rafmacalaba/datause-encoder-data huggingface dataset:
# **Estimation with GARCH model**

An alternative specification to test the sensitivity of our results is implemented with the GARCH model. A number of studies have used a GARCH model to analyze maize price volatility ( **Gilbert and Morgan, 2010** ; **Minot, 2013** ). In a GARCH model ( **Bollerslev, 1986** ), an autoregressive moving average (ARMA) model is assumed for the error variance. A GARCH (p,q) model may be presented in the same manner as the ARCH model except that the variance equation is now as follows: Non-negativity of the conditional variances requires ω, λi, βi > 0.

ln Pit = θ0 + θ1ln Pit−1 + θ2Trendt + θ3ln RERt + θ4lnIPt + ∑μi i Sit + ∑ʊj j Mjh + ρlnRemote+ δ1lnBorder+ δ2Production+ εit **(17)** hit = λ0 + λ1ε<sup>2</sup> t−i<sup>+ β</sup> 1<sup>Ϭ2</sup> t−i<sup>+ Ω</sup> 1<sup>ln P</sup> it−1<sup>+ Ω</sup> 2<sup>Trend</sup> t<sup>+ Ω</sup> 3<sup>ln RER</sup> t<sup>+ Ω</sup> 4<sup>lnIP</sup> t<sup>+</sup> ∑πi i Sht + φlnRemote+ ω1lnBorder + ω2Production + vit **(18)** The corresponding estimation is shown in **table 5** which reports a significant and positive impact of market remoteness on maize price volatility, with similar findings for other variables. However, the coefficient associated with the GARCH model is non-significant. It is not easy to explain the non-significance of the GARCH term. One possibility is that monthly data usually do not exhibit GARCH effects ( **Baillie and Bollerslev, 1990)** . Even if a GARCH process exists, it will be due to the structural break of unconditional variance. Furthermore, GARCH application is more appropriate for high frequency data; however, in our application we use monthly data. Both ARCH and GARCH processes usually generate persistence in price volatility, i.e. high volatility is followed by high volatility, and the same holds for low volatility. The ARCH process features high persistence of price volatility but with short memory in that only the most recent residuals (shocks) have an impact on the current volatility. The GARCH model gives a much more smoothed volatility profile with long duration, in which past residuals and lagged volatility terms affect the current price volatility. This means that price volatility in Burkina Faso’s maize market is mainly due to recent shocks and the geographic situation within the country.

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and GVC‐related trade (see Box 3). The depth of an agreement is captured by the number of legally enforceable provisions that it includes. This methodology has been extensively used by economists to test empirically the determinants of trade flows, and to estimate the effect of preferential trade opening on trade flows.

Box 3: Methodology for the Estimation of the Impact of Deep Integration on GVC Integration Gravity equations are derived from models that seek to explain or predict the relationship between a (dependent) variable (in this case bilateral GVC‐related trade) and a set of other (independent or explanatory) variables whose values can be estimated (in this case elements of deep integration). Endogeneity occurs when both the variable being explained (the left‐hand side variable in the equation) and the explanatory variable (the right‐hand side variable in the equation) may be determined by a third factor not in the model. For example, firms that want to invest in a country may also lobby for free trade agreements. Consequently, a free trade agreement may not increase FDI, but both FDI and FTAs may both come about due to perceived economic benefits of firms and their political lobbying efforts.

In order to control for endogeneity and for the existence of zero trade flows the following structural gravity regression is estimated for a set of 189 countries between include 1990 and 2014 using Poisson pseudo maximum‐likelihood (PPML)<sup>16</sup> : GVC��� �exp�β�Depth��� �δ�� �δ�� �δ����ε��� (1) Where GVCijt is a measure of GVC‐related trade between country i and j at time t and it is captured with gross trade flows in parts and components from the UN Broad Economic Categories classification (BEC)<sup>17</sup> Depth��� is a measure of the depth PTAs. A statistically significant and positive coefficient β1 implies that signing a deeper agreement is associated with greater GVC‐related trade. This variable is calculated as the number of enforceable provisions that are included in a certain agreement. The δs are a series of fixed effects: i for importer, j for exporter and t is year. Finally, εijt is the error term.

Empirical evidence suggests that trade policy aiming at making international production smooth and without disruptions is very important for GVC integration.<sup>18</sup> Figure 19 illustrates that the value of GVC‐related trade in 2014, proxied with trade in parts and components,<sup>19</sup> is on average higher for country pairs that signed deeper agreements. Preliminary results also suggest that higher levels of depth, defined as the number (or share) of legally enforceable provisions included in an agreement, are associated with higher levels of GVC‐related trade.

> 16 See Piermatini and Yotov (2016).

> 17 Parts and components include non‐fuel BEC intermediates (111,121,21,22,42 and 53).

> 18 See also Orefice and Rocha (2012).

> 19 We follow Yeats (1998) and Hummels et al. (2001) and use trade in parts and components to proxy for global production sharing.

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Table 2 shows the mean growth rates of external and internal imports before and after 1993. While several factors beyond AFTA have probably affected trade growth rates in the ASEAN countries (the currency crisis of 1997-1998 being only the most prominent one), a few regularities are worth noting. First, intra-AFTA trade grows faster than external trade in five of the six original members even in the _pre_ - agreement period (the only exception being Indonesia). This suggests that the choice of members was not random, but rather influenced by their growing trade ties. In the post-agreement period, internal trade growth was faster than external import growth in all countries, and by a significantly larger margin than it was before 1993.

Although using aggregate trade data obviously makes the sample very small, we can still perform t-tests on the difference in the means of the pre-union and postunion growth rates and also between the growth rate of internal trade and trade with nonmembers. As the results reported in table 2 show, the t-tests of the difference in the mean growth rates before and after the agreement are not conclusive. On the other hand, the difference in the mean growth rates of imports from members and nonmembers are different enough for Indonesia and the Philippines in the post-AFTA period that the t-test on their difference is statistically significant, despite the very small sample.

Furthermore, if increased trade with bloc members displaced imports from third countries, we would expect to see a decline in the correlation between internal and external trade growth subsequent to the formation of AFTA. However, as shown in the last column of table 2, the correlation between external trade growth and internal trade goes up in five out of the six countries—and by a large amount in Thailand and the Philippines. The correlation falls in the post-1993 period only in Brunei, and the drop was minimal.

# **IV. The effect of tariffs on trade in ASEAN**

Having looked at the pattern of aggregate trade and average tariffs in the ASEAN countries, we turn now to examine the relationship between imports and 14
indicators are comprehensive and rely on data from many important sources, and tend to be _de facto_ measures, but the methodologies underlying their indicators are not uniform, and their sample of firms is not representative of a country’s universe of firms.

Given the pros and cons of each data source, how robust are the business environment effects and what additional insights can be gained from using all three instead of only one? Are related indicators from different sources closely correlated? How can we explain why and how the estimated effects of the same business environment theme on firm performance differ for specific indicators, depending on whether those indicators are from de facto and de jure sources? Do performances of firms with different bargaining power (such as domestic vs foreign firms, and small versus large firms) depend on the business environment differently? While important, these questions have not been addressed by the existing literature. They are the focus of this paper.

This paper makes three contributions to the literature.

First, given that each series of the business environment measures has its own measurement errors, using all three data sources serves as robustness and soundness checks. We find that they offer independent sources of variations in explaining firm performance. The _Enterprise Survey_ indicators seem to explain the largest share of variations in firm performances. Their qualitative results on the performance-BE relationship tend to be the most expected. For instance, they suggest that corruption depresses firm growth, while physical safety, access to finance, and ease in access to land enhance firm performance. These results, however, are either insignificant or have the opposite signs, when examined with the other two data sources.

Second, we offer novel evidence of the importance of contract enforcement in understanding the effects of de jure business environment indicators. Our analysis suggests that the de jure _Doing Business_ indicators are more effective in improving firm performance in countries with better contract enforcement. In particular, in countries with better contract enforcement, _Doing Business_ Get Electricity has more positive association with firm growth, and _Doing Business_ Contruction Permit has more positive association with productivity.

Third, we find substantially different effects of the business environment on firm performance, by firm ownership and size. While domestic and foreign firms do not differ much in their sensitivity of firm _growth_ to the business environment, they differ significantly in their sensitivity of firm _productivity_ . Foreign-owned firms, i.e., firms with positive foreign owner share, benefit more in their productivity performance from the maintenance of physical safety and from the ease in obtaining construction permits, and gain a competitive advantage in 3
The paper is organized as follows. Section 2 briefly reviews the literature and discusses our hypotheses. Section 3 describes the dataset. The estimation strategy is presented in Section 4 and results are discussed in Section 5. Potential channels through the impacts materialized are presented in Section 6 and Section 7 concludes.

# **_2. Literature and Hypotheses_**

Emerging evidence indicates that information technologies can promote growth and increase welfare. Using cross-country data, Roller and Waverman (2001) argue that improvements in telecommunications have a positive and significant impact on growth. More forcefully, Jensen (2007), in a study of the fisheries sector in south India, finds that the adoption of mobile phones increased both consumer and producer welfare. For example, fishermen profits went up about 10 percent after the introduction of mobile phone service. Aker (2008) demonstrates that the introduction of mobile phones led to significant reduction in price dispersion among grain markets in Niger. In addition, Goyal (2008) shows that providing wholesale price information has a positive impact on the price received by producers. Finally, Klonner and Nolen (2008) show that in South Africa cell phone network roll-out has a positive impact on household income. We improve on the literature by assessing the household-level welfare impacts of purchasing a mobile phone.

As indicated in Figures 1 and 2, mobile phone coverage has spread quickly in the Philippines. This has been accompanied by a robust growth in mobile phone ownership. While the share of the population with a mobile phone was 15.6 percent in 2001, it rose to 27.7 by 2003 and reached 49.7 percent by 2006. In neighboring Indonesia, the figures were respectively 3.1 percent, 8.6 percent and 28.6 percent.<sup>2</sup>

> 2 World Development Indicators (2008) Accessed on 05/12/2008 <u>http://ddp-ext.worldbank.org/ext/DDPQQ/member.do?method=getMembers</u>

4
# Research Components of World Bank Education<sup>P-ojects,</sup> since 1982.

|Area|Country and Loan/Credit<br>Number|Approved|<br>Research Components|Current Status of Studies,<br>including<br>CompletedDocuments/Data|
|---|---|---|---|---|
||l||(ii) schools inventory<br>survey (para.2.21 p.<sup>1</sup>5).||
|ENEMA|Yemen PDR CR F019-YDR|1984|Fourth Education. Future project preparation<br>to<br>design suitable criteria for plaming<sup>and</sup><br>implementation<br>of munpower training activities<br>(para. 2.21 p.15).|No informat on avaitable.|

58
NSS region are part of the _direct_ effect and the lights from the part of a city that has spilled over the border are part of the _indirect_ effect that comes from neighbors.<sup>22</sup>

# **V. Types of Urban Growth and Rural Poverty**

# _5.1 Effects of Unmasked Regional Lights on Rural Poverty_

We start by examining whether night lights have any effect at all, before we compare effects of lights coming from big cities with those from smaller towns. We refer to these results as ‘unmasked’ since we consider the entire area of each NSS region without first masking the pixels lit by the big cities.

Table 2 reports the estimates of the SDM for the headcount index, H and the poverty gap index, PG that consider four different ways to use the night lights data. In columns (1) and (5), the total lit area for each NSS region, for any clusters of lights exceeding 20 percent of maximum brightness (DN=13) is used as the proxy for urban development. Variables in these models are in logarithms for the convenience of using elasticities.<sup>23</sup> The results suggest an absolute elasticity of rural poverty with respect to own-region lit urban area of about 0.1 to 0.2, with a slightly more precisely estimated elasticity for H than for PG. The elasticities with respect to the spatially weighted average of lit area in neighboring regions are larger, at between 0.3 and 0.4, and the spatial lag of the poverty rate has an elasticity of 0.3. Allowing for the spillover to neighbors and the feedback effects shown in equation (7), the total effect of growth in lit area on H has an elasticity of -0.6 and on PG has an elasticity of almost -0.9. These indirect effects will have both local and global components, given that  2 _k_<sup></sup> 0 and   0.

When the average DN value is used, either solely in columns (2) and (6), or jointly with lit area in columns (3) and (7), it has no statistically significant association with the poverty gap index, and a positive relationship with rural headcount poverty (albeit one that is not significant when lit area is also in the model). In contrast to the mixed and imprecise

> 22 Chandigarh and Delhi lit area includes Union Territory (UT) area that is not part of our data set of 19 major states. The omitted area averages 7.6% of all big city area. In sensitivity analyses we apportion the UT area into the neighboring NSS regions, according to each region’s share of the Delhi or Chandigarh lit area agglomeration in the particular year. The differences in effect of big city growth and smaller town growth on rural poverty are somewhat more precisely estimated in these analyses, while the qualitative pattern of results is unchanged. 23 When urban growth is split into big city and smaller town components not all regions have a big city and the logarithm of zero is undefined. To address this problem, we use the inverse hyperbolic sine transformation 2 5.0 whereby _ihs_ ( _x_ )  ln( _x_  ( _x_  )1 ) which is shown in Appendix Table 1 to give identical results to using logarithms.

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_Figure 7: Governorate level poverty estimates based on nominal and spatially adjusted welfare_ <!-- Start of picture text --> $2.15 - 2017 PPP $3.65 - 2017 PPP<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>nominal spatially adj nominal spatially adj<br>Source : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey<br>Overall, while acknowledging the importance of controlling for spatial price differences, we do not think<br>that using an imperfect spatial deflator would be the best way forward, particularly when considering how<br>much a fixed basket approach would deviate from the theoretical underpinnings of welfare measurement.<br>Moreover, using a nominal aggregate could also be aligned with the impossibility to account for spatial<br>price differences in pre-conflict poverty estimates based on 2009 grouped data.<br>Figure 8 below maps poverty at the governorate level, using the nominal aggregate.<br>Figure 8: Poverty estimates based on HNAP 2022 data, nominal aggregate<br>$ 2.15 (2017 PPP) $3.65 (2017 PPP)<br>Al-Hasakeh Al-Hasakeh<br>Aleppo Aleppo<br>Ar-Raqqa Ar-Raqqa<br>Idleb Idleb<br>Lattakia Lattakia<br>Hama Hama<br>Deir-ez-Zor Deir-ez-Zor<br>Tartous Tartous<br>Homs Homs<br>Damascus Rural Damascus Poverty Rate(%)88 - 100 Damascus Rural Damascus Poverty Rate(%)88 - 100<br>Quneitra 75 - 8862 - 75 Qu neitra 75 - 8862 - 75<br>Dar'a As-Sweida 50 - 6238 - 5025 - 38 Dar'a As-SweidaAs-Sweida 50 - 6238 - 5025 - 38<br>12 - 25 12 - 25<br>0 - 12 0 - 12<br>Percent Percent<br><!-- End of picture text --> _Overall, while acknowledging the importance of controlling for spatial price differences, we do not think that using an imperfect spatial deflator would be the best way forward, particularly when considering how much a fixed basket approach would deviate from the theoretical underpinnings of welfare measurement. Moreover, using a nominal aggregate could also be aligned with the impossibility to account for spatial price differences in pre-conflict poverty estimates based on 2009 grouped data._ _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey
APA (American Psychological Association) and through PsyContent,<sup>17</sup> a database specialized in psychology and psychiatric journals.

We also implemented a snowball approach by searching the citations of relevant papers we found in the demand-for-skills literature even when these specifically did not base their analysis on employment surveys. Likewise, we followed a similar approach to improve the key words list. We selected key words from research documents that were relevant for our analysis and added them to our list of key words.

Finally, we employed combinations of key words in Table 1, in order to improve the likelihood of finding papers closely related to our subject of interest. This is important since in some cases only a combination of key words would bring relevant search results. For example, within the literature related to entrepreneurship there is a set of research looking into the specific non-cognitive skills that entrepreneurs have that others do not and that distinguish them from others. In this case, for relevant literature we would use in our search the key words entrepreneur and skills to obtain pertinent results. Also, we often added the words “employer survey” to other key words in order to find research based on employer surveys.

We included in the sample any study that presents data on employer demand for or difficulty in acquiring a range of cognitive and non-cognitive skills. We limit the sample to studies that examined both cognitive and non-cognitive skills to allow for a comparison of preferences between the two.

The data have several shortcomings that we cannot correct. First, employer identification of preferred skills and naming the skills most lacking is a stated preference rather than a revealed preference, perhaps introducing measurement error. Second, the questions are necessarily constrained since employers are responding to the current skills equilibrium. Thus, a skill may be ranked low in priority because it is in abundant supply, rather than because it is not necessary for the production process. Third, the employer responses are qualitative rather than quantitative so while we know that a skill is important or lacking, we do not know the extent of the demand (perceived skill gap) for that skill relative to a skill ranked just below it (Rutkowski 2010). Finally, we cannot control for labor market institutions across countries, which may affect the supply and thus the perceived shortage, of skills (Rutkowski 2010). In spite of these limitations, the data provide us with a global picture of constrained skills demand and areas where the skills formation system could further develop the supply of skills.

> 17 www.psycontent.com.  It includes the following databases: PsyJOURNALS, PsycARTICLES®, PsycINFO® and PsyCOLLECTION®.

8
# Table 2: **Robustness Checks**

_Dependent variable: Biannual remittances (log)_

|_Panel A. USING ALTERNA_|_TIVE MEASUR_|_ES OF DISTA_<br>All Observa|_NCE AND SAMPLES_<br>tions<br>|Restrict<br>|ed sample<br>|
|---|---|---|---|---|---|
||||Min. distance|Exc|luding|
||Distan|ce|to border/airport|1% tails of|_>_5% HH relat.|
||in log(km)<br>(1)|_<_350km<br>(2)|in log(hr)<br>(3)|rem. change<br>(4)|working in US<br>(5)|
|C-19_×_Distance|-0.029***|0.079**|-0.032***|-0.033***|-0.025***|
||(0.007)|(0.039)|(0.011)|(0.009)|(0.010)|
|Observations|25600|25600|25600|21000|25409|
|_Panel B. PLACEBO TESTS_|_AND INCLUSI_<br>|_ON OF ADDI_<br>|_TIONAL CONTROLS_<br>|||
||Changing|Defning|COVID|Exports|CARES|
||North-South|COVID=|incidence|control|channel|
||Defnition|2019|control|||
||(1)|(2)|(3)|(4)|(5)|
|C-19_×_Distance|-0.014|-0.001|-0.037***|-0.033***|-0.034***|
||(0.016)|(0.005)|(0.008)|(0.008)|(0.009)|
|C-19_×_pc exports (log)||||0.004<br>(0.003)||
|C-19_×_CARES exposure|||||0.045<br>(0.137)|
|Observations|22354|25600|25600|25600|25600|

**_Notes:_** Table 2 shows robustness checks of the main results. _Panel A_ presents results using alternative measures of distance between the municipality and the US-Mexico border. Column (1) uses a measure of distance in log(km) and column (2) presents results using a north municipality indicator that takes the value of one if the distance between the municipality and the USMexico border is less than 350 km. Column (3) uses the minimum distance to the border or airport measured in log(hr). Columns (4) and (5) present results for restricted samples using log(hr) as in the main model. Column (4) drops observations below or above percentiles one and 99 of the change in biannual remittances distribution and column (5) restricts the sample to municipalities in which less than five percent of households have relatives living in the United States. _Panel B_ summarizes results of placebo tests and after the inclusion of additional controls in the main model that might confound the main effects. In column (1), we restrict the sample to non-north municipalities (distance _≥_ 350 km) and define a north municipality dummy that takes a value of one if its distance is below the median within the restricted sample. Column (2) in panel B defines the COVID dummy = 1 for the year 2019 instead of 2020. Column (3) controls for COVID-19 incidence rates at the municipal level measured as the number of cases per 100,000 inhabitants. Columns (4) and (5) include the value of per capita (pc) exports (in log) and the exposure to the CARES index at the municipal level as controls, respectively. _C_ -19 is a dummy indicator for 2020. All models include municipality and time-quarter fixed effects, and municipality trends, and are weighted by average *** ** * quarterly remittances at the municipal level. Standard errors are clustered at the municipal level. _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

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