rafmacalaba/datause-encoder-data huggingface dataset:
(Andree et al., 2017) are of help. While the stability conditions of SVARMA are straightforward in terms of high-level conditions, they involve many parameters and in practice it may be less straightforward to calculate them for testing purposes. We suggest that for practical purposes, it may be less cumbersome to simulate from the model under impulses, and see if the responses converge as the researcher should be interested in this either way.

# **B.1 Small sample distribution of the MLE**

To explore the adequacy of the S(V)ARMA in filtering out space-time-dynamics, we conduct a simulation study. We investigate both the MLE that arises by setting _λ_ = 0 and the PMLE with _λ_ = 1 _/√NT_ in situations where _T_ is small. We set the sample size and parameters to realistic values given the empirical application. Apart from the behavior of the ARMA components we are interested in the adequacy of the (P)MLE in dynamically estimating appropriate spatial structures. We explore whether the spatial structure improves the ARMA estimates, and explore robustness to over-fitting under the null of an ARMA process. The _DGP_ is where **x** is drawn uniquely in every experiment from a Student’s- _t_ distribution with _ν_ = 120, **_ε_** _t_ is drawn from a Student’s- _t_ distribution with _ν_ = 5. We explore both a spatial structure with few but strong links with _b_ = _._ 15 and a smoother network with _b_ = 2. The decision to focus on the heavy tail case is guided by our empirical results. We focus on _t − p_ = 12, which is identical to, and _N_ = (10 _,_ 25 _,_ 75 _,_ 125) which covers, our empirical cases.

As we can see in fig. 6 the PMLE performs reasonably well already in small samples, but even in the largest samples we do not obtain the limit result for the individual parameters. This is not surprising given the small _T_ . The initialization of the moving averages at zero cannot fade, causing a bias towards zero. In fact, by increasing _N_ and fixing _T_ , the impacts increase further as the ratio of distorted information _N/T_ grows. Nonetheless, the ARMA parameters are jointly well behaved, even when both _N_ and _T_ are small. We conclude that inference on the joint parameters is therefore valid, while statements that involve differentiation between short- and long-term effects should be made with caution in small _T_ panels. Figure 7 shows the results for the MLE. It is clear that the penalization improves the empirical distribution of the bandwidth parameter substantially.

Figure 8 and fig. 9 document results for _b_ = 2. Again the unpenalized distribution of _b_ is not well-behaved. The penalized distribution of _b_ improved substantially and is also better than the distribution under _b_ = _._ 15. In both results, the ARMA parameters remain jointly well-behaved confirming that the model is useful for inference about the time dynamics.

Figure 10 shows results for ARMA estimation on the identical _DGP_ s. This reveals that when the cross-sectional process exhibits both ARMA and spatial effects, and the spatial effects iv
remittances for 2000, we obtain very similar results to those reported in Table 3 and 4 (see appendix Table A.3).

A second source of endogeneity is the fact that some portion of the migrants out of Mexico returns after a period abroad. These return migrants may return with knowledge of U.S. financial markets, and hence have higher demand for financial services in Mexico. We view this channel as primarily affecting the interpretation of the results. If knowledge of financial markets acquired abroad is an important factor in increasing demand for banking services in Mexico, then the coefficient on remittances should be interpreted as reflecting the broader impact of migration on bank depth and outreach.

A final source of endogeneity appears to be more serious. The lack of financial services may cause a lack of economic development, or both may be related to some omitted third factor. The lack of development, in turn, might lead to out migration and, subsequently, higher remittance flows. That is, our regression may be mis-specified because we lack a control for _municipios_ with “bleak futures.” In that case, we can definitively sign the direction of the bias only for the set of regressions on columns (1) through (4) of tables 2 and 3. These regressions include only remittances on the right hand side. Since the omitted variable is negatively correlated with banking development and positively correlated with migration and remittances, the Tobit coefficients for this specification will be biased downward.

If, as we expect, these unmeasured characteristics of the _municipios_ are the main source of endogeneity, the Tobit results for the specification including only remittances should be interpreted as representing a lower bound of the impact of remittances on banking sector depth and breadth. These results show that remittances are positively associated with the opening of bank branches, and the number of accounts and level of deposits. The effect of remittances on credit remains somewhat ambiguous, however, because the remittances variable is significant in the credit regression only after we control for GDP per capita as well.<sup>22</sup> In general, the direction of bias can be determined in the presence of other independent variables only when those variables are orthogonal to the variable of interest. Because the correlation between remittances and GDP per capita is low (-0.035 in the full sample), it is unlikely that its inclusion in the

> 22 The specification on tables 2 and 3 includes both GDP per capita and density, but the magnitude of the coefficient on remittances is similar in all four regressions when the density variable is dropped.

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**Figure 4: Global Fuel and Fertilizer Prices**

<!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>2002 2003 2004 2005 2006 2007 2008Jan 2008Feb 2008Mar 2008Apr 2008May 2008Jun 2008Jul 2008Aug<br>Petroleum Urea<br>Price Index<br><!-- End of picture text --> _Source: IMF IFS Yearbook, World Bank Commodity Prices_

# **_Expected Near to-Medium-Term Outlook for International Food Prices_**

While short-term fluctuations in food production compounded by trade restrictions had a negative impact on short-term food prices, one good news for near-term prices is the bumper global harvests for wheat in 2008-09. Rice production has also increased. As a result, stocks are being replenished putting a downward push to global prices. The lifting of the trade bans will help lower prices further. Yet, it is clear that the underlying economics of food markets has changed substantially and unless energy prices were to collapse to the levels found in 2004, the global price of rice and wheat will not likely return to the pre-crisis prices of 2004. Expected outlook for prices in the near to medium term are shown in Table 2. These projections show the substantial reduction in projected annual average oil and fertilizer prices from the peak levels in 2008. While these projected input prices are significantly lower than the local peak (especially for oil that reached a monthly average of $134/per barrel in July 2008), they are substantially higher than the average prices in 2004 reflecting the realities of world demand and supply. Also, as the global recession eases commodity prices are likely to rebound.

**Table 2: Projected average global wheat and rice prices, 2007-2010**

||**2007**|**2008**|**2009**|**2010**|
|---|---|---|---|---|
|Oil ($/barrel)|72.5|101.2|74.5|75.8|
|Rice ($/MT)|326.4|660.0|446.0|459.0|
|Wheat ($/MT)|255.2|330.0|255.0|262.0|
|Fertilizer ($/MT)|309.4|544.0|388.0|299.0|
|Source: World Bank Prospe|cts for the Global Ec|onomy (November|2008)||

6
In general, our results suggest that investment orders of magnitude and country rankings are not highly sensitive to our assumptions about pi. For pi = OED (the OED success rate), cross-regional variation is considerably greater than within-region variation. The consequence is general similarity in optimal investment rankings and relative magnitudes for p1=1<sup>4</sup> and pi = OED. The greatest water and sanitation investments are concentrated in Sub-Saharan Africa, South Asia and East Asia. For air pollution reduction, the greatest investments are in South Asia and China. The pattern for natural resource management is more diverse, with some large country investments indicated for all regions except Eastern Europe and Central Asia.

Differences in OED success probabilities are, however, reflected in some patterns of cross-regional allocation. Many African countries, for example, get substantially higher allocations when policy doesn't matter (pi = 1),5 since there is no countervailing weight for poverty in case 2. In contrast, China's allocation is significantly higher when pi = OED.

Figure 8 summarizes the results for total optimal investment when pi = OED. The largest indicated investments are in South and East Asia, Sub-Saharan Africa, and the two largest countries in Latin America (Brazil and Mexico). Lower levels are generally (although not always) indicated for Eastern Europe and Central Asia, Northwest Africa, Western South America, and Central America.

# 6. **Summary and Conclusions**

In this paper, we use several new datasets and a model of World Bank decisionmaking to estimate optimal. environmental investments for the Bank across countries and

> 4 We have chosen **pi** = **I** for expositional clarity, but any constant probability will yield the same result. 5 Examnples are Nigeria, Congo, Somalia, Central African Republic, Congo (DR), and Cameroon.

12
Table 1: **African Sovereign Wealth Funds in billion of US$**

|**Country**<br>**C**|**apitalizatio**|**n**<br>**Type**|**Origin**|
|---|---|---|---|
|Botswana|6.9|Future Generations|Diamonds|
|Angola|5|Development|Oil|
|Nigeria|1.4|Stabilization|Oil|
|Congo, Dem. Rep.|1.39|Stabilization|Oil|
|Senegal|1|Development|Non-commodity|
|Mauritius|0.5|Development|Non-commodity|
|Gabon|0.4|Future Generations|Oil|
|Mauritania|0.3|Stablization|Oil and Gas|
|Sudan|0.15|Stabilization|Oil|
|Equatorial Guinea|0.08|Future Generations|Oil|
|Ghana|0.07|Stabilization|Oil|
|Chad|0.03|Stabilization|Oil|
|Sao Tom ́e Principe|0.01|Development|Oil|
|Namibia|n/a|Development|Minerals|

Sources: Sovereign Wealth Fund Institute available at `http://www.swfinstitute.org/` . The information for the following SWFs: Sao-Tome-Principe, Sudan, Chad and Congo, are taken from Triki and Faye (2011).

and the available data, only Nigeria’s SWF is quite transparent with a value of 9, followed by Botswana’s SWF with a value of 6. According to the available information taken from SWI, African SWFs are all managed by local managers.

In Table 2, we present African SWFs capitalization over the period 2007-2013, using the information given by the SWI in terms of the international capitalization of SWFs around the world. Given that the share of African SWFs is equal to 0.24 percent, this allows us to estimate the capitalization of African SWFs over the period 2007-2013. The mean capitalization of African SWFs over the period 2007-2013 is $11.21 billion, while the minimum is $8 billion, and the maximum reaches $15 billion.

Table 2: **African SWFs capitalization over the period 2007-2013 in $ billion**

|**Year**|**World capitalization**|**African SWFs capitalization**|
|---|---|---|
|2007|3,259|8|
|2008|4,149|10|
|2009|4,032|10|
|2010|4,418|11|
|2011|4,842|12|
|2012|5,198|12.5|
|2013|6,106|15|

Sources: SWF world capitalization data are taken from the Sovereign Wealth Fund Institute, and the African SWFs capitalization data are based on the authors’ calculations.

4
Women-headed households are not a homogeneous group and there are varied definitions of what actually constitutes such a household. Most vulnerable are smallholder households headed by women without husbands. Their income is about half that of the income of male-headed households. These women face an education and employment disadvantage since they have less access to "income, credit and technical support, the necessary conditions for adoption of innovations, improved technologies, higher yields and adequate livelihood' (GOK and UNICEF, 1984). Since the majority of these women are older, they are unlikely to remarry and gain access to these resources through marriage (GOK and UNICEF, 1984).

Table 8

# Percentage Distribution of Productive Assets of Male and Female Headed Smallholdings

|Productive Assets/|Married|Married|Unmarried|
|---|---|---|---|
|Status|Men|Women|Women|
|Cash Crops||||
|Coffee|21|15|13|
|Tea|9|7|4|
|Pyrethrum|8|9|6|
|Cotton|10|6|9|
|Cattle||||
|Improved|74|87|84|
|Unimproved|57|61|66|
|Monthly Off-farm Income||||
|No off-farm income|19|22|29|
|Less than KS 300|55|56|64|
|Btw. KS 300 - 600|20|6|5|
|More than KS 700|6|6|2|
|Size of Holding||||
|Less than 1 ha.|45|61|64|
|Btw. 1-<br>1.9 ha.|20|20|15|
|More than 2 ha.|28|14|17|
|Education of Head||||
|None|55|60|90|
|Stds. 1-4|18|17|8|
|Stds. 5-8|23|19|2|
|Forms 1-2|2|3|0|
|Forms 3+|2|1|0|

Source: Barnes and Werner, 1982 based on IRS Labor Module 1978/79. Cited in ILO, 1986.

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- 19 -

Given our empirical model, therefore, it is difficult to solve the endogeneity issue associated with this index.

Second, the rural ICT adoption index is available for most countries; to take into account the possible digital divide between urban and rural areas the index is calculated by multiplying the sum of fixed line and cellular phone users per capita by the ratio of teledensity outside the largest city to teledensity (Lio and Liu, 2006). Unfortunately, the original data of teledensity—for only main lines—in the largest city and the rest of the areas are no longer published. The latest available data are for 2000 (ITU, 2002).<sup>18</sup> We use the teledensity outside the largest city in 2000 as a proxy of lagged rural telecommunications infrastructure.

Third, the share of rural residents with access to improved water is used for a proxy of water infrastructure development in rural areas. The data in 1990 and 2004 are available for most countries in _World Development Indicators_ . The share of irrigated land to total cropland is also used for our complementary analysis.

Finally, the data on electricity consumption for agricultural purposes are available from _Energy Statistics_ by International Energy Agency, but only for a limited number of countries. Particularly in Africa, less than ten countries are covered. We divide the total agricultural electricity consumption by the number of workers who are active in the agriculture sector. As commonly believed, it is true that agricultural areas are lagging behind in electrification (Figure 9). At the same time, however, there appears to be a significant positive association between overall and agricultural electricity consumption.

> 18 After 2002, no mainline teledensity data in the largest city, urban and rural areas are reported in International Telecommunication Union’s _World Telecommunication Development Report_ .
due to transportation costs. This echoes the findings of other studies that transportation costs account for 20-25% of the urban retail prices at regional hub cities in Nigeria (Liverpool-Tasie and Takeshima, 2013). This effect is likely exacerbated at rural markets and (even further in remote villages) to capture the costs of getting the fertilizer to more remote areas with poorer road networks<sup>23</sup> .

**Table 5: Share of total fertilizer acquisition prices due to transport costs in Nigeria**

||**2010**|**2012**|
|---|---|---|
|Market price for fertilizer ( per kilogram)|104.55|100.62|
|Total acquisition cost|425.57|450.21|
|Share of total acquisition cost of fertilizer that is due to<br>local transportation cost*|0.70|0.71|

_Prices are adjusted to 2012 prices using the CPI from the Nigerian National Bureau of Statistics.* Transportation cost is the difference between the total acquisition cost and the market price. The share represents the proportion of the acquisition cost that goes towards transport_ With the low MPP of nitrogen for maize production in Nigeria, the proportion of maize plots for which nitrogen application is profitable (for a risk averse farmer) at the observed fertilizer acquisition prices and maize price is quite low. In the C-RCFS, it is only profitable for about 16% of all maize plots. This farming system is the largest farming system in our sample (comprising over 60% of all maize plots) and likely most representative of maize production in Nigeria. Even in the TCFS where the MPP of fertilizer application was about 13kgs of maize per kg of nitrogen applied, nitrogen application is only profitable in expectation for about 35% of maize plots (Table 6)<sup>24</sup> .

> acquisition costs) we present the more conservative estimates (at about 50% of the cost) that still enable us to make our point about the effect of high transportation costs on fertilizer profitability.

> 23 We use both total winsorized (at 95%) transportation costs and winsorized transportation costs with a cap of N700. While the uncapped transportation costs  gives us much larger values (with transportation being up to 70% of total acquisition costs) we present the more conservative estimates (at about 50% of the cost) that still enable us to make our point about the effect of high transportation costs on fertilizer profitability.

> 24 We do not lay too much emphasis on the results for the RCFS because they were never statistically significantly different from zero in the production function estimates and this was consistent across all methods; fixed effects, pooled OLD and correlated random effects.

22
**Table 11: Trade Openness and Industry Labor Demand for Blue Collar Workers in Mexico (1987-90)**

||||**(s**|**tandard error**|**s in parenthes**|**is)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Ln L t-l|Ln**WBt**|Ln WBt-l|Ln WWt|Ln WWt-l|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|Trade Policy|0.475(**)|-1.002|-0.909|0.896|1.053|0.092(*)|0.001|--|0.828|0.157|8196|
|Specification(b)|(0.225)|(1.298)|(0.910)|(0.953)|(1.033)|(0.035)|(0.061)||||(2049)|
|Variable* Tariff Rate|-0.017(***)<br>(0.009)|<br>-0.007<br>(0.047)|-0.058(**)<br>(0.030)|0.036<br>(0.036)|0.046<br>(0.042)||0.084<br>(0.099)|0.057||||
|Variable* Inputs Tariff|0.016(***)|0.007|0.038(**)|-0.035|-0.026||-0.091|0.034||||
||(0.009)|(0.032)|(0.020)|(0.023)|(0.027)||(0.091)|||||
|Variable* Non-Tariff-|0.0001|-0.001|-0.001|0.002|-0.0001||-0.001|0.111||||
|Barriers|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)||(0.005)|||||
|Variable* Real|0.002|0.008|0.017|-0.011|-0.016|||0.122||||
|Exchange Rate|(0.002)|(0.018)|(0.012)|(0.013)|_(0.015)_|||||||
|Wald Test: p-value(c)|0.226|0.080|0.069|0.009|0.319||0.700|||||
|Trade Flows|2.488(*)|0.310|-0.081|-1.139(**)|0.104|0.035|-0.121(***)|--|0.299|0.727|9508|
|Specification(d)|(0.910)|(0.629)|(0.698)|(0.579)|(0.453)|(0.031)|(0.071)||||(2377)|
|Variable* Import Ratio|0.001<br>(0.005)|-0.051(***)<br>(0.027)|0.041<br>(0.030)|0.042(**)<br>(0.020)|-0.040(***)<br>(0.024)||-0.029<br>(0.067)|0.416||||
|Variable* Export Ratio|-0.0002|0.021|-0.036|-0.028|0.029||-0.038|0.722||||
||(0.006)|(0.028)|(0.039)|(0.023)|(0.028)||(0.075)|||||
|Variable* Real|-0.011(**)|0.00001|0.002|0.008(***)|0.001|||0.165||||
|Exchange Rate|(0.006)|(0.005)|(0.006)|(0.005)|(0.004)|||||||
|Wald Test: p-value(c)|0.150|0.107|0.401|0.155|0.159||0.053|||||
|<br>Price-Cost-Margin|0.731(**)|0.473(***)|-0.179|0.210|0.200|0.103(**)|-0.003|--|0.863|0.399|9524|
|Specification(e)|(0.326)|(0.275)|(0.269)|(0.205)|(0.248)|(0.048)|(0.013)||||(2381)|
|Variable* Price Cost|-0.003|0.009|0.009|-0.005|-0.006||0.036(***)|0.588||||
|Margin|(0.002)|(0.009)|(0.009)|(0.006)|(0.008)||(0.022)|||||

Notes: GMM estimates with first-differenced data. Time dummies were included but are here omitted. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and further lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Wald Test of Joint Significance of the variables interacted with a given openness variable. (b) Additional Instrument: the second lag of the tariff rate. (c) Wald Test of Joint Significance of openness variables interacted with a given variable. (d) Additional Instruments: third and fuirther lags of import and export ratios. (e) Additional Instruments: second and further lags of price-cost-margins.
variation in the policy implementation, we cannot disentangle timing effects from the effect of policy implementation. Secondly, improvements in measuring TCC may also improve our research capabilities. One way to think about this is to split the cost into fixed and variable costs and focus on each group separately. The importance of dynamic analysis was mentioned above but needs further elaboration. We could take only a static approach in this analysis due to limitations of the data; however longitudinal data has a great potential to enhance our understanding of phenomena analyzed here. Mainly, this will allow us to control for all time invariant firm characteristics, which is quite important to deal with potential endogeneity problems. In the current version, we tried to control for all direct and indirect firm characteristics and their perception of tax systems and government services, which we hope will mitigate the endogeneity problem.

Another shortcoming of our analysis is the sample size. Due to various reasons explained above, the focus of the analysis was on the firms that do all TC inhouse and this approach resulted in a loss of a fair part of the data. If the effect of e- filing on TCC is indeed relatively small and one estimates the effect with less information, then large standard errors (loss of precision of our estimates) are inevitable. Under such a scenario, estimates simply would become statistically unreliable although they may not necessarily be wrong estimates of the corresponding real population parameters.

# **5. References**

Che Azmi, Anna, and Yusniza Kamarulzaman. 2010. “Adoption of Tax E-filing: A Conceptual Paper.” African Journal of Business Management 4 (5): 599–603 Coolidge, J. (2012) “Findings of Tax Compliance Cost Surveys in Developing Countries”, _e-Journal of Tax Research, Vol. 10, no. 2, Oct. 2012, pp 250 – 279_ Coolidge J., Ilic D. and Kisunko G. (2009) “Small Businesses in South Africa: Who Outsources Tax Compliance Work and Why?” _World Bank Policy Research Working Paper no. 4873._ 54
Figure 1: Distribution of overall management scores, PISA vs WMS <!-- Start of picture text --> BRA CAN COL<br>-4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4 6<br>KS test p-value: 0.181 KS test p-value: 0.887 KS test p-value: 0.116<br>GBR GER ITA<br>-4 -2 0 2 4 -2 0 2 4 -4 -2 0 2 4<br>KS test p-value: 0.965 KS test p-value: 0.700 KS test p-value: 0.008<br>MEX SWE USA<br>-4 -2 0 2 4 -4 -2 0 2 4 -2 0 2 4<br>KS test p-value: 0.350 KS test p-value: 0.870 KS test p-value: 0.956<br>PISA 2012 index WMS index<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br><!-- End of picture text --> Note: Data for the World Management Survey index for all countries except for Mexico and Colombia can be found at www.worldmanagementsurvey.org. Distribution of overall management indices standardized within countries. Kernel density curves estimated using WMS sampling weights (calculated as the inverse probability of being interview on log of number of students, public status, and population density by state, province, or NUTS 2 region as a measure of location) for the WMS data and _school final weights_ for the PISA data. Samples include both public and private secondary schools for both datasets, with the exception of Colombia where WMS data is only available to public primary schools. Number of WMS/PISA observations are as follow (WMS/PISA): Brazil = 510/561, Canada = 129/770, Colombia = 468/268, Great Britain = 89/422, Germany = 102/158, Italy = 284/926, Mexico = 157/1327, Sweden = 85/179, United States = 263/136.

33