rafmacalaba/datause-encoder-data huggingface dataset:
- 17 -

population density could be correlated with the error term in the equation. To test this possibility, the spatial autoregressive instrumental variable (SPIV) model was performed. Taking advantage of lagged variables is one approach. The population density in 2007 is used as an instrument in this model.

35. As shown in **Table 5** , the results are broadly unchanged. The Hausman endogeneity test statistics indicate that the SPIV models are better. The test statistics are estimated at 8.68 and 16.51, which can reject the hypothesis of no autocorrelation. Based on the SPIV estimation, the land prices are found to decrease with the accessibility to jobs (JAI). The elasticity is 0.108. The other coefficients are broadly similar to the above results: The access to taxi-be is important. Road infrastructure and all-weather road connectivity are also found to be essential. While proximity to amenity zones is appreciated, the areas that are vulnerable to flood are not preferred.

**<u>Table 5. Spatial autoregressive IV estimation results</u>**

||<br>Coef.|<br>Std.Err.||<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_MIN_|-0.131|(0.132)|||||
|ln_JAI_||||0.108|(0.065)|*|
|ln_POPDEN_07|0.063|(0.037)|*|0.049|(0.037)||
|ln_CSB_|-0.031|(0.029)||-0.030|(0.029)||
|_VUL_|-0.025|(0.008)|***|-0.024|(0.008)|***|
|_RD_|1.911|0.5694|***|1.848|0.5678|***|
|_ALLWEARD_|0.149|(0.071)|**|0.152|(0.071)|**|
|_RICE_|-0.019|(0.163)||-0.080|(0.164)||
|_AMENITY_|1.552|(0.782)|**|1.558|(0.771)|**|
|_TAXIBE_|0.142|(0.087)|*|0.157|(0.087)|*|
|_SUBU_|-0.027|(0.059)||-0.037|(0.059)||
|Constant|6.938|(3.825)|*|5.152|(3.222)||
|Obs.|548|||548|||
|No. of district|||||||
|dummies|3|||3|||
|Wald chi2|139.05|||142.39|||
|Spatial parameters:|||||||
|_λ_|0.187|(0.356)||0.290|(0.329)||
|_ρ_|3.549|(0.708)|***|3.453|(0.746)|***|
|Hausman endogeneity<br>Chi2 test statistic|test:<br>8.680|||16.510|||
trends, especially as ART scale-up increases, can be misleading about the rate of acquisition of new infections in the overall population.

However, prevalence trends within specific population strata can be used as a proxy for incidence within larger populations and have been established as national-level indicator data. Estimating HIV incidence from prevalence within the entire HIV positive population is challenging, as the long average duration of HIV infection makes it difficult to detect changes in new infections within older age groups and these estimate are largely dependent on assumptions made on mortality, which are becoming more complex as ART prolongs survival. Therefore, prevalence trends within the younger population (adults 15-24) are regarded as a proxy for HIV incidence within the entire population, and the highest rates of new infections typically fall within young adults, especially females (UNAIDS 2012a, 2013a). For generalized epidemics, such as seen in the majority of Sub-Saharan Africa, HIV surveillance among antenatal clinic (ANC) attendees and routine Demographic and Health or AIDS Indicator Surveys (DHS/AIS) provide important HIV prevalence data to track HIV trends at national and global levels, including for the United Nations General Assembly Special Session on HIV/AIDS (UNGASS) and the Global AIDS Response Progress Report (GARPR) data collection. It is worth noting, however, that using HIV prevalence trends within youth may become more complicated as long-term survivors of mother-to-child transmission of HIV age, and therefore may bias prevalence increases within this indicator population, such as has been seen in Eastern Zimbabwe (Eaton et al. 2013).

# **2.1.1. Empirical measurements of HIV prevalence used to inform HIV incidence estimates**

## **2.1.1.1. ANC Sentinel Surveillance**

Anonymous and unlinked testing of samples from women attending antenatal clinics is used to measure HIV prevalence within a cross-section of the sexually active population and is used to generate HIV incidence estimates. These surveys have been completed every 1-3 years and thus, can be used to longitudinally monitor HIV epidemic trends, including being the primary (and until recently, with repeat DHS, the only available data on HIV infections over time in Africa) source of prevalence trends in young people for UNGASS and GARPR indicators.

However, sentinel surveillance has many limitations and biases. First, ANC data are limited to pregnant women using ANC sentinel site clinics, biasing the data upward, as these women are sexually active and at risk for sexual transmission of HIV, and HIV prevalence data are greatly influenced by changes within this demographic. Prior to the ART era, the ANC-derived HIV prevalence was chiefly affected by lower fertility in women with advanced HIV infections and by a selection bias for unprotected sexual activity especially among younger women. Now, ART scale-up has helped restore 12
controls. These variables are: parity, age of mother, length of preceding birth interval, sex of child, region (Lower and Upper), size of place of residence (Cairo and Alexandria, other urban places, rural), maternal and paternal literacy and schooling, and whether contraception is used in the interval following the birth. In selecting these variables, we draw on the extensive analysis of the EFS (analyses of fertility, child survival, and breastfeeding) that has already been completed (Hallouda, Farid, and Cochrane, 1988). (See Appendix Z for definition of the control variables.) 16
allowing foreign entry into services industries may be the key channel through which services liberalization contributes to improved performance of downstream manufacturing sectors.

TABLE 2. Estimation with firm fixed effects using OLS productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Vari|able: ln(TFP)|I|II|III|IV|V|VI|VII|VIII|
|---|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|1.003**||||||||
|linkages||(0.399)||||||||
||FDI (share)||6.220***|||||5.661**||
||||(1.934)|||||(2.316)||
|||||2.472**|||||2.665***|
||FDI (threshold)|||(0.967)|||||(0.962)|
||Concentration||||0.983|||2.919|1.166|
||||||(2.876)|||(2.732)|(2.707)|
||Privatization|||||10.087**||5.398||
||(share)|||||(4.856)||(5.819)||
||Privatization||||||-24.591||-27.388|
||(threshold)||||||(19.823)||(19.101)|
|Manufacturing|FDI (share)||-0.332|||-0.720*||-0.458||
|input linkages|||(0.461)|||(0.419)||(0.487)||
|||-0.341||-0.366|-0.582||-0.528||-0.393|
||FDI (threshold)|(0.391)||(0.383)|(0.393)||(0.391)||(0.403)|
||MFN Tariff|-0.184|-0.368***|-0.258**|-0.075|-0.183*|-0.079|-0.377***|-0.272**|
|||(0.112)|(0.130)|(0.126)|(0.109)|(0.107)|(0.105)|(0.134)|(0.124)|
|Within sector|FDI (share)||0.076|||0.326*||0.154||
|effects|||(0.192)|||(0.182)||(0.207)||
|||0.22||0.198|0.378**||0.344**||0.207|
||FDI (threshold)|(0.150)||(0.148)|(0.169)||(0.153)||(0.166)|
||MFN Tariff|0.105*|0.190***|0.136**|0.056|0.097*|0.055|0.193***|0.143**|
|||(0.056)|(0.064)|(0.062)|(0.055)|(0.055)|(0.053)|(0.067)|(0.060)|
||No. of obs.|7155|7155|7155|7155|7155|7155|7155|7155|
||R<sup>2</sup>|0.095|0.095|0.095|0.094|0.094|0.094|0.095|0.095|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are lagged one year. All models include firm and year fixed effects. Robust standard errors clustered on industry-year combinations are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.

TABLE 3. Estimation with firm fixed effects using Olley-Pakes productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Vari|able: ln(TFP)|I|II|III|IV|V|VI|VII|VIII|
|---|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|0.891**||||||||
|linkages||(0.404)||||||||
||FDI (share)||5.844***|||||4.732*||
||||(1.979)|||||(2.471)||
|||||2.090**|||||2.241**|
||FDI (threshold)|||(1.012)|||||(1.013)|
||Concentration||||2.146|||4.164|2.37|
||||||(2.939)|||(2.957)|(2.812)|
||Privatization|||||12.392***||9.332||
||(share)|||||(4.581)||(5.933)||

13
design tariffs, about social tariffs, about universal service funds, about how to target subsidies, how to think about the need to be open to alternative technologies and modes of provision within the regulatory framework. We also know about the relevance of the frequency of billing and the damage that minimum consumption requirements and fixed charges can do to the poor. There has also been a lot of experimentation with prepayment, financing connection costs, and demand management devises. We know much more and yet it is not happening as well as it should.

The good news is that there are new ideas as to how to try to make things happen, thanks to some of the insights from behavioral economics. Governments, businesses, and other stakeholders have increasingly realized that behavior is influenced by more than just financial incentives and information. Efforts to nudge people to do the right thing have proven to be quite effective in hygiene campaigns to remind individuals to wash their hands at critical junctures, automating water purification processes. Nudging makes it easier to match the choices of price and quality of service mix with local social norms, educational or other local institutional characteristics.

With respect to water consumption, the emphasis has been on water resources management and demand management. In most regulated industries, including water, behavioral interventions have been linked to both individual choices and interactions between consumers and producers and society. For instance, Ferraro and Price (2013) showed the effectiveness of technical recommendation, pro-social messages and social comparisons sent to consumers. Non-pecuniary strategies do influence water conservation, with a higher effectiveness than social comparison in a group of high-use households. But they also find that the largest effects are in the short run and this has been a recurring observation in the use of behavioral and nudging tricks in regulated utilities.

With respect to poverty and equity concerns, there are many more new insights and most have very general implications. For instance, Martins and Moura e Sá (2011) validate for the water sector a result that is already quite well established in energy regulation: the poorest tend to be penalized by the excessive complexity of billing and payment options. Their result is in fact broader since it suggests that complexity also confuses efforts to improve demand management when trying to get users to consume less. Simpler bills deliver more. Olmstead et al. (2007) use experimental household data from a previous study and suggest that the difference between the price-elasticities under increasing block tariffs (IBT) and uniform rates could be explained by behavioral responses to price structures or citylevel heterogeneity. Higgs and Worthington (2001) had already established a “flat-rate bias” in an experiment in Brisbane (Australia). In their experiment, consumers generally prefer a pricing system minimizing the impact on payments of uncertain future demand, even if there are no clear reasons to do so considering only current consumption.

37
Policy Research Working Paper 7093

# **Abstract**

This paper examines the determinants of agricultural productivity and its link to poverty using nationally representative data from the Nigeria General Household Survey Panel, 2010/11. The findings indicate an elasticity of poverty reduction with respect to agricultural productivity of between 0.25 to 0.3 percent, implying that a 10 percent increase in agricultural productivity will decrease the likelihood of being poor by between 2.5 and 3 percent. To increase agricultural productivity, land, labor, fertilizer, agricultural advice, and diversification within agriculture are the most important factors. As commonly found in the literature, the results indicate the inverse-land size productivity relationship. More specifically, a 10 percent increase in harvested land size will decrease productivity by 6.6 percent, all else being equal. In a simulation exercise where land quality is assumed to be constant across small and large holdings, the results show that if farms in the top land quintile had half the median yield per hectare of farms in the lowest quintile, production of the top quintile would be 10 times higher. The higher overall values of harvests from larger land sizes are more likely because of cultivation of larger expanses of land, rather than from efficient production. It should be noted that having larger land sizes in itself is not positively correlated with a lower likelihood of being poor. This is not to say that having larger land sizes is not important for farming, but rather it indicates that increasing efficiency is the more important need that could lead to poverty reduction for agricultural households.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at goseni@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
for heteroskedasticity gives greater weight to countries with larger surveys, since the labor outcomes are estimated more precisely in these countries. This forms the data for the descriptive changes of the job ladder indicators across years. However, in the appendix we also report key results without the heteroskedasticity correction, to verify that they are robust (Table A17-A19) without heteroscedasticity.

We also estimate linear regressions of different forms. The simplest form is used to estimate trends in the share of workers in different rungs: (1) R R cc,tt<sup>= αα</sup> cc<sup>+ β</sup> cctt<sup>,</sup> where R R is the proportion of adults (15+) on a given rung of the ladder in country<sup>(tt) + εε</sup> in (cc, tt) cc year , is country fixed effects, and , so that the coefficient represents the 10 tt cc tt αα ββ(tt) = ββ average change in the share of workers working in a particular rung over ten years.

To assess whether changes in the jobs ladder are correlated with changes in poverty. We estimate regressions of the form: (2) P P + ∑ R R 1 2 3P , rr=7 ccttcc cc tt rr=2 rr rrcctt cc,tt cc,tt cc,tt cctt where P P PPttyy is headcount poverty in country = αα + δδ γγ + ββ at time XX + ββ , measured at poverty line l. We GGββPP + ββ PP + εε ccttcc conside three separate poverty lines, set at $1.90, $3.20, and $5.50 in PPP 2011 terms. These PPttyy cc tt correspond to the extreme poverty line, and the poverty line for lower-middle-income countrie and upper- middle-income countries. R R rrcctt<sup>is the proportion of adults on rungPP, with the</sup> proportion of people in lower-tier self-employment being the omitted category. We also include the unemployed and those out of the labor force, leading to seven total categories. In all regressions, we cluster standard errors at the country level.

13
Learning from Developing Country Power Market Experiences: The Case of Peru Table 8-3 Detail of wholesale market governance indicators for Peru. Source: own

|**Level**|**India score**|
|---|---|
|**Market Governance**|**61%**|
|**Decision Making Autonomy**|**78%**|
|Is there an Independent System Operator (ISO), or Independent Transmission System<br>Operator(ITSO)?|1|
|Does the system operator hold ultimate authority on real-time system operation (i.e.<br>above market operator instructions orgenerator self-schedules)?|1|
|Does the board of the market (or system) operator allow one class vetoes (e.g. allowing a<br>class of market participants to veto rule changes sought by other classes, thus producing<br>deadlocks and impede changes to the system operator and itsprocedures)?|0|
|Is regulatory backstop allowed, whereby the regulator (or minister) can review, revoke or<br>modifydecisions taken bythe board of the market(or system)operator?|1|
|Is the market (or system) operator legally independent from market participants and<br>government?|0|
|Can the market(or system)operator decisions be appealed?|1|
|Are there regulatory requirements for the board members of the market (or system)<br>operator to be independent from market participants and the government to prevent<br>conflicts of interest?|1|
|Is there a formally established mechanism for dispute resolution among market<br>participants?|1|
|Does the board of the market(or system)operator have real decision-makingauthority?|1|
|**Transparency**|**88%**|
|Are operationprocedurespubliclyavailable?|1|
|Is bid datapubliclyavailable?|0|
|Are marketpricespubliclyavailable?|1|
|Are dispatchquantities and schedulespubliclyavailable?|1|
|Is settlement informationpubliclyavailable?|1|
|Are the grid code and all the operating procedures (including market clearing processes)<br>publiclyavailable to all interestedparties?|1|
|Are there well-defined boundaries on acceptable market outcomes?|1|
|Is the form of regulatoryintervention clarified in advance?|1|
|**Accountability and Monitoring**|**17%**|
|Is there an external overseeingor monitoringentity?|0|
|Can the external monitoring entity access information from system and market operation,<br>includingcommerciallysensitive and confidential information?|NAV|
|Does the external monitoringentityreport to the board of directors of the operator?|0|
|Does the external monitoringentityreport to the regulator?|0|
|Does the system operator report regularlyto the regulator,andpolicymakers?|1|
|Was sufficient time (> 3 years) allowed for fully phasing in the operation of the wholesale<br>power market?|NAV|
|During the transition to a wholesale power market were any measures taken to reduce<br>marketpower in thegeneration segment?|0|
|Was any transition mechanism put in place to ensure a minimum degree revenue certainty<br>forgenerators and acceptable degree ofprice risks for retailers?|0|

28
Lanjouw, and Neri (2001), 20 percent of all households had a household head employed in agriculture and these households contributed 34 percent to overall poverty in 1996. Changes in poverty, not only its levels, also differ across regions, rural and urban areas, and activities. Verner’s (2004) PNAD (Pesquisa Nacional por Amostra de Domicílios)based figures suggest that the poverty headcount in the Northeast declined from almost 60 percent in 1990 to 42.3 percent in 2001, whereas poverty in Brazil’s most populous state São Paulo rose slightly from 8.6 to 9.4 percent during the same period. For urban areas, Ferreira and Paes de Barros (2005) show that extreme poverty increased between 1976 and 1996. According to Paes de Barros (2004) however, the poverty incidence in rural areas in general and among households engaged in agricultural activities, in particular, declined from levels of about 60 percent to around 50 percent between 1992 and 2001.

One important factor for understanding these developments is the structural change in Brazilian agriculture in the 1980s and 1990s. In their assessment of the impact of sectorspecific as well as economy-wide reforms on Brazilian agriculture, Helfand and Rezende (2004) conclude that agriculture became one of the most dynamic sectors in the Brazilian economy. Between 1980 and 1998 real GDP grew by about 40 percent and real agricultural output by about 70 percent. In many sub-sectors, yields increased significantly and more harvested area was dedicated to exportables, in particular soybeans and sugarcane. Agriculture benefited from favorable macroeconomic environment and trade reforms that led to less industrial protection and the elimination of taxes and quantitative restrictions on agricultural exports. In addition, specific agricultural reforms – in particular a reform of agricultural credit and price support policies; an agrarian reform program, including a land reform; and, finally, the deregulation of domestic markets for agricultural goods – were important drivers of the observed agricultural performance.<sup>5</sup> However, the increase in agricultural productivity was accompanied by a massive lay-off of hired labor and by important changes in the size distribution of farms. According to the agricultural census from 1996, the number of small farms declined dramatically and agricultural employment shrank by 23 percent between 1986 and 1996, although these figures should be taken with some caution (Helfand and Rezende 2004). Our analysis based on the 1997 and 2001 household surveys (PNAD) suggests that this decline in agricultural employment has continued after 1996. In 2001, agriculture accounted for 20.6 percent of employment in Brazil down from 24.2 percent in 1997.

> 5 See Helfand and Rezende (2004) and Dias and Amaral (2002) for details.

6
Table 2: The effects of services development on manufacturing export RCA, using U.S. services input intensity averaged over 1995-2007

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Df|-5.102***<br>(1.733)||||||
|**Df*SIIf**|<br>**111.109*****|||**111.829*****|||
||**(35.993)**|||**(37.762)**|||
|Db||0.857<br>(2.066)|||||
|**Db*SIIb**||<br>**38.719*****|||**39.306*****||
|||**(12.895)**|||**(13.937)**||
|Dfb|||-3.240**<br>(1.405)||||
|**Dfb*SIIfb**|||<br>**27.217*****<br>**(6.695)**|||**27.721*****<br>**(7.182)**|
|log(GDP/capita)|0.153<br>(0.168)|0.008<br>(0.160)|0.084<br>(0.168)||||
|TFP|<br>0.069***|<br>0.075***|<br>0.072***|0.064**|0.066**|0.065**|
||(0.022)|(0.021)|(0.022)|(0.029)|(0.028)|(0.028)|
|log(emp)|<br>0.938***|<br>0.935***|<br>0.952***|<br>0.958***|<br>0.952***|<br>0.970***|
||(0.091)|(0.092)|(0.092)|(0.098)|(0.099)|(0.099)|
|SKratio|0.588|0.521|0.542|0.802|0.833|0.796|
||(0.621)|(0.636)|(0.638)|(0.801)|(0.826)|(0.822)|
|K/L|<br>0.069***|<br>0.066***|<br>0.067***|<br>0.071***|<br>0.068***|<br>0.069***|
||(0.025)|(0.025)|(0.025)|(0.026)|(0.026)|(0.026)|
|rwage|<br>-0.001|<br>-0.001|<br>-0.001|<br>-0.000|<br>-0.001|<br>0.001|
||(0.007)|(0.007)|(0.007)|(0.009)|(0.008)|(0.009)|
|GVC Participation|<br>2.463***|<br>2.452***|<br>2.470***|<br>2.577***|<br>2.558***|<br>2.584***|
||(0.448)|(0.445)|(0.444)|(0.500)|(0.497)|(0.495)|
|Country FEs|Yes|Yes|Yes||||
|Sector FEs|Yes|Yes|Yes||||
|Year FEs|Yes|Yes|Yes||||
|Country*Year FEs||||Yes|Yes|Yes|
|<br>Sector*Year FEs||||Yes|Yes|Yes|
|Observations|5,686|5,686|5,686|5,686|5,686|5,686|
|R-squared|0.575|0.575|0.581|0.593|0.593|0.599|

Notes: The dependent variable is manufacturing export RCA. Df (Db) refers to the share of financial (business) services value added in GDP. Dfb equals the sum of Df and Db. Sf is the ratio of the U.S. embodied domestic financial services to U.S.’ manufacturing value added, averaged over 1995-2007. All WIOD manufacturing sectors 3-16 are covered (not grouped together). Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

35