 3 percent in low-income countries to 30 percent in high-income countries.

The average correlation coefficient among country scores across all seven dimensions is 0.27; the coefficients between two dimensions range from -0.22 (between regulations for sharing public intent data and protecting nonpersonal data) to 0.82 (between regulations for protecting personal data and allowing cross-border data flows) (table 5). The extent to which such bilateral correlation coefficients are statistically significant, and indeed rather large in some cases, suggest that governments may tend to simultaneously address certain clusters of issues in the regulatory framework. For instance, countries with regulatory frameworks that allow cross-border data flows are highly likely to allow access to private intent data, provide protection for personal data, and have strong cybersecurity regulations in place. Or again, countries 12
19 OEA bank i's operating expenses-to-total assets ratio MDGR = market deposits growth rate D1 = 1 for private, 0 for public banks A finding of b,>O and b2=O supports the traditional S-C-P hypothesis. On the other hand, if b1=0 and b>0, then the efficient structure is supported. It is, of course, possible that both b, and b2 maybe positive and significant, indicating that both factors are operative simultaneously. The effects of market concentration and market share on profits will be given by partial derivatives equal to the coefficients b, and b2 respectively.

The performance measure, the dependent variable, is bank profits measured as the return on assets (ROA), i.e., net income divided by total assets. Other possible performance measures are return on equity (ROE) and bank stock prices. The use of ROE may not be the best measure because banks can divide capital between debt and equity, making the comparison of equity values across banks difficult. The banks in Turkey are closely-held by various industrial groups, individuals, and the government, and their stocks are not traded. Hence, we are unable to observe and use bank stock prices. ROA is the most widely used bank performance measure and, as suggested by Evanoff and Fortier (1988), dividing net income by total assets, which is ROA, makes it a more comparable measure across banks because "bank assets are a more common denomination." Therefore, ROA is the dependent variable." The independent variables include both firm and market specific variables, and they are similar to those utilized in previous studies. Following previous studies, we proxy market structure, with the share of 5 leading banks in total deposits. Hence, concentration ratio is defined as the sum of shares of the leading 5 "<sup>1</sup> Business Week (April 9, 1984), p. 83, and Business Week (April 8, 1985), p. 106
Bloom, H.S., and C. Weiland. 2015. _Quantifying variation in Head Start effects on young children's cognitive and socio-emotional skills using data from the National Head Start Impact Study._ Available at SSRN 2594430.

- Bouguen, A., D. Filmer, K. Macours, and S. Naudeau. 2018. "Preschool and parental response in a second best world evidence from a school construction experiment." _Journal of Human Resources_ (The World Bank Working Paper Series) 53 (2): 474-512.

- Bowne, J.B., H. Yoshikawa, and C.E. Snow. 2016. "Experimental impacts of a teacher professional development program in early childhood on explicit vocabulary instruction across the curriculum." _Early Childhood Research Quarterly_ 34: 27-39.

- Brinkman, S.A., A. Hasan, H. Jung, A. Kinnell, and M. Pradhan. 2017. "The impact of expanding access to early childhood education services in rural Indonesia." _Journal of Labor Economics_ 35 (S1): S305-S335.

- Brotman, L.M., S. Dawson-McClure, D. Kamboukos, K.Y. Huang, E.J. Calzada, K. Goldfeld, and E. Petkova. 2016. "Effects of ParentCorps in prekindergarten on child mental health and academic performance: Follow-up of a randomized clinical trial through 8 years of age." _JAMA Pediatrics_ 170 (12): 1149-1155.

- Carneiro, P., and R. Ginja. 2014. "Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start." _American Economic Journal: Economic Policy_ 6 (4): 13573.

- Clements, D.H., J. Sarama, M.E. Spitler, A.A. Lange, and C.B. Wolfe. 2011. "Mathematics learned by young children in an intervention based on learning trajectories: A large-scale cluster randomized trial." _Journal for Research in Mathematics
higher wages than workers with temporary contracts. Therefore, the policy implemented after Independence to protect employees seems not to have affected wages directly as they are higher than those of unprotected workers are. This finding is coherent with what Fallon and Lucas (1991) found estimating wage equations using time series data. However, the labor legislation may have affected the number of permanent jobs created negatively because firms have invested more in physical capital than in long-term engagement to employment. There does not seem to be a trade offbetween enhanced job security and wages. Rather the contrary, job security endows insider workers with bargaining power enhancing the possibility of achieving higher wages.

**Table 5 Apprenticeship and Contracts**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itj|Coef.|Std. Err.|<br>t|P>JtI|
|dpermem|-0.245|0.098|-2.500|0.013|0.118|0.048|2.468|0.014|
|apprmeta|0.027|0.056|0.478|0.633|0.107|0.027|3.939||
|dappr|-0.064|0.121|-0.531|0.596|0.282|0.059|4.780|0.000|

The productivity differential associated with holding a permanent contract is negative and statistically significant. Is the impact from the type of contract of the same size on wages and productivity? We reject the hypothesis of equality of productivity and wages of permanently employed, with a p-value of effectively zero, (F=13.17, Prob
stronger, the longer the exposure to the new region (i.e., the difference between origin and destination is reflected in their chances in a larger magnitude when the move happens in earlier ages). These two steps are explained in detail in the next subsections.

# **III.1 Predicted upward mobility using permanent residents**

As a first step, I estimate the level of upward mobility in education (by region and birth decade) defined as the likelihood of obtaining at least a primary education for individuals whose parents did not finish primary school using a sample of permanent residents with age between 14 and 25. I define permanent residents as individuals who live at the time of the census interview in the same region (i.e., province or district) in which they were born. Hence, the indicator of upward intergenerational mobility ( _γrb_ ) used corresponds to the estimation of the following conditional probability by region _r_ and birth cohort _b_ : where _Dirb_<sup>_children_</sup> is an indicator variable equal to one if individual _i_ , living in region _r_ , and born in decade _b_ obtains at least primary education. Similarly, _Dirb_<sup>_parent_</sup> = 0 is an indicator variable equal to one if the parents of the same individual were able to complete at least primary school.

The sample estimates of _γrb_ are then used to construct the main variable of interest for the next step, which is defined as: This variable captures the difference in upward intergenerational mobility of permanent residents of a given birth decade _b_ living in region _i_ and those living in region _j_ .

10
, and a set of socioeconomic controls (the same as that included in the main specification for health outcomes). In column (2), we control for a set of interactions between household controls with _Post_ (indicating _t ≥_ 1985). The table reports the F-statistic from a test for the joint-significance of the set of interaction terms in column (2). Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

51
the Zanzibar and Mafia channel, where the shelf extends to some 60 to 80 km (Ruitenbeek and others, 2005). Along most of Tanzania’s coast line, fishing is an open access activity in which anyone can participate.<sup>4</sup> Most marine fishing activity takes place in the shallow near shore waters, using traditional vessels and gear. Table 1 provides an overview of the different types of fishing gear commonly used by artisanal fishermen in Tanzania. About 95 percent of the total marine catch is estimated to come from this type of small scale artisanal fishery (Jiddawi and Ohman, 2002). Fishing is an attractive activity for the poor in particular, as it does not necessarily require the ownership of any assets and has very low start up costs. It also generates food and cash income on a daily basis. While most fishing households in coastal areas are also engaged to some extent in agriculture, there is less dependence on agriculture on the coast due to the lack of suitable soils for farming which is characteristic of certain areas where coral rag soils dominate the terrain. Fishing also provides some insurance for the poor as a fallback source of food and income in between crops or bad crop years, and during times of economic crisis and instability. Fishing is therefore a crucial activity for the livelihoods of many poor rural households along the coast of Tanzania.

The number of fishermen in the coastal areas of Tanzania has increased over time, although reliable time series statistics are difficult to obtain due to the engagement of many part time fishermen and fluctuations due to changing economic conditions (FAO, 2000). Many fishermen in Tanzania attribute the increasing number of fishermen as one of the major causes of declines in fish catches (MANREC, 2005; Leon and others, 2004). They also cite the lack of vessels and gear to fish offshore as reasons for over exploitation of near shore reef fisheries. The lack of fishing gear, many fishermen say, also leads them to resort to cheap but destructive