rafmacalaba/data-use-mentions-extended huggingface dataset:
# **V.  Cadre Status and Household Incomes**

Table 1 presents summary statistics of household characteristics of the sample. On average, households have 6.4 years of education. In addition, arable land per capita for a typical household is 1.36 _mu_ (or 0.09 hectares) while productive assets per capita are 470 Yuan. Fourteen percent of the households have a Communist Party member and, on average, 4.61 percent of rural households have either a township or village cadre. The average cadre tenure for households for cadre members is three years, but in the RCRE data source, some are cadre households for as long as16 years and others for only one year. It is important to note that the village committee chair and village party committee chair tend to stay in their positions for much longer than other cadres, such as village accountants or heads of the village security office.

The statistically significant higher average income per capita, both earned and total, of cadre households relative to non-cadre households is evident from direct comparisons shown in Table 2. Over the entire period from 1986 to 2003, the average per capita income of cadre households was 28 percent more than that of non-cadre households (Panel A); and if we restrict attention to earned income per capita, earnings were 25 percent higher (Panel B). The higher average income of cadre households also demonstrates an interesting pattern over time (Figure 1, Panels A and B). First, the income gap appears to increase over time in both absolute and relative terms. Second, during the period 1995 to 1999 when a sharp decline in farm prices and cropping incomes occurred (Benjamin, Brandt and Giles, 2005), per capita income for non-cadre households actually fell while cadre households still experienced an average income growth rate of 2.4 percent per year, from 1,068 Yuan in 1995 to 1,173 Yuan in 1999.

16
,<sup>8</sup> we split the sample into countries with low financial development (i.e., in the first quartile of the distribution of the IMF financial development index), and high financial

> 8 The explanation and data for the index are available at the following link: <u>https://data.imf.org/?sk=F8032E80-B36C-43B1AC26-493C5B1CD33B.</u>

18
January 2015. Eligible households received their first payment in June 2015, the second in October, and the third in January 2016.

Instead of sticking to a quarterly schedule, operational issues delayed the fourth and fifth transfers, which took place in July 2016 and January 2017. The sixth and seventh transfers were made in the spring 2017, while the last transfer happened at the end of the following school year and just before the endline, in June 2018.

# 6. Data

Baseline data was collected between May and August 2014 and endline data was collected four years later, between May and August 2018. The sample from the control villages included households who would have been eligible, i.e., were poor and had children. Unfortunately, because of coding errors in the household roster in the endline data, we are only able to follow households, but not individuals, across both survey waves, limiting our ability to conduct individual-level panel analyses.

# 7. Sample balance at baseline and household characteristics

In Table 2 we show the baseline balance of the sample in terms of education measures. At baseline, the characteristics of treatment and control villages were similar. The education module of the baseline surveyed children aged between 5 and 15 years old, although two-thirds of the surveyed children were aged 10 and below, in both waves. The total number of children surveyed in the first wave is 11,350.

Table 2 shows that there are no differences, at the baseline, between treated and control villages for the following variables: age of children, proportion of girls, tests scores in mathematics, French and Raven (an ability measure) and whether the child can read. Household size is significantly higher in control villages (9.4 vs 9.0). Finally, within the control group, 4 percent are from “other ethnicities” versus 2 percent in the treatment group. These are the only significant differences between the groups at baseline, and we do not expect these differences to drive our results.
# **1. Introduction**

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Inclusive financial systems— allowing broad access to financial services, without price or nonprice barriers to their use—are especially likely to benefit poor people and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or become entrepreneurs—and small enterprises must rely on their limited earnings to pursue promising growth opportunities. This can contribute to persistent income inequality and slower economic growth.<sup>1</sup> African countries experienced positive developments in access to financial services in recent decades. In many African countries with the deepening of the financial sector, more financial services, especially credit, is now provided to individuals and enterprises. Similarly, new technologies such as mobile money help broaden access to financial services, including savings and payment products. However, the financial systems of many African countries still remain under-developed as compared to other developing economies even though most of these countries have undergone extensive financial sector reforms in the last two decades. Indicators of the use of financial products and services by adults and enterprises in the region show that many challenges remain toward building a more financially inclusive financial sector in Africa.

For instance, recent evidence from Global Findex<sup>2</sup> database shows that less than a quarter of adults in Africa have an account with a formal financial institution and many adults in Africa use informal methods to save and borrow. Similarly many small and medium size enterprises (SMEs) in Africa cite access to finance as a major obstacle. Compared to other economies, many firms in Africa lack proper access to a bank line of credit - a major obstacle for firm growth. In addition, other financing sources such as equity markets are underdeveloped.

This paper summarizes the status of financial inclusion in Africa. We first provide a brief overview of the African
</sup> Over the 28 year period, we find that the percentage of the population of the developing world living below $1.25 per day was halved, falling from 52% to 22%. The number of poor fell by 600 million, from 1.9 billion to 1.3 billion over 1981-2005 (Table 2). The trend rate of decline in the $1.25 a day poverty line over 1981-2008 was 1% point per year. (Regressing the poverty rate on time the estimated trend is -1.03% per year with a standard error of 0.06%, with

> 24 Note that there is a ―hole‖ in coverage for South Asia in 1999. This reflects the well-known comparability problem due to India’s National Sample Survey (NSS) for 1999/2000. (Further discussion and references can be found in Datt and Ravallion, 2002). We decided to drop that NSS survey round given that we now have a new survey for 2004/05 that we consider to be reasonably comparable to the previous survey round of 1993/94. We also decided to only use the 5-yearly rounds of the NSS, which have larger samples and more detailed and more comparable consumption modules (aside from the 1999/00 round). Unfortunately, this leaves a 10-year gap in our survey coverage for India; the estimates for India over the intervening period use our interpolation method. Including all available survey rounds for India adds to the variability in the series but does not change the trend.

> 25 Further details on these estimates and results for other poverty lines and for the poverty gap index can be found in Chen and Ravallion (2012).

13
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2442 A Firms's-Eye View of Policy and<br>Fiscai Reforms in Cameroon|Bernard Gauthier<br>Isidro Soloaga<br>James Tybout|September 2000|L. Tabada<br>36896|
|WPS2443 The Politics of Economic Policy<br>Reform in Developing Countries|Richard H. Adams Jr.|September 2000|M. Coleridge-Taylor<br>33704|
|WPS2444 Seize the State, Seize the Day:<br>State Capture, Corruption, and<br>Influence in Transition|Joel S. Hellman<br>Geraint Jones<br>Daniel Kaufmann|September 2000|D. Billups<br>35818|
|WPS2445Subsidies in Chilean Public Utilities|Pablo Serra|September 2000|G. Chenet-Smith<br>36370|
|WPS2446 Forecasting the Demand for<br>Privatized Transport: What Economic<br>Regulators Should Know, and Why|Lourdes Trujillo<br>Emile Quinet<br>Antonio Estache|September 2000|G. Chenet-Smith<br>36370|
|WPS2447 Attrition in Longitudinal Household<br>Survey Data: Some Tests for Three<br>Developing-Country<br>Samples|Harold Alderman<br>Jere R. Behrman<br>Hans-Peter Kohler<br>John A. Maluccio<br>Susan Cotts Watkins|September 2000|P. Sader<br>33902|
|WPS2448 On "Good" Politicians and "Bad"<br>Policies: Social Cohesion,<br>Institutions, and Growth|Jo Ritzen<br>William Easterly<br>Michael Woolcock|September 2000|A. Kibutu<br>34047|
|WPS2449 Pricing Irrigation Water: A Literature<br>Survey|
7-11 years old due to almost full enrollment in urban areas (Acción Social, 2010a). Like the _Oportunidades_ program, the transfers are specifically given to mothers, a mechanism designed to ensure that the money is invested in children and as an incentive for empowering women within their communities.

Within each municipality FA targets the poorest households based on a proxy-means test system constructed with information from a census of poor people (known as _Sisben_ ). In Colombia, all households surveyed by _Sisben_ are assigned to one of six brackets of a poverty index score called _Sisben_ that is used to identify the most vulnerable population. The index runs from 0 to 100, and it is a function of a set of household characteristics and variables related to the consumption of durable goods, human capital endowments, and current income to calculate a score that indicates the household economic well-being. The first level includes households that are extremely poor. While many social programs target the population with scores falling in the first two brackets, FA is only offered to households in the first bracket. Municipal governments are responsible for ensuring adequate coordination with schools and health centers working with the FA program to ensure its successful local implementation.<sup>9</sup> Local banks deliver the cash transfers to beneficiaries every two months. It was estimated that the annual costs of the program in 2009 were going to be equivalent to 0.27 percent of GDP in that year (World Bank, 2008).

The program FA expanded over the years to reach national coverage in 2010. Initially, the program was targeted geographically. Only municipalities that were not departmental capitals, with fewer than 100,000 inhabitants, with at least one bank branch working in the municipality, and had access to facilities that allowed for the implementation of the program were eligible (691 out of the 1,024 municipalities). Within these communities, a total of 340,000 households in 622 municipalities were registered to participate (Attanasio et al., 2006). In 2005, the
of maize produced can be reported in different states of the crop. For example, at the time of harvest, maize is still on the cob. Before being sold, stored, or consumed, maize grains may be removed from the maize cob and dried. In this process, the weight of the same harvest may vary depending on the state in which its weight is reported. Moreover, this may be related to the length of the recall period if, for example, farmers more often report the harvest weight of maize on the cob immediately after the harvest and the weight of dried grains more often as more time passes between the harvest and the interview. This, in turn, could mean that the observed recall effect is the result of changes in reported harvest state over time. We assess this possibility using information on the harvest state – shelled (grain) and unshelled (on the cob) – which is available for Malawi IHS4 and IHPS 2016/17 data in three steps. First, we assess whether the state in which harvest weight is reported changes with the length of the recall period. We find that the share of farmers reporting the weight of maize on the cob decreases with recall length at 0.8 percentage point per month in Malawi IHPS 2016/17 and 0.4 percentage point in Malawi IHS4. Next, we include the state in which harvest was reported as a control variable in the main regression. Finally, we convert the quantity of unshelled maize (on the cob) to grain-equivalent shelled weight and then repeat the analysis with this new variable. The unshelled to shelled conversion factor was obtained from the MAPS experiment in which maize was weighed twice, before and after shelling. The main results are robust to these two specifications. Further, we assess whether the correction of the harvest date and hence of the recall length variable (section 3) drives our results. We estimate the main specification including the difference (in months) between the raw and the corrected harvest date variable (
, 4-year (CAB)|[0.056]|[0.055]|[0.054]||
|EU 10 and EU candidate|-0.609|-0.660|-0.568||
|dummy times CAB|[0.343]|[0.277]<br>*|[0.304]<br>***|*|
|EU neighborhood country|0.488|0.474|0.480||
|dummy times CAB|[0.126]|[0.121]<br>***|[0.128]<br>***|***|
|Number of observations|565|573|568||
|Number of countries|88|88|88||
|Number of instruments|40|40|40||
|Hansen test for over-|0.236|0.206|0.227||
|identifying restrictions|||||
|Arellano-Bond AR(1) test|0.000|0.000|0.000||
|Arellano-Bond AR(2)test|0.706|0.711|0.736||

Notes:<sup>[A]</sup> GDP data are adjusted for international differences in purchasing power of the dollar. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. All equations include time period and group-specific dummies. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

# **6. Concluding Remarks**

This paper provides evidence that the new EU member states and the EU candidate countries have been able to speed up their income convergence through greater reliance on foreign capital. The growth equations reveal that the current account deficits are positively correlated with medium-term economic growth
The findings in Table 5 are consistent with the predictions from trade models with heterogeneous firms whereby growth in the number of firms contributes relatively little to total exports as compared with growth in average firm size because entrants tend to be marginal firms.<sup>41</sup>

**_Stylized Fact 3: Across countries, entry and exit rates are strongly positively correlated with each other and both are negatively correlated with entrant survival rates, average exporter size, and diversification at the exporter level (in terms of products and destinations)._**

Table 6 shows the matrix of correlation coefficients among several measures in the Database at the country-year level (averaged over the 2006-2008 period) and the corresponding p-values.<sup>42</sup> The table presents also the correlations between the dynamics and diversification measures with total exports. Exporter entry and exit rates are strongly positively correlated in Table 6, implying that countries with high entry rates also have high exit rates. This evidence is similar to that based on industry data for individual countries such as Peru by Freund and Pierola (2010). In a typical year there are naturally high levels of experimentation (entry) accompanied by similar levels of failure (exit) in all countries. This intuition is reinforced by the strong negative correlations of both entry and exit with one-year survival of new entrants into export markets. Entry and exit rates are also strongly negatively correlated with average exporter size. This is not surprising since export markets are more contestable in countries where exporters are relatively smaller, observed churning is likely to be higher.

> 41 This finding differs, however, from cross-sectional evidence provided by Mayer and Ottaviano (2008) and our stylized fact 5 below on the correlation between the number of exporters and standard gravity variables. We discuss this in more detail below. 42 Kuwait and Portugal are part of the sample used to compute the correlations in Table 6 despite their data covering a period other than 2006-2008. However, similar correlation patterns are obtained if they are excluded
11 While these studies are useful to assist in specifying the costs models, they are not sufficient since they do not include any information on the prices of all inputs (including the price of capital) as is required by Cobb-Douglas or translogarithmic specifications typically found in the literature.

# **4. Data and estimation of the costs frontier for Asia**

The cost frontier for the Asian water companies was estimated from a database published by the Asian Development Bank (1997). Their sample covers 50 firms surveyed in 1995. The information included data on operational and maintenance costs (COST), number of clients (CLIEN), daily production (PROD), population density in the area served (DENS), number of connections (CONE), percentage of water from surface sources (ASUP), treatment capacity (CAPAC), market structure (STRU, represented by the relation between residential sales and total sales in cubic meters), number of hours of water availability (QUALI), staff (PERS), salary (SALAR) and a set of qualitative variables on the type of treatment used: conventional (DUMCONV, with a value of 1 when the treatment is conventional, 0 otherwise), rapid sand filters (DUMFRAP, with a value of 1 is the firms use the filter, 0 otherwise), slow sand filters (DUMFLEN, with a value of I if used, 0 otherwise), chlorification (DUMCLO, with value I if used with sand filters, 0 otherwise and desalinisation (DUMDES, in, fact only 1 does).

The basic statistics are summarised in table 1. The costs refer to operational and maintenance costs and are expressed in thousands of US$. The client's numbers and the population are expressed in thousands. Treatment capacity is expressed in cubic meters per day. Average salaries are found as the ration of total salary cost to the number of workers The strategy adopted to estimate the model could be summarised as follows. In a first stage, all the variables
|-1.7%|-1.5%|-5.8%|

Note. _∗∗_ Standard _∗∗∗_ errors are clustered at the municipality level. Asterisks indicate statistical significance:<sup>_∗_</sup> _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. The sample is restricted to insurable crops (maize, beans, sorghum barley). All regressions include crop fixed effects and are estimated using only pre-CADENA data from each municipality. Column 1 displays the results of regressions log yield on the minimum deviation from the precipitation threshold that is established once CADENA is in place, while column 2 includes a quadratic term in the explanatory variable. Column 4 displays the results of regressions log yield on the total precipitation within the growing season defined by future insurance policies, while column 2 includes a quadratic term in the explanatory variable.

26
Scheffler RM, Mahoney CB, Fulton BD, Poz MRD, Preker AS. Estimates Of health care professional shortages in Sub‐Saharan Africa by 2015. Health Aff (Millwood) 2009; 28: w849–62.

- Scheffler RM, Liu JX, Kinfu Y, and Poz D (2008).  Forecasting the global shortage of physicians: an economic‐ and needs‐based approach. Bull World Health Organ; 86: 516–23B

- Scheil‐Adlung X. Health workforce benchmarks for universal health coverage and sustainable development. Bull World Health Organ 2013; 91: 888–888.

United Nations General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015; published online Sept 18.

http://www.un.org/ga/search/view_doc.asp?symbol=A/70/L.1&Lang=E (accessed Oct 10, 2015).

- United Nations. Probabilistic Population Projections based on the World Population Prospects: The 2012 Revision. http://esa.un.org/unpd/ppp/ (accessed Aug 29, 2014).

- United States Department of Agriculture. ERS International Macroeconomic Data Set. USDA Econ. Res. Serv. 2014; published online Dec 18. http://www.ers.usda.gov/data‐ products/international‐macroeconomic‐data‐set.aspx (accessed Sep 27, 2015).

Vujicic, M. and Zurn P. (2006), “The Dynamics of the Health Labor Market”, International Journal of Health Planning and Management, 21, 101–15.

World Bank. 2015. World Development Indicators, published online.

http://data.worldbank.org/data‐catalog/world‐development‐indicators (accessed Oct 11, 2015).

- World Health Organization (no date). Global Health Observatory of the World Health Organization.  http://www.who.int/gho/en/ (
.464)|(0.476)|(0.491)|(0.459)|(0.353)|(0.367)|(0.384)|(0.362)|(0.498)|(0.463)|(0.465)|(0.508)|
|Number of observations|469|469|469|469|473|473|473|473|522|522|522|522|427|427|427|427|
|r2_w|0.340|0.358|0.335|0.355|||||||||||||
|r2_b|0.066|0.035|0.077|0.074|||||||||||||
|r2_o|0.066|0.038|0.074|0.073|||||||||||||
|rho|0.669|0.697|0.674|0.701|||||||||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Sector information used as the interaction terms is taken from the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

64
# **Mangroves as Protection from Storm Surges in a Changing Climate**

Brian Blankespoor<sup>1</sup> , Susmita Dasgupta and Glenn‐Marie Lange Key words: mangroves; climate change; storm surge; coastal protection JEL classification: Q54, Q57 1 Authors’ names are in alphabetical order. The authors are respectively Environmental Specialist, World Bank; Lead Environmental Economist, World Bank; and Senior Environmental Economist, World Bank.

We would like to thank Chandra Giri (United States Geological Survey) for providing the mangrove presence data necessary to conduct the analysis. We extend a special thanks to Anna McIvor (University of Cambridge) for her insight on the analysis, particularly the formulation of the wave attenuation functions. We also thank Mark Spalding (University of Cambridge and The Nature Conservancy) for his guidance on the mangrove results, Ed Barbier (University of Wyoming) for his thoughtful review of this research, Peter Mumby (University of Queensland), and Mike Beck (The Nature Conservancy) for their insights on this analysis. We are thankful to Zahirul Huque Khan (Institute of Water Modeling, Bangladesh) for sharing the technical analysis of mangrove afforestation in Hatia island. We also thank the participants of the “State of the Knowledge of the Protective Services and Values of Mangrove and Coral Reef Ecosystems”, organized by The Nature Conservancy and the World Bank WAVES Partnership, at the University of California, Santa Cruz, United States, December 3 – 4, 2014. We also thank the participants of the presentation at the Association of American Geographers Annual Conference, Chicago, US, April 25, 2015. The research was partially funded by the Wealth Accounting and Valuation of Ecosystem Services Partnership.