rafmacalaba/datause-encoder-data huggingface dataset:
Biomass scarcity may be less problematic in slightly richer countries or in countries where there are substitutes available, but in Malawi, the almost singular dependence on forests for energy implies that scarcity can have an impact on household welfare, particularly for poor households. Households faced with scarcity may reduce their consumption of energy, walk longer distances to collect fuelwood or use lower quality fuelwood (Brouwer _et al._ 1997). Lower biomass also implies that there are fewer wild foods and products to be had and there may be indirect effects from changes in eco-system functions. We hypothesize that all of these factors can affect household well-being either by affecting household consumption directly, affecting income (from any limited sales of fuelwood and labor re-allocation) or through its impact on household health or leisure.

To test the link between household welfare and biomass, we estimate a reduced form equation with log of real annual per capita consumption expenditure as the measure of household welfare on the left hand side. Consumption expenditure covers a variety of food and non-food expenditures, including estimated expenditures on some products obtained from forests such as charcoal, poles, grasses, wild foods and meat. It does not include estimated expenditures on collected fuelwood. Thus, any effect of biomass on consumption is an overall ‘net’ effect that captures behavioral changes undertaken as a result of scarcity in fuelwood and other forest products.

We build our model of consumption expenditure based on an understanding of economic theory. Thus, our exogenous variables are those that are identified by economic theory and we test to see if there is a causal relationship between these variables and poverty. Our model relies strongly on a previous effort to estimate the determinants of poverty in Malawi using 1997-98 household data (National Economic Council, National Statistical Office, Government of Malawi and International Food Policy Research Institute 2001). This model is also documented in Mukherjee and Benson (2003). This model is also similar to the model used in Chapter 2 of this volume to assess determinants of poverty. Our main extension is in the use of biomass as a determinant of welfare.

In our reduced form model, the variable of interest on the right hand side is amount of biomass per hectare in the community. We postulate that household welfare and biomass stock have a nonlinear relationship. As biomass increases, welfare does not linearly increase but increases at a 11
# **Appendix B: The Multivariate Cointegration Analysis of Johansen**

The Johansen procedure allows us to test for cointegration in a multivariate system./ Starting from an unrestricted vector autoregressive model (VAR), the hypothesis of cointegration is formulated as a hypothesis of reduced rank of the long run impact matrix Π (Johansen, 1988, Johansen and Juselius, 1990). The VAR is generated by the vector zt, which defines the potential endogenous variables of the model. Taking first differences of the variables, the VAR can be transformed into an error correction model ∆ z t = Γ ∆ 1 z t − 1 + ... +Γ k − 1 ∆ z t − k = 1 + Π z t − k + ψ Dt + ε t , ε t ~ IN(0, Σ ) where the estimates of Γ _i_ = − ( _I_ − _A_ 1 − ... − _Ai_ ), ( _i_ = 1,..., _k_ − )1 describe the short run dynamics to changes in zt and Π = − ( _I_ − _A_ 1 −− ... _Ai_ ) captures the long run adjustments and _D_ contains deterministic terms.

Cointegration occurs in the case of reduced rank of Π . Only if the rank is reduced (r<n) is it possible to factorize Π into Π ( = αβ ') where _α_ denotes the adjustment coefficients and _β_ the cointegration vectors. The cointegration vectors _β_ have the property that _β_ ' zt is stationary even though _zt_ itself is non-stationary.

If the rank is reduced it is possible to interpret the VAR in first differences as a vector error correction model and to obtain estimates of _α_ and _β_ via the reduced rank regression. Since the rank of Π is equal to the number of independent cointegration vectors and the rank of Π is also equal to the number of non-zero eigenvalues, the test of cointegration thus amounts to a test for the number of non-zero eigenvalues. The trace statistics, λ trace, is a non-standard distributed likelihood-ratio test, which is commonly used to determine the number of cointegration vectors, (Johansen, 1988). The trace statistic tests the null hypothesis that there are at most _r_ cointegration vectors: _H0:_<sup>_λ_</sup> _i_<sup>=0, for i = r+1,..n</sup> where only the first _r_ eigenvalues, _λ_ , are non-zero against the unrestricted hypothesis that r = n.<sup>16</sup>

> 16 The null hypothesis of at most _r_ cointegration vectors implies that there are _n-r_ unit roots and, theoretically, _n-r_ zero eigenvalues. This is because the hypothesis of cointegration is formulated as the ' ' reduced rank of Π = _αβ_ and the full rank of _α_ ⊥<sup>Γ</sup><sup>_β_</sup> ⊥ , where _α_ and _β_ are _n_ × _r_ matrices and _α_ ⊥ and

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ethnicity might be a barrier to entry in the public procurement market. In our sample, about 40 percent of bidders come from the districts where the dominant ethnic group is the same. The ethnicity barriers look high in certain districts (Figure 7).<sup>4</sup>

**Figure 7. Share of bidders that come from districts with the same dominant ethnicity as project areas (percent)**

<!-- Start of picture text --> 80<br>7060 68 63 61 60 59<br>50<br>504030 38 43 41 38 33 31 29 29 29 27 24<br>20<br>20 17<br>10 5<br>0<br>All Dhanusa Kaski Makwanpur Syangja Rupandehi Kailali Dhading Bardiya Kapilvastu Nawalparasi Nuwakot Siraha Udayapur Sarlahi Banke Mahottari Salyan Palpa Rautahat<br><!-- End of picture text --> Weather may be another important determinant of road procurement and contract performance in Napal. Some of the selected districts have much precipitation, particularly in summer (Figure 8). In our sample districts, the average monthly precipitation over the past 10 years was about 200 mm per a month. The average monthly precipitation was relatively low in Banke and Kaski. But the precipitation exceeded 300 mm per month in Banke and Nuwakot (Figure 9).<sup>5</sup> Precipitation has two implications in our context. First, heavy rains and storms will interrupt civil works, causing project delays and cost overruns. Second, precipitation can have an implication to the quality of roads. Particularly, unpaved or gravel roads are vulnerable to precipitation. The quality roads would deteriorate with precipitation if roads are not well maintained.

> 4 For instance, 47 percent of district population in Makwanpur is Tamang people. 30 out of 49 bidders who applied for road projects in the district were local―registered in Makwanpur―or come from other districts where the dominant ethnic group is also Tamang, such as Dhading and Nuwakot.

> 5 Our monthly precipitation data are based on 20 weather stations that are close to our surveyed roads. Thus, the precipitation variables that we use in this paper are district-specific but time-variant and thus contract-specific.
DR-CAFTA, we compute the tariff cuts by product for the U.S.<sup>22</sup> and the other CAFTA members. Appendix 8 reports the results. The median tariff cut in the case of the U.S. is 3.1% and 1% in the case of the CAFTA members. In the U.S. sectors that experienced high tariff cuts include agriculture, prepared foodstuff, footwear, textiles and clothing. In the case of the other CAFTA members, important tariff cuts took place also in agriculture, textiles and clothing, prepared foodstuff, as well as in machinery and appliances.

# **5.2. Did tariff reductions affect the extensive margin?**

In order to explore the relationship between the extensive margin and the tariff reduction, we look at the extensive margin according to the size of the tariff cut. If the tariffs had an effect on the exporters’ behavior, one expects the sectors with the larger tariff cuts to exhibit a relatively bigger increase in exports.

We first classify sectors (HS2-digits) according to whether they had a low or a high tariff cut. A sector is a high cut sector if its median tariff cut is larger than the overall median tariff (i.e. 3.1% in the case of the U.S. and 1% in the case of the other CAFTA members). We use tariffs at sector level (HS2-digits) rather than at product level, in order to have the largest product concordance between DR exports and its partners’ product classifications. Exports by tariff cuts are reported in figures 6a and 6b.

Figures 6a and 6b: The extensive margin by tariff cut <!-- Start of picture text --> Extensive margin by tariff cut (US market) Extensive margin by tariff cut (CAFTA market)<br>highlow highlow highlow highlow highlow highlow highlow highlow highlow highlow highlow highlow highlow highlow<br>2003 2004 2005 2006 2007 2008 2009 2003 2004 2005 2006 2007 2008 2009<br>existing firm, existing product, new market existing firm, existing product, new market<br>existing firm, new product, existing market existing firm, new product, existing market<br>existing firm, new product, new market existing firm, new product, new market<br>new firm new firm<br>200 25<br>150 20<br>15<br>100<br>10<br>in US$ millions in US$ millions<br>50 5<br>0 0<br><!-- End of picture text --> In the case of the exports to the Central American countries, the extensive margin behaves as expected. The growth in the extensive margin between 2007 and 2009 is driven by the export growth in sectors that experience high tariff cuts. This suggests that the DR-CAFTA did promote new exports to the CAFTA members, and incumbent exporters seem to have benefited the most. These sectors include cotton, tobacco and apparel. However, the effect seems to vanish with time, as the extensive margin in both low and high sectors exhibit a progressive decline from 2008 onwards. This could reflect the effects of the 2008 economic crisis rather than a decline in the exporters’ enthusiasm for the agreement.

22 U.S. tariff figures excludes products whose tariff scheme depended on the good characteristics (weight, length, etc.), as well as some tobacco products whose tariffs were equal to 350%. In total, we exclude 963 products.

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**Table 9: Do Differences in Sector of Work explain Gender Differences?**

Dependent Variable: Real profits adjusted for own hours worked

||All Firms|All Firms|Only industries with<br>participation from<br>both genders above<br>25%|Only industries with<br>participation below<br>25% from one gender|
|---|---|---|---|---|
|**Panel A: Treatment effect**|||||
|Amount|6.91**|5.91*|8.28**|7.64*|
||(2.72)|(3.58)|(4.16)|(3.91)|
|Amount*Female|-7.51*|-9.48**|-7.83|-13.86*|
||(3.92)|(3.77)|(4.96)|(7.11)|
|Obs.|3722|3697|1994|1703|
|Firms|365|365|196|169|
|**Panel B: Returns to Capital**|||||
|Capital Stock|10.06**|9.63|13.67*|9.54|
||(4.33)|(6.12)|(7.54)|(5.81)|
|Capital Stock*Female|-10.71*|-13.34**|-12.8|-18.01*|
||(5.87)|(6.62)|(8.59)|(8.71)|
|Obs.|3541|3518|1944|1574|
|Firms|364|364|196|168|
|Industry*Wave effects|no|yes|no|no|
|Industry*Amount controls|no|yes|no|no|

Robust Standard errors in parentheses, *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Column 2 include 8 industry dummies and 80 industry*wave dummies. The samples in Columns (3) and (4) are restricted using the distribution of male and female ownership in 20 industries with more than 4 enterprises in the sample.

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# **1.  Introduction**

In this paper, we use information from recent Demographic and Health Surveys (DHS) for India and Bangladesh to identify regional differences in measures of maternal and child health that are not explained by commonly cited socioeconomic determinants. Our analysis assesses the health status of mothers and children using the incidence of anemia and wasting, respectively. These measures have two advantages for our purposes: They are widely cited as critical health indicators, and they are the best-recorded health measures in the DHS surveys.

Wasting, or a substandard weight/height ratio,<sup>1</sup> indicates nutritional deficiencies that significantly increase the risk of child morbidity and mortality. These risks are well-documented in the literature (Ghosh-Jerath et al. 2017; Tickell et al. 2017), and are attributable to diverse factors, including weight loss from infectious disease; loss of muscle and fat tissue from inadequate diets (Lenters et al. 2013); associated chronic infections (Bhutta et al. 2017; Scrimshaw 2016); and other forms of developmental impairment (Menon et al. 2012; Kara and Dolan, 2014). Wasting afflicts more than 50 million children worldwide (Harding, et al. 2018, citing UNICEF 2017) and causes about 875,000 preventable deaths annually (13% of the total) for children less than five years of age (Black et al. 2013). More than half of the children with wasting are in South Asia (Harding et al. 2018), where IFPRI (2016) identifies wasting as a critical public health problem because its incidence surpasses the 15% threshold. In related work, Galasso and Wagstaff (2018) find that stunting (substandard height/age ratio), another nutritional deficiency measure, has a significant impact on adult wages through its effects on schooling, cognitive skills and height. For developing countries, they estimate that the associated per capita income penalty from stunting is around 7 percent.

> 1 Wasting is technically defined as a weight-for-height Z-score (WHZ) less than -2, based on WHO’s Child Growth Standards (WHO, 2006).

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# **6. Results**

We applied the analytical framework in the case of two climatic phenomena, namely basin variability of precipitation and basin variability of runoff. Descriptive statistics of the variables discussed and used in the paper are presented in Appendix 2.

We report separately the results for the basin precipitation variability and for the basin runoff variability. One important caveat we should address upfront is that our analysis at this stage doesn’t account for water regulation in the rivers in our sample. While the IPCC (2001:Section 4.3.6.1) suggests that “...Runoff tends to increase where precipitation has increased and decrease where it has fallen over the past few years,” it is important to note that dams may skew the runoff pattern. However, we found an empirically positive correlation (R<sup>2</sup> =0.280) between mean basin precipitation and mean basin runoff in the 215 basins we could compare; and a higher positive correlation between the coefficient of variation of basin precipitation and runoff (R<sup>2</sup> =0.729). Another interesting finding is the high correlation (R<sup>2</sup> =0.927) between the mean country-basin precipitation values ( _MeanPb1_ and _MeanPb2_ ). The country-basin precipitation variation values ( _CVPb1 and CVPb2_ ) were also found to be highly correlated (R<sup>2</sup> =0.860) among the two riparians. Therefore, we will use only the basin level variable _CVPb_ . This high correlation suggests that even in very large river basins in our sample, the climate characteristics are similar across the basin territories of the two riparians. Another explanation is that the model data was created from limited meteorological / runoff observations in certain geographic areas (e.g. Africa) and does not have high variance.

# <u>Basin precipitation and runoff estimates</u>

We first present results of an analysis that estimated whether or not the basin precipitation variability itself and basin runoff itself can explain cooperation. Table 1 contains 3 equations. Equation (1) includes the basin precipitation variation while equations (2) and (3) include the basin runoff variation. The results indicate that basin precipitation variability ( _CVPb_ ) and basin runoff variability ( _CVRb_ ) explain the variance in the level of treaty cooperation across the analyzed basins, with a fitness of fit tests that are significant at a 5 percent level and better. The results confirm as well the inverted U-shape of the relationship between water variability and treaty cooperation. The finding are encouraging, but, taking the logit Pseudo R<sup>2</sup> of 0.044 as an indication, suggests that precipitation and runoff variability alone cannot fully explain 21
Deterring Kickbacks and Encouraging Entry in Public Procurement Markets: Evidence from Firm Surveys in 88 Developing Countries Stephen Knack*, Nataliya Biletska and Kanishka Kacker The World Bank

# JEL: O10, H50

Keywords: procurement, governance, corruption, bribery, kickbacks

*Corresponding author, The World Bank, 1818 H St. NW, Washington DC 20433 USA (sknack@worldbank.org).  We are grateful to Luis Serven and Joel Turkewitz for helpful comments, but all errors remain the responsibility of the authors.  The conclusions of this paper are not intended to represent the views of the World Bank, its Executive Directors, or the countries they represent.
scholarships are equal. The models relying on unannounced school visits, where the sample sizes are large, are estimated quite precisely. The average per-dollar percentage point increase in attendance that results from the first $45 ranges from 0.40 to 0.62; in contrast, the average per-dollar percentage point increase in attendance that results from the additional $15 ranges from –0.07 to 0.31. Broadly speaking, the results in Table 2 are consistent with very sharply decreasing marginal returns in school attendance to transfer size in Cambodia.

We extend our basic results in two ways. First, we present separate estimates for each school visit. These results are in Table 3, which shows that we find a similar pattern of effects for each of the four school visits. However, there is some evidence that the impact of the $45 scholarship diminishes over time: The coefficient on the CSP treatment from the June 2007 visit, when applicants would have been enrolled in 8<sup>th</sup> grade if they continued in school and did not repeat grades, suggests a program effect of approximately 20 percentage points—as compared to a program effect of approximately 26 percentage points for the three earlier visits, all of which took place when applicants would have been enrolled in 7<sup>th</sup> grade, if they continued in school. We note that the estimated program effect from the basis of the June 2007 school visit is close in magnitude to that estimated using the household survey, 18 percentage points; the household survey also corresponds to enrollment in 8<sup>th</sup> grade for children who do not repeat grades. Most importantly for the discussion in this paper, and as with the specifications that pool data from all of the school visits, we find no evidence that the additional $15 significantly affected school attendance in any of the results based on a single school visit.

The second way in which we extend our results is by considering the question of the possible heterogeneity of program effects by the underlying socio-economic status of recipients. This is important because, by construction, the average applicant who was offered a $60 scholarship had lower socioeconomic status than the average applicant who was offered a $45 scholarship. Therefore, if scholarships generally have larger effects on the school attendance of better-off children, our results could confound the effect of the size of the scholarship ($60 versus $45) with the underlying socio-economic status (very 11
# **7. Conclusions**

We have combined a natural experiment on emigration with survey data on would-be emigrant’s expectations about employment and incomes in the migration destination. Contrary to anecdotal stories which raise fears of over-optimistic expectations, we find striking evidence that emigrants tend to underestimate the employment likelihood and the income they can earn abroad. The degree of underestimation appears to be in due to potential migrants placing excess weight on negative employment experiences of a few migrants, to extended family lowering remittance pressure, and to changes in the male wage premium between Tonga and New Zealand. They suggest that more accurate information about earnings opportunities abroad may actually increase migration pressure.

More broadly, our work demonstrates the feasibility of asking probabilistic expectations questions to individuals in developing countries, even in cases in which the event for which expectations are elicited is not one for which the individual has direct prior experience. Since individual decisions are driven by their subjective expectations, the large gap we find between realized outcomes for immigrants and expected outcomes for comparable would-be emigrants suggests that inference based on realized outcomes alone may be very misleading.

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# **3.  The Tunisian Pesticide Stockpile Database**

Like many low- and middle-income countries, Tunisia has hundreds of sites of obsolete pesticide stockpiles with potentially-serious health hazards to nearby populations. Product bans, outdated products, donations or purchases in access of requirement, poor stock management and inadequate storage are primary reasons behind this accumulation. Under the ongoing Africa Stockpiles Program, Project1 (ASP-P1), The Ministry of Environment and Sustainable Development’s waste management department, (Agence Nationale de Gestion des Déchets – ANGed) of the Government of Tunisia, has currently finalized and verified a detailed inventory of all the publicly-held pesticide stockpiles in the country, determining the geo-location of the storage sites, whether the storage sites are protected or not, identity of the contaminant, its quantity, and general condition of the stockpiles.

Overall, Tunisia has approximately 1,915 metric tons of obsolete pesticide _formulations_ in 197 storage sites. A total of 692 metric tons of _active ingredients_ were identifiable from the database, while 415 tons were not identifiable (or 22% of the total).<sup>6</sup> Preliminary investigation revealed only 11% of the stockpiles were contained in “undamaged” packages; 47% of the packages were either broken or showed surface damage, 8% indicated leakage, 34% were considered to be contaminating the soil and equipment.

At the storage site (stockpile) level, the database included pesticide volume, corresponding active ingredient and pesticide volume by damage class for containers (leaking, broken, contaminated soil, etc.). The Chemical Abstract Number (CAS) of each active ingredient was used to assign hazard indicators: the World Health Organisation toxicity class. The analysis revealed the presence of 13 metric tons of WHO-Ia (extremely hazardous), 2 metric tons of WHO-Ib (highly hazardous), 196 metric tons of

> 6 The chemical in the pesticide formulation that actually kills the pest(s) is termed the _active ingredient_ . The added chemical(s), those which make the product easy and safe to formulate or apply, are termed the inert ingredients or carriers. Each formulation has a specific percentage concentration of active ingredient usually measured as a percentage of the total formulation weight, in grams per kilogram or liter. In this study we are primarily interested in the toxicological properties of the active ingredient.

8
6

# **Figure 1**

<!-- Start of picture text --> Fiscal Policy and Output Volatility<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>GDPvol= 2.48 + .21 FPvol<br> (8.13)<br>R2=.43 D.W.=1.98<br>25<br>20<br>15<br>IDN<br>10<br>CHN<br>MYS<br>5 THA<br>IRE HKG CHL<br>0<br>0 2 4 6 8 10 12 14<br>GDP volatility<br>Fiscal Policy Volatility<br><!-- End of picture text --> Source: Author’s calculations