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21
retirement age are not readily available. Anecdoctal evidence suggests that the incidence of early retirement is higher in developing and transition economies.

Another problem with this measure arises from the use of life expectancy at the official retirement age based on the mortality experience of the entire population of a country. Because coverage is low in poorer countries, and because covered workers tend to have higher incomes and better access to health services, the life expectancy figures used here will tend to underestimate retirement duration in many countries. Furthermore, this bias will be greater the poorer the country and the lower the coverage. The implication is that retirement duration in poor countries is even higher than that presented.

# **D. Poverty Indicators**

Figures 3.1 and 3.2 compare poverty rates among different types of individuals in five Latin American countries and eight industrialized countries. Data for Figure 3.1 are based on an unpublished mimeograph commissioned for the report and written by World Bank consultant, Jennifer Keller. The data were obtained from country surveys which are fully described in "Poverty and Income Distribution in Latin America" by Psachoropolous e.. al. 1993. The five countries were chosen from the 13 included in that study because of their comprehensive income definitions (including pension income) and national representative samples.

According to Keller's calculations, the total income of each household was calculated by adding the total incomes of all related household members. Income per household member was calculated by dividing the total household income by the number of members with the following equivalency weights: 1) Adults were counted as 1, 2) children between 6 and 14 years of age were counted as .45 and 3) children under _5_ were given an equivalency weight of .25. This method was consistent with the method employed by Deaton and Paxson (1991) which looked at demographic consumption patterns in Thailand and Cote d'lvoire.

No adjustment was made to account for possible economies of scale within households which might affect the results if large households also tended to be young households. However, the 54
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30
# **3 Conceptual Framework**

Our analysis is guided by a conceptual framework on how the probability of detection can affect the evasion decision by taxpayers, as has been discussed in Allingham and Sandmo (1972). We proceed with the baseline case in which government monitoring acts as sole enforcement as cited in Naritomi (2019).

Suppose a risk-neutral firm generates reported revenue _Y ≥_ 0 and makes proportional tax payment _t ∈_ [0 _,_ 1] out of it. The firm’s reported revenue, _Y_ , could differ from its actual pretax revenue _Y_ . _E_ ( _Y_ ) = _Y − Y_<sup> ̄</sup> is the total evasion by the firm—in the case of under-reporting, it becomes _Y < Y_ .

Let _p_ ( _A_<sup>_E_</sup> ) _∈_ [0 _,_ 1] be the probability of audit in the event of evasion and _d ∈_ [0 _,_ 1] the government’s ability in detecting evasion in an audit. Firm faces detection probability _p ≡ p_ ( _A_<sup>_E_</sup> ) _d_ , where _p_<sup>_′_</sup> ( _E_ ) = _p_<sup>_′_</sup> ( _A_<sup>_E_</sup> ) _d >_ 0. When firms get caught for evading, a fine _θ ≥_ 0 applies in proportion to the size of evasion _t_ ( _Y − Y_<sup> ̄</sup> ). Therefore, firms maximize _π_ by reporting revenue _Y_ as follows: Where firm chooses the interior optimal solution _Y_<sup>_∗_</sup> that satisfies first-order condition _dπ/dY_ = 0 and [ _p_ ( _A_<sup>_E_</sup> ) + _p_<sup>_′_</sup> ( _A_<sup>_E_</sup> ) _.E_ ] _d_ (1 + _θ_ ) = 1. In practice, POS usage acts as an extra surveillance tool to monitor tax compliance, which enhances government’s evasion detection ability _d_ . As _d_ increases, the chance of getting caught, _p_ (which is a function of _d_ ), rises. Given the convexity of _Ep_ ( _A_<sup>_E_</sup> ), _Y_<sup>_∗_</sup> will be rising in _d_ -and therefore in _p_ .

In other words, when POS device is used properly and quality audit is in place, we can also expect the magnitude of evasion _E_ also shrinks. Therefore, it reduces firms’ taxevading behavior and drives the optimum reported revenue _Y_<sup>_∗_</sup> upward—manifested in overall higher tax receipts.

# **4 Empirical Strategy**

We evaluate the impact of giving POS installations to hotels and restaurants by running balanced panel difference-in-difference (DiD) specification for each of the POS types covering a 48-month window throughout 2019 – 2022 (West Manggarai) and a 60-month one – during 2018 2022 (Gorontalo). Hotels and restaurants which received any of the POS in or before month-of-year _t_ belong to the treatment group, while the rest belong to the control group. By design, each recipient hotel and restaurant were given only one type of POS, therefore the observed impact of different types of POS on hotel/restaurant tax payment were not likely to overlap with each other.

Following the nature of our data on West Manggarai, treatment time varies among restaurants within the treatment group as POS distribution came in waves. An exception applies for the case of tapping box in the districts’ data where all restaurants in the treatment group received their tapping box installation in about the same month (Octo12
-26 -

The results appear in table 4. School life expectancy in country B is estimated to be 1.8 times that in country A-10.8 years compared with 6.0 years. The pupil-teacher ratio, on the other hand is lower, 19.8 pupils per teacher, compared with 38.5 in country A, implying that other things being the same, per pupil spending in country B is 1.95 times (=38.5/19.8) as high as in country A. The product of these two ratios reflect the aggregate resources expended per school-aged child in country B as a ratio of the resources expended per child in country A. The ratios imply that 46.9 percent of the extra resources in country B are deployed to expand coverage, while the remaining 53.1 percent are invested to improve the learning environment via a reduction in the pupil-teacher ratio.<sup>27</sup>

**Table 4: Expansion of coverage and decline in the pupil-teacher ratio as countries grow rich**

|Indicator|Item in<br>equation (1')|Country an<br>G<br>AP<br>A: $200|d per capita<br>(<br>B: $3,000|Country B's<br>advantage<br>over Ac/|% contribution<br>to country B's<br>advantage|
|---|---|---|---|---|---|
|Overall coverage|Pe/Psa|6.0|10.8|1.80|46.9|
|Pupil-teacher ratio|Pe/T|38.5|19.8|1.95|53.1|
|Spending per school-aged childi||||3.51|100|

Source: columns 3 and 4 are simulated from regression estimates of the relation between the corresponding indicator and per capita GNP, based on the data for some 125 countries in 1993; the last two columns are authors' computation, following procedures explained in the text.

a/ Proxied by the school life expectancy which is defined as the expected number of years of schooling given the current structure of enrollment rates across the three levels of schooling.

b/ This is the product of the components on the right-hand-side of equation (1"'), i.e. the product of the first item in the next column and the inverse of the second item.

c/ In the framework of equation (1"'), B's advantage is computed, for the first item listed, as the ratio of its indicator to that of country A; and for the second item, as the inverse of its ratio to that of A.

Because equation (1"') is an identity, we expect the product of the components of both sides of it to have the same value. Yet based on the data in table 4., the product on the right-hand-side is 3.51 (=1.80 x 1.95), which is only about 83 percent of 4.22, the corresponding figure on the left-hand-side shown in the previous table. Data flaws and incompleteness are a possible source of the discrepancy, but the aggregation of data for the three levels of educationon coverage, teacher pay and pupil-teacher ratio-into a single synthetic indicator representing the system as a whole, may also have contributed to it. To avoid this problem, we repeat the disaggregation exercise below, focussing on only one level of education, the primary level, for which the available data are more complete.

> 27 These percentages are calculated in the same way as those completed earlier showing the sources of contribution to the rise in resource availability. For example, the estimated share of resources used to expand coverage, 46.9 percent, is evaluated as ln(1.8)/(ln(1.95 x 1.80)) x 100.
upper middle class<sup>6</sup> is significantly more covered against public sector inefficiency in the field of power supply, which could theoretically hamper they demand for improvement in the sector.

In sum, this example illustrates how the Dominican middle and high class has internalized the costs derived from both state and market failures in the provision of public goods. In this case, the existence of private auto generation capacity of electricity, together with widespread non-payment and fraud, results in persistent low quality of service. As we will argue in the next section, weak collective action and limited public accountability mechanisms are influenced by this poor service provision.

# **II.2 Education**

The education system in the Dominican Republic is characterized by high repetition rates and low completion. According to ECLAC (2010), the late completion rate for children aged 9-11 years in the Dominican Republic is 20%, among the highest in the region, compared to 3% in Mexico or 10% in Brazil. On the quality side, the performance of students in primary education (3<sup>rd</sup> and 6<sup>th</sup> grade) on regional math and reading tests was very poor, a strong signal of remaining challenges in service delivery. According to UNDP (2010), impoverishment in the quality of public education together with the lack of sufficient classrooms motivates attendance to private schools. Similarly, countries with less degree of public expenditure in education are expected to present higher enrollment rates in private schools. As shown in the graph, the Dominican Republic has presented over the last decade the lowest level of public expenditure as a percentage of GDP in Latin America, and it is a country with a relatively high rate of enrollment in private secondary schools.

> 6 The average monthly income of individuals belonging to Q5 households is of just US$350, it is likely that the survey is not capturing the real picture of Dominican elites.

[10]
TABLE 11: Impact of leaders’ exposure on intermediary outcomes, by closest leader’s commitment

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|ITT Leader|-0.16<sup>_∗_</sup>|-0.01|-0.02|-0.13<sup>_∗_</sup>|-0.25<sup>_∗∗_</sup>|-0.05|-0.13<sup>_∗_</sup>|
||(0.08)|(0.06)|(0.04)|(0.07)|(0.10)|(0.06)|(0.07)|
|Committed leader|-0.15|-0.16|-0.06|0.19<sup>_∗∗_</sup>|-0.10|-0.25<sup>_∗∗∗_</sup>|-0.15|
||(0.12)|(0.11)|(0.07)|(0.08)|(0.10)|(0.08)|(0.13)|
|ITT leader X Committed leader|0.09|0.08|0.10|-0.11|0.11|0.23<sup>_∗_</sup>|0.06|
||(0.17)|(0.14)|(0.09)|(0.11)|(0.14)|(0.12)|(0.15)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|ITT Leader + ITT Leader X Committed leader|0.67|0.61|0.38|0.02|0.26|0.14|0.65|

_Note:_ Committed leader is a leader predicted to be in the top quintile of leaders with highest participation in the quizzes. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 45
5.10 The multi-dimensional poverty proxy used for illustrative purposes in this study is based on a weighting of five variables demonstrated to have a strong correlation with poverty, namely: overcrowding; inappropriate housing; unsanitary conditions; presence of infants; and head of household education (Table 7). This proxy is used to estimate per capita household income and hence to predict whether a particular household is above or below the poverty (or extreme poverty) line.

5.11 The results of using this device indicate that errors of exclusion for the poverty proxy weighting all five indicators are substantially lower than the errors of exclusion associated with any one of the five indicators individually. Nevertheless, the poverty proxy performs substantially better at predicting poverty that at predicting extreme poverty, illustrating that this smaller second group is much more difficult to identify.

**Table 8: Targeting** **<u>performance of the multi-dimensional poverty proxy</u>**

|%|Overcrowding|<sup>Inappropriate</sup><br>housing|Unsanitary<br>conditions|Presence<br>infants|of<br>Head<br>household<br>education|of<br> <br>Multi-<br>dimensional<br>proxy|
|---|---|---|---|---|---|---|
|**Poverty**|||||||
|Error of inclusion|19.6|27.1|10.5|32.7|39.8|39.7|
|Error of exclusion|90.7|96.1|91.1|67.2|85.1|36.3|
|**Extreme poverty**|||||||
|Error of inclusion|42.2|77.0|42.1|73.3|65.7|36.2|
|Error of exclusion|82.7|95.6|85.1|83.0|56.9|76.3|

_Source:_ OPSM, May 2002 5.12 Furthermore, the performance of the multi-dimensional poverty proxy varies significantly by geographic region (Figure 15). As far as poverty is concerned, the best targeting results are obtained in the Northeast and Northwest Regions, where poverty tends to be long term and structural in nature, and therefore well aligned with the variables used in the multi-dimensional proxy. In these cases, both errors of inclusion and exclusion are as low as 20%. The proxy also performs quite well in the greater Metropolitan Area as well as the Pampeana and Cuyo Regions. However, it does not perform very well for Patagonia or the Federal District, where the errors of exclusion climb to the 60-80% range. In the case of the Federal District, this could be explained by the phenomenon of the ‘new poor’ that have been created as a result of the recent economic crisis in Argentina. These are people who suffer from low incomes but do not exhibit the emblems of structural poverty (poor housing and education). The results obtained for targeting extreme poverty are much less satisfactory across all geographical areas, particularly with respect to errors of exclusion that in all cases exceed 70%.

35
||**Scenar**<br>|**io**||<br>|**Poverty r**<br>**(USDper**<br>|**ate in 20**<br>**capitap **<br>|**30 at pov**<br>**er day, 20**<br>|**erty line**<br>**05 PPPs)**<br>|**s**<br><br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Growth**|**m**|**1.25**|**1.45**|**2**|**2.5**|**4**|**6**|**8**|**10**|
||Zero|-2|39.7%|40.3%|53.9%|69.6%|89.4%|94.7%|97.6%|99.0%|
|||-1|36.8%|40.2%|56.5%|71.9%|89.8%|95.1%|97.9%|99.1%|
|||0|23.8%|34.8%|59.8%|74.5%|90.1%|95.6%|98.2%|99.3%|
|||1|11.6%|21.7%|64.3%|77.7%|91.0%|96.2%|98.6%|99.4%|
|||2|2.6%|6.6%|64.6%|83.3%|92.8%|97.4%|99.1%|99.7%|
|Sub-Saharan Africa|10 year|-2|33.4%|37.1%|45.7%|55.3%|70.7%|84.0%|90.0%|93.1%|
|||-1|28.2%|33.9%|44.2%|53.7%|71.7%|84.7%|90.5%|93.4%|
|||0|23.4%|29.0%|42.5%|52.5%|72.6%|85.5%|90.9%|93.7%|
|||1|19.2%|24.3%|40.0%|51.5%|73.5%|86.5%|91.5%|94.1%|
|||2|15.0%|19.1%|31.3%|48.8%|69.4%|87.8%|92.3%|94.7%|
||20 year|-2|39.2%|43.7%|54.9%|62.8%|78.8%|89.0%|93.1%|95.3%|
|||-1|35.6%|40.9%|54.1%|62.9%|79.6%|89.5%|93.4%|95.6%|
|||0|32.2%|38.0%|52.5%|62.7%|80.7%|90.0%|93.8%|95.8%|
|||1|26.2%|34.9%|50.5%|61.1%|81.6%|90.6%|94.2%|96.1%|
|||2|20.6%|28.1%|46.5%|59.6%|82.4%|91.5%|94.7%|96.5%|
||Zero|-2|48.8%|54.3%|68.6%|77.0%|88.8%|94.3%|96.6%|97.8%|
|||-1|48.4%|54.7%|69.3%|78.0%|89.2%|94.5%|96.8%|97.9%|
|||0|47.4%|55.1%|70.3%|78.8%|89.7%|94.8%|97.0%|98.0%|
|||1|42.4%|55.5%|70.9%|79.9%|90.4%|95.2%|97.2%|98.1%|
|||2|37.7%|48.8%|72.2%|81.2%|91.4%|95.7%|97.4%|98.3%|

27