rafmacalaba/datause-encoder-data huggingface dataset:
_Table 2: Main Entry Points for Imports in the Western DRC, 2007_

|**_Customs office:_**|**% shar**<br>|**e of import**<br>|**s interms of **<br>|
|---|---|---|---|
||_value_|_weight_|_transactions_|
|Matadi|65.05|84.17|65.20|
|Boma|0.02|0.02|0.06|
|Kinlau(fuelport)|0.54|0.65|0.32|
|Banana|1.22|0.16|0.57|
|Kimpangu(Angolan border)|0.00|0.00|0.00|
|Kinshasa Port(ONATRA freightport)|15.92|10.50|10.68|
|Kinshasa Airport|9.47|0.27|15.82|
|Kinshasa Beach Ngobila(ONATRA ferry port)|0.07|0.11|0.28|
|Other Kinshasaports|7.72|4.12|7.07|
|_Total_|_100_|_100_|_100_|

Source: own computations, based on data from DGDA.

**Almost no DRC imports from RC find their way into official trade statistics.** According to the data summarized in Table 2, the two customs offices likely to clear imports originating in neighboring countries, the ferry port Kinshasa Beach (RC) and Kimpangu (Angola), record next to no imports.<sup>9</sup> Only ONATRA’s Kinshasa freight port declares a significant volume of imports, accounting for some 16 percent of the value of Western DRC imports. Given that there is no formal upriver trade (with CAR and Cameroon) left, this points towards non-negligible formal imports from Brazzaville. These imports could be of two kinds: overseas imports that have been unloaded at Pointe Noire (RC) or Douala (Cameroon) and transited through Brazzaville, or imports of goods produced in RC. However, we could obtain detailed trade data only for Kinshasa Beach, not for the freight port. Detailed data obtained from DGDA show only five small RC-originated shipments recorded for all of 2007, representing but a fraction of a percent of Western DRC imports. Recorded volumes of imports that have transited through RC are somewhat larger, but also account for well under one percent of Western DRC imports. The main recorded DRC import goods from RC are fuel, vehicles, building materials, cement and mineral water.<sup>10</sup>

> 9 For Kimpangu, the 2007 statistics report a single shipment (USD 660 worth of beans). Most trade with Angola clearly is informal and therefore goes unreported. For Kinshasa Beach, see also Table 3.

> 10 Some DRC imports from RC are not recorded by DRC customs but by the port authority in Brazzaville (see Table 5).

11
designed to measure. Data are available for each of the 18 countries for the years 1996, 1998, 2002, and 2004. We have imputed for the firm survey years of 1994 to 1997 the governance indices of 1996. Likewise, we have assumed that governance indicators for 1998 apply to 1999 and 2000, while those of 2002 apply for 2001 as well. We impute for 2003 the observations of 2004.

Our analysis involves three sets of indicators of economic geography. One of these consist of the three key regressors of the structural exporting equation that we would like to estimate, namely, national supplier access, local supplier access and foreign market access. The second consists of aspects of natural geography that influence market access and supplier access through their direct effect on trade costs. These include distance from national and international markets, and proximity and access to the sea. The third consists of the size and productivity of domestic industry which is assumed to influence exports through its effect on the domestic component of supplier access, by which we mean the price index of domestic inputs.

The exact list of variables in the second set are given in Table 1, and includes the following: a) a dummy for whether or not the country in which an establishment operates is land locked; b) the area of the country where the establishment is based; and c) a variable that we have termed “international distance, which we define as the log of the distance (in km) from the nearest port of the city in which the establishment is based scaled up by the national average of the ratio of CIF prices to f.o.b.. prices of imports in 1995.<sup>12</sup> We include in the third set country GDP’s, local (or city level) GDP’s, and the paved percentage of a country’s road network. City GDP being unavailable in every case, we estimate these by assuming that a city has the same share in a country GDP as it has in the country’s population.

Turning to the geographic variables of the structural exports equation, we estimate (national) supplier access and foreign market access by using the following formulae that Redding and Venables (2000) propose in an earlier version of their 2004 paper. The formulae used to estimate foreign market access and national supplier access are, respectively: and Λ = [exp( _isli_ )] ρ ˆ5 [exp( _isl j_ )] ρ ˆ6 [exp( _llocki_ )] ρ ˆ 7 [exp( _llock j_ )] ρ ˆ8 [exp( _openi_ )] ρ ˆ9 [exp( _openj_ )] ρ ˆ10 (14)

> 12 The ratio of c.i.f prices to f.o.b. prices of imports is a direct measure of iceberg transport costs which bilateral physical distances can at best proxy. The ratio minus unity gives the iceberg transport of imports, which depends not only on physical distance from sources of imports but also on the efficiency of ports and the competitiveness of markets in shipping and air services. The importance of the latter is illustrated by the fact that the 1995 transport cost for Cote D’Ivoire imports were 25% of the total value of imports. This contrasts sharply with the corresponding figure of 7% of its immediate neighbor, Ghana, which also had more or less the same sources of imports as Cote D’Ivoire in 1995.

10
> For example, if _B j_ is the quality of the electricity supply from the grid, and _Gij_ is the firm’s generator, then _B j_ and _Gij_ combine to create the firm’s electricity input _Eij_ .

> Spending money on _Gij_ means the firm can generate its own electricity when there is an interruption in supply from the main electricity grid ( _B j_ ).   Electricity is then combined with labor to create output.  Although the quality of the business environment at country level cannot be directly observed, there are country-level

> proxies for _B j_ such as the degree of reliability of the electricity supply (e.g., in terms of outages).

> Corruption is another example.  In this case, _B j_ is a measure of the honesty of government bureaucrats in country _j_ , but the public input _Bij_ supplied to firm _i_

> depends not only on _B j_ but also on productivity _Aij_ : high productivity firms will attract attention from dishonest government officials looking for bribes. As a consequence, the quality of the public input supplied to high productivity firms will be lower than that supplied to low productivity firms that are ignored by the bribeseeking officials.  Thus if _B j_ is the honesty of the bureaucracy in country j, _Bij_ is the inverse of the number of inspections that a firm with productivity _Aij_ attracts (more inspections means a lower quality public input _Bij_ supplied to the firm), and _Gij_ is bribes.

Central to our analysis are the “Subjective Severity” indicators collected in the Enterprise Surveys. These are responses to questions about a feature of the business environment faced by the firm, where the question takes the form, “How much of an obstacle is X to the operation and growth of your business?”, and the respondent rates the severity on a 5-point scale of 0 (“no obstacle”) to 4 (“very severe obstacle”).

The key point about these subjective severity indicators is that these are _not_ estimates of the country-wide public input _B j_ , or even of the public input _Bij_ supplied to firm _i_ ; they are _valuations_ of the public input. A simple and intuitive interpretation is that the “reported cost” _Rij_ of a public input is the gap between the firm’s profit in the 7
WPS3945 rates and the large observed gender gap.<sup>11</sup> At the household level, school enrollment and retention rates typically vary signi fi cantly with income and the gender gap declines as income increases. This pattern is evident in Figures 1-6 in the appendix. As wealth increases, enrollment rates rise and dropout rates decline across the board. The gender gap in both also narrows. as does the gap in completed grades, and both boys and girls do equally well in terms of completed grades for age if they remain in school. This suggests that migration should not only increase school enrollment and retention rates, it should yield relatively higher bene fi ts for girls. This is indeed borne out in a simple comparison, by household migration status, of mean enrollment and dropout rates in our sample. Children in migrant households have higher levels of enrollment and lower dropout rates. Girls also do better in terms of completed grades (see table A2 in the appendix) and there is some evidence of smaller gender gaps in all outcomes.

Migration could, in principal, generate countervailing effects on child labor market activity. The relaxation of credit constraints should reduce participation in the labor market, while the potential disruption in family life and lack of available adults could place greater labor demands on the time of school age children, particularly in home production activities. Fortunately, we can use data on the time spent by children in household production and wage labor to directly examine this question. While these simple mean comparisons are only suggestive at this stage, we do not appear to be in world where the income effect of migration is dampened or reversed by greater labor demands on the time of school age children due to migration induced family life disruption.

Nonetheless, we can examine this question directly by looking at the labor market activity of school age children. We have labor market participation information for 5780 children age 7 to 17 who belong to 1992 sample households. There is data on fi ve major categories of work. Work on the family farm, agricultural wage work, work on a family enterprise or home based productive activity of any kind, non-farm wage work and care of livestock. For children up to age 13 we also have information on time spent on fetching fi rewood and water. We construct two de fi nitions of work. The more restrictive de fi nition (I) , includes only directly income generating activities. It therefore excludes livestock care, and the fetching of fi rewood and water. The less restrictive de fi nition (II) includes all work.

Using the more restrictive de fi nition, 18% of all children in the age group 7-17 report

> 11 See the Pakistan Country Gender Assessment, Chapter 2, The World Bank. 2005 for an extensive overview and analysis.

7
Table 6 reports the percentage shares of experts that changed their WTP estimates for Europe for Plan A and B, from Round 1 to Round 2. We see that more experts retained their original mean estimate for Plan B than for (the more extensive) Plan A; 59 and 33 % respectively. On the other hand, changes were greater on average for Plan B than for Plan A. More experts lowered than increased their mean WTP estimates for both plans. For Plan A, 27 % increased their mean WTP while only 8 % did so for Plan B. Those one-third and one-half of experts who did _not_ change their estimates from Round 1 to 2 have likely felt little pressure toward a consensus. Those that did change their stated values seemed overall to be affected by the information given about Round 1 results. A hypothesis, that experts more experienced in conducting SP surveys had a higher probability of changing their WTP estimate, was rejected .Other background variables for the experts could also not explain why some changed their WTP estimate while others did not.

Table 6 also shows the average adjustments (up or down) for those that changed their estimates, which were around 8 euros for Plan A answers, and much higher (15 euros) for Plan B answers.

# **Table 6: Percent of experts who lowered, increased or stated the same WTP estimates  for Europe in Round 2.**

|**Plan**|**WTP**<br>**estimate**<br>**sought**|**Lower**|**Same**|**Higher**|**Average**<br>**change, all**<br>**(euros/hh/y)**|**Average**<br>**change,**<br>**changers**<br>**(euros/hh/y)**|
|---|---|---|---|---|---|---|
|**Plan A**|**Mean**|40|33|27|5.7|8.5|
||**Median**|27|42|31|4.8|8.3|
|**Plan B**|**Mean**|33|59|8|6.2|15.1|
||**Median**|35|53|12|2.7|5.7|

# **4.2 Income Elasticities of WTP**

Tables 7 and 8 present elasticities of expert-assessed average own-country WTP, with respect to own-country income, from Rounds 1 and 2 for the European WTP estimates for Plan A and B; for two measures of income: regular GDP per capita, and Purchasing Power (PPP) corrected 14
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 19: Individual Yearly Earnings - Tradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 2.9<br>2.4<br>1.9<br>1.4<br>0.9<br>0.4<br>-0.11990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.6<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the yearly earnings in the year listed on the x-axis as a multiple of the worker’s average pre-liberalization (1986-89) earnings, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply larger declines in yearly formal earnings for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

58
documents necessary for their operations; and the remaining group received a message appealing to tax morale, claiming that "with your taxes we build a better country". The effects of appealing to tax morale (Luttmer & Singhal, 2014) in similar interventions have so far been mixed. De Neve et al. (2021) find that appeals to the social norm of payment or the importance of public goods have null or negative effects across the compliance spectrum in Belgium, while Castro & Scartascini (2015) find null effects in Argentina for local property taxes. Kettle et al. (2016), on the other hand, find that informing late filers in Guatemala that they were in a minority of non-compliers was as effective as sending a threatening message. Although the exact treatments are not the same, it is somewhat surprising that appeals to tax morale are more effective in Guatemala than in Argentina or Belgium, given low levels of confidence in the government: according to Latinobarometro, only 22% of Guatemalans answer having some or a lot of confidence in the government between 2015-2018, lower than the 30% reported by Argentinians. The experts we surveyed systematically predicted that the tax morale treatment would likewise be less effective than the other two. In our main estimates all three treatment arms are estimated to have null effects on compliance and we cannot reject that the effect of the tax morale arm is equal to the other interventions.

Finally, our exercise using the causal forest algorithm<sup>5</sup> contributes to a recent literature exploring heterogeneity analysis in experimental settings to inform program targeting, ranging from cash and asset transfers to the poor (Alatas et al., 2012) to the use of cellphones to monitor agricultural extension workers (Dal Bó et al., 2021). In a similar exercise but in a different setting, Hussam et al. (2020) worked with experimental grants to entrepreneurs in India, and showed that the causal forest algorithm can do no better in predicting returns to the program than assessment by other community members. We compare our treatment effect estimates with the baseline risk assessment of each taxpayer, showing that experimental results are a useful tool for the tax authority to improve targeting.

# **2 Context Taxation in Honduras**

Honduras is a lower middle-income country in Central America with GDP per capita of $5,800 PPP in 2018. Similar to other countries with comparable income levels, the country collects less than 20% of GDP in taxes and is very reliant on taxes on goods and services, making up over 50% of total tax collection (International Monetary Fund, 2018).

The fiscal year runs from January 1st to December 31st and taxpayers must file an income tax declaration by April 30th of the following year. Corporations are taxed at

> 5See Davis & Heller (2017) and Bertrand et al. (2017) for detailed discussions on the application of causal forest algorithm in experimental settings.

5
# **Concluding remarks**

Development organizations have devoted considerable resources over the last two decades to supporting the emergence of viable insurance mechanisms in developing and emerging markets<sup>39</sup> . This investment has been validated by a growing empirical literature on the role of the insurance sector in fostering inclusive growth, in reducing poverty and in mitigating the primary, secondary and indirect impacts of natural disasters.

Dealing first with the insurance-growth nexus the correlation between GDP and insurance demand has long been clear, as demonstrated in Figure 1, and the correspondence between regional insurance sectors and emergent middle classes is if anything even more striking (Table 4). Thus the challenge from a developmental perspective becomes to establish whether the insurance sector leads economic development or follows.

Table 4: Insurance and the middle class (defined as daily expenditure between US$10 and US$100)

||**Middle**<br>**class**<br>**pop. mill**|**% global**<br>**middle class**<br>**consumption**|**% life**<br>**global**<br>**premium**|**% P&C**<br>**global**<br>**premium**|
|---|---|---|---|---|
|**North**<br>**America**|338|27|25.0|39.7|
|**Europe**|664|38|35.7|29.7|
|**Central/**<br>**South**<br>**America**|181|7|2.3|4.7|
|**Asia**<br>**Pacific**|525|23|36.5|23.6|
|**Sub**<br>**Saharan**|32|1|0.1|0.5|
|**MENA**|105|4|0.5|1.9|
||1,845||||

Source: World Bank In this regard the source and nature of data and the methodologies employed are important considerations in assessing the reliability of any causality findings. Single country time series data (spurious correlation) and cross country data (omitted variable bias) need to be handled particularly carefully, especially when different classes of insurance are aggregated. More recent data sets have enabled relatively large panels to be assembled and modern methodologies to be adopted: greater weight needs to be given to these in drawing inferences. The later studies broadly support the general proposition that causality runs from insurance sector development to economic development. The evolution of the findings can be illustrated as follows (Figure 6): 39 The World Bank’s investment banking arm, the International Finance Corporation, has more recently reflected this resource allocation. Its portfolio of investments in the insurance sector now comfortably exceeds US$1 billion (of which 83 percent is in direct equity) and quintupled between 2011 and 2013.

27
**1.g** 4

- 11 -

# DISTRIBUTION OF PRE-TAX PROFITS

<!-- Start of picture text --> 0.4<br>0.35<br>045_ ...<br>0.25<br>0.1<br>0.05<br>010 2091 3001 4091 1492 2092 3022<br>- wvMfl t bonuw lPAid tck devia. Amd s7ocdl fti<br>FI8 5<br><!-- End of picture text --> <!-- Start of picture text --> Russia: Price Changes<br>Retail ond Industrial Producer Prices<br>450<br>400 -<br>330 -<br>300-<br>.C<br>200-<br>E SO<br>100_<br>50'<br>Jon1 f mor f moy j jul I sOP nv 0 jon92 - mar may 1 jul 1 Sp<br>feb opr jun oug Oct dec91 feb opr jun oug<br>0 retail prices + ind prod prices<br><!-- End of picture text -->
measure of income inequality. The Gini index is equal to one minus twice the area under the Lorenz curve.

Although there exist several altemative welfare measures-, this study is confined to using W as a basis for analyzing welfare in Ukaine. We give two reasons for this choice. First, we regard W to be a reasonable welfare measure which takes into account both the size and distribution of income. Second, since income distribution data are available only in grouped form, W is the only welfare function which can be estimated most accurately from such data.

# 3. Welfare by Income Components

Since the individual income is the sum of several income components, it will be useful to analyze total welfare (and inequality) in tenns of individual income components. The methodology for disaggregation of Gini index by income components was first developed by Rao (1969) and subsequently refined by Kakwani (1977, 1980), Field and Fei (1974), Fei, Ranis and Kao (1978), Fields (1979) and Pyatt, Chen and Fei (1980). In this section we extend this methodology to analyze the contribution of each income component to total welfare.

Suppose there are k income components and **_g4_** is the mean of the jth component. Then it is obvious that

**_k_** -1

and the disaggregation of Gini index in terms of income components is written as (Kakwani 1980): <!-- Start of picture text --> G -E 'Lici ~~~~~~~~~~~~~~~~~~(3)<br><!-- End of picture text --> where Cj is called the concention index of the jth income component. The concentration index C3is smar to the Gini index excep **t** th ranking of individuals is by the total income ande not the jth income component. As a result, the index can be negafive.

The concenton index of an income component measures how evenly or unevenly that income component is distributed over the total individual income. If C; is greater (smaller) than G, it implies that the jth income component is distributed over the total income in favc- of richer (poorer) individuals.

# Combining (l) with (2) and (3) gives

2 See for **instance** Atkinson (1970) who derived **a** clas of **welfare measurs** based **on** a homothetic utility **fi_n.** 4
exchange rate, financial reform, development assistance, and capital importation on our measurement of productivity change, %ΔTFP.

# _Measurement of variables_

Output is measured by Gross Domestic Product (GDP) at constant prices published by the _Ministry of Finance and Economic Planning_ . Direct employment series are not available, so labor is estimated by data on the economically active population (labor force) published by the International Labor Organization (ILO). Physical capital, as in most developing countries, is not readily available, but following a number of previous studies<sup>10</sup> , the law of motion for capital stock is specified as follows: Kt = ((1δ )*Kt-1)+It, where Kt denotes capital stock at period t, δ is the depreciation rate of capital stock and It is investment at period t. Following Ghura and Mercereau (2004), δ is set at 6 percent<sup>11</sup> and a capital-output ratio in 1960 was arbitrarily set at 1.5. Investment is proxied by gross capital formation that equates gross fixed capital formation with change in the inventories of capital stock. Gross capital formation and gross fixed capital formation are measured in local currency (constant price). The series on gross capital formation, gross fixed capital formation and change in stock of inventories are extracted from the World Bank database.

# **4.  Estimation of Cointegration and Production Functions and Statistical Tests**

Estimation of the production relation for the period 1961 to 2003 produced the following empirical result: GDP/L = const. - .0075 * t + .43 * K/L, (4.13) (6.73) 9