rafmacalaba/datause-encoder-data huggingface dataset:
That area expansion has been an important source of growth is evident from the low cropping intensity of 130 percent. Egypt, a country with strikingly similar resource endowments, has a cropping intensity of 180 percent. This is surely one reason why Pakistan with an agricultural land base six times as large as that of Egypt, its agricultural product was less than twice that of Egypt. Egypt does have the benefit of greater water resources than Pakistan - which highlights the need for Pakistan to use its water resources as efficiently as possible.

# Pductiit Grwth

Production estimates by themselves do not provide sufficient information to evaluate the growth process. One needs to consider output growth relative to inputs -- that is, productivity and productivity growth.

Byerlee (1994) divides technical change in land intensive systems into four stages, distinguished by the sources of growth in each phase: (1) the pre-Green Revolution phase, when growth is driven by (irrigated) area expansion, and productivity growth is modest, (2) the Green Revolution phase, when growth is driven by high yielding varieties with increased responsiveness to inputs, (3) the first post-Green Revolution phase, when growth is driven by intensification of input use, especially chemical ferdlizer, and irrigation water (which facilitates multiple cropping), and (4) the second post-Green revolution phase, when input use begins to plateau, and the source of growth becomes increases in input efficiency, coupled with the ongoing release of new varieties. According to this framework, the Green Revolution shifted the production function upwards and raised the marginal responsiveness to inputs. Farmers did not operate initially on the production frontier. In the first post-Green Revolution phase, use of complementary inputs rose, and farmers improve allocative efficiency (equalizing marginal products and prices). In the second post Green Revolution phase, farmers encountered diminishing returns to inputs, and moved towards the production frontier by raising their efficiency. Resource degradation is a form of technical regress which would shift the production function downward.

Following this framework, Pakistan is now in the second post-Green revolution phase of productivity growth, where improvements in input efficiency are the main source of growth. High yielding varieties have already diffused widely, and input use is at high levels. However, neither absolute levels of productivity, nor productivity growth over time, have matched the progress on the input side. Various methods can be used to measure productivity, but most measures yield similar conclusions.

A straightforward measure is the yield per ha. Yields have grown strongly only for cotton. YiMds have grown slightly in the case of wheat and for remaining crops, yields have remained more or less stgnant. Yield figures suggest that productivity is much lower than comparable countries such as India and Egypt. For instance, in the past decade, wheat yields have grown at 1.6 percent per annum, compared to 2.9 percent in the Indian Punjab and 2.7 percent for all developing countries.

6
Policy Research Working Paper 10862

# **Abstract**

With about 80 percent of poor households in Timor-Leste dependent on agriculture for their livelihood, it is widely acknowledged that growth in the agriculture sector is an important channel for poverty reduction in the country. That notwithstanding, the country’s agricultural production system is one of the least developed in the world, with the sector’s productivity being well below that of other small island developing states and below the average for other low-income states. Using data from the 2007 and 2014 waves of the Timor-Leste Surveys of Living Standards, this paper provides insight into the extent to which increases in agricultural productivity can contribute to poverty reduction, and assesses the drivers of and constraints to increasing agricultural productivity in the country. The findings show that improvements in agricultural productivity reduce the probability of being poor among agricultural households. The paper also finds that for agricultural productivity to increase, the following factors should be considered: facilitation of farm mechanization, improvement in the use of chemical (fertilizer, pesticides, and herbicides) inputs, enhancement of access to credit and extension, encouragement to farmers to join farmer groups, expansion of the commercialization of farm produce, and reduction in the gender gap in agricultural productivity.

This paper is a product of the Agriculture and Food Global Practice. 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 fdarko@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 A.2: Robustness check using endogenous switching regression

|||In 2007<br>||Fr|om 2007 to 2<br>|009|
|---|---|---|---|---|---|---|
|||(1)|||(2)||
||Mai|n eq.|Selection<br>eq.|Main|eq.|Selection<br>eq.|
|VARIABLES|y_F,07|y_NF,07|Fish in 07|∆y_F|∆y_NF|Fish in 09|
|AGGSHOCK|0.480|0.0468|-1.382***|-0.350*|-0.218|0.0704|
||(0.503)|(0.339)|(0.457)|(0.189)|(0.217)|(0.183)|
|IDIOSHOCK||||0.0319|-0.0484|-0.145|
|||||(0.173)|(0.211)|(0.196)|
|D_bad house in 07|0.0346|-0.170|-0.367||||
||(0.283)|(0.184)|(0.293)||||
|D_bad house in 09||||-0.0533|-0.113|0.250*|
|||||(0.143)|(0.249)|(0.145)|
|Boat aid in 09||||-0.397|-0.338|0.492*|
|||||(0.263)|(0.300)|(0.253)|
|Boat owner and captain pre-tsunami|0.240|-0.119|-0.113|-0.151|0.0680|0.214|
||(0.212)|(0.178)|(0.260)|(0.165)|(0.199)|(0.188)|
|D_large boat pre-tsunami|0.221|-0.293*|0.146||||
||(0.210)|(0.176)|(0.264)||||
|Ln (number of NGOs in 07)|-0.127|0.213|-0.751***||||
||(0.236)|(0.230)|(0.286)||||
|Fraction of fish population in 07||||-0.806***|-0.838**|0.151|
|||||(0.244)|(0.382)|(0.320)|
|Constant|10.66***|10.80***|1.727**|0.490|-0.552|0.666|
||(0.570)|(0.841)|(0.864)|(0.357)|(0.746)|(0.490)|
|**Instrumental variables**|||||||
|D_debt to Toke Bangku pre-tsunami|||0.564*||||
||||(0.309)||||
|Number of captains in 05|||0.0156**||||
||||(0.00721)||||
|D_arisan group in 07||||||0.493**|
|||||||(0.223)|
|D_debt to Toke Bangku in 07||||||0.544|
|||||||(0.398)|
|**Correlation coefficients:**|||||||
|ρ_F|||0.667|||-0.808***|
||||(0.749)|||(0.295)|
|ρ_NF|||0.077|||-0.052|
||||(0.443)|||(0.218)|
|Observations||148|||357||
|Sample||Original||Su|pplementary|1/|
|Regional fixed effects||Y|||Y||
|Samplingheterogeneityis controlled||Y|||Y 2/||

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model.  Robust standard errors clustered at the village level are in parentheses.  The regression implements the maximum likelihood estimation using _movestay_ command developed by Lokshin and Sajaia (2004).

1/ In column (2), the inverse probability of staying in the sample ("stayers") is used as a weight to account for attrition bias and sampling heterogeneity is controlled by including _1[s_orig=1]._ AGGSHOCK=Fraction of bad aid boats IDIOSHOCK=D_have a bad aid boat in 09 47
and Yemen.<sup>2</sup> One possible reason for women's share of this burden is that access to water affects several domestic tasks, such as cooking, laundry, cleaning and caring for children.

Furthermore, given that water (unlike fuel) has few alternatives, and that access to water in rural areas is often limited to wells, public standpipes or natural sources, substantial time can be spent in collecting water. Although few nationally-representative time use studies have been conducted (see Rosen and Vincent, 1999, and Blackden and Wodon, 2006), rural women in Africa and South Asia are frequently reported to spend at least an hour and up to several hours a day fetching water for the household. Water shortages and uneven supply compound this effort.

The relationship between women's water collection burdens and the transition to off-farm work, however, depends not only on direct time savings but on numerous individual, household and community factors that affect the ability and desire to work offfarm. A large literature emphasizes various factors that may impede women’s off-farm market activities in the rural areas of developing countries (Mammen and Paxson, 2000; Feder and Lanjouw, 2001). Women's ability to participate in off-farm work may be affected by social norms and cultural restrictions on their time use and mobility (Kevane and Wydick 2001; Jayaraman and Lanjouw, 1998). The tradeoffs between farm and offfarm work are likely to depend on a household's economic situation, its access to land and labor, seasonality, local agro-climatic factors and exposure to risks, as well as work opportunities and markets in the community.<sup>3</sup> Studies also emphasize the effects of education and wages, the ability to control fertility, access to child care, access to credit, as well as household composition and interactions with men's occupational choices (Lokshin and Glinskaya, 2008, Matsche and Young, 2004; Khandker, 1998).

A limited but growing economic literature addresses the role of infrastructure and natural resource availability in women's labor supply (including Ilahi and Grimard, 2000, and Menon, 2009, for water; Dinkelman, 2009, and Grogan and Sadanand, 2009, for electricity; and Lokshin and Yemtsov, 2005, for various types of infrastructure). Part of

> 2 See, for example, World Bank (2005b); African Development Bank (2006).

> 3 Community-level factors such as transportation costs and access to markets/information about jobs are also important (Schultz, 2001). Changes in agricultural technology may divert labor back from off-farm to farm activities, as was the case for women during India’s Green Revolution (Mukhyopadhyay, 1994).

5
**Table 4.3: Outage-Climate correlation**

|**Division**|**Correlation Outages/Lightnings**|**Share of national outage**<br>**events that occurred in**<br>**this division**|
|---|---|---|
|Barisal|-|0|
|Chittagong|**0.49**|**19.4**|
|Dhaka|**0.75**|**46.3**|
|Khulna|0.19|0.9|
|Rajshahi|**0.72**|**20.5**|
|Rangpur|0.26|9.8|
|Sylhet|0.18|3.2|
|**Country**|0.39|100|

# 4.3. System vulnerability to storms

This section aims at determining by how much the probability of observing an outage is increased when wind speed increases. Different definitions of storms are derived from the ERA5 wind data. As highlighted in Section 2.2.2, in Bangladesh these climatic events reflect storms associated with the monsoon season and to a lesser extent to Nor'westers.

For a basic definition of “storms” we count the number of days on which wind speed observations exceed a certain threshold in each state. Starting from 15 km/h, we increase the threshold with constant steps of 5 km/h, in order to identify days with stronger storms. The strongest storms we consider correspond to wind above 35km/h. Table 4.4 summarizes the number of storm days according to the respective thresholds in the period 2000 to 2017 in Bangladesh and the US. Each entry of the climate data corresponds to the average daily wind speed recorded at the centroid of one division or state. In Bangladesh, 0.07% of the days between 2000 and 2017 are classified under the strongest storm (wind above 35km/h), while it is 0.4% in the US during the same period.

**Table 4.4: Number of storms per country.**

||> 15km/h|>20km/h|**_Wind speeds_**<br>>25 km/h|>30km/h|>35km/h|
|---|---|---|---|---|---|
|**_Bangladesh_**|7,356|2,528|703|166|35|
|**_US_**|77,250|30,119|11,433|4,329|1,407|

_Source: authors based on ERA5 data._ We then match the dates on which these storms occurred to the dates on which storm-induced power outages were reported by the energy agencies. This allows us to compute the share of storms that caused power outages in Bangladesh and the US.

28
<!-- Start of picture text --> 400<br>300 4vilyM aes Sh Ominal ame Fea!<br>fi<br>| I<br>|<br>= ih 7 _<br>a<br>1970 1975 1980 1985 19909 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

Source: World Bank
# **Impacts of Large-Scale Expansion of Biofuels on Global Poverty and Income Distribution**

Caesar B. Cororaton, Virginia Polytechnic University Govinda R. Timilsina, Development Research Group, World Bank

**Key Words** : Biofuels, income inequality, poverty, general equilibrium modeling **JEL Classification** : C68, D31, Q16, Q43

Sector: **Energy**
- 11 -

# Table 2: Outcomeis of Irvestmnnts in Monitoring **and** Evaluation Systems, Projects Completed in **FY85-86 (continued)**

|CGuntry|Design|Inputs|Outcomes|
|---|---|---|---|
|Mali|Evaluation in each of<br>the 5 conponents.<br>All<br>focused on project<br>activities.<br>National<br>Pedagogical Institute<br>for project<br>curricula;<br>AGtraining<br>evaluation<br>specialist;<br>Enterprise<br>training<br>needs survey<br>system by National<br>Managemnt Institute;<br>Regular evaluations<br>of<br>literacy<br>program;<br>Follow-up of pre-<br>investnent<br>study.<br>National Research Team|**5**Years TA|Almost ignored<br>in**PCR.**<br>Somestudies conducted;**no**<br>evidence of utilization|
|Seregal|Evaluation of all<br>project activities.<br>Build educational<br>planning capacity.<br>Case stLdies,<br>"indepth<br>evaluation;"<br>tracers.<br>GovernmentandIDA to<br>consult on findings|TA. Evaluation<br>Specialist|Severe inplemlentation<br>problems.<br>Evaluationnot<br>doneas local staffnot<br>traimed and found evalua-<br>tion threatening.<br>Tracer<br>system**not**established.|
|**Somalia**|Build permanent<br>evaluation<br>system|2 Years TA -<br>**Evaluation Specialist;**<br>**Fellowships; Local**<br>**training**|Local training<br>provided;<br>**fellows falltoreturn to**<br>**their posts.**|
respective relationships, and their expectations of one another, creating a framework for extended interaction. According to Barrett, treaties help states coordinate their actions, especially when unilateralism fails to sustain a mutually satisfying outcome and to prevent free-riding (Barrett 2003, xiii). To do this, treaties must be self-enforcing by being individually rational as well as collectively rational. According to Barrrett, these two types of rationality imply that no party to the treaty can gain by withdrawing and that no party can gain by failing to comply given the treaty’s design (Barrett 2003, xiii). To be self-enforcing, treaties must also be fair and perceived by the parties as being legitimate (Barrett 2003, xiv).

Specific to water, evidence suggests that the likelihood of political tensions is related to the interaction between rates of change (physical or institutional) within a basin, and the absence of institutions (such as treaties) to absorb that change (Wolf, Stahl, and Macomber, 2003; Stahl, 2005). Consequently, regions and basins that are not governed by treaties or water-related institutions and facing increased future variability may be more vulnerable to tensions and conflict. Treaties that govern river basins, therefore, constitute an important means for managing transboundary water resources thereby assuaging the escalation of disputes. According to McCaffrey (2003: 157), “treaties stabilize [the relations of states sharing a river] giving them a level of certainty and predictability that is often not present otherwise.” In turn, water agreements promote wider cooperation and enhance basin security more generally by altering state behavior, their relationships and expectations of one another in accordance with the terms of the treaty (Conca and Dabelko, 2002; Dinar 2011; Intelligence Community Assessment, 2012; Brochmann 2012).

Empirical studies have largely confirmed such arguments pertaining to treaties and cooperation. Brochmann and Hensel (2007; 2009), for example, find that while the existence of river treaties does not necessarily prevent disagreements (operationalized as conflicting country claims) between states over their shared water resources, the presence of at least one treaty over the specific subject of the claim reduces the likelihood that the claim becomes militarized. In addition, the treaty provides an important starting point that greatly increases the likelihood of negotiations over such claims.<sup>2</sup> Empirical studies provide support that treaties likewise have a

> 2 Despite the success of treaties in providing a starting point for negotiations over a conflicting claim, Brochmann and Hensel (2012) find that the presence of a treaty does not necessarily guarantee successful negotiations over the claim and a subsequent end to the claim.

3
Policy Research Working Paper 5244

# **Abstract**

Despite strong beliefs that property titling and registration will enhance credit access, empirical evidence in support of such effects remains scant. The gradual roll-out of computerization of land registry systems across Andhra Pradesh’s 387 sub-registry offices allows us to combine quarterly administrative data on credit disbursed by all commercial banks for an eleven-year period (1997–2007) aggregated to the sub-registry office level with the date of shifting registration from manual to digital. Computerization had no credit effect in rural areas but led to increased credit-supply in urban ones. A marked increase of registered urban mortgages due to computerization supports the robustness of the result. At the same time, estimated impacts from reduction of the stamp duty are much larger, suggesting that, without further changes in the property rights system, impacts of computerization will remain marginal.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to explore the impact of innovative approaches to improving security of land tenure and functioning of land markets. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org and agoyal3@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
# **Teacher Opinions on Performance Incentives:**

# **Evidence from the Kyrgyz Republic**<sup>**1**</sup>

Marlaine E. Lockheed<sup>2∗</sup>

## **JEL classification: I28**

**Keywords:** Education Policy, Teacher Incentives

**Sector Board:** Education

> 1 I am grateful to the Kyrgyz Republic Ministry of Education and Sciences for authorizing and facilitating the survey, to M-Vector Consulting of Bishkek for conducting the interviews and preparing the data sets, and to the schools and teachers who participated in the surveys. In addition I wish to thank the World Bank staff who also supported this study, in particular Dingyong Hou, Paul Cahu, Aisulu Bedelbayeva, and Aisulu Sulaimonova. This paper is based on an independent evaluation  supported by the Europe and Central Asia Region of the World Bank. Its findings and conclusions are the author’s alone, and do not represent the World Bank Group.

> 2 Correspondence: Marlaine Lockheed, The World Bank (retired), 561 Lake Drive, Princeton, NJ 08540, email: mlockheed@verizon.net.

<mark>∗</mark>
his or her subjective frame of reference. An issue perceived as a major obstacle to doing business in one country may actually impose a lower cost in actuality than it does in a country where the problem is rated as merely a minor problem. For example, based on data from Enterprise Surveys in 33 African and Latin American countries that used instruments similar to those used for the BEEPS, González, López-Córdova, and Valladares (2007) show that perceptions adjust slowly to firms’ experience with corrupt officials and hence are an imperfect proxy for the true incidence of graft.

Consequently, quantitative measures of an issue in the business environment are always selected over perception-based indicators whenever available. For example, the number of power outages or surges from the public grid is used rather than the perceptions of the manager on how problematic electricity is for the operation and growth of the business. Similarly, the level of bribes paid as a percentage of total annual sales number is used rather than the perceptions of the manager on how problematic corruption is for the operation and growth of the business. However, because of the inadequate number of quantitative measures available in the areas of governance (legal system), labor market flexibility, and labor quality, one perceptionsbased question is used in the construction of these indicators in order to meet the three variables per component minimum threshold recommended for exploratory factor analysis (Thurstone, 1935; Kim and Mueller, 1978b).

All synthetic indicators are given by the first principal component of their respective set of underlying BEEPS variables, and three separate tests confirm the decision to retain only the first principal component. The first test is the most frequently used Guttman-Kaiser criterion, which states all components with eigenvalues greater than 1 should be extracted as variables. The rationale behind this criterion is that the interpretation of proportions of variance smaller than the variance contribution of a single variable is of dubious value (Guttman, 1954; Kaiser, 1961). The second test is the Cattell’s scree test, which plots the components along the X-axis and the corresponding eigenvalues along the Y-axis and is also a widely used criterion. Cattell (1996) suggests visual inspection to identify an inflection point of the resulting curve (scree), where components to the left are retained and those to the right are dropped.<sup>13</sup>

> 13 “Scree” is the geological term referring to the debris that collects on the lower part of a rocky slope (Cattell, 1966).

17