rafmacalaba/datause-encoder-data huggingface dataset:
identifying actions and policy measures that will improve the ability of developing countries to use trade for development. Section 5 concludes.

# **1.  The mechanics of erosion**

It is helpful to start with a brief discussion of the basic mechanics of preferences and preference erosion.<sup>3</sup> Figure 1 represents an archetype OECD country importing varieties of good _X_ from two suppliers, an LDC (SLDC) and a non-LDC (Snon-LDC). Trade preferences imply a reduction in the tariff applied to imports from the LDC. This increases LDC exports from _X_ LDC,0 to _X_ LDC,1, with associated benefits for the LDC exporter represented by area A. There is also a concomitant shift in demand away from imports from the non-preferential supplier, resulting in a loss in exporter surplus equal to area B. The magnitude of the costs and benefits depend on supply and demand responsiveness to price changes, as well as the degree of substitution between preferential and non-preferential suppliers. The impact on the country granting the preferences depends on a mix of effects – terms of trade, trade creation, and trade diversion. For a beneficiary country preferences change the relative returns of producing a product – thus either promoting diversification or impeding it (as in the case of sugar or bananas). They also affect the overall terms of trade of the country, thus implying the equivalent of a net financial transfer.

Trade preferences therefore involve a mix of benefits for preferential exporters, costs imposed on third-country exporters, and potential losses for the importer as well. Only if the (more) preferred country (countries) is (are) small in the sense of not at all affecting the internal price in the importing nation will there be no detrimental effect on third country competitors. If so, the preference only creates trade (expands imports), to the detriment of local suppliers in the preference granting country, but not to other foreign suppliers, as they continue to confront the same price.<sup>4</sup> Preference erosion

> 3 What follows draws on Francois, Hoekman and Manchin (2005).

> 4 See Baldwin and Murray (1977) for an early discussion. Most empirical studies conclude however that preference programs are associated with negative terms of trade effects for excluded (less preferred) countries, i.e., there is trade diversion as well as trade creation. Much depends on having good estimates of the elasticities of substitution between foreign and domestic goods and between foreign products of

4
# **5 Conclusion**

Deciding to adhere to their tax obligations is a complex decision for taxpayers, and several factors motivate it. A large body of the literature has sought to understand the determinants of tax morale with limited evidence on the role of trust.

As a novelty, this paper considers simultaneously the role of vertical and horizontal relationships, captured by trust in public institutions and the neighborhood, on attitude towards taxes. First, the paper proposes a theoretical framework, where both trust in public institutions and the neighborhood are included in the standard model of tax evasion. Further exploiting the outcomes of the model, the paper examines the effects of both types of trust on tax morale in Algeria, Ghana, Morocco, and Nigeria using the WVS. As the relationship between trust and tax morale is potentially endogenous, the paper devotes efforts to address such issues employing the instrumental variable method. The identification strategy takes advantage of historical data on slave shipments by ethnic group and community-level social norms about trust in public institutions and in the neighborhood. The underlying hypothesis of using historical data on slavery is that the slave trade caused a culture of mistrust in Africa, which may persist to this day and alter taxpayers trust in public institutions and the neighborhood. It is well documented that the slave trade generated a general environment of insecurity as slaves were captured through state-organized raids and warfare, and caused individuals to turn against each other. So, I rely on the intensity of the slave trade measured as the ratio the number of slaves exported and the initial land area of the ethnic group. I use two additional instruments, which are the average trust in public institutions and the neighborhood at the community level. Both instruments rely on the literature on the cultural heritage of trust according to which cultural heritage about trust is a critical component of individual-level trust.

The estimation results indicate that both trust in public institutions and the neighborhood are significant determinants of individual tax morale in Algeria, Ghana, Morocco, and Nigeria. The magnitude of the effects is large and statistically significant at the usual signif35
Table 4: Dynamic Panel Regressions with Government Stringency Score

|||Ethi|opia|||Mal|awi|||Nig|eria||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||FS Index|Mild|Moderate|Severe|FS Index|Mild|Moderate|Severe|FS Index|Mild|Moderate|Severe|
|Lagged Fractional Index (FI)|7.570*|2.792|0.723|1.288|0.312|-0.795|0.505|-0.185|1.100|0.423|0.131|0.271|
||(3.036)|(2.887)|(1.754)|(0.832)|(0.430)|(0.577)|(0.421)|(0.219)|(0.644)|(0.670)|(0.480)|(0.225)|
|Lagged FS _×_ Lagged FI|5.086|-2.887|7.670|14.879|-0.466|0.927|-0.486|-0.442|-0.502|-0.348|0.358|-0.360|
||(3.054)|(3.623)|(4.371)|(8.628)|(0.393)|(0.615)|(0.539)|(0.919)|(0.540)|(0.710)|(0.607)|(1.079)|
|Lagged Food Security (FS)|-3.837|2.239|-6.233|-11.453|0.579*|-0.041|0.593|0.515|0.383|0.539|-0.032|-0.193|
||(2.578)|(2.820)|(3.626)|(7.484)|(0.273)|(0.420)|(0.371)|(0.682)|(0.354)|(0.455)|(0.384)|(0.712)|
|COVID-19 Stringency|0.120***|0.069*|0.013|0.012|0.015*|0.000|0.008|0.004|0.021*|0.011|0.007|0.003|
||(0.034)|(0.030)|(0.019)|(0.009)|(0.007)|(0.007)|(0.005)|(0.003)|(0.010)|(0.006)|(0.006)|(0.002)|
|Lagged FS _×_ COVID-19 Stringency|0.050|-0.028|0.081|0.138|-0.009|0.001|-0.011|-0.011|-0.007|-0.006|-0.002|0.000|
||(0.033)|(0.036)|(0.046)|(0.095)|(0.005)|(0.007)|(0.006)|(0.012)|(0.006)|(0.006)|(0.006)|(0.013)|
|Lagged FI _×_ COVID-19 Stringency|-0.097*|-0.035|-0.009|-0.018|-0.003|0.015|-0.008|0.003|-0.011|-0.006|-0.002|-0.003|
||(0.038)|(0.036)|(0.022)|(0.011)|(0.008)|(0.010)|(0.007)|(0.004)|(0.009)|(0.008)|(0.006)|(0.003)|
|Lagged FS _×_ COVID-19 Stringency _×_ Lagged FI|-0.066|0.035|-0.101|-0.182|0.008|-0.017|0.009|0.009|0.010|0.005|-0.002|0.007|
||(0.039)|(0.046)|(0.055)|(0.110)|(0.007)|(0.011)|(0.009)|(0.016)|(0.008)|(0.009)|(0.009)|(0.019)|
|Observations|12,031|12,031|12,031|12,031|9,303|9,303|9,303|9,303|3,277|3,277|3,277|3,277|

_Note_ : The table displays regression results from our dynamic panel specification with household fixed effects, round dummies, and region-time trends (see Equation 3.2). FI stands for Fractional Index while FS stands for our standardized index of food insecurity. Standard errors, clustered at the household, are reported in parentheses (*** p _<_ 0.001, ** p _<_ 0.01, * p _<_ 0.05).
maps of communes in Mali, and of ‘departments’ in Burkina which are administrative units similar to counties in the United States. We use digital maps from censuses to create commune/department level panel data. The panel data set based on the population censuses includes 371 departments in Burkina and 629 communes in Mali, giving us a relatively rich sample of 1,000 sub-districts for our empirical analysis.<sup>14</sup> The source of built-up and population density data is the Global Human Settlement Layer (GHSL) data set made available by European Joint Research Centre (Pesaresi et al. 2015) and Columbia University, Center for International Earth Science Information Network - CIESIN (2015). With spatially consistent information from four Landsat image collections, these data are available for 1975, 1990, 2000 and 2014. The built-up data are 250 meters by 250 meters, whereas we use 1 km squares for the population grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the percentage of built-up area and population density at the commune/department levels. The GHSL data on built-up density have at least two periods before the war which allow us to check the pre-war trends for both population density and built-up density.

The data on the luminosity of night-lights are drawn from global satellite database. The night-light data are available from 1992 to 2013.<sup>15</sup> We focus on 1992-2011 and exclude the last two years from the sample as data for some of sub-districts are missing in those two years. We use the census borders to construct a panel at commune/department levels. Our focus is on the average night-light luminosity per sq km. An important advantage of the night-light data is that they are available before, during and after the CIV civil war. To guard against possible serial correlation, we follow the suggestion of Bertrand et al.

> 14Total number of departments/communes in Burkina Faso is 374 and Mali 695. Some of the subdistricts lack data on building density and are dropped from the analysis presented here. When we repeat the analysis of employment shares without dropping those extra sub-districts and all results reported here are robust to the inclusion of those sub-districts. Using high resolution data that became available since the data construction of the Burkina Faso department boundaries, we slightly modified the urban boundaries to align with the expected location of urban areas using ancillary and satellite imagery keeping the same shape and resolution of the original data.

> 15The night-light data are collected from a series of DMSP-OLS satellites during the period 1992-2013. In order to enable temporal analysis, we correct for the lack of in-flight calibration using the method in Wu et al. (2013) to adjust for the differences between sensors, acquisition times, and saturation. A newer satellite VIIRS started in 2013. The data from the earlier satellite (DMSP) may not be comparable to that from the new satellite.

17
As a result, given that parameters and market-clearing conditions are quarter speci...c, equilibrium prices will also be quarter speci...c. That is, their values will vary from quarter to quarter, even in steady state.

# 3.2 Information Structure

Information is key to forming expectations for speculators, and therefore the information structure is a relevant issue in the model. This section constructs it to mimic reality as closely as possible.

Production shocks and annual demand shocks are revealed the quarter after they take place, so agents do not know at present moments what supply and demand are. They use both public and private signals to form expectations regarding these. Private signals are formed each quarter. Public signals, on the other hand, are realized only in quarters 1 and 4. The model incorporates public signals this way in order to map USDA reports; hence, it must mimic the same timing schedule. Private signals are private in the sense that they are developed by the private sector. They are observable to speculators, but not to the econometrician.

Information regarding demand and supply fundamentals is only revealed after each quarter is ...nished. During each quarter, therefore the agent does not know with precision what is driving price changes. As an example, in quarter 1, yield, annual demand, and quarter-speci...c demand shocks are realized, but the agent only knows them after the quarter is over. Therefore, in quarter 2 the annual component of demand is known, but not the quarter-speci...c one for quarter 2, and so on.

Uncertainty about current shocks takes place only in quarter 1, since in quarters 2 ,3 and 4 they only speculate with respect to next years fundamentals (which by de...nition have not taken place yet). So, in quarter 1, speculators can observe the current price, but since the current price will depend on the realization of three shocks (annual supply shock, annual demand shock and quarter 1 speci...c demand shock), they cannot identify or map back to fundamentals (there is no 1 to 1 mapping possible). The assumption used here is that if there is any information that can be captured from this it is negligible and for simplicity will be disregarded.

Agents have access to public reports that are issued in the ...rst and last quarters of the marketing year. The ...rst-quarter report predicts current annual demand and yield and the latter predicts the upcoming year’s amounts. Private forecasts regarding next year’s yield and demand, on the 20
SRI LANKA

**-** 2. Projections NRR-1 by 1995

|**Tw**<br>|**PapmAnti.n**|_p.<br> **ratft**|**_Pwled_**|6ut<br>**rate**<br>tC|en||**TFN**|O<br>**Iwim**|Ob<br>**Fmline**|In|q<br>x000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>1995<br>2000<br>2005<br>2010<br>2015<br>2020|16956<br>17923<br>10354<br>19657<br>2076<br>21845<br>22732|59.4<br>53.8<br>47.6<br>45.5<br>46.0<br>8.2<br>49.7|1990-94<br>1995-99<br>2000-04<br>2005-09<br>2010-14<br>a014-19<br>2020-24|1.11<br>1.01<br>1.04<br>1.00<br>0.91<br>0.80<br>0.71|19.0<br>17.7<br>17.5<br>17.1<br>16.1<br>14.9<br>14.1|S.8<br>5.7<br>S.?<br>5.9<br>6.2<br>6.4<br>6.8|2.23<br>2.10<br>2.09<br>2.09<br>2.09<br>2.0S<br>2.08|69.5<br>70.8<br>72.0<br>73.0<br>73.9<br>74.9<br>75.9|7n.9<br>75.3<br>76.8<br>77.8<br>78.8<br>79.9<br>81.0|18<br>14<br>12<br>10<br>9<br>7<br>6|21<br>17<br>14<br>13<br>11<br>9<br>a|
|2025<br>2050<br>2075<br>2100<br>2125<br>2150|23549<br>26414<br>_277_<br>27m<br>2S071<br>2U20|51.7<br>66.6<br>_69.6_<br>72.4<br>73.5<br>74.3|2025-49<br>2050-74<br>2075-99<br>2100-24<br>2125-49|0.46<br>0.13<br>0.07<br>0.04<br>0.02|13.1<br>12.2<br>11.9<br>11.8<br>11.7|8.6<br>10.9<br>11.2<br>11.3<br>11.5|2.07<br>2.06<br>2.06<br>2.06<br>2.06|78.1<br>80.2<br>81.3<br>82.2<br>82.5|83.6<br>86.2<br>87.7<br>U.9<br>89.3|4<br>3<br>2<br>2<br>2|5<br>4<br>3<br>3<br>3|

# 3. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 70-74<br>65.69<br>60-84<br>55-be<br>50-54<br>45.49<br>40-44<br>35.39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 S 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and In-ome Group, **1985-30**

TFR **e0** Growth rate <!-- Start of picture text --> Country legfon Income group<br><!-- End of picture text --> _Cewmtry_ Region IncOme grotp Country Region Income group 101
# **_(b)  The geography of dual-currency banking_**

The growth in the dollarization of bank deposits – creeping up, as has been mentioned, at about 1 percentage point per annum during the 1990s – remained largely unnoticed when the primary challenge was to bring inflation under control. But now, with few countries displaying high inflation rates, attention has turned to dollarization, which in many cases has in effect been a legacy of their inflationary past. This section asks what kinds of country have high dollarization, defined essentially as having more than half of bank deposits in foreign currency (Table 1).<sup>3</sup> Extraordinarily high rates of dollarization, in countries such as Cambodia<sup>4</sup> and Congo, DR, are a fossil remain of earlier episodes of hyperinflation and currency collapse. Such high rates can persist despite a protracted period of currency stability, as witness the case of Lebanon. In some such cases, the fear of another bout of currency weakness or turbulence in the presence of extensive dollarization is a contributing factor to wedding the authorities even more closely to a currency peg, though such pegs can become brittle as in Argentina, 2001.

> 3 How shall we define high dollarization countries?  First, recall that we are dealing with dollarization of the banking system and data is most widely available for deposits (rather than loans).  Any banking system with more than half of its deposits denominated in foreign currency can, on a reasonable common-sense criterion, be described as highly dollarized.  A roughly equivalent alternative is the top quartile of the 130some countries for which data is available.  And these criteria could be applied to the latest year, the average of the latest few years, or the maximum reached since 1990.  Actually most of these criteria select roughly the same group of countries.  Let us therefore work with all countries that makes the grade on any of these criteria.

> 4 Bolivia had the highest ratios before the Cambodia data enters the series.  Since then Cambodia has had the highest value in each year, with no sign of decline – indeed the last year is also the highest for Cambodia.

6
<mark>The endline survey was conducted immediately following the pilot intervention (in March 2017). While the baseline survey had both qualitative and quantitative elements to gauge beliefs and behaviors around latrine use, the endline survey used primarily quantitative measures to identify changes in individual behavior and beliefs, as well as changes in social empirical and normative expectations. A total of 252 households were surveyed at endline. However, only 156 of these respondents were common across both rounds, as respondents from some baseline households could not be reached or located at endline. The analysis of the pilot intervention results is thus restricted to these 156 households.</mark> Respondents were asked to answer questions using Likert scales—answers to questions about where people defecate ranged from “Always defecate in latrine” to “Always defecate in the open” (five options), while questions about beliefs were posed as agree‐disagree statements (agree, neither agree nor disagree, disagree). Scores were coded on a scale of 0 to 1, with 1 being the most pro‐latrine answer in each case and 0 being the least. For example, when asked where respondents usually defecate, those answering “Always in the latrine” were given a score of 1 and those answering “Always defecate in the open” a score of 0. Answers in between—“Usually defecate in latrine”, “Sometimes in the latrine, sometimes in the open”, and “Usually defecate in the open” —received a score of 0.75, 0.5 and 0.25, respectively. Negative statements (e.g. “It is okay to defecate in the open if no one sees you”) were reverse‐coded using the same Likert scale coding method to ensure all pro‐latrine behaviors or beliefs were similarly coded. Questions regarding norm enforcement were binary variables, with positive enforcement behavior receiving a score of 1.

Normative statements (both about respondents’ own beliefs and social normative expectations) were grouped together into three indices—whether open defecation is bad (bad, harmful and shameful), whether exceptions are not okay, and a combination of both to reflect overall favorability of latrines over open defecation. An additional index was created for all social norms (inclusive of both social normative and social empirical expectations). Indices reflect the average of scores across all relevant questions. All indices report relatively high internal consistency within their respective scales, with Cronbach’s alphas ranging from 0.70 to 0.85 (see Table 3 below).

**Table 3. Alpha Scores**

|**Index**|_Alpha_|
|---|---|
|_Personal normative beliefs_||
|OD is bad (3 items)|0.7016|
|Exceptions not okay (5 items)|0.8092|
|All norms (8 items)|0.7696|
|_Social normative expectations_||
|OD is bad (3 items)|0.7422|
|Exceptions not okay (5 items)|0.8512|
|All normative beliefs (8 items)|0.7858|
|All social norms(9 items)|0.7807|

**Baseline Findings**

8
Table 3. Spillovers from KF-METRs outside the Host Country?

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Rate Diferential|1.501***|1.497***|1.487***|1.384**|
||(0.514)|(0.513)|(0.510)|(0.543)|
|Host KF-METR|-0.002|-0.002|0.001|-0.006|
||(0.004)|(0.004)|(0.009)|(0.006)|
|Parent KF-METR||-0.005|-0.002|-0.005|
|||(0.006)|(0.009)|(0.006)|
|Mean Non-host KF-METR|||0.137<br>(0.342)||
|Lowest Non-host KF-METR||||-0.018<br>(0.013)|
|Constant|-31.598**|-32.426**|-34.436**|-32.072**|
||(12.503)|(12.679)|(13.496)|(12.673)|
|Observations|8,008|8,008|8,008|8,008|
|Pseudo-_R_<sup>2</sup>|0.0816|0.0817|0.0817|0.0819|
|Controls:|Host, Parent|Host, Parent|Host, Parent|Host, Parent|
||Bilateral|Bilateral|Bilateral|Bilateral|
|FEs:|Year, Parent|Year, Parent|Year, Parent|Year, Parent|

_Notes:_ All specifications are estimated in PPML regression using an unbalanced panel of all available host-parent-year observations for which the parent-country KF-METR is available. All specifications include parent country and year fixed effects, and the following characteristics of the host and parent countries: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Standard errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

52
exporters are particularly reliant upon imported inputs.<sup>7</sup> If domestically produced goods are lower quality than imported goods (especially from industrialized economies), this might make it difficult for exporters to produce goods to international standards when imports are restricted.

In practice, tariffs have been falling throughout much of the region over the past decade for capital goods, intermediate goods and total imports. Among the seven countries in this study for which data were available, average tariff rates fell for all three categories in six of them (see Table 4). In the final country, Mali, tariff rates increased slightly for capital goods and for overall trade and increased more significantly for intermediate goods. However, this appears to be partially due to tariff rates being relatively low in the mid-1990s. As a result, average rates in Mali were comparable to rates in the other countries by the early 2000s. Furthermore, tariff rates do not appear any higher in Africa than in the more successful exporters in East and South Asia. In fact, except for the Philippines where rates are significantly lower than elsewhere, average tariff rates were generally lower in Africa than they were in East and South Asia.

Tariffs and non-tariff barriers are not the only policy-related barriers to trade. In addition to these barriers, customs and trade regulations can also affect exports and imports. In many developing countries, it takes a relatively long time for exports and imports to clear customs procedures and in some countries additional informal payments to customs officers are needed to ensure timely processing. In addition to long processing times, the paperwork associated with importing and exporting can be burdensome (Milner _et al.,_ 2000). Further, some enterprises need to complete additional procedures, such as getting import or export licenses, to import intermediate inputs, raw materials or capital goods and to export their final production. Finally, rebates under duty drawback schemes and for value-added taxes are often slow, sometimes for less than the full amount and even can require additional informal payments or bribes in some cases.

Although it is difficult to quantify all aspects of trade and customs regulations, the investment climate surveys include some questions that address these questions. The most direct

> 7 In the sample of firms in this surveys exporters are more likely to use imported intermediate inputs than non-exporters.  Imports account for 50% of inputs for exporters, compared to 37% for non-exporters.  This is consistent with results for Tanzania in Grenier _et al_ . (1999)

7