rafmacalaba/datause-encoder-data huggingface dataset:
around the world, this analysis can also help to provide guidance on how to improve existing subsidies. This is the topic taken up in the following sections of this paper.

# **3. HOW TO DEFINE AFFORDABILITY IN THE TRANSPORT SECTOR**

Given that subsidies are usually justified based on the premise that they increase affordability of low income segments of the population, it is convenient to define the concept of affordability and how it can be used in practice.

Most studies on poverty and transport estimate the percentage of monthly income or expenditure devoted to transport by poor families<sup>14</sup> and compare this figure to a benchmark considered “affordable” to households. Armstrong-Wright and Thiese (1987) consider that there is an affordability problem with public transport when more than 10% of households spend more than 15% of their income on work related trips. According to Venter and Behrens (2005), the South African government has established a 10% of income as a policy benchmark in its 1996 White Paper on Transport Policy (Department of Transport, 1996). Gomide, A., S. Leite and J. Rebelo (2004) use a 6% limit to estimate the affordability of public transport in Belo Horizonte, Brazil.

This approach is not exclusive to the transport sector. Foster (2005) uses 15% of a household monthly income or expenditure as the way to define affordability of three public services (water, electricity, and gas). In the water sector, there is a well established rule of thumb —whose origin is attributed to the World Health Organization (WHO)— whereby a water bill representing more than 5% of monthly household income or expenditure is considered unaffordable.

Although intuitively appealing, there are several problems with this affordability measure. The main one, as noted by Venter and Behrens (2005), is that the relation between welfare and expenditure on transport as a percentage of income may not be monotonic.<sup>15</sup> Therefore, it is not clear that households that spend less than 10% of 14 Examples of studies are Fouracre (2002) for Lagos, SITRASS (2004a and 2004b) for Conakry and Douala, Gómez-Lobo (2007a) for Santiago, among many others. In formal terms, this affordability measure can defines as: where _xi(pi,y)_ are the number of trips —usually public transport trips or work related trips— taken during the month by household member _i_ , and _y_ is household income or expenditure. The number of trips is presented as an explicit function of the price of trips and household income.

15 Venter and Behrens (2005) also discuss some practical problems with this affordability index. Besides the reliability of survey data, especially income figures, there is the problem that household level indices may obscure access and affordability by non-earners or non-household head members of the household. What is affordable from the household perspective may not necessarily imply affordability at the individual 10
Policy ReseaRch WoRking PaPeR 4577

# **Abstract**

This paper uses uniquely matched household, enterprise and community survey data from four major regions in rural Ethiopia to characterize the performance, constraints and opportunities of nonfarm enterprises. The nonfarm enterprise sector is sizeable, particularly important for women, and plays an important role during the low season for agriculture, when alternative job opportunities are limited. Returns to nonfarm enterprise employment are low on average and especially so for female-headed enterprises. Women nevertheless have much higher participation rates than men, which attest to their marginalized position in the labor market. Most enterprises are very small and rely almost exclusively on household members to provide the required labor inputs. Few firms add to their capital stock or increase their labor inputs after startup. Local fluctuations in predicted crop performance affect the performance of nonfarm enterprises, because of the predominant role played by the agricultural sector. Enterprise performance is also affected by the localized nature of sales and limited market integration for nonfarm enterprises. The policy implications of these and other findings are discussed.

This paper—a product of the Agriculture and Rural Development Unit in the Africa Region (AFTAR) and the Development Research Group (DEC)—is part of a larger effort to understand factors determining the rural investment climate and private sector initiative. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jloening@worldbank.org, bob.rijkers@economics.ox.ac.uk or mans.soderbom@economics.gu.se.

_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
projected data (this change is positive under both the crisis and the benchmark scenarios) and allocate the dividend as follows: (i) across divisions, remittances are allocated proportionally to the 2005 across‐ division distribution; (ii) among households within divisions: recipient households are selected at random and given a remittance transfer equivalent in real terms to the median remittance transfer in that division in 2005, with the _number_ of total transfers to be made within each division being equal to the total amount of additional remittances to be distributed divided by the 2005 median value.<sup>13</sup> As a result of this process the overall distribution of remittances across divisions remains unchanged; while there is an increase in number of recipient households (both types of households, those who did or did not receive remittances in 2005, _can_ receive new remittances in 2009‐10).

Finally we simulate changes in other sources of non‐labor income. For this we assume that capital and financial income grow at the same rate as real GDP, public transfers (primarily Social Security transfers) remain constant in real terms at 2005 levels, and domestic remittances change at the same rate as labor income. These assumptions appear to be reasonable for Bangladesh, and can be modified for other countries depending on country circumstances.

**Assessment of Impacts.** Impact assessment is the process by which we use the information on individual employment status and labor income, as well as on non‐labor income at the household level, to generate income and consumption distributions and construct various poverty and distributional measures that can then be used to compare the crisis and the benchmark scenarios. This is done in three steps.

First, we account for the fact that between 2005 and 2009‐2010, food prices have increased at a higher rate than other prices. To ensure that the same food basket is affordable at the new prices, we adjust the 2005 poverty line to reflect the increase in food prices relative to that of other prices (for more details, see Section 4.4 below)<sup>14</sup> .

Second, we calculate total household income by aggregating labor income across all employed individuals and adding non‐labor income, and then use information on household size to construct a measure of per‐capita household income, as in (3).

Third, because poverty in Bangladesh is measured on the basis of per‐capita consumption, we need to map income to consumption. We do this by assuming that the household‐level consumption‐to‐income

> 13 To illustrate, 23% of additional remittances are allocated to rural Dhaka, equal to its share of remittances in 2005. Every randomly selected recipient household then receives 5,417 taka/month, equivalent to the median international remittance transfer in the division in 2005 (<U$ 90 dollars per month with an exchange rate of tk.61/U$S)

> 14 An alternative, more sophisticated way of dealing with this problem would be to construct household‐specific consumption deflators that take into account differences in consumption patterns across the income/consumption distribution.

10
received immediate rather than delayed feedback from classroom<sup>quizzes,</sup> with results more consistently positive for adults than for children. Two studies with grade 8 students as subjects reported contradictory findings, one showing a positive effect size of .60 (Paige, 1966) and the<sup>other a</sup> negative effect size of -.55 (More, 1969), Of the experimental studies reviewed in the same paper, seven dealt with children's learning (paired associates or stimulus discrimination); three studies found delayed feedback superior to immediate feedback (average effect<sup>size -.31) and four</sup> studies fcund the converse (average effect size +.74).

Observational studies of teacher behavior and student<sup>achieveme-,</sup> however, provide more consistently positive evidence in favor of ongoing monitoring and evalu-tion effects on student learning (Brophy<sup>and Good,</sup> 1986).

In developing countries, few studies of the effects of monitoring, evaluation, or feedback have been conducted, but results<sup>are consistently</sup> positive. For example, Arriagada (1983) found a<sup>positive effect for</sup> teacher monitoring and evaluations of student achievement; teacher evaluations (progress reports) were positively<sup>related to achievement in</sup> science in Colombia. Heyneman and Loxley (1983)<sup>report that teacher time</sup> spent grading tests at school was related to science<sup>achievement in</sup> Argentina and Colombia, teacher time spent discussing<sup>exercises was related</sup> to science achievement in Paraguay, and teacher time<sup>spent correcting</sup> exercises was related to science achie- bnt in Argentina.<sup>Lockheed and</sup> Komenan (1988) found that teacher time spent monitoring and<sup>evaluating</sup> 9
The empirical context of this paper is Vietnam's liberalization of its rice markets during the 1990s. Between 1993 and 1998, the real price of rice increased on average by almost 30% relative to a consumer price index in Vietnam. Brandt and Benjamin (2004) show that international and national rice market integration are driving forces behind these rice price changes. In 1989, out of a concern for domestic food security, Vietnam imposed stringent export controls on its rice exports. Coupled with internal restrictions on the flow of rice across regions, these trade restrictions suppressed the domestic price of rice and lowered the relative incomes of rice producing households. However, from 1993 to 1998, the government gradually liberalized its export regime, increasing the quota from less than 1 million metric tons in 1992 to 4.5 million in 1998. The rise in international demand from the relaxation of the quota will put upward pressure on domestic prices of rice. In addition, the government lifted internal barriers to rice trade across regions within Vietnam in early 1997, beginning the integration of the national market for rice. National integration leads to relative price shifts with rice surplus areas experiencing larger price increases than rice deficit areas.

The empirical approach of this study is to relate regional and intertemporal variation in the relative price of rice to variation in child labor using rural, panel households from the Vietnam Living Standards Survey (VLSS). The VLSS is a multi-purpose household survey with community-level price surveys conducted in 1993 and 1998. This study captures sibling and household composition effects by allowing the effect of rice prices to vary with household and sibling composition. Of course, not all of the variation in rice prices observed in the data is attributable to national and international product market integration. However, to the extent that market integration leads to relative price variation, the results of this study suggest effects that could result from a market integration that induces a similar degree of price variation. In fact, 2
20

- Piketty, Thomas. 1997. “The Dynamics of the Wealth Distribution and the Interest Rate with Credit Rationing.” _Review of Economic Studies_ **64** : 173-189.

- Rawls, John. 1971. _A Theory of Justice_ . Cambridge. Mass.: Harvard University Press.

- Roemer, John E. 1998. _Equality of Opportunity_ . Cambridge, Mass.: Harvard University Press.

- Sen, Amartya. 1980. “Equality of What?” In S. McMurrin (ed.) _Tanner Lectures on Human Values_ . Cambridge: Cambridge University Press.

- --------. 1985. _Commodities and Capabilities_ . Amsterdam: North-Holland.

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- World Bank. 2005. _World Development Report 2006: Equity and Development_ . Washington, DC: World Bank and Oxford University Press.
# _Village Fixed Effects_

Finally we turn to village fixed effects in table 5 column 4. For elections, the issue is that villages yet to have elections by the end of 2007 or 2009 may have had a history of greater volunteerism pre-tsunami driven by unobservables, and that history could be driving results despite all the controls we use to try to capture village effects. Pre-tsunami versus 2009, villages with elections experienced a 44% drop in volunteer days, while those without only had a 31% drop. If we impose village fixed effects to try to control for the unobservables, what happens? Before answering that we note there are two issues with such a specification. First, imposing fixed effects assumes village characteristics have the same effects over time (in 2007 versus 2009, or pre-tsunami) on volunteerism. As table 7 shows, for village observables this is clearly not the case in our context, in which case fixed effects is arguably a misspecification. Second, to get identification, we must pool the three years of data on volunteerism (pre-tsunami, 2007, and 2009), using a Poisson model with year and village fixed effects. We need the three years because most elections occur before surveying in 2007, and we can’t identify just off of elections that occur in 2008 and 2009. The issue in adding in the third year is that pre-tsunami days are based on recall about what was typical, which might be influenced by the response to current volunteerism. With these caveats in mind, in column 4 of table 5, the fixed effects estimator has an election effect of -0.27, a still strong effect, although smaller than those we found in different specifications in the same row.

_(d) Controls in the basic specification_ 25
**Table 2: Two Different Measures of New Firms**

|||N<br>|umber of f<br>|irms<br>||
|---|---|---|---|---|---|
||newly|newly|Gap year|(yearentered-|yearregistered)|
|year|registered|entering|0|1|2|
|2003|4,925|4,160|100%|0%|0%|
|2004|5,021|4,804|86%|14%|0%|
|2005|8,739|8,604|90%|10%|1%|
|2006|8,885|8,376|89%|10%|1%|
|2007|8,931|9,532|86%|13%|1%|
|2008|8,568|8,859|91%|7%|2%|
|2009|7,456|7,542|93%|5%|1%|
|2010|7,151|7,500|93%|5%|1%|
|2011|6,383|6,686|95%|3%|1%|
|Total|66,059|66,063|91%|8%|1%|

Source: Data from Serbia’s Financial Accounts Registry As shown in table 2, for most firms, the year of registration and year of entering the database are the same, but in some cases, they differ by one or maximum two years. We believe these differences are due to administrative delays in reporting financial accounts. We perform our analysis using both measures and find equivalent results either way. Table 3 presents the number of new firms based on their reported year of registration by commercial court and year.

**Table 3: Number of New Firms by Year and Commercial Court Region**

|No|Commercial court|2003|2004|2005|2006|2007|2008|2009|2010|2011|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1|Belgrade|2,121|2,364|4,507|4,460|4,455|4,243|3,717|3,630|3,245|32,742|
|2|Kragujevac|228|195|331|329|353|323|251|291|275|2,576|
|3|Kraljevo|273|246|375|370|353|358|259|245|242|2,721|
|4|Leskovac<sup>*</sup>|165|157|198|187|172|156|146|153|119|1,453|
|5|Niš|259|232|346|337|360|346|291|296|309|2,776|
|6|Novi Sad|549|609|889|1,056|1,011|1,020|901|824|748|7,607|
|7|Pančevo|183|187|263|257|290|224|235|196|186|2,021|
|8|Požarevac|127|130|189|185|215|191|155|184|124|1,500|
|9|Sombor|90|85|168|135|169|163|168|150|117|1,245|
|10|Sremska Mitrovica<sup>*</sup>|128|123|234|248|276|290|220|208|179|1,906|
|11|Subotica|223|200|367|372|423|379|336|289|231|2,820|
|12|Užice<sup>*</sup>|119|69|168|179|149|149|109|88|106|1,136|
|13|Valjevo|153|185|266|320|297|311|264|215|218|2,229|
|14|Zaječar|91|73|100|96|114|97|136|141|84|932|
|15|Zrenjanin|91|70|172|190|148|160|146|118|110|1,205|
|16|Čačak<sup>*</sup>|125|96|166|164|146|158|122|123|90|1,190|
||**Total**|4925|5021|8739|8885|8931|8568|7456|7151|6383|66059|

* We do not have or have a relatively small number of responses for the distrust data in the commercial courts Leskovac, Sremska Mitrovica, Užice, Čačak, thus the 4 courts are excluded in our analysis later. Source: Data from Serbia’s Financial Accounts Registry

8
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/ (accessed October 11, 2015).

- ________________ (2006). The World Health Report 2006: Working Together for Health. 2006 http://apps.who.int//iris/handle/10665/43432 (accessed March 12, 2015).

- ________________ (2012), “The Labor Market for Human Resources for Health in Low‐ and Middle‐income Countries”, _Human Resources for Health Observer_ , 11.

- ________________ (2015).   “Health workforce and services: Draft global strategy on human resources for health: workforce 2030”.  Report by the Secretariat to the Executive Board EB138/36.

<u>http://apps.who.int/gb/ebwha/pdf_files/EB138/B138_36‐en.pdf?ua=1</u> (accessed December 20, 2015).

15
# **Securing Property Rights in Transition:**

# **Lessons from Implementation of China’s Rural Land Contracting Law**

**Klaus Deininger,**<sup>*** 1**</sup> **Songqing Jin**<sup>******</sup>

** Michigan State University, East Lansing, MI

> 1 1818 H St. NW, Washington DC, 20433; Tel 202 4730430, fax 202 522 1151, email kdeininger@worldbank.org. We thank the rural survey team of NBS for an extraordinary data collection effort and Li Guo, Tim Hanstad, Li Ping, Camilla Toulmin, Keilang Zhu, and two anonymous referees for valuable comments that significantly improved the quality of the paper. Funding from the DFID-World Bank collaborative program on land policy, and the World Bank’s Knowledge for Change Trust Fund is gratefully acknowledged.
Table 4: Foreign Tariffs on Honduran Exports and Honduran Export Values, 2004 (Percent and Millions of Lempiras)

||CACM|and DR||EU||US|R|oW|
|---|---|---|---|---|---|---|---|---|
|Sector|_τx_|_X_|_τx_|_X_|_τx_|_X_|_τx_|_X_|
|Bananas|0.0|36.8|6.3|398.8|0.0|2884.8|12.2|9.1|
|Cofee|0.0|72.5|0.0|3718.6|0|650.1|0.1|964.3|
|Sugar|0.0|26.2|0.0|0.0|0.0|313.9|9.3|97.5|
|Mining|0.0|61.3|0.0|183.3|0.0|994.7|4.0|386.9|
|Livestock|0.0|12|0.0|1.2|0.0|3.5|0.7|7.0|
|Wood|5.5|147.2|0.0|112.3|0.0|687.4|2.3|856.4|
|Non-traditional crops|0.0|1339.7|1.1|564.3|0.0|3251.4|4.0|82.7|
|Domestic crops|11.1|338.1|0.0|0.0|0.4|8.6|11.8|30.6|
|Oil|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Food, beverages, tobacco|4.7|1414.0|0.0|23.8|5.0|595.3|12.1|1039.2|
|Textiles|0.3|384.9|0.0|16.7|11.6|708.2|17.1|57.1|
|Paper|11.5|401.8|0.0|3.7|0.0|57.1|14.7|143.0|
|Chemicals|2.6|448.7|0.0|14.8|0.0|109.8|14.0|92.4|
|Metals, minerals, mach.|15.0|1158.1|0.0|159.6|0.0|500.6|6.1|320.7|
|Other manufacturing|0.9|1678.0|0.0|53.2|0.0|432.7|7.4|184|
|Merchandise trade|4.8|7519.4|0.6|5250.6|1.0|11198.3|6.0|4270.9|
|Agriculture|2.3|1972.5|0.7|4795.3|0.0|7799.7|2.6|2047.5|
|Manufacturing|5.7|5546.9|0.0|455.2|3.3|3398.6|9.1|2223.3|

_Note:_ For each trading partner, the first column ( _τx_ ) contains the ad-valorem tariff rates collected by that trading partner on Honduran exports and the second column ( _X_ ) contains total exports for each commodity. “RoW” stands for rest of the world, i.e., all trading partners other than CACM countries and the Dominican Republic, the EU, and the US. The manufacturing aggregate includes oil and mining.

_Source:_ Author’s calculations using data from UN COMTRADE and UN TRAINS.

# **4.4 Structure of production and demand**

The economy of Honduras is dominated by service sectors, which account for 72 percent of GDP at factor cost and 49 percent of total production.<sup>14</sup> The first column of Table 5 provides additional detail on the structure of production by sector. Agriculture is dominated by domestic crops, followed by non-traditional crops (e.g., melons, sweet peppers, chiles, tilapia, shrimp) and livestock. Food, beverages, tobacco, and textiles account for slightly less than two-thirds of total value added and production in manufacturing.

The second and third columns of Table 5 show the export orientation and import dependence of each sector. With the possible exception of food, beverages, and tobacco, all other sectors appear to have a clear orientation toward either exports or imports, confirming that the sectoral disaggregation of the model is sufficiently detailed. There is a clear bias toward exports in most farm activities, with coffee and bananas selling virtually all of their output abroad, which makes these sectors vulnerable to global demand shocks and commodity price fluctuations. With the exception of metals, minerals, and machinery (a small sector), export shares in manufacturing are

> 14The difference between the two numbers is that the former does not include demand for intermediate inputs while the latter does, i.e., the distinction is between value added and value added plus intermediates.

11