rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8757

# **Abstract**

This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels through spatial weights matrices. the paper explores a kernel method to estimate the network topology based on similarities in the data. It discusses the model and estimation, focusing on a penalized Maximum Likelihood criterion. The empirical performance of the estimator is explored in a simulation study. The model is used to study a spatial time series of pollution and household expenditure data in Indonesia. The analysis finds that the new model improves in terms of implied density, and better neutralizes residual correlations than the VARMA, using fewer parameters. The results suggest that growth in household expenditures precedes pollution reduction, particularly after the expenditures of poorer households increase; that increasing pollution is followed by reduced growth in expenditures, particularly reducing the growth of poorer households; and that there are significant spillovers from bottom-up growth in expenditures. The paper does not find evidence for topdown growth spillovers. Feedback between the identified mechanisms may contribute to pollution-poverty traps and the results imply that pollution damages are economically significant.

This paper is a product of the Environment and Natural Resources 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/research. The authors may be contacted at bandree@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
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- Indicators of family care for development for use in multicountry surveys. _<mark>Journal of Health, Population and Nutrition,</mark> 30_ (4), 472–486.

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||||||regulation worsens<br>incentives|
|---|---|---|---|---|---|
|Cullman and<br>von<br>Hirschhause<br>n (2008a)|parametric/<br>non-<br>parametric|Poland|32 distribution<br>companies<br>between 1997 to<br>2002|DEA/SFA|technical<br>efficiency<br>improved with<br>reforms but<br>allocative<br>efficiency<br>deteriorated|
|Cullman and<br>von<br>Hirschhause<br>n (2008b)|non-<br>parametric|Poland,<br>Czech<br>Republic,<br>Slovakia,<br>Hungary|47 Eastern<br>European regional<br>companies, 37<br>German<br>companies|DEA/Free<br>Disposal Hall|Czech Republic<br>and Slovakia<br>feature the highest<br>efficiency,<br>privatisation had a<br>positive effect in<br>all countries|
|Lam and<br>Shiu (2004)|non-<br>parametric|China|panel of 30<br>municipal<br>autonomous<br>regions and<br>provincial thermal<br>generation plants<br>1995-1996|DEA with 2<sup>nd</sup><br>stage<br>regressions|average efficiency<br>increased at 2%|
|Bagdadioglu<br>, Price and<br>Weyman<br>Jones (1996)|non-<br>parametric|Turkey|cross section of<br>70 distribution<br>operators in 1991|DEA|Private operators<br>are more efficient<br>than public<br>operators|
|Celen (2013)|non-<br>parametric|Turkey|21 companies for<br>the period 2002-<br>2009|DEA with 2<sup>nd</sup><br>stage<br>regression/Tob<br>it|private ownership<br>positively affect<br>efficiencies|
|Jain, Thakur<br>and<br>Shandilya<br>(2010)|parametric/<br>non-<br>parametric|India|30 state –owned<br>utilities for the<br>year 2007-2008|DEA/SFA|unbundling drives<br>efficiency in<br>electricity<br>generation|
|Wattana and<br>Sharma<br>(2011)|non-<br>parametric|Thailand|Thai electric<br>industry, time<br>series data from<br>1980-2006|DEA|industry reforms<br>not significant in<br>driving efficiency|
|Bautista,<br>Agnes and<br>Valderrama<br>(2011)|non-<br>parametric|Philippines|120 electric<br>cooperatives,<br>2001 to 2006|DEA|reforms did not<br>drive productivity<br>in the sector|
|Nakano and<br>Managi<br>(2008)|parametric/<br>non-<br>parametric|Japan|10 companies,<br>1965-2003|DEA/<br>generalised<br>method of<br>moments|regulatory reforms<br>have contributed<br>to productivity<br>growth in the<br>steam power-<br>generation|

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compliance with FVA’s technical standards is extremely small, less than 30 over the period 2013-2015. Also, between 2013 and 2014 the non-compliance rate increased while the sampling rate decreased. This is rough evidence suggesting that the FVA was implementing a better targeting strategy, although firmer conclusions require conditional statements. One issue is that the aflatoxin outbreak in 2013 could have raised sampling rates and measured rates of non-compliance at the same time.

**Table 3. Summary statistics on numbers of shipments, sampling and non-compliance rates** **<u>Panel A. All import shipments</u>**

||Number of<br>import<br>shipments<br>sampled|Total number<br>of import<br>shipments|Number of<br>sampled<br>import<br>shipments<br>non-<br>compliant|Sampling<br>rate|Non-<br>compliance<br>rate|
|---|---|---|---|---|---|
|2013-1|835|8167|12|10.30%|1.40%|
|2013-2|446|8479|1|5.30%|0.20%|
|_Total 2013_|_1281_|_16646_|_13_|_8.00%_|_1.00%_|
|2014-1|451|7932|2|5.70%|0.40%|
|2014-2|780|8643|21|9.10%|2.70%|
|_Total 2014_|_1231_|_16575_|_23_|_7.00%_|_2.00%_|
|2015-1|4|8844|0|0.00%|0.00%|
|2015-2|59|9615|0|0.60%|0.00%|
|_Total 2015_|_63_|_18459_|_0_|_0.00%_|_0.00%_|

**Panel B. Import shipments of food of animal origin (requiring license N853)**

||Number of<br>import<br>shipments<br>sampled|Total number<br>of import<br>shipments|Number of<br>sampled<br>import<br>shipments<br>non-<br>compliant|Sampling<br>rate|Non-<br>compliance<br>rate|
|---|---|---|---|---|---|
|2013-1|753|7678|2|9.80%|0.30%|
|2013-2|418|8011|0|5.20%|0.00%|
|2014-1|422|7510|2|5.60%|0.50%|
|2014-2|744|8136|20|9.10%|2.70%|
|2015-1|4|8320|0|0.00%|0.00%|
|2015-2|56|9050|0|0.60%|0.00%|
|_Total_|_2397_|_48705_|_24_|_4.90%_|_1.00%_|

**Panel C. Import shipments of animal feeds (requiring license I047)**

||Number of<br>import<br>shipments<br>sampled|Total number<br>of import<br>shipments|Number of<br>sampled<br>import<br>shipments<br>non-<br>compliant|Sampling<br>rate|Non-<br>compliance<br>rate|
|---|---|---|---|---|---|
|2013-1|82|489|10|16.80%|12.20%|
|2013-2|28|468|1|6.00%|3.60%|
|2014-1|29|422|0|6.90%|0.00%|
|2014-2|36|507|1|7.10%|2.80%|
|2015-1|0|524|0|0.00%||
|2015-2|3|565|0|0.50%|0.00%|
|_Total_|_178_|_2975_|_12_|_6.00%_|_6.70%_|

Notes: in all panels the table shows information calculated based on the FVA lab sample data set (1<sup>st</sup> , 3<sup>rd</sup> , 4<sup>th</sup> , and 5<sup>th</sup> columns) and the EXIM data set (2<sup>nd</sup> and 4<sup>th</sup> columns). The data sets are combined based on the broad product group.

Table 3 also shows that in the second semester of 2014 the sampling rate increased and the noncompliance rate increased, reaching its maximum value in the sample period. Imports of food of animal origin represent the largest share, covering approximately 93% of the total number of shipments and of the total number of shipments sampled of the combination of food of animal origin and animal feeds. Imports 28
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Table 4: COINTEGRATION UNIT ROOT TEST RESULTS /a

|Critical Level 90Z<br>Critical Level 95%|CRDW<br>0.32<br>0.39|DF<br>3.03<br>3.37|ADF<br>2.84<br>3.17|Lags_/b_|2<br>R<br>/c|
|---|---|---|---|---|---|
|Meranti||||||
|Sapelli|0.85|-2.95|-2.87|2|0.93|
|Niagon<br>|1.38<br>|-3.96<br>|-3.31|2|0.94|
|Samba<br>|1.15<br>|-3.47<br>|-3.60<br>|2|0.93|
|Teak<br>|1.46|-3.33|-2.80|4|0.92|
|Sawnwood|1.15|-3.33|-4.15|2|0.92|
|Sapelli||||||
|Niagon<br>|1.19|-3.47|-4.76|2|0.98|
|Samba<br>|0.55<br>|-2.15|-2.88|2|0.92|
|Teak|0.80|-2.18|-2.67|4|0.80|
|Sawnwood|0.89|-2.70|-3.63|2|0.89|
|Niagon||||||
|Samba<br>|0.58<br>|-2.02|-2.41|2|0.93|
|Teak<br>|0.91|-2.37|-2.83|5|0.82|
|Sawnwood|1.09|-2.95|-4.31|2|0.89|
|Samba||||||
|Teak|0.86|-2.43|-2.32|4|0.83|
|Sawnwood|0.80|-2.58|-3.83|2|0.83|
|Teak||||||
|Sawnwood|1.06|-2.66|-2.80|5|0.93|

/a The cointegration regression is: Yt = a + KYt + Zt /b "Lags" refers to the number of lags of the ADF test.

/c 2 refers to the adjusted R2 of the cointegration regression.
Rubavu). The data collection was undertaken in the context of the World Bank’s Great Lakes Trade Facilitation and Integration Project and carried out by IPSOS, the market research firm. The field surveys were administered by trained enumerators as face-to-face, structured interviews of small-scale traders who were intercepted at the different border crossing locations.<sup>7</sup> Traders were asked to report on their personal characteristics (e.g., trading experience, association membership, business status), their typical product portfolio, transport means, and traveling distance, and their participation in and assessment of group trade. A total of 664 questionnaires were completed.

SSCBT at the Eastern DRC borders is largely unidirectional, with the vast majority of interviewed traders reporting that they mainly or exclusively ship merchandise into DRC. There were only 22 traders in the sample (3 percent) who specialize on exports from DRC to Burundi or Rwanda. A further 9 percent trade at least sometimes in both directions. Given that the group of DRC exporters is very small, and that the magnitude and direction in which different products are traded by the bi-directional traders is unobserved in the dataset, the empirical analysis was carried out based on the 581 traders that exclusively ship products into DRC. Among these 581 DRC importers, 142 individuals reported that they had experience with group trade.

3.1 Potential determinants of group trade participation The first question concerns the determinants that make some traders use group trade while others continue to trade individually. This question is important from a policy perspective as some observers see group trade as a promising strategy to formalize SSCBT, reap benefits

> 7 We use a broad definition of “trader” that also comprises “transporters”, who do not own the products they carry across the border (9 percent of the interviewed individuals). This choice was motivated by some of the “transporters” also reporting that they were participating in group trade arrangements.

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# **Figure 3  Poverty transition matrix, 1993 to 2000, IFLS panel data, unweighted**

<!-- Start of picture text --> NP 5.78 1.28 0.15 0.11<br>P 5.20 0.77 0.08 0.06<br> Ag Nag Ag Nag<br>Where they went...<br>[Percent (out of 5308 workers<br>in total) of the 713 individuals<br>who started as rural, poor<br>agriculture]<br>URBAN<br>RURAL 2.1->2.7 24.5->24.2<br> = +0.6 = -0.3<br>Non- 19.8->24.2 26.2->25.2<br>Poor = +4.4 = -1.0 1.3->1.5 5.6->6.3<br> = +0.2 = +0.7<br>Poor 13.4->10.9 7.1->5.1 Agric. Non-Ag<br> = -2.5 = -2.0<br> Agric. Non-Ag<br><!-- End of picture text --> Where they came from... [Percent (out of 5308 workers in total) of the 577 individuals who ended up as rural, poor agriculture]

|NP|3.05|1.15|0.06|0.08|
|---|---|---|---|---|
|P|5.20|1.30|0.04|0.00|
||Ag|Nag|Ag|Nag|

The central tables show the percent of the total IFLS workers who were tracked between 1993 and 2000 (5308 in number). In each cell, the first number is the percent in that cell in 1993, with the arrow pointing to the number in that cell in 2000. The bottom number is then the net difference between 1993 and 2000, and thus this net number corresponds to similar numbers in Figure 2. But these are only net flows, and Table 5 actually permits the identification of which workers made up that net flow and where they came from and went. That is what the upper and lower cells show for just one of the net flows, for the rural, agricultural, poor.

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Registered IMR in Egypt portrays a picture of wide fluctuations up to the mid-seventies, after which a smoother, more systematic trend is established. It should be emphasized that these rates are believed to suffer from under-registration, but the available studies do not point to recent improvements in registration completeness. Adjusted rates for Egypt 28 portray trends similar to those of the registration data from 1970 onwards (but of course at a higher level). A starting date for the series of 1975 seems reasonable in view of the smaller erratic variations that occur thereafter, while at the same time providing a time series of adequate length. The NCDDP started nationally in 1984, but it seems reasonable to allow one year for the program to get underway before expecting effects. Figure 1 also suggests a break in the series in 1985. Thus 1985 is taken as the beginning of post project period, and pA is set equal to zero up to 1984, and equal to one for 1985 onwards.

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(i.e., which can represent household tastes), occupation, and household assets or incomes. Occupation-related characteristics can generally include whether the household head works, the share of household members that work, the type of work that household members participate in, as well as context-specific variables such as the share of female household members that participate in the labor force, or some variables at the region level. Other community or regional variables can also be added since these can help control for different labor market conditions.

The following linear model is typically employed in empirical studies to project household consumption on household and other characteristics ( _x_ ) for household _i_ in survey _j_ , for _i_ = 1,..., N. Equation (1) is often extended to a more general model where the error term μij is now broken down into two components, one (υcj) is a cluster random effects and the other (εj) the idiosyncratic error term. Note that we suppress the subscript that indexes households to make the notation less cluttered.<sup>4</sup> For convenience, we also refer to the survey that we are interested in imputing poverty estimates for as the target survey, and the survey that we can estimate Equation (1) on as the base survey. The former survey is usually more recent (or offers more disaggregated information, as in the case of a census) and has no consumption data, while the latter is usually older and has consumption data.

We discuss the various poverty imputation methods starting from the next section.

# **II. Non-Monetary Poverty Estimates**

> 4 Conditional on household characteristics, the cluster random effects and the error terms are usually assumed 2 2 uncorrelated with each other and to follow a normal distribution such that υcj|xj~N(0, συj ) and εj|xj~N(0, σεj ). While the normal distribution assumption results in the standard linear random effects model that is more convenient for mathematical manipulations and computation, it is not necessary for this type of model. As can be seen later, we can remove this assumption and use the empirical distribution of the error terms instead, albeit at the cost of somewhat more computing time.

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protection, which is likely to be particularly costly to the poor, who spend a large share of their incomes on food.<sup>13</sup>

# **Fig. 3. Nominal Rates of Assistance to Developing Country Agriculture by Trade Status, %**

<!-- Start of picture text --> 50<br>30<br>10<br>-10 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>-30<br>-50<br>import-competing exportables total<br><!-- End of picture text --> _Source_ : Five year averages from Anderson (2009) and www.worldbank.org/agdistortions Producer Support Estimates (PSEs) produced by the Organization for Economic Cooperation and Development (OECD) provide a measure of the extent to which farmers are being assisted over time by governments through various payments and price support policies. The PSE expresses the monetary value of policy-induced transfers from consumers and taxpayers to producers and can be expressed as a percentage of gross farm receipts. Support to producers in high income countries was estimated to be US$227 billion in 2010, accounting for 18 percent of gross farm receipts – the lowest percentage on record (OECD, 2011). The changes in the levels of PSEs in 2010 were mostly driven by changes (often increases) in world prices and exchange rate movements. However, more than half of support to farmers continues to be delivered in ways that are highly distortive of trade and competition. The EU and China currently

> 13 As is the case with any reform or price shock there will be winners and losers: winners from higher food prices include producers in poor countries. Much also depends in the longer run on the supply response to higher prices.

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|Duflo (2000)|Does the increase in<br>household<br>resources for<br>grandmothers -<br>following the<br>expansion of the<br>South Africa<br>pension program -<br>improve health<br>outcomes for<br>female<br>grandchildren?|National<br>household survey<br>of South Africa<br>carried out by the<br>World Bank and<br>the South African<br>Labor and<br>Development<br>Research Unit at<br>the University of<br>Cape Town|Difference-in-<br>difference approach<br>that compares<br>differences in height<br>of children in<br>eligible households<br>and non-<br>households, and<br>differences in height<br>of children who<br>have been exposed<br>to the program for a<br>shorter period and<br>children who have<br>had full exposure to<br>the program.|Pensions received by<br>grandmothers had a<br>substantial positive<br>impact of female<br>grandchildren’s<br>health. The effect<br>was large enough to<br>bridge half the gap in<br>height-for-age<br>between South<br>African and<br>American girls.|
|---|---|---|---|---|
|Luke and<br>Munshi<br>(2011)|Does the increase in<br>income for women<br>– after the<br>expansion of<br>employment<br>opportunities in tea<br>estates in Tamil<br>Nadu, India – have<br>a positive impact on<br>female children in<br>the household?|Survey of ~ 4000<br>female workers<br>from 2002-2003|Different<br>instrumental<br>variables are used to<br>instrument variation<br>in female income,<br>and estimate its’<br>causal impact on<br>child outcomes|Increase in female<br>income leads to an<br>increase in female<br>children’s schooling,<br>particularly for<br>historically<br>disadvantaged<br>groups|
|Qian (2008)|Do increases in<br>female agricultural<br>income (relative to<br>male income) cause<br>an improvement in<br>the survival<br>prospects and<br>educational<br>attainment of girls?|1990 Population<br>census for China,<br>and 1993<br>household-level<br>survey data from<br>Ministry of<br>Agriculture|Difference-in-<br>difference strategy<br>that compares the<br>gender gap in<br>survival and<br>education in the<br>Maoist era and post-<br>Mao era, between<br>counties that plant<br>tea and counties that<br>do not plant tea. A<br>county’s production<br>of tea is a<br>differentiating<br>factor because post-<br>Mao reforms led to<br>an increase in tea<br>prices, and an<br>increase in wages<br>for tea cultivators|Increases in female<br>income increases the<br>survival prospects<br>and educational<br>attainment of girls|

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