rafmacalaba/datause-encoder-data huggingface dataset:
county average dibao expenditure.<sup>5</sup> This approach effectively assumes an egalitarian distribution within villages or within counties of dibao benefits among dibao recipients.

The dibao participation rates in the CHIP rural survey are lower than the aggregate rates implied by official data.<sup>6</sup> To some extent this reflects the selection of provinces in the CHIP sample, but the discrepancy remains even for the nine CHIP provinces (to be discussed in more detail below). The reason why the CHIP sample has lower dibao participation rates than the official data is not clear. It is possible that dibao households are under‐sampled in the CHIP survey. Under‐sampling of poor households—which are presumably more likely to be dibao recipients—is a known feature of the NBS household survey samples from which the CHIP samples are drawn. It is also possible that some dibao households do not report their dibao participation. Households may not be aware that the transfers they received were from the dibao program, or they may not want to disclose their participation in the program. A third possibility is that the official numbers overstate true participation rates. It is widely accepted that local‐level governments in China massage their statistics so as to appear to comply with central government policy targets and in order to obscure local irregularities in program implementation (Hvistendahl 2013).

# **IV. Patterns of dibao participation, thresholds and transfers**

Consistent with the official data in Table 1, our data show substantial expansion of the dibao program since 2007 (Table 3). The mean dibao threshold, calculated using MOCA county‐ level data for all provinces, increased from 1,064 yuan per capita in 2007 to 1,428 yuan per capita in 2009; changes were similar for the nine sample provinces. The mean dibao transfer per capita also increased. Dibao thresholds were, on average, higher than, and dibao transfers lower than, China’s official poverty lines at the time (785 yuan in 2007, 1,067 yuan in 2008, and 1,196 yuan in 2009).

> 5 In the few cases of missing village‐level (county‐level) data we use county‐level (village‐level) information to impute missing values.

> 6 Gao, Garfinkel and Zhai (2009) find that in the CHIP urban data (for 2002) the rate of dibao participation is also lower than the officially reported rate.

12
middle income countries of the region, from 5 percent in Chad to 61 percent in Cape Verde. This compares to 141 percent in South Africa and 87 percent in Mauritius.

While Africa’s financial systems are shallow in international comparison, there have been marked improvements over the past decade, as documented in Figure 2. All three standard indicators of financial development have substantially improved over the period 2000 to 2011.<sup>iv</sup> The median value for Liquid Liabilities to GDP increased from 20 to 31 percent, while that for Deposits to GDP increased from 12 to 22 percent. Median Private Credit to GDP increased from 11 percent to 18 percent. And this improvement has been broad based. If one considers the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentiles of Private Credit to GDP over the same period, it is evident that countries at different points of the distribution have all witnessed improvements.

**Figure 2: Financial deepening in Africa over the past decade**

<!-- Start of picture text --> 35<br>30<br>Bank deposits to GDP<br>(%)<br>25<br>20<br>Liquid liabilities to GDP<br>15 (%)<br>10<br>Private credit by<br>5 deposit money banks<br>and other financial<br>0 institutions to GDP (%)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Global Financial Development Indicators, World Bank Africa’s banking systems are not only shallower than banking systems in non-African developing countries, they are also less inclusive (Figure 3). Here we present four indicators of access to and use of financial services. First, we present two aggregate indicators: bank 7
there was potential for the ATI intervention to improve government capacity and thus knowledge on existence of OAIs, our evaluation findings show no relevant impact of the intervention on knowledge about existence of an OAI. It appears that this knowledge has increased similarly in both treatment and control groups which would be consistent with the presence of spillover effects across treated municipalities. It also appears that knowledge is greater in the chosen ATI municipalities both in the baseline and endline surveys, thus implying no differential effect from the intervention.

In terms of general civic knowledge, when households were asked to identify whether a certain public service is provided by the central or local government, the proportion of correct answers is higher in treatment than in control municipalities for key services. Specifically, the proportion of correct response on education – that it is a central government responsibility – was 4.2 percentage points higher in treatment areas than in control areas. Surprisingly, despite similarity between the ATI program and other transparency projects such as the Participatory Budgeting, knowledge on the latter wasn’t influenced by the ATI program. Neither of these findings was found to be statistically significant.

Regarding the use of information, the surveys show a large increase in requests for information to public offices or officials in the ATI municipalities and a small decrease in the control municipalities. The differential effect is positive, 1.5 percentage point, but not statistically significant. That this parameter is small also reflects findings raised by the qualitative baseline study that identified several barriers to accessing information, especially in the poorer communities in the DR. First, participants in the focus groups blamed pervasive illiteracy for making people unaware of their rights as citizens. Because people do not understand their rights, they do not make demands – as a result, professionals or lawyers are the ones most able to pursue these rights on their behalf. Second, although people wanted to know more about what the public authorities were doing, they feared that demanding information about financial matters could appear confrontational. Participants of the study attributed fear of reprisal to the legacy of dictatorship, under which they lived for 32 years. Thus they seemed to believe that the 17
Although medical evidence about the benefits of handwashing with soap is clear, changing handwashing behavior is a complex phenomenon related to personal habits that has multiple determinants (economic, cultural, social, etc.).<sup>54</sup> These characteristics of handwashing result in important difficulties in changing and sustaining these hygienic practices. This paper studies a large-scale handwashing promotion intervention that took place in Peru between 2008 and 2010, and that introduced an innovative mix of communication strategies to induce handwashing with soap at critical times. In particular, the study examined the effect of two different treatments—a province-level mass media campaign and a district-level community treatment—on several outcomes: exposure to the program, handwashing determinants, handwashing behavior, environmental contamination, and child health. We combined different quantitative and qualitative data collection techniques to measure these variables, including detailed questionnaires; structured observations; microbiological analysis of samples of child stools, capillary blood, and drinking water collected in the field; and anthropometric measures performed by health experts.

This paper is the first to assess the effect of a large-scale handwashing with soap intervention using such a wide range of indicators. Previous studies in the literature of randomized handwashing experiments focus on intensive (in terms of labor, inputs and/or monitoring) and controlled treatments, showing that they are effective in reducing diarrhea and pneumonia incidence in children.<sup>55</sup> This study, in contrast, examines a significantly less intensive intervention at scale under real-world conditions. In addition, we study a full set of intermediate outcomes, such as the effectiveness of the campaigns and behavior change, in order to better understand the results in the context of the causal path of disease transmission identified in the medical and public health literature. To the best of our knowledge, ours is the first study of handwashing to focus on all components of this causal chain.

The results of this evaluation show that the mass media intervention alone was not effective in reaching the targeted audience with the handwashing message and therefore failed to improve the handwashing knowledge of mothers and caregivers and to generate a behavior

> 54 Chapman 2010

> 55 Luby et al. 2005

30
# Figure 5: Hiring regulations

<!-- Start of picture text --> 2000 2002 2004 2006 2008 2010<br>Year 2000 2002 2004 2006 2008 2010<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU10 Balkan/Turkey CIS<br>Source: WB Doing Business, Fraser Institute Source: WB Doing Business, Fraser Institute<br>Figure 6: Shadow economy<br>1999 2001 2003 2005 2007<br>Year 1999 2001 2003 2005 2007<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU10 Balkan/Turkey CIS<br>Source: Schneider et al (2010) Source: Schneider et al (2010)<br>Figure 7: Ease of doing business<br>2005 2006 2007 2008 2009 2010<br>Year 2005 2006 2007 2008 2009 2010<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU11 Balkan/Turkey CIS<br>Source: WB Doing Business Source: WB Doing Business<br>10 10<br>9 9<br>8 8<br>7 7<br>6<br>6<br>5<br>5<br>4<br>4<br>Hiring regulations 3<br>Hiring regulations 3<br>2<br>2<br>1<br>0 1<br>0<br>70 50<br>60<br>50 40<br>40<br>30<br>30<br>20<br>20<br>Shadow Economy (% of GDP) 10<br>Shadow Economy (% of GDP)<br>0<br>10<br>80 80<br>70 70<br>60<br>60<br>50<br>50<br>Distance to Doing Business Frontier<br>Distance to Doing Business Frontier<br>40<br>40<br><!-- End of picture text -->

# Figure 6: Shadow economy

# Figure 7: Ease of doing business

8
to respond to higher foreign demand for skilled workers as they have the financial ability to invest more in education. In contrast, the domestic demand for skilled labor declines with higher migration possibilities (but in the education sector), as its relative cost increases in comparison with physical capital and unskilled labor. Besides, higher remittances affect export competitiveness through a real exchange rate appreciation and the overall demand for domestic goods and services.

The impact <u>of higher remittances on the likelihood for brain gain</u>

|Migration Possibilities|Low<br>(S1r)|Medium<br>(S2r)|High<br>(S3r)|
|---|---|---|---|
|_Annual growth rates_||||
|Population|1.2%|1.1%|1.0%|
|Real Gross Domestic Product|3.9%|3.9%|3.8%|
|Per capita real disposable income|3.5%|3.6%|3.8%|
|Per capita domestic human capital|3.5%|3.2%|2.8%|
|Real physical investment|5.9%|5.9%|6.0%|
|Real educational investment|6.8%|7.0%|7.2%|
|Labor supply, unskilled|0.8%|0.7%|0.7%|
|Labor supply, skilled|4.7%|4.4%|4.0%|
|Wage, unskilled|3.4%|3.4%|3.5%|
|Wage, skilled|-0.3%|0.2%|0.8%|
|Physical capital remuneration|-2.0%|-2.0%|-1.9%|
|Educational investment costs|0.1%|0.4%|0.8%|
|Physical investment costs|0.2%|0.3%|0.4%|
|Consumer price index<br>_Others_|0.2%|0.3%|0.4%|
|Proportion of domestic skilled workers after 25 years|12.7%|11.8%|10.9%|
|Skilled workers migration rate after 25 years|2.5%|3.1%|3.9%|
|Elasticity of per capita domestic human capital accumulation t<br>Source: authors’ calculations.|o skilled mig|ration|-0.46|

Raising further remittances per migrant in proportion of GDP per capita (to a ratio of 4:1 for instance) only marginally affects the picture without changing the overall conclusion. Raising further remittances also becomes questionable from a likelihood perspective. Indeed, at 4 times GDP per capita, migrants from developing to developed countries would broadly remit a quarter of their incomes, and much more if initially originating from middle-income countries. Another possibility for higher remittances is the existence of a much larger initial Diaspora. But it would also induce a lower impact 14
2 At the same time as reducing fertilit, and possibly partly related to this, education seems to have a positive influence on health. In section 2.4, a variety of effects are discussed. In developing countries parental education leads to a reduction in child mortality. Related to this, despite not being an external effect, is the suggestion that healthy children learn more efficiently.

While the effects of improved health are very important, they are also reasonably straightforward to appreciate. The theoretical basis for the impact of education on fertility and economic growth, however, merits further investigation. In section 3, a simulation model is developed. This model assumes that family size is an economic choice and sets out, first of all, the links between education and fertility, on the assumption that technical progress is exogenous. It is demonstrated that fertility increase is decreasing in the cost of raising children, but increasing in the rate of exogenous _per capita_ growth. Education is introduced into the model by making the duration of education (and thus the cost of raising children) sensitive to the return to education. The interaction between the return to education and the endogenously-determined return to physical capital appears to be the dominant route by which the return to education influences fertility.

In section 4, the model is extended by making technical progress depend on the duration of education; the simulation results enable one to identify the overall effects of an extension of education in terms of population growth and technical progress, as well as reflecting the effects which enter into conventional rate of return calculations. These simulations show a link between fertility and growth reasonably similar in magnitude to that identified in empirical studies.

There is good reason to believe that the demand for education is sensitive to economic factors. Several studies (for example, (King and Ullard (1987)) look at the effects of the cost of education and confirm that demand is sensitive to its price. They also stress the importance of accessibility as a factor influencing the take-up of education. But the external effects alluded to above mean that one cannot rely solely on market forces to deliver a socially desirable amount of education.

2. Economic Development and Education: Some of the Issues Involved There are several important routes by which education can influence an economy. First and foremost, education raises labor productivity. Improvements in the educational attainment of the population will tend to be associated with economic growth. This may be enhanced by the externalities discussed above. An individual may be more likely to benefit from a given level of education if there are others with whom to co-operate. And a high flow of investment in education is likely to lead to more rapid growth in knowledge and thus to faster technical progress. Secondly,
# **3**

We assume in this section that there is no market between the two dimensions and that achievements in the two dimensions are cardinally measured.<sup>22</sup> Consumption is typically cardinally measured.<sup>23</sup> This can also be the case of health when measured in quality-adjusted life years or healthyyears equivalents (Mehrez and Gafni, 1989). This can also be the case of education when measured in learning-adjusted years of schooling (Corral et al., 2021). We use our theory in order to compare linear identification methods that rely on different information bases.

## **3.1 Three families of linear methods**

In practice, mainstream identification methods are linear identification methods (LIMs). LIMs have two main characteristics. First, they are based on a reference **deprivation bundle** z = (z1, z2) ∈ R<sup>2</sup> ++<sup>whosecom-</sup> ponent zj > 0 is dimension j’s deprivation cutoff. Second, their aggregation function M makes a weighted sum of outcomes in all dimensions. Under information constraints, a linear aggregation constitutes a natural default choice, especially when it is backed by axiomatic justifications (Alkire and Foster, 2011b).

We distinguish three families of LIMs, which differ by the information bases they use in order to measure outcomes. The mainstream family uses the deprivation-status basis in order to identify the poor (Alkire et al., 2015). For any individual i the deprivation bundle z and her bundle xi define a vector of **deprivation statuses** si ∈{0, 1}<sup>2</sup> such that This information basis has the key advantage that it can be used both on ordinal and cardinal data. Let w<sup>S</sup> = (w1<sup>S, w</sup> 2<sup>S)∈[0, 1]2beavectorof</sup> weights such that<sup>�</sup> j<sup>w</sup> j<sup>S=1.LetkS∈[0, 1]denotetheidentification</sup> threshold. A **status-based linear identification method** (status-LIM) is defined as a triplet (z, w<sup>S</sup> , k<sup>S</sup> ) that identifies i as poor when For instance, both the UNDP-OPHI’s global MPI and the World Bank’s MPM use status-LIMs, though they consider more than two dimensions. We illustrate this for the World Bank’s MPM in Section 7.1.

A comment is in order. We do not consider that the deprivation bundle z is exogenously-given. We rather consider z as a parameter of an LIM.

> 22 A dimension is cardinally measured when achievements in this dimension are ordered and the distances between achievements can be meaningfully compared.

> 23 Monetary poverty is typically measured using cardinal data. However, when exactly one dimension is cardinally measured and the other dimensions are ordinally measured, we are virtually in a case for which all dimensions are ordinally measured. Indeed, when exactly one dimension is cardinally measured, the identification method cannot take advantage of the cardinal nature of this dimension.

12
# **1  Introduction**

Livestock systems globally are changing rapidly in response to a variety of drivers. Human population is projected to increase from around 6.5 billion today to 9.2 billion by 2050 (UNPD, 2008). About 1.4 billion of this increase will occur in Asia, with more than 1 billion occurring in Africa. Rapid urbanization is expected to continue in developing countries, and the global demand for livestock products is projected to continue increasing very significantly in the coming decades. The trends in demand in developing countries will be for both increased quantity, especially as incomes rise from USD 2 to 10 per day, and for increasing quality, particularly among urban consumers who purchase livestock products from supermarkets. Meeting this increased demand may put substantial pressure on a wide range of natural resources such as land and water. At the same time, the climate is changing, and climate variability with it; this is inevitable in the coming decades, given the lags in the climate-earth system, even if greenhouse gas emissions stabilized tomorrow. These drivers of change add up to a formidable set of development challenges that affect (and are affected by) developed countries as well as developing countries.

This paper attempts to summarize what is known about the linkages between the burgeoning demand for livestock products, the subsequent growth in livestock production, and the impacts that this may have on natural resources, as well as how these may both affect, and be affected by, climate change in the coming decades. The paper is structured as follows. Section 2 contains a summary of the increased demand for livestock products globally, highlighting various factors that drive changes in the patterns of consumption. The increases in production that may result are then outlined, drawing on recent global assessments that have addressed this issue using scenario analysis. This is followed in section 3 by a discussion of the impacts of these production changes on natural resources, particularly water use, land use and land-use change with special reference to deforestation and biodiversity. This section also draws on recent global and regional assessments that have considered these issues, and is illustrated by some examples of livestock-natural resources "hotspots" in different parts of the world.

Section 4 contains a summary of what is known about the impacts of livestock on climate through the emission of greenhouse gases, and the impacts of climate change on livestock, in relation to water and feeds, livestock diseases and disease vectors, and livestock system 8
Table A.9. Di¤erence-in-di¤erence estimates of the program impact on user charges at the health facility.

|Dependent variable|Drugs|General treatment|Injections|Delivery|
|---|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|(4)|
|Program impact|-0.01|-0.06*|-0.14**|-0.07|
|(Treatment*2005)|(0.01)|(0.029)|(0.07)|(0.11)|
|2005|0.002|-0.018**|0.11**|-0.13*|
||(0.005)|(0.007)|(0.04)|(0.07)|
|Mean control group 2005|0.01|0.02|0.37|0.50|
|Facility ...xed e¤ects|Yes|Yes|Yes|Yes|
|Observations|5660|5734|2511|507|
|R<sup>2</sup>|0.003|0.18|0.27|0.42|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Speci...cation: (1)-(4) Dummy variables indicating whether the health facility charged for service provided during the last visit. c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas.

# A.3.3 Utilization

The di¤erence-in-di¤erences estimates on the number of outpatients and deliveries are reported in table A.10. For the number of outpatients, we present the results from estimations of di¤erence-in-di¤erences speci...cations in both levels and logarithms.

The treatment e¤ect is positive and signi...cantly di¤erent from zero for both outpatients served and the number of deliveries. The point estimates in the out-patient speci...cations suggest a substantial treatment e¤ect.

Table A.10. Di¤erence-in-di¤erences estimates of the program impact on health facility utilization.

|Dependent variable|Out-Patient|Log of Out-Patient|Delivery|
|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|
|Program impact|215.5**|0.28**|3.48*|
|(Treatment*2005)|(93.4)|(0.11)|(1.98)|
|2005|-247.3|-0.25***|1.73|
||(70.1)|(0.07)|(0.89)|
|Mean control group in 2005|661||9.2|
|Facility ...xed e¤ects|Yes|Yes|Yes|
|Observations|100|100|100|
|R<sup>2</sup>|0.77|0.82|0.90|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Robust standard errors in parenthesis.

40
l

# Table 21: ANNUAL PRIVATE COSTS OF VTE (PER STUDENT) (CFAF)

||Seconda|ry Level|Post-sec<br>Leve|ondary<br>l|Teacher|Training|<br>Apprenticeships|
|---|---|---|---|---|---|---|---|
|Out-of-pocket Costs|163,426||232,435||184,886|||
|Scholarships|22,328||161,414.||48,602||__|
|Net out-of-pocket Costs|141,098|(23.43)|<br>71,021.|(5.8%)|136,284|(14.8%)X||
|Opportunity Costs|461,796|(76.6%)|1,155,732|(94.2%)|783,684|(85.2%)|426,240 (100.0%)|
|Total Private Costs|602,894|(100.0%)|1,226,753|(100.0%)|919,968|(100.0%)|426,240 (100.0%)|