rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The global food and fuel price spike in 2007-08 motivated many researchers to examine the welfare effects of these aggregate shocks on households in various developing countries. These price spikes were found to have a significant net negative impact on food security and poverty, with studies estimating that between 141.2 million and 155.6 million people could have fallen under the $1.25 per day poverty line due to the price increase (de Hoyos & Medvedev, 2011; Ivanic & Martin, 2014). However, there is widespread heterogeneity in the results of food price welfare incidence analysis, with the effects varying with location (both across and within countries), the temporal scale of the analysis, price levels as well as their volatility, and the degree of price transmission to local markets (Headey & Martin, 2016). Given this heterogeneity, the appropriate policy responses to these price rises are not always clear.

While global agricultural prices have been relatively low, especially after 2011, many individual countries have continued to experience sudden rises in prices. A case in point is Mozambique, which saw steep food price inflation during 2016-17, going up as high as 40%. With a higher risk of natural disasters, a large share of the population dependent on agriculture and unequal progress in development across regions, the likelihood of negative welfare impacts of food price inflation in Mozambique cannot be ignored. The context specific nature of the impacts of price hikes and their policy responses necessitates a deeper understanding of the welfare incidence of the price spikes in Mozambique to better target the segments of the population who are likely to require support.

This paper performs food price incidence analysis in Mozambique, combining data from household and farmer surveys on consumption and agricultural activities collected from Mozambique in 2014 and 2015. We exploit the spatial location information of households across two different data sets to match household consumption data with production data from the set of nearest neighbors. In addition, we also incorporate disaggregated, market-level price data on major staples to simulate the welfare and poverty effects using observed price changes. As a result, in contrast to many previous studies, this allows us to analyze the spatial variation in the impact of food costs using actual prices faced by the households.

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# **_Empirical Findings_**

**Table 7 below shows the regression results, presented for developing countries only (first four columns) and for the world as a whole, including both developing and developed countries (last four columns).** Developing countries are those with upper middle income status or below.<sup>29</sup>

**Columns 1 and 5 present the base model results for the developing countries and the world samples and the remaining columns show alternative specifications.** For the most part, the variables have the expected signs. The initial FDI/GDP share is negative, suggesting conditional convergence. FDI as a share of GDP is growing faster in countries that have lower FDI stocks, but this is significant only in the world sample (column 5). This variable’s higher significance in the world sample is probably due to greater FDI flows going from developed to developing countries over the sample period; hence, this factor has more variance in the world sample. The size of the coefficient suggests that countries with low FDI/GDP are conditionally catching up to countries with higher FDI/GDP at a rate of about 0.06 percentage points per year.<sup>30</sup>

> 29 World Development Indicators (http://data.worldbank.org/data-catalog/world-development-indicators).

> 30 The rate of convergence is calculated as λ= −ln (1 + β̂)/t, where t is 1 (since growth is annualized, the period over which growth is calculated is 1) and β̂ is the estimated coefficient on the level of FDI/GDP in the starting period.

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We determine that one project is high risk (H) and the other is low risk (L). Assume that each project has two possible cash flows depending on two possible states of the world, which occur with certain probabilities. Table 1 provides numeric assumptions to be used in this example. Figures in Table 1 would typically result from scenario analysis.

**<u>Table 1</u>**

|Project|State of t<br>|he world 1<br>|State of t<br>|he world 2<br>|
|---|---|---|---|---|
||Cash flow<br>volume|Probability of<br>state|Cash flow<br>volume|Probability of<br>state|
|L|175|90%|0|10%|
|H|250|60%|0|40%|

As can be observed, projects L and H differ in that they provide a small cash flow with high probability if the risk is low and a high cash flow with a lower probability if the risk is high.

Based on the information provided, we can calculate the present values (PV) and net present values (NPV) for the two projects:

|PV(L)|=|(175*0.9+0*0.1)/1.05|=|150.00|
|---|---|---|---|---|
|NPV(L)|=|-100+PV(L)|=|50.00|
|PV(H)|=|(250*0.6+0*0.4)/1.05|=|142.86|
|NPV(H)|=|-100+PV(H)|=|42.86|

Using the “NPV rule,” the businessman chooses project L since its NPV of 50 is higher than the NPV of project H, which is only 42.86. To calculate the NPVs solely the exogenously given probabilities for the two states of the world are needed; hence, this method falls under the efficiency or stochastic approach. The decision is taken without considering that the bank is an active player and offers two types of credit contracts. Is this realistic?

<u>Table 2</u> provides numeric assumptions regarding the credit contracts offered by the bank. One contract requires collateral (C) but comes at a low interest rate; a second contract does not require collateral (N) but comes at a high interest rate. Note that interest rates in this example were purposefully chosen to be unrealistically high to be able to demonstrate clear differences in size.

6
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similar change, suggesting that there may have been a common program or change affecting some states in the region. However, the change is not as noticeable for Bahia.

**Figure 1: Child Mortality Change 2007–2011 and Child Mortality in 2007**

**Figure 2: Child Mortality and GDP per Capita, 2011**

<!-- Start of picture text --> AP<br>AP<br>DF RSSCSOSPPRESRJSE MWRRGOMGMT ROBR TOAMNR PA MA PI PA BAACTO AM MT<br>MS PIBA AL PB RNSE RO<br>CE AC MA CE PE RR GOMG<br>PENERNSEPB MS RJ<br>PRESRSSC SP DF<br>AL<br>10 20 30 40 8 8.5 9 9.5 10<br>Child Mortality (per 1,000 born alive) 2007 LN GDP per Capita (constant 2000)<br>95% CI Fitted values 95% CI Fitted values<br>Difference Child Mortality (per 1,000 born alive) 2007-2011 Chil Mortality (per 1,000 born alive)<br>Source : Ministry of Health, SVS 2013. Source : IBGE, Ministry of Health, SVS 2013.<br>Figure 3: Child Mortality in Bahia, Ceará, and Figure 4: Child Mortality in Minas Gerais and Rio de<br>Pernambuco (per 1,000 born alive), 2002–2011 Janeiro (per 1,000 born alive), 2002–2011<br>PE: MG: MG:<br>Pernambuco Performance Performance<br>para Todos Agreements Agreements<br>– 1 stage – 2 stage<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Year Year<br>Bahia Ceará Minas Gerais Rio de Janeiro<br>Northeast Pernambuco Southeast<br>10 25<br>0 20<br>-10 15<br>-20 10<br>-30 5<br>40 40<br>30 30<br>20 20<br>Chil Mortality (per 1,000 born alive) Chil Mortality (per 1,000 born alive)<br>10 10<br><!-- End of picture text --> _Source_ : Ministry of Health, SVS 2013.

**Figure 3: Child Mortality in Bahia, Ceará, and Pernambuco (per 1,000 born alive), 2002–2011**

**Figure 4: Child Mortality in Minas Gerais and Rio de Janeiro (per 1,000 born alive), 2002–2011**

_Source_ : Ministry of Health, SVS 2013.

_Source_ : Ministry of Health, SVS 2013.

In the case of Minas Gerais, there is no clear discontinuity in its declining trend that could be linked to the 2004 introduction of performance agreements in the health sector or their refinement in 2008 (Figure 4). While the rate decreased by an average of 3.7 percent between 2007 and 2011, child mortality levels remain above the regional average, and the distance between the two has been fairly constant. Minas Gerais’ trend is similar to that of the southeast region and that of Rio de Janeiro, a state that does not use results agreements in the health sector.

**_Security Sector._** After three consecutive years of decline, the national homicide rate per 100,000 inhabitants started increasing again in 2007.  Since that time, only eight states have recorded reductions (Figure 5).  The largest drop was observed in Pernambuco and Rio de Janeiro, with a

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Policy Research Working Paper 9462

# **Abstract**

Parental involvement programs aim to increase schooland-parent communication and support children’s overall learning environment. This paper examines the effects of low-cost, group-based parental involvement interventions in Mexico using data from two randomized controlled trials. The first experiment provided financial resources to parent associations. The second experiment provided information to parents about how to support their children’s learning. Overall, the interventions induced different types of parental engagement in schools. The information intervention changed parenting behavior at home—with large effects among indigenous parents who have historically been discriminated and socially excluded—and improved student behavior in school. The grants did not impact parent or student behaviors. Notably, the paper does not find impacts of either intervention on educational achievement. To understand these null effects, the paper explores how social ties between parents and teachers evolved over the course of the two interventions. Parental involvement interventions led to significant changes in perceived trustworthiness between teachers and parents. The results suggest that parental involvement interventions can backfire if institutional rules are unclear about the expectations of parents and teachers as parents increase their involvement in schools.

This paper is a product of the Education 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 hpatrinos@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
67

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**15**

slightly above the rural mean consumption for 1981/82. As one would expect, the distributionally sensitive poverty measure is far more responsive to changes in overall inequality.

It should be recalled, however, that the change in the Gini coefficient postulated here is associated with a specific shift in the Lorenz curve, whereby inequality falls across the whole -ange of the distribution. A reduction in inequality which is concentrated more amongst the poorer half (say) would naturally have a stronger impact on a distributionally sensitive assessment of aggregate poverty.

The results in Table 3 can be used to calculate the distributionally neutral growth rates which would be needed to achieve any specified targets for poverty alleviation in Bangladesh. This uses the fact that the rate of change in poverty over time is given by (to a first order approximation) the product of the growth rate in mean consumption per capita and the elasticity of poverty with respect to the mean.

For example one may ask: What is the minimum growth rate in mean consumption that would be needed to reduce the aggregate number of the poor in Bangladesh, without altering relative inequalities? To a first-order approximation, the answer is simply the ratio of the population growth rate to the elasticity of the headcount index with respect to the mean, as reported in Table 3. Since the growth elasticity is found to be about one using the BBS poverty line, we can identify a simple rule-of-thumb: Unless the rate of growth in aggregate real consumption is at least twice as high as the rate of population growth, the absolute number of poor in Bangladesh will increase. Some caveats should, however, be noted:
New Zealand’s wine industry has seen even more rapid export growth, growing from NZ$300 million in export sales in 2004 to almost NZ$1.2 billion in 2012.<sup>4</sup> Collectively it is estimated that the horticultural and viticulture industries need at least 50,000 workers a year at peak times to pick and pack fruit, prune, and prepare orchards and vines for the next season (Ramasamy et al, 2008).

Historically these employers had relied on a range of students, local casual workers, people entering New Zealand as working holiday-makers, and migrant workers coming through a range of other temporary work schemes to help carry out these tasks. But the rapid growth in these industries, coupled with low levels of unemployment in New Zealand, meant that by the mid-2000s employers were increasingly having trouble obtaining sufficient numbers of workers in peak times. Moreover, on top of concerns about the number of workers, employers were also concerned about worker quality, with high levels of worker turnover resulting in few opportunities for sustained training of workers and poor quality work reducing the value of crops harvested. Ramasamy et al. (2008) report that the horticultural industry estimated that the shortage and poor quality of labor was annually costing NZ$180-300 million in lost output and NZ$140-230 million in lost value added.

In response, in 2004 the New Zealand government introduced temporary policies to allow employers to recruit labor on seasonal work permits during the 2005-06 season while working to develop a more comprehensive policy response. Some employers brought in temporary workers on employer approval-in-principle (AIP) schemes whereby foreign workers could be offered work provided they could show they could not meet this need with the local labor force. Approximately 2,000 workers came through such categories in the 2005-06 financial year, including 60 workers from Samoa, 7 from Tonga, and 11 from Vanuatu (Gregory, 2006).

During the same period, there was growing pressure from Pacific Island governments to allow more movement of unskilled and low-skilled migrants from these countries to New Zealand and Australia. The New Zealand government undertook an interagency consultation (involving the Department of Labour, Ministry of Foreign Affairs and Trade, and New Zealand Agency for International Development), leading to a 2006 cabinet policy paper. A major World

> 3 <u>http://www.hortnz.co.nz/Overview/About-Us/Industry-statistics.htm</u> [accessed October 18, 2013]. NZ$1  = US$0.85 [October, 2013]

> 4 <u>http://www.nzwine.com/assets/sm/upload/da/9i/te/eu/NZW_Annual_Report_2013_web.pdf</u> [accessed October 18, 2013].

4
# I **INTRODUCTION**

B1ankers' incentives matter. Banks are key institutions for attracting savings, in the form of short-term deposits, and converting them into longer-term investments, in the form of loans. When private capital is genuinely at risk, bankers have strong incentives to gather information about the credit-worthiness of potential borrowers, which they can then use to determine how, and on what terms, credit is allocated. This ensures that investment is directed towards the most productive purposes and imposes a hard-budget constraint on firms. i However, when political pressure distorts bankers' incentives, credit may be directed without due regard to commercial lending criteria. These pressures are likely to be especially pronounced for state-owned banks. **In** theory, bank privatization might, therefore, have a large effect on financial sector performance and, in turn, on aggregate long-term growth. 2 However, in practice, bank privatization has not always been successful. For example, Chile privatized many public banks in the early 1970s as part of its privatization program. In 1982, the financial distress of the industrial conglomerates caused by high interest rates and currency devaluation meant that many firms were unable to service their loans.<sup>3</sup> This forced the governmenit to rescue, and re-nationalize, many of the recently privatized banks. World Bank (1989) suggests that an inadequate regulatory framework "allowed [the privatized banks] to be World Bank (1995) found a strong link between performance of state-owned enterprises and hard budget constraints. That report also summarizes the literature on hard budget constraints. Egypt's problems with overdrafts and government assumption of the liabilities of state-owned enterprises are described in Sherif (1992) and Sherif and Soos (1993); Poland's efforts to harden budget constraints in Baer and Gray (1994); and China's tendency to allocate state-owned bank credit and direct governmental subsidies to enterprises with relatively low productivity in Hwa (1992).

- 2 Demirguc-Kunt and Levine (1994) show that a variety of indicators of financial development (including general measures of financial depth) are all closely aligned with income levels. Similarly, King and Levine (1993 a,b) and Levine and Zervos (1996) highlight the strong link between financial development and aggregate growth rates. Not only do King and Levine find a strong connection between real per capita growth rates and financial development, they also find a link between total productivity growth and finance -- which is, perhaps, a more direct indication that well-developed financial sectors allocate resources better than others.

World Bank (1989, p. 123) notes that some estimates indicate that the non-performing assets of Chile's banks might have been as large as 79% of capital and reserves in 1982 and 150% in 1983.