rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: First Stage Regressions**

||% of Area<br>under LDO|Travel time<br>to urban Centers|Area under LDO*<br>Travel Time|
|---|---|---|---|
|Instruments||||
|Malaria*Inland water|0.015|-0.145|-0.007|
||(3.70)***|(1.91)*|(0.34)|
|Malaria*% of area above 1000 feet of elevaton|-0.067|1.215|-0.111|
||(3.44)***|(1.91)*|(1.36)|
|difference in slope|0.003|0.094|0.007|
||(3.78)***|(6.50)***|(2.46)**|
|difference in slope*malaria*water|-0.004|-0.081|-0.035|
||(5.74)***|(6.99)***|(10.05)***|
|District Fixed Effect|Yes|Yes|Yes|
|Individual & area controls|Yes|Yes|Yes|
|Relevance of Instruments||||
|Angrist-Pischkec<sup>2</sup>|29.48|43.36|44.62|
|P-value|0.00|0.00|0.00|
|Angrist-Pischke F-statistics|14.73|21.59|22.22|

Robust t statistics in parentheses. Standard errors corrected for clustering

* significant at 10%; ** significant at 5%; *** significant at 1%
# **Table 4: Public Works**

|**Crisis and Context**|**Authors**|**Methodology**|**Main Findings**<br>**Comments**|
|---|---|---|---|
|**Argentina (1994-2003)**<br>Jefas Y Jefas programme|Galasso and<br>Ravallion<br>(2003)|Matched subset of applicants who are not yet<br>accepted into the program are used as a<br>comparison for program participants using<br>matching methods to control for selection on<br>observables. Matches double differenced estimates<br>of program impact are used to minimize bias due<br>to selection on unobservable but estimates are<br>imprecise, rendering the matched single-<br>differenced estimates the preferred estimation<br>method.|<br>Program reduced unemployment by 2.5% and had<br>a small impact on poverty rate, but a large impact<br>on the number of people in extreme poverty which<br>would have been 10% without the program.<br><br>The impact could have been higher if the program<br>had been targeted since the program attracted many<br>inactive people into the workforce.|
|**Argentina (1994-2003)**<br>Jefas Y Jefas programme,|Iturriza, A.<br>and Bedi, A<br>and Sparrow,<br>R (2008)|Comparison of probability of exiting<br>unemployment of participants and non-<br>participants using logit and multinomial logit<br>models, single-differenced and double-differenced<br>matcing estimators|<br>Participation is associated with a 12-19% lower<br>probability of transiting to employment<br><br>Women are especially less likely to exit the<br>program.|
|**Indonesia (1997-2000)**<br>Two social safety net programs:<br>the_Jaring Pengaman Sosial_) and a<br>rice subsidy program|Sumarto,<br>Sudarno, et<br>al. (2003)|Dynamic<br>benefit<br>incidence<br>analysis<br>using<br>representative household panel data.|<br>The job creation program was much better at<br>targeting the most affected than the rice subsidy<br>program,<br><br>The rice program was better at targeting the<br>poorest.|

24
there is significant variation in knowledge concerning different aspects (Peden et al., 2007). The response of increased temperatures on water demand by livestock is well-known, for example. For _Bos indicus_ , for example, water intake increases from about 3 kg per kg dry matter (DM) intake at 10°C ambient temperature, to 5 kg at 30°C, and to about 10 kg at 35°C (NRC, 1981). The impacts of climate change on water supply changes in livestock systems, on the other hand, are not well-studied. The key contribution of groundwater to extensive grazing systems will probably become even more important in the future in the face of climate change, although the impacts on recharge rates of the aquifers involved are essentially unknown (Masike, 2007; see Box 2). The coming decades are likely to see increasing demand and competition for water in many places, and policies that can address allocation and efficiency issues will increasingly be needed.

Impacts of climate change on livestock production will often be mediated through changes in feed resources, which can be affected by climate change through several pathways:

1.  Land use and systems changes. As temperature increases and rainfall amounts change and become more variable, the niches for different crops and grassland species change. Transitions from one crop to another, or between crops and rangelands, can occur.  As temperate areas become warmer, substitution for crop species more suited for warmer climates may take place.  In parts of East Africa, reductions in the length of growing period are likely to lead to maize being substituted by crop species more suited to drier environments such as sorghum and millet (Thornton et al., 2008b).  In marginal arid places of southern Africa where crops grow, the reductions in length of growing period and the increased rainfall variability is driving systems to a conversion from a mixed crop-livestock system to a rangeland-based system, as farmers find growing crops too risky in those marginal environments (A van Rooyen, personal communication).  These land-use changes can lead to different compositions of animal diets and to changes in the ability of smallholders to manage feed deficits in the dry season.  These two effects can have substantial effects on animal productivity and on the maintenance of livestock assets.

2. Changes in the primary productivity of crops, forages and rangelands. The effects are significantly different depending on location, production system, and on crop and pasture species. In C4 species, temperature increases to 30-35°C will generally increase the productivity of crops, fodders and pastures, as long as other factors do not significantly limit 54
More skilled workers are generally expected to excel in socio-emotional skills. The skills that employers most value in their more skilled employees in Peru, India, and LAC, can be classified as socioemotional skills (World Bank 2011, Blom and Saeki 2011, Bassi et al. 2012). Of the seven skills Peruvian employers were asked to evaluate,<sup>52</sup> they most demand interpersonal skills (17 percent) of their workers with some tertiary education. Indian employers of university educated engineers also classified socio-emotional skills as most important. The set of “core employability” skills are more important, on average, than professional skills (technical) and communication skills, and specific skills within that set – including integrity, reliability, teamwork, willingness to learn, and entrepreneurship – rank highest among the 25 skills measured (Table 8). More than 56 percent of Argentine, Brazilian, and Chilean employers ranked socio-emotional skills as the most important skills set of “high paid” employees. Vietnam may be an exception, where “job-related” skills are most demanded of college graduates, but this category likely includes all four of our skill sets.

Technical skills are more important for employers of low-skilled workers, but employers also highly value socio-emotional skills among less skilled workers. Peruvians who employ people with less than completed secondary education list teamwork (23 percent) and being capable (a mix of all three skills sets) as the top ranked of seven skills explored (World Bank 2011). Vietnamese employers name job-related skills, punctuality (24 percent), and practical skills as most important for technical school graduates (diGropello 2010). In Latin America, socio-emotional skills emerge as the most important skill set for those hiring low-wage workers (Bassi et al. 2012).

Within country comparisons reveal that employers prioritize similar skills among more and less skilled workers. Vietnamese employers give top ranking to the same skills sets for more and less skilled workers, though they demand more of those skills from their less-skilled workers (diGropello 2010). In Peru, the top ranked skills do not differ by skill level of the worker, though there are subtle differences by education levels: employers demand more teamwork of those without a secondary education and more inter-personal skills of more educated workers (World Bank 2011). Cognitive skills top Latin American employer’s preference ranking for low- and high-wage workers, though they demand more socio-emotional skills of high-wage employees as compared to lower-wage employees. Basic cognitive skills and technical skills were ranked low for both education levels (Bassi et al. 2012).

> 52 The skills employers were asked to rank were: interpersonal skills, creativity, verbal fluency, capability, proactive, working under pressure, and teamwork.

25
# **_5) Business Environment_**

The business environment corresponds to the institutional, policy, and regulatory environment in which firms operate. While most previous studies relied on managerial opinions and perceptions about the business environment, our survey has the advantage of including objective measures that capture institutional and policy weaknesses affecting firms. Our measures cover different dimensions of the business environment. Bureaucracy and government efficiency in providing services are captured by the number of days needed to clear customs for imports and the percentage of weekly time spent by managers dealing with regulation. The reliability of the public infrastructure is captured by the number of power outages suffered and a dummy variable for firms that have a generator. Corruption is captured by a dummy variable for whether firms in a given industry pay bribes to government officials to “get things done” and the percentage of sales paid as bribes. Finally, potential crime is captured by protection payments made as a percentage of firm sales.<sup>30</sup> The aforementioned business environment measures cover the major obstacles to growth and operations faced by manufacturing firms in Bangladesh, as revealed by perceptions’ data.<sup>31</sup> Specifically, the major obstacles pointed out by firms are (a) corruption (65% of firms), (b) customs (63% of firms), (c) power from the public grid (44% of firms), (d) business licensing and operating permits (22% of firms), frequent changes in government regulation and regulatory policy (16% of firms), and other bureaucracy-related aspects (e.g., fire department or environmental permits). Although crime is not among the top obstacles, it is crucial for 13% of firms. We are confident to be covering the most crucial aspects of the business environment for firms in Bangladesh, however our TFP regressions could suffer from an omitted variables’ problem if other relevant obstacles are not accounted for because they were not included in the list of issues covered by the survey questionnaire.

> 30 In the survey questionnaire, (i) “get things done” was explained as bribes needed for firms to resolve issues related to customs, taxes, regulations, and services and (ii) protection payments are those to organized crime to prevent violence. We use firm values for the ratio of protection payments to sales in the regressions.

> 31 Firms in Bangladesh were asked to rank 28 business environment issues according to their degree of obstacle to firm operations and growth. The results from these rankings are available upon request.

17
exceptions are the short term interest rate for Brunei, trade for China and Indonesia, GDP growth for Lao PDR and Thailand, real effective exchange rate for Malaysia. This is a very desirable result as it confirms the suitability of a GVAR model for this region. Based on the eigenvalues<sup>4</sup> , the model is also found to be stable.

# **3.3 Time varying weights**

As mentioned in section 2, trade weights are calculated for each country in order to create the country specific foreign variables. These trade weights reflect the proportion of a country’s trade with other countries in the sample. Yearly bilateral trade flow data among each of the countries are used in order to create these weights. Use of time varying trade weights (instead of fixed trade weights) in this GVAR model means there are trade weights for each country for each year in the sample. This large trade weight matrix gives insights on how trade flow fluctuated between countries over the years. Figure(1) shows the trade weights of China for the 10 ASEAN countries over the sample period (1994-2016). Trade weights here represent the relative importance of China in terms of trade for these countries compared to other countries in the sample (i.e. rest of the ASEAN countries + Japan).

It is evident from the figure that for almost all the ASEAN countries, the importance of China as a trading partner has increased over the time. While the trade weights of China increased most sharply for Vietnam, the weakest increase is displayed by Brunei. As a trading partner, China’s importance is greatest for Vietnam, Cambodia and Myanmar at the end of the sample period. Myanmar shows the most fluctuation in terms trading relation with China. In 1994, China was their most important trading partner among all the ASEAN countries, but the weight decreased until 2000 and started to rise again after the creation for ACFTA in 2010. In short, the graphs in general show an upward trend of the weights of China as a trading partner for the ASEAN countries and it is likely that they will continue

> 4The results are available on request

11
that global poverty estimates should be held constant with the adoption of the new 2011 PPP data. As one example, Kakwani and Son (2014) take quite literally the question of whether new PPP data should affect our estimates of poverty and answer with a resounding no.

In their approach, they develop a set of “equivalent poverty lines” in 2011 PPP terms for each country, which corresponds to the poverty line in 2005 PPPs. One way to view this approach is to say that they take the poverty headcount for 2010 as estimated by extrapolations of the 2005 PPP index as truth. They then back out the value of the poverty line in 2011 PPP terms that maintains the same headcount for each country. They are implicitly taking the view that we learn nothing about the true poverty status of individuals from the 2011 PPP data – if someone is identified as being poor based on extrapolating the 2005 PPP data out to 2011, then they should be identified as poor based on the 2011 PPP data.

The appeal of their approach is that it essentially washes away any communication concerns – poverty headcounts are kept exactly the same. International organizations can present themselves as not manipulating the poverty headcount, while also superficially presenting themselves as adopting the 2011 PPP data. But, following this approach leads one to an interesting position – the poverty line continues to either be measured in terms of 2005 PPP dollars or if measured in 2011 PPP dollars, no longer reflects the same standard of living across countries.

Another proposal for adopting the 2011 PPPs has been the so-called “continuous headcount approach.” This approach sets the poverty line at whatever value is needed to maintain the global headcount ratio at 14.5 percent in 2011 when using the 2011 PPP conversion factors. Underlying this method, there is no interest in ensuring that the new global poverty line reflects the same standard of living in the world’s poorest countries in 2011 as in 2005. The 28
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We recognize the potential diversity of stakeholder views about the relative importance of species-, ecoregion- and risk-related factors in this context. To accommodate diverse concerns, we adopt a conservative strategy for indicator construction. First, we divide each of our 50 indicators by its maximum value and multiply by 100 to create an index in the range 0-100. This ensures comparability in measurement. Then, for each 500 m Hansen cell in our database, we select the maximum value among 50 indices as our risk indicator. This ensures significant representation for the concerns of stakeholders whose focus may range from protection of critically-endangered amphibians to conservation of small Laotian biomes.

Figure 7 displays the geographic distribution of the resulting indicator, aggregated to 0.1 degree grid cells for ease of interpretation.<sup>11</sup> We have overlaid a province map of Lao PDR as a geographic reference. Figure 7 reveals a dominant wedge-shaped “axis” that narrows from the extreme northwest (where the highest indicator values are concentrated in Louang Namtha and Bokeo) to east-central Lao PDR (with the highest indicator values in Bolikhamxai and Khammouan). Large areas with high indicator values are also evident in Phôngsali, Oudômxai and Louangphrabang.

Figure 7 also reveals a much smaller “axis” in southeastern Lao PDR, with the highest indicator values concentrated in the eastern parts of Xékong and Attapu. Pockets of high indicator value are also visible in the southern frontier areas of Champasak and Attapu. These may well reflect small Laotian portions of ranges for species and/or ecoregions that extend into Cambodia. Our methodology focuses on Laotian national considerations, so the small Laotian portions of these regions/ecoregions would be assigned high endemicity value.

> 11 Our aggregative indicator is the maximum indicator value within each 0.1-degree grid cell.

26
WPS3860

# **Incentives for Public Investment under Fiscal Rules**

By

## Jack M. Mintz and Michael Smart

## World Bank Policy Research Working Paper 3860, March 2006

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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

Jack Mintz is the Deloitte & Touche LLP Professor of Taxation, J. L. Rotman School of Management at the University of Toronto and President and CEO of the C. D. Howe Institute. Michael Smart is Associate Professor, Department of Economics at the University of Toronto. This paper was prepared as part of the World Bank's Latin American Regional Studies Program.
In contrast to the rest of the literature, the high frequency of the data allows us to focus on the short-run dynamics during and right after the disaster. The results show that local-level aggregate activity declined for some months but then recovered quickly and even overshot within the first year.

# **3.2 Natural Disasters and Household Finances**

Amid developing countries’ high vulnerability to natural disasters, an important concern is their impact on rural households. Many papers focus on Southeast Asia and South Asia, two particularly vulnerable regions in terms of natural disasters.<sup>10</sup> Bui et al. (2014) and Arouri, Nguyen, and Youssef (2015), for example, find that natural disasters reduced household expenditure and income in Vietnam in the 2000s. Lohmann and Lechtenfeld (2015) show that droughts deteriorated health conditions and increased the share of health expenditure in rural Vietnam between 2007 and 2013. Karim (2018) finds that recurrent flood risks negatively affect the income and non-food expenditure of agricultural households. In line with these findings, Kurosaki (2015) shows that floods significantly reduce the consumption of rural households in Pakistan. In contrast, Bandyopadhyay and Skoufias (2015) find that proneness to floods does not affect the consumption of rural households in Bangladesh, but rainfall variability in non-flood-prone subdistricts has a negative effect. This strand of the literature highlights access to microcredit, internal remittances, education, social allowances, and land ownership as potential mitigating factors.<sup>11</sup> Case studies of specific disasters contribute to a better understanding of their impact on household finances and consumption. Akter and Mallick (2013), for example, find that while poor households in Bangladesh suffered greater damage during Cyclone Aila than others due to low-quality housing and distant cyclone shelters, they experienced higher income

> 10They tend to use cross-sectional or longitudinal data from primary surveys or nationally representative household surveys such as the Vietnam Household Living Standards Survey, the Household Income and Expenditure Survey of Bangladesh, or the Indonesian Family Life Survey.

> 11See, for example, Arouri, Nguyen, and Youssef (2015), Karim (2018), and Kurosaki (2015).

8
. 7 Moreover, e.he quantities KRR-ERR and EPR-CEPR should normally have the seme sign, i.e., be simultaneously positive (or negative).

14. Factor y in India is significantly above 1 for several reasons. Firstl, collected tariff duties on imported capital goods for projects are high. They average some 70% (Table 3). Only electronics industry machinery enjoys a lower tariff duty o ;os. Other industries pay an average tariff duty of 80% on their imported equipment. Secondl locally procured capiLal goods, generally representing the major share of equipment, carry purchase prices which average 40% aoove international prices. _1 Thirdly, other goods and materials usnd in investment projects, cement in particular, are charged substantial excise and other taxes which increase further the financial costs of invsstment above international equivaler.ts.

15. Two estimates of factor y were computed for the sample projects. The first estimate yl was computed as the ratio of the financial investment cost to the same adjusted for tariff duties on imported equipment and for the high prices of local equipment (the first two factors described in para. 14). This factor yl captures only the impact of the high prices of capital goods. The second estimate y2 is the ratio between the financial investment cost and the 'economic' investment cost (used for ERR computation), and captures not only the effect of capital goods prices but also the price distortions of other goods and factors (e.g., cement, interest during construction, ...). In a sense, the factor y2 represents an upper limit of the cumulative effects of all forms of investment taxation in India.

16. The sample data confirm that investment costs in India are significantly higher than international costs, and that the taxation of. capital goods is the major factor accounting for that, as illustrated below: Tabte 3: Effect of Investfint Costs on Effective Protection /I

|Export<br>Ca"soorv|Tariff (t) on<br>Imorted Machinery|Investwent<br>xi|Cost Factor<br>_x2_|Cqpersatory<br>EPI (CEPR) /b<br>(in S)|Actual<br>EF<br>(in X)|<br>Projects (X)in<br>Excess/Shortfatl|
|---|---|---|---|---|---|---|
|None|74.2|1.41|1.55|30-41|60|65/35|
|PotentiaL|76.1|1.52|1.60|45-52|-27|0/100|
|marginaL|56.8|1.36|1.52|23-33|54|33/67|
|Significant|om 0||LR|2740|**-jt**|23/7|
|OveraLl|70.5|1.40|1.55|30-42|30|**_42/58_**|
|of which.|||||||
|ELectronics|3$.9|1.27|1.40|15-22|7||
|Others<br>(A.)|78.7|1.43|1.58|34-46|34||

_/a_ Actual EPRs In this tabLe **are** weighted by VAi /b The Lower (resp. higher) value of **the** indicated range correspords to y1 (resp. y2). Source: Arnea IV 17. These results call for a number of observations and remarks. First, exporting projects (whether marginal or significant exporters) pay lower duties on their imported equipment than non-exporters (about 60% versus 75% on L' Derived from the conversion factors used by the DFIs for local capital goods in their ERR computations. This 1.4 ratio is reasonably close to our own estimates.