rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4515

# **Abstract**

This paper uses a new, nationally-representative household survey from Ghana to analyze within a rigorous econometric framework how the receipt of internal remittances (from within Ghana) and international remittances (from African or other countries) affects the marginal spending behavior of households on a broad range of consumption and investment goods, including food, education and housing. Contrary to other studies, which find that remittances are spent disproportionately on consumption (food and consumer goods/durables) or investment goods (education and housing), the findings show that households receiving remittances in Ghana do not spend more at the margin on food, education and housing than households with similar income levels and characteristics that do not receive remittances. When the analysis controls for endogeneity and selection bias, the findings show that any differences in the marginal spending behavior between remittance-receiving and non-receiving households are explained completely by the observed and unobserved characteristics of households. Households in Ghana treat remittances just like any other source of income, and there are no changes in marginal spending patterns for households with the receipt of remittance income.

This paper—a joint product of the Development Prospects Group, Development Economics Department and the Africa Region— is part of a larger effort to understand the impact of migration and remittances on investment and consumption in the developing world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at radams@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
<u>Table A.11: Parameter Estimates: Basic Metals and Fabricated Metal Products</u>

||VC|M|P-|R|
|---|---|---|---|---|
||coef.|s.e.|coef.|s.e.|
|Labor Share Equation: Wage|0.1<sup>_∗∗∗_</sup>|(0.01)|0.1<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Capital|0.06<sup>_∗∗∗_</sup>|(0.01)|0.07<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Energy Price|0.01|(0.01)|0.003|(0.010)|
|Labor Share Equation: Materials Price|-0.04<sup>_∗∗∗_</sup>|(0.01)|-0.05<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Output|-0.1<sup>_∗∗∗_</sup>|(0.01)|-0.1<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Time Trend|-0.002<sup>_∗∗∗_</sup>|(0.0004)|-0.005<sup>_∗∗∗_</sup>|(0.0004)|
|Labor Share Equation: Constant|0.4<sup>_∗∗∗_</sup>|(0.006)|0.4<sup>_∗∗∗_</sup>|(0.005)|
|Energy Share Equation: Wage|0.006|(0.007)|0.02<sup>_∗∗_</sup>|(0.006)|
|Energy Share Equation: Capital|0.01<sup>_∗_</sup>|(0.008)|0.02<sup>_∗∗_</sup>|(0.008)|
|Energy Share Equation: Energy Price|0.004|(0.006)|-0.005|(0.005)|
|Energy Share Equation: Materials Price|-0.007|(0.008)|-0.01<sup>_∗_</sup>|(0.007)|
|Energy Share Equation: Output|-0.004|(0.007)|-0.002|(0.006)|
|Energy Share Equation: Time Trend|-0.0003|(0.0003)|-0.0006<sup>_∗∗∗_</sup>|(0.0002)|
|Energy Share Equation: Constant|0.04<sup>_∗∗∗_</sup>|(0.004)|0.04<sup>_∗∗∗_</sup>|(0.003)|
|Euler Equation for Capital: Adjustment Term|0.8<sup>_∗∗∗_</sup>|(0.2)|0.8<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Wage|0.9<sup>_∗∗∗_</sup>|(0.1)|0.6<sup>_∗∗∗_</sup>|(0.1)|
|Euler Equation for Capital: Capital|0.5<sup>_∗∗∗_</sup>|(0.1)|0.6<sup>_∗∗∗_</sup>|(0.1)|
|Euler Equation for Capital: Energy Price|0.04|(0.10)|0.08|(0.09)|
|Euler Equation for Capital: Materials Price|0.10|(0.1)|0.03|(0.1)|
|Euler Equation for Capital: Output|-0.9<sup>_∗∗∗_</sup>|(0.10)|-0.8<sup>_∗∗∗_</sup>|(0.10)|
|Euler Equation for Capital: Time Trend|0.007<sup>_∗_</sup>|(0.004)|-0.01<sup>_∗∗∗_</sup>|(0.004)|
|Euler Equation for Capital: Constant|-1.5<sup>_∗∗∗_</sup>|(0.06)|-1.4<sup>_∗∗∗_</sup>|(0.04)|
|Number of observations|2|63|26|3|
|Labor Share Equation: _R_<sup>2</sup>|0.|95|0.9|5|
|Energy Share Equation: _R_<sup>2</sup>|0.|72|0.7|3|
|Euler Equation for Capital Stock: _R_<sup>2</sup>|0.|90|0.8|9|
|LR Test: _φl_ =_φe_ =_φm_ =_ζ_ = 0, _χ_2 (pval)||40.7|(0.00)||

Notes. VCM: Vintage Capital Model. P-R: Pindyck and Rotemberg (1983) Model. coef.: estimated coefficient. s.e.: standard error.<sup>_∗∗∗_</sup> p _<_ 0.01,<sup>_∗∗_</sup> p _<_ 0.05,<sup>_∗_</sup> p _<_ 0.1 46
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> where _w_ are demographic controls. The estimation of (7) provides the fitted coefficients β and γ ˆ as well as estimated residuals ε ˆ . In order to reproduce the observed levels of inequality, the imputation of per capita consumption into sample _S m_ (the “main” DHS survey) is constructed by adding the linear prediction, _X_ β ˆ + _w_ γ ˆ , and a prediction of the residual ε<sup>~</sup> . The predicted residual ε<sup>~</sup> is drawn, for the sample _S m_ of the main survey, from the empirical distribution of residuals obtained when fitting (7) to the auxiliary sample _S a_ . The procedure allows for heteroskedasticity by drawing ε<sup>~</sup> from the distribution of residuals for households with similar assets<sup>15</sup> . This is done in six steps: (1) The regression in (7) is estimated using the common set of wealth indicators, and the parameters β , γ ˆ and residuals ε ˆ are obtained.

(2) The sample _S a_ of the HBS survey is divided into G = 10 groups, defined according to the deciles of the distribution of the first principal component (the wealth index) _y_ for the set of wealth indicators common to the two surveys.<sup>16</sup> Separate distributions of the predicted residuals are identified for each of the 10 groups.

(3) The sample _S m_ of the DHS survey is then divided into the same 10 groups, using the same cut-off values for _y_ as in the auxiliary sample.

(4) For each household _i_ in group _g_ in _S m_ , a residual<sup>ε~ is drawn from the empirical</sup> _i_ distribution of residuals for households in group g in _S a_ . The imputed value of per capita consumption is given by: (5) Measures of inequality of opportunity are computed using the imputed distribution of per capita consumption.

(6) Following the bootstrap principle, steps (4) and (5) are repeated for a number _R_ of drawn replicate distributions of the residuals, and the measures of inequality of opportunity are computed as the mean over the measures obtained for each replication. In our analysis, we use _R_ =20 replications. This replication process allows averaging out the

> 15 Heteroskedasticity might stem from a non-linear relationship between log consumption  and wealth assets, or from the higher noise in this relationship for richer households than for poorer ones. 16 We partition the sample into 10 groups in order to allow for a sufficiently high degree of heteroskedasticity, while keeping group sizes to the order of a few hundred observations.
Different from the MDGs which were considered to be a donor-led agenda, the SDGs were the outcome of a far more participatory process where lower-income countries (e.g., Brazil, Colombia) and smaller Western European countries could take an active role. Furthermore, the constituency in this process includes not only the development community, but also the environment community, which traditionally have different political alignments from that of the former. As such, the SDGs offer more diverse ideas on development outcomes, as well as a significantly richer agenda than that of the MDGs (Dodds, Donoghue, and Roesch, 2017; Fukuda-Parr and McNeill, 2019).

Yet, there are concerns about a mismatch between the (goals and) targets and the indicators with the SDGs. On one hand, MacFeely (2018) raises the concern that although the SDGs were agreed to by all UN states, it is the global statistical community that effectively select the indicators, thus ultimately determine whether the targets will be a success or failure. On the other hand, technical issues (such as data availability) aside, there were concerns that the selection of an indicator can be highly political.<sup>22</sup> A recent special issue of the journal _Global Policy_ offers an interesting discussion of the dynamics behind the politics of indicator selection—particularly the process of moving from goals to targets to indicators—for various outcomes including education, environment, justice, and sustainable agriculture. For example, Fukuda-Parr (2019) provides a detailed account of the struggle to include inequality as a stand-alone goal, and observes that while various stakeholders, ranging from academics, civil society groups, UN agencies, to governments in the South, support this goal, most of the governments of the North consider it to be redundant. The end outcome is that inequality was primarily treated under the SDG framework from the

> 22 Notably, the drafting of the goals and targets themselves was considered as part of the political negotiations as well (UNSC, 2015).

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by completely reengineering tax administration, improving taxpayer service, and facilitating compliance. PricewaterhouseCoopers (2010) presented mandatory e- invoicing as one of the alternative VAT collection methods to the European Commission indicating that under this model “tax authorities gain access to information on sales transactions at a very early stage, i.e. at the time the invoice is issued.”

# **3. Study Objectives and Scope**

This paper presumes Korea’s success with electronic tax invoice and the accompanying early warning system—one of the most effective electronic tax administration tools in Korea—as a holistic approach that harmonizes the institutional and legislative approach and the normative and cognitive approach in effectively raising tax compliance.

The introduction of ETI is a _prima facie_ institutional and legislative approach. However, ETI not only launches a new institutional reform, but also enhance taxpayer service and trust by facilitating taxpayer compliance. ETI gives a tax authority an instant access to transaction data just as an invoice is issued (PricewaterhouseCoopers, 2010), generating a more powerful effect on tax compliance than paper-based invoice. The instant access to and processing of digital information by tax authorities increase taxpayer’s vigilance on an audit possibility and hence reduce noncompliance in VAT filings and input tax refund claims. ETI also contributes to reducing taxpayer costs that are incurred in selling and purchasing, invoicing, making payments and settlements, and filing VAT with increased transaction efficiency and accuracy. Tax authorities can also lower substantial cost and time related to VAT filings and refund claims while improving taxpayer service by reallocating the savings to the more valuable core service areas. Such changes would eventually contribute to VAT compliance. Therefore, this paper reviews Korea’s ETI under the broad assumption that ETI enhances tax compliance through both institutional and normative changes. Although this paper does not conduct quantitative causal analyses to examine the effects of ETI on tax compliance, it provides a descriptive analysis by utilizing various sources including studies, materials, and information regarding Korea’s ETI introduction, statistical data provided by the National Tax Service (NTS), and the results of a perception survey on ETI conducted by the author for this paper.

This paper describes Korea’s motivation for ETI, the implementation process, the legal and regulatory regimes, the key issues associated with the introduction, the accomplishments and lessons learned, and future challenges for tax policy makers and tax authorities in developing countries that are contemplating ETI. Chapter 2 presents an overview of ETI including its concept, the process of e-voicing, and the experience of various countries with e-invoicing. Chapter 3 then reviews the “drivers” of ETI in Korea and the implementation process and challenges. Chapter 4 evaluates the effects of ETI on compliance cost reduction and combating tax fraud 5
include other possible synonyms for subsidies by including ‘discount’ and ‘vouchers’ in the database search. Our search criteria are described in more detail in the appendix. Our main productivity measure is yield, output, or the value of yield or output. We also include income or expenditure as an additional outcome of interest to capture changes in living standards, which should relate to poverty. Figure _1_ describes the number of articles at each screening stage. The initial search yielded 655 articles across the 3 databases: 213 articles from _Web of Science_ , 149 articles from _Scopus_ , and 293 articles from _jstor_ .

The title-and-abstract screening stage was performed simultaneously by two individuals based on the following exclusion criteria: meta-analysis or review article, subject matter is not agriculture, setting is not a low- or middle-income country, simulation or calibration study, not an input subsidy intervention or program. Conflicts in inclusion and exclusion between the two screens were then reconciled to yield 36 articles for the full text screening stage.

The full screening stage was conducted to obtain the standardized effect of input subsidies on productivity. For consistent comparison, we collected the estimates for the percentage increase in yields as a result of participating in an input subsidy program. After reading the full-text articles, we excluded articles that do not involve specific input subsidy programs, or if one of the following was not included: the number of observations, baseline or comparison group mean, and confidence interval or standard errors. Studies that use simulation or calibration methods, non-causal methods, and non-quantitative methods were also excluded. After full-text screening, 9 articles remained. The references of these 9 articles were reviewed to find additional relevant articles. This step identified another 3 eligible studies, of which 2 articles were included in the meta-analysis. The entire screening process yielded a total of 11 articles that we include in the meta-analysis. Lastly, to obtain additional estimates, we reached out to the authors of several studies which included data on subsidies and productivity or income, but did not report the program effect on productivity or income in the articles. We received a response from Abman and Carney, and include this study (Abman and Carney 2020) in our analysis. The final sample includes a total of 12 articles.

_Figure_ 1 _Number of articles at each screening stage_ 4
**Table 1: Summary Statistics**

|Variable||B|alanced Panel<br>|<br>||Full<br>|BEEPS S|ample<br>|
|---|---|---|---|---|---|---|---|---|
||Obs.|Mean|Std. Dev.|Min|Max|Obs.|Mean|Std. Dev.|
|Finance No Obstacle (dummy)|920|0.35|0.48|0|1|22340|0.34|0.47|
|Small Firms (<=19 workers)|920|0.50|0.50|0|1|22340|0.47|0.50|
|Medium Firms (20-99 workers)|920|0.32|0.47|0|1|22340|0.32|0.46|
|Large Firms (>=100 workers)|920|0.18|0.38|0|1|22340|0.22|0.41|
|Firm Age (Years)|920|16.6|16.8|1|141|22340|15.6|16.4|
|Employment Growth (%)|920|24.0|96.7|-96.7|1400|22340|33.9|220.8|
|Private ownership share (%)|920|79.0|38.1|0|100|22340|82.5|35.7|
|Foreign ownership share (%)|920|12.8|30.9|0|100|22340|9.2|26.6|
|Privatized? (dummy)|920|0.23|0.42|0|1|22340|0.14|0.35|
|Exports as a % of sales|920|10.1|24.1|0|100|22340|10.5|24.8|
|GDP Growth %|920|5.8|4.6|-4.6|26.4|22340|5.8|4.0|
|GDP per capita (1000s $US 2002)|920|3.2|3.1|0.3|13.7|22340|3.4|2.5|
|% of banking assets in state banks|920|14.7|20.6|0.0|75.2|22340|18.6|20.4|
|% of banking assets, foreign banks 1999|920|31.6|30.1|0.0|92.3|22340|31.4|28.5|
|KKM, Institutional development|920|-0.02|0.66|-1.06|1.04|22340|-0.001|0.61|
|Population density, residents per sq .mile|920|76.3|32.2|5.5|135.0|22340|77.1|38.3|
|Population (millions)|920|12.4|21.5|1.3|145.0|22340|28.4|40.0|
|Inflation (%)|920|9.1|9.6|0.2|53.4|22340|9.4|8.5|
|Hotel, Services Industry (dummy)|920|0.13|0.34|0|1|22340|0.14|0.35|
|Construction Industry (dummy)|920|0.13|0.33|0|1|22340|0.09|0.29|
|Trade Industry (dummy)|920|0.37|0.48|0|1|22340|0.26|0.44|
|Transport Industry (dummy)|920|0.06|0.24|0|1|22340|0.06|0.23|
|Manufacturing Industry (dummy)|920|0.30|0.46|0|1|22340|0.40|0.50|
|<br>Pegged Exchanged Rate (dummy)|920|0.36|0.48|0|1|22340|0.22|0.42|
|Crawling Peg Exch. Rate (dummy)|920|0.53|0.50|0|1|22340|0.54|0.50|
|Band, Managed floating Ex. Rate (dummy)|920|0.09|0.29|0|1|22340|0.16|0.37|
|Current Account Deficit as % GDP|920|7.0|6.2|0.0|25.2|22340|6.6|6.5|

Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey, BEEPS data and from other sources

**Table 2: Firm status at time of Financial Crisis Survey**

||Number of firms|
|---|---|
|Group 1: Interviewed|1,686|
|_Of which: In bankruptcy or insolvency_|_26_|
|Group 2: Not interviewed but operating|545|
|Group 3: Could not be located|201|
|Group4: Confirmed as closed|69|
|Total|2,501|

Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey 40
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23 <!-- Start of picture text --> Total Firm Wage and Benefits Costs<br>1991.1 prices: 1991.1 - 1992.3<br>4.5<br>4<br>5.5<br>5<br>2.5<br>2<br>1.5<br>0.5!<br>91.1 91.2 91.3 91.4 92.1 92.2 92.3<br>0 Asset Size Closs I + Asset Size 11 0 DAset SiZe IIl<br><!-- End of picture text --> Fig 24 <!-- Start of picture text --> Total Real Housing Expenditures<br>(prices)<br>4<br>3.5<br>5<br>2.5<br>2<br>1.5<br>0.5<br>0<br>1 0 1991 2 0 1991 3 0 1991 4 0 1991 1 0 1992 2 0 1992 3 5 1992<br>0 Size Class t + SC2 o SC3 a SC4 x SC5 v All<br><!-- End of picture text -->
# **iii. Degree of commodity details**

Another key difference between the two surveys arises from the degree of commodity details. The list of non‐food items collected in the HBS is more extensive than in the NPS. This is particularly true for HBS 2011/12 where households were provided a very detailed list of the items to be reported in the recall module. For example, HBS 2011/12 solicited information for over 300 non‐food items compared to the 52 non‐food items solicited in NPS 2012/13.

According to Beegle _et al_ . (2012), a more detailed commodity list is expected to lead to higher consumption aggregates and lower poverty levels, but the differences between HBS and NPS poverty measures is contrary to what should be expected as HBS poverty indicators are significantly higher than NPS indicators.

Further, it is worth noting that the HBS survey instruments have improved significantly over time, while there were no substantial changes in survey methods between the NPS waves except for a few additions in the questionnaire for the third round. Great attention is generally devoted to the supervision of the NPS. To ensure strict control over data quality during fieldwork, the NPS survey uses a smaller and more closely supervised group of enumerators. The survey uses mobile teams, each consisting of seven people (1 supervisor, 4 enumerators, 1 data entry operator and 1 driver). These mobile teams spend only a few days in each enumeration area or village. The HBS on the other hand, used locally recruited enumerators who resided in their respective survey areas year‐round (i.e. resident enumerators). The HBS 2007 faced some supervision issues, but this problem was adequately addressed in 2011/12. There also have been substantial changes in the HBS design which can be summarized as follows:

**_Number of items and aggregation:_** The HBS 2011/12 captured food consumed outside the household which was not captured in the HBS 2007. The HBS 2011/12 also probed for a much larger number of non‐food items compared to the HBS 2007. For example, the HBS 2011/12 asked explicitly for expenditures on 70 different clothing items. In contrast, the HBS 2007 only probed for three broad categories of clothing (of males, females and children), though enumerators still recorded item‐ specific expenditures (using codes provided in a separate manual). The increase in the number of item categories in the HBS 2011/12 was expected to enhance the household’s recollection of expenditures and hence to increase measured consumption. However, at the same time, the HBS 2011/12 omitted certain non‐food categories that were included in the HBS 2007, which may

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At this point, it is worth reiterating that our results suggest insignificance for several management modes that figure prominently in discussions of forest governance and protection. These include management by national ministries, individual landowners, profit-making enterprises, local nonindigenous communities, non-profit organizations, and private entities.<sup>9</sup> Our results are also sparse for legally-defined status -- designated, inscribed or proposed. Only the latter has any significance, and that is in one region: LAC, where protection is much lower in parks that have been proposed but not yet legally designated.

# **4.2.2 Testing the Impact of Park Establishment**

Since our database identifies each park by start year, it permits us to perform quasi-experiments to test the effect of park establishment on deforestation for protected areas that have been established since 2002. We produce our estimation sample by extracting these 726 parks. Then we use statistical analysis to pose the question: Has acquisition of official protection status changed the outside/inside ratio significantly? If so, we have highly suggestive evidence that official protection has actually made a difference. If not, then we can tentatively conclude that the outside/inside ratios we observe are principally reflections of other factors. We should note that these factors may also be related to protection, which could be provided by a host of local and/or informal arrangements that are not reflected in official park establishment years. Advance notice of establishment may also create a perverse effect, in which clearing accelerates in an area before official protection begins. All things considered, our results might best be interpreted as measuring the net impact of enhanced effectiveness ex post vs. anticipatory acceleration of clearing ex ante.

> 9 For example, see the metaanalysis conducted by Geldmann et a, 2013..

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