rafmacalaba/datause-encoder-data huggingface dataset:
Data is shown separately for different payments methods employed by nonbanks, such as credits, direct debits, cheques, e-money payment transactions, and card transactions of different types. Interbank transactions through the major automated clearing systems are also shown.

In 2007, aggregate payments of nonbanks reported in the Red Book came to USD 479 trillion, with USD 2459 trillion in interbank payments. Adding these two together gives us a round figure of USD 3000 trillion in payments. Since this is almost one hundred times the aggregate GDP of the countries included in the Red Book,<sup>21</sup> it becomes clear why it could seem superficially plausible that a very small tax rate – a fraction of one percent – might generate almost all the revenue any government could need.

Interestingly, though, there is a sizable variation across countries in the ratio of payments transactions to GDP, varying – for the most recent year available, i.e. 2007, from 36 times in Italy and 55 times in Sweden (2006) to 129 in the US and 147 in Hong Kong (even though the Hong Kong data only includes interbank transactions (Figure 1). This is not merely a function of whether or not the country hosts a global financial center: Germany and France also have multiples in excess of 100, while Singapore is the fourth smallest country.

The wide variation suggests that payments transactions may not be stable in response to influences such as the imposition of a transactions tax. The volatility over time in the ratio is also sizable in some countries (Figures 2, 3; Tables 1, 3), with a coefficient of variation as high as 40 percent in Switzerland – though it is likely that much of that is attributable to some institutional or definitional changes.

Turning to non-interbank payments transactions, the aggregate value ratio to GDP for the reporting countries is _much_ lower at under 15. Furthermore, the figure for the UK – 77 – is a wide outlier, certainly reflecting its status as a financial center and likely especially

> and deepened its coverage but added only two additional countries (Hong Kong and Singapore), as well as the Euro zone, to the original 11.

> 21 The ratio is actually 89 for 2007, and varies between 75 and 89 in the period 2000-2007

18
expansion across Cox’s Bazaar since 2010. More recently, forests declined by nearly 20 percent from 2017 to 2020, while settlements expanded by 21 percent between 2017 and 2020.

Across the district of Cox’s Bazar, we use a stratified random sampling method to select 10,000 points and build a spatially explicit pixel-year panel data set that identifies the geodesic distance between each point and the nearest camp (in kilometers), as well as each point’s land cover class by year. Based on our initial descriptive analysis, we designate pixels 0-5 km from the nearest camp as part of the “treatment” group and pixels 10-15 km from the nearest camp as “control.” Using a difference-in-difference framework that includes year and pixel fixed effects, we find evidence of significant forest loss 0-5 kilometers from camp boundaries. All else equal, we find that pixels within this distance buffer were 7.6 percentage points more likely to become deforested relative to the control area after camp openings. Our heterogeneity analysis suggests that these forest losses are concentrated outside protected natural areas. Moreover, we find that settlement areas are expanding faster in the 0-5 km buffer area relative to control: pixels within the treatment buffer were 13 percentage points more likely to convert to settlement after camp openings.

# **2. Literature Review**

Why might refugee camps trigger forest cover losses? The arrival of thousands of displaced people constitutes a sudden and large increase in the local population count. Population growth has the potential to drive forest loss because of the resulting increase in the area needed for shelter and for agriculture, which (in the absence of appropriate technologies) requires an expansion in agricultural land use, often at the expense of forests.

There are other reasons why refugee camps can trigger forest cover loss. Evidence from Kakuma Camp in Kenya shows that camps can stimulate economic activity (Alix-Garcia et al., 2018). This is partly because refugees often participate in local markets. They expand the local supply of certain goods when they sell portions of their in-kind aid package. They also influence demand when they draw on savings, remittances, credit, and (in some cases) income to make purchases (Betts, Bloom, Kaplan, & Omata, 2017). If camps stimulate additional market activity at the outskirts of the camp, then this activity may lead to forest cover loss.

4
|Training for innovation||0.343***||0.348***||0.337**||0.292*|
|---|---|---|---|---|---|---|---|---|
|||(0.106)||(0.109)||(0.155)||(0.157)|
|Equipment for innovation||0.884***||0.911***||1.055***||1.063***|
|||(0.092)||(0.096)||(0.134)||(0.134)|
|Purchase of knowledge||0.127||0.178||‐0.300||‐0.230|
|||(0.168)||(0.175)||(0.283)||(0.286)|
|Constant|‐0.621*|‐1.014***|‐0.633*|‐1.337***|‐0.284|‐0.878***|‐0.409|‐1.195***|
||(0.332)|(0.213)|(0.358)|(0.224)|(0.522)|(0.313)|(0.513)|(0.315)|
|Observations|8,550|8,550|7,854|7,854|1,868|1,868|1,747|1,747|

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Country & ISIC 2‐digit FE not shown 35
some countries such as Costa Rica, Uruguay and Chile devote a large fraction of government spending to public goods, most of the countries considered spend a much smaller fraction in public goods, many of them less than 50%. That is, they spend a large fraction of the government budgets in non-social subsidies which as shown by López, Thomas and Wang (2008) are mainly geared to subsidize large corporations and the income of the elites. Table 6 provides more details about the evolution of government spending for four countries, China, India, Brazil and Chile. The table shows the share of expenditures in items that can be clearly identified as subsidies to the elites (the “Type B” expenditures); all four countries spend a large fraction of the budget in such subsidies. Table 7 shows that in several countries just fuel subsidies absorbed an inordinate part of the government total expenditures. Government spending in private goods thus tends to crowd out spending in public goods.

The impact of these policy biases is reflected in the dynamics of the various assets over time. In general the development of asset growth accounting data is still in its early stage. However, with the limited information available one can at least get an idea of the asset growth for certain countries, especially middle-income and high income countries which tend to have a better informational base. Table 8 compares the growth rates of tangible versus intangible assets for a sample of countries in Latin America over the 1990-2001 period. The table shows that while in all but two countries tangible assets (physical capital) exhibit positive per capita growths, in all but two countries intangible assets have experienced declines. Obviously there is no reason to believe that asset growth should be balanced (although a key tenet of endogenous growth theory is to postulate balanced asset growth as an optimal equilibrium condition) but such a big disparity in asset growth over a relatively long period of time and for all countries is likely to reflect economic inefficiency.

Table 9 shows a more aggregate picture of the disparities between growth in physical versus human capital for a large sample of middle-income and high-income countries based on recent data of wealth indicators developed at the World Bank. The physical versus human capital gap is particularly large among lower middle income countries where the growth rate of physical capital almost doubles the rate of growth of human capital. These indicators tend to overestimate the true growth of human capital because they do not account for the qualitative aspects of education. In general countries are able to increase school enrollment quite often but the goal of education quality 10
intake for richer households (75<sup>th</sup> quantile) is over three times as large as that for poorer households.

These adjustments in caloric intake link directly to concessions in dietary quality. Households at the bottom of the dietary diversity distribution must make larger reductions in the diversity of their diets to maintain energy levels, relative to those at the top of the distribution. The richer households (75<sup>th</sup> quantile) make larger percentage reductions in the quantity (calories) of food consumed than in the quality (dietary diversity) of food consumed.

These findings are consistent with the literature on the impact of high food prices on nutritional outcomes. For example, Klotz _et al._ (2008) argue that during times of economic crisis and when households cannot increase the amount that they spend on food, they are forced to cut back on expensive, micronutrient-rich foods to maintain their consumption of core staples. Therefore economic shocks will lead to micronutrient deficiencies before weight loss. Jensen and Miller (2008) similarly find that in the face of food price inflation, poor urban households in China substitute toward cheaper foods.

These findings are also consistent with past studies related to the impact of economics shocks on nutrition. Diagana _et al._ (1999) find decreases in levels of dietary diversity and changes in food consumption patterns after the 1994 devaluation of the CFA franc using data from West Africa. Martin-Prevel _et al._ (2000) find reductions in maternal and nutritional status following the same currency devaluation. And Block _et al._ (2004) find declines in maternal and child nutritional status following drought and financial crisis in Indonesia in 1997-98. See Ruel _et al._ (2010) for a more detailed review of the literature on the effects of economic crises on household wellbeing.

# _Policy-relevant thresholds_

In terms of the value of food consumption and caloric intake, the marginal effects estimated at the policy-relevant cutoff by UQR are smaller than those estimated by OLS, implying that the average household is more affected by price shocks than those living near subsistence levels (Table 7). For example, OLS estimates indicate that the wheat-price elasticity of food consumption is -0.43, but when we estimate this elasticity at the food poverty line using 17
39 simulate the welfare implications of reducing subsidies for various types of food. For instance, Laraki (1989) finds that reduced subsidies would adversely affect the poor, despite the pro-rich distribution of the food subsidy.34

# Consumer Price Subsidies

Eastern Europe's desire to move to a private market-based economy raises questions concerning the equity effects of removing consumer subsidies. The Govemment of Hungary (1989) has undertaken a partial equilibrium analysis for Hungary. Data from a household survey undertaken in 1987 and updated to 1989 are analyzed. The main conclusion of the Hungarian report is that the distribution of the per capita total subsidy is pro-rich, as the amount of the per capita subsidy increases with income decile. More specifically, per capita subsidies for rent, fuels, milk and diary products, transportation, cinema and theater, and mortgage interest are prorich (see Government of Hungary, statistical appendix, page 39).

On the other hand, the subsidy amount as a percentage of income decreases with income decile implying that the subsidy has an equalizing effect on the distribution of income. For specific products, the subsidies to milk products, fuel, transportation, school books and mortgage interest are pro-poor when measured as a percentage of income, while subsidies to cinema and theater are sdll pro-rich (see Govermment of Hungary, statistical appendix, page 42). In addition, the analysis reveals that the total subsidy as a percentage of income increases with family size and has an urban bias.

# VI. Conclusions and Suggestions for Further Research

The available empirical results for developing countries suggest that the benefit incidence of spending on human services, such as education and health, is pro-poor when measured as a percentage of income and either pro-poor or pro-rich when measured in dollar terns depending on the public service under analysis. Benefit recipients are also particularly concentrated in urban areas, perhaps because of lower delivery costs. Government agricultural programs are typically believed to be distributionally neutral in percentage terms arnong farmers--who may be poorer than the national average, but wealthier than agricultural laborers or rural nonfarn laborers. The
# **Figures and Tables**

Figure 1: The Effect of VAT Rebates on the Use of Electronic Payment Technology Raw and De-Seasonalized Data <!-- Start of picture text --> A. Raw Trends B. De-seasonalized Trends<br>I. Number of Card Transactions<br>⇐ August 2014<br>VAT Rebates of up to<br>4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>3 3<br>2.5 2.5<br>2 2<br>Log Number of Card Transactions Log Number of Card Transactions<br>1.5 1.5<br><!-- End of picture text -->

- II. Volume of Card Transactions

<!-- Start of picture text --> ⇐ August 2014<br>VAT Rebates of up to<br>4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>III. Number of POS<br>⇐ August 2014<br>VAT Rebates of up to<br>4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>IV. Number of Firms with a POS<br>⇐ August 2014<br>VAT Rebates of up to<br>4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>10<br>10<br>9.5<br>9.5<br>9 9<br>Log Volume of Card Transactions Log Volume of Card Transactions<br>8.5 8.5<br>11.6 11.6<br>11.4 11.4<br>11.2 11.2<br>Log Number of POS Log Number of POS<br>11 11<br>10.8 10.8<br>10 10<br>9.9 9.9<br>9.8 9.8<br>9.7 9.7<br>Log Number of Firms with POS Log Number of Firms with POS<br>9.6 9.6<br><!-- End of picture text --> Notes: Panel A plots the monthly aggregate values for each of the outcomes. For row I, the outcome is log of millions of transactions. For row II, it is log of millions of pesos. POS stands for point-of-sales terminal, i.e. credit/debit card machine. We average over the months of April and May 2014, for reasons discussed in Figure B.1. Panel B plots the de-seasonalized trends after taking out month-of-year fixed effects, as per equation 1 (linear specification). This Figure is discussed in Section 4.1.

26
commercialization have been running down their stocks of cocaine with the expectation that the current level of anti-drug policies cannot last much longer. However, there is no significant evidence to support this explanation. In any case, if it were true, the stocks “should be soon exhausted and a contraction of the market should then become visible.” (UNODC, 2005a).

While it is very hard, if not impossible, to directly verify some of the above explanations due to obvious reasons including the lack of price records and transaction quantities at each of the different commercialization stages (or the lack of access to the drug traffickers’ accounting books), the availability of better and more reliable data on coca cultivation, yields, consumption, etc.—as it becomes available—will help clarify the validity of other possible explanations. Specifically, field studies in coca growing regions such as the ones currently being cunducted by UNODC, will continue to help clarify whether there has been an increase in yields, better planting techniques, etc. In other words, better assesments of productivity parameters (and how they change over time) are key to understanding the cocaine market. And, going back one step in the cocaine production chain, field studies would be very helpful to confront the measures of coca cultivation obtained from satellite images with those that would be obtained directly in the fields. This is because, as explained above, there is anecdotal evidence that coca growers have found ways to avoid being detected by the satellite images, and therefore, the measures obtained from satellite pictures might be biased downwards. Random, in-the-field measures will provide an estimate of this bias. Also crucial for the analysis of the cocaine market is the understanding of the response of drug producers to anti-drug policies. The following section elaborates this point.

27
**Table 4:  Summary Statistics on Governance Indicators**

||**Voice and**<br>**Accountability**|**Political**<br>**Stability/**<br>**Absence of**<br>**Violence**|**Government**<br>**Effectiveness**|**Regulatory**<br>**Quality**|**Rule of Law**|**Control of**<br>**Corruption**|**Overall**|
|---|---|---|---|---|---|---|---|
|**Number of Cou**|**ntries**|||||||
|1996|194|180|182|183|171|154|177|
|1998|199|189|194|194|194|194|194|
|2000|200|190|196|196|196|196|196|
|2002|201|190|202|197|197|197|197|
|2003|201|200|202|197|202|198|200|
|2004|208|207|208|204|209|205|207|
|2005|208|208|209|204|209|205|207|
|2006|209|209|212|206|211|207|209|
|2007|209|209|212|207|211|208|209|
|**Median Numbe**|**r of Sources Per**|**Country**||||||
|1996|4|4|3|4|6|4|4|
|1998|5|5|4|5|7|5|5|
|2000|5|5|5|6|8|6|6|
|2002|7|6|8|8|11|7|8|
|2003|8|6|8|8|11|8|8|
|2004|8|7|9|8|12|9|9|
|2005|9|7|9|8|12|9|9|
|2006|10|8|10|9|13|11|10|
|2007|11|8|11|9|13|11|11|
|**Proportion of C**|**ountries with On**|**ly One Data Sou**|**rce**|||||
|1996|15|16|21|11|6|18|15|
|1998|11|7|10|10|9|10|10|
|2000|11|8|8|7|7|8|8|
|2002|10|7|5|7|7|8|7|
|2003|3|10|5|7|5|7|6|
|2004|6|6|8|7|9|8|7|
|2005|6|5|8|7|8|7|7|
|2006|6|3|9|8|8|8|7|
|2007|6|3|8|9|8|8|7|
|**Average Stand**|**ard Error**|||||||
|1996|0.25|0.37|0.34|0.41|0.30|0.32|0.33|
|1998|0.27|0.31|0.18|0.30|0.22|0.24|0.25|
|2000|0.26|0.32|0.22|0.28|0.19|0.22|0.25|
|2002|0.21|0.30|0.22|0.25|0.19|0.22|0.23|
|2003|0.20|0.30|0.22|0.21|0.20|0.20|0.22|
|2004|0.21|0.29|0.23|0.21|0.19|0.20|0.22|
|2005|0.20|0.28|0.21|0.21|0.19|0.19|0.21|
|2006|0.18|0.26|0.23|0.21|0.19|0.19|0.21|
|2007|0.18|0.26|0.23|0.22|0.19|0.19|0.21|

32
allocated budget, the share of the allocated budget that comes from the exploitation of natural resources, total population in 2007, the average number of years of schooling of the population aged 15 and older in 2007, the number of whitecollar personnel as percentage of total population, the poverty rate in 2007, the percentage of urban population in 2007, the percentage of winning votes of the elected mayor in the 2006 elections, a binary variable of value 1 if the elected mayor is an incumbent, and year-specific effects. Spending data cover all 1,834 Peruvian municipalities; however, because of other data missing, the final sample consists of an unbalanced panel of 1,688 municipalities and 3 years of data (rendering 4,858 observations).

The basic estimation methodology is ordinary least squares regression (OLS) with robust standard errors, clustered by province (a political demarcation of adjacent districts/municipalities). The results are robust to the use of weighted least squares regression (WLS) and median regression (MR) that controls for the influence of outliers. In addition, we apply quantile regressions (QR) in order to allow the estimated effects to vary according to the degree of budget execution.

Table 1 presents the basic OLS regression results for total spending, for spending disaggregated by current and capital expenditures, and for defined capital expenditures. In what follows, we organize the discussion of results using the framework introduced above, which separates the determinants of the budget execution rate along the issues of the budget size and allocation process, local capacity, local needs, and political economy constraints.

Let us first discuss the variables related to the budget. A larger allocated budget is significantly related to a lower execution rate; and this is the case for both total and disaggregated expenditures, with a larger effect on capital than current expenditures. Thus, it seems to be harder to spend a larger budget, especially when this requires planning, project preparation, and implementation capacity. The budget share of natural resource revenues has an ambiguous impact on the execution rate of total expenditures. This ambiguity is, however, clarified once expenditures are disaggregated. While for current expenditures the share of natural resource revenues has a significantly negative impact on the budget execution rate, for capital expenditures this impact is significantly positive. This can be explained by the fact that 11
country level, most FDI to Africa comes from the U.S. Consequently, it is not surprising that G7 countries accounted for 72 percent of the combined BRICS’s and G7’s direct investment stock in Africa between 2001 and 2012 (Table 2). The majority of BRICS’s direct investment went to Tanzania, followed by Zimbabwe, Angola and Botswana. In contrast, G7 countries’ direct investment was predominantly in Angola, Equatorial Guinea, the Republic of Congo and Tanzania. Moreover, G7 countries’ direct investment tends to dominate that of the BRICS in most African countries with the exception of Zimbabwe, Ethiopia, Democratic Republic of Congo and Botswana, where the proportion of BRICS’ direct investment is relatively higher than that from G7 countries.

Table 2: BRICS’s and G7’s average direct investment in millions of USD in selected African countries, 2001-2012

||BRICS' FDI-in-Stock|% of total BRICS' FDI|G7's FDI-in-Stock|% of total G7's FDI|
|---|---|---|---|---|
|Angola|402.99|10.21|5959.37|29.07|
|Botswana|387.45|9.82|146.24|0.71|
|Cameroon|26.85|0.68|967.47|4.72|
|Chad|42.77|1.08|119.10|0.58|
|Congo, Rep.|93.42|2.37|2263.88|11.04|
|Congo, Dem.<br>Rep.|252.09|6.39|36.11|0.18|
|Equatorial<br>Guinea|66.85|1.69|2455.54|11.98|
|Ethiopia|172.52|4.37|96.00|0.47|
|Gabon|78.44|1.99|1518.35|7.41|
|Côte d’Ivoire|22.72|0.58|948.07|4.62|
|Kenya|220.73|5.59|1076.14|5.25|
|Liberia|98.04|2.48|480.18|2.34|
|Madagascar|34.79|0.88|1040.25|5.07|
|Tanzania|1208.23|30.62|1734.26|8.46|
|Uganda|304.69|7.72|1494.15|7.29|
|Zimbabwe|533.16|13.51|165.54|0.81|
|Total|3945.72|100|20500.63|100|

Source: Authors’ computation based on UNCTAD 15