rafmacalaba/datause-encoder-data huggingface dataset:
that the operating costs are substantially lower if Azteca serves a municipality previously covered by Elektra.

## **Consumption floor:** <u>c</u>

This parameter determines the minimum consumption that a household receives. Its estimated value is 0.365, which corresponds to a daily percapita consumption of $0:83USD.

**Exogenous parameters.** The parameters that are not estimated inside the model and that are considered exogenous are the following: information about interest rates and collateral requirements was obtained from CONDUSEF, Azteca’s Financial Reports and the households’ data. According to this information, the average APRs of pawnshops, Banco Azteca, traditional banks and friends or relatives in 2005 were 220%, 130%, 40% and 0% respectively. Regarding collateral, 90% of the households in the dataset that obtained credit from friends and relatives reported they were not required to own any collateral, therefore in the model, the collateral that friends and relatives require is set to zero. According to reports from PROFECO, the average collateral required by a pawn shop in Mexico is three times the value of the loan. According to CONDUSEF and Banco Azteca’s reports, Banco Azteca requires that the value of collateral is equivalent to the value of the loan. The maximum loan that households can borrow from traditional banks and friends and relatives is also determined outside the model as a function of the head’s education. The maximum loan size from friends and relatives is $3; 000USD, while bank loans can reach $5; 500USD. These values were obtained from the distribution of loans from banks and friends and relatives from the households data. The depreciation of the durable goods, �, was fixed to 0.10. The relative price of the durable goods with respect to the consumption goods, p, was obtained from the Consumer Price Index in 2002, and was set to $10. The discount factor, �, was fixed to 0.99.

# **7.2 Model Fit**

As seen from table 13, the estimated model matches closely the simulated statistics to the real ones. The first panel presents the moments for the share of households employed in the formal sector in 2005, conditional on their education level and previous sector employment. The likelihood of being employed in the formal sector is strongly correlated with having participated in the formal sector in the previous period, for both low and high educated households. But also, regardless of whether households were formal or not in the previous period, education increases the probability of being formal. The simulated moments replicate these data patterns closely. Interestingly, while 7% and 13% of low educated households receive formal job offers each period, only 4% and 11% of these 28
altogether something else. It seems safe to say, however, that using the findings of a purely ‘local’ analysis of a rural feeder road is not obviously heavily biased one way or the other where assessing its social profitability is concerned. The external costs are, perhaps, more tangible and thoroughly researched than the corresponding benefits; but if pressed, I think the latter will outweigh the former in the present setting.

One other form of ‘externality’ must be mentioned. There are numerous reports that once a road has been built, politicians and bureaucrats take a sudden interest in – – visiting the communities it and they serve. This development is to be welcomed, though no attempt will be made here to put a (money-metric) value on it.

# **7 Other Approaches: Capitalization**

Those who own land in a new road’s catchment area will benefit from it in various ways, and if the markets for this land and credit function well, the willingness to pay for such benefits will be fully reflected in the resulting changes in land prices. This socalled ‘capitalization’ approach to estimating the benefits (net of any costs entailed by ownership, such as land taxes) is well-established in the regional and urban economics literature; but there are drawbacks when applying it to poor rural regions. First, credit markets are very imperfect and the land market is usually thin. When respondents are asked what particular plots are worth, their answers are therefore bound to be rather speculative, especially if a road has been built only recently. Second, the new road may affect other factor markets. The ensuing effects on landowners are fully captured in the values of their plots, in the textbook version at least. Yet whilst households that depend mostly on agricultural labor almost invariably own a small house-plot and garden, any changes in their welfare stemming from changes in wage rates and employment possibilities will not be captured by summing up the changes in the values of individual landholdings in their communities. Third, of all the wider effects outlined in Section 6, only the chance that new non-farm activities will arise in the catchment area will be covered in such an accounting. Fourth, as a purely practical matter, mapping all the land in a catchment and assigning it a rental gradient before and after the advent of a road is likely to be a burdensome affair, more so than identifying the communities and sampling their inhabitants. It is worth remarking, however, that one careful study of Madagascar (Jacoby and Minten, 2009) arrives at the conclusion that the ‘capitalization’ approach yields a much larger estimated benefit than that pursued in Section 4, whereby the former is naturally rather sensitive to the discount rate.

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**Chart 18. The quantitative impact of the NBRM measures on the banking system**

<!-- Start of picture text --> 70.00%<br>58.77%<br>60.00%<br>50.00%<br>42.14%<br>40.00%<br>30.20%<br>30.00%<br>19.80%<br>20.00% 16.14%<br>10.3% 15.03% 9.3%<br>10.00%<br>2.58%<br>0.00% 0.51%<br>Credit growth rate Credit growth (households) Liquid assets/Total assets Capital adequacy ratio NPL ratio<br><!-- End of picture text --> Note: NPL= Nonperforming loans Source: NBRM As we have seen, the main target of the NBRM measures were the credit growth (especially the growth of the loans to households), the quality of banks’ credit portfolio, the banks’ liquidity position, and the enhancement of the capital adequacy of the banking system. The results are rather evident (Chart 18). Most of the macroprudential measures (three out of four) were undertaken in a period of one year, from the first quarter of 2008 till the same period in 2009. In this period, all of the analyzed indicators experienced positive movements; they are particularly apparent for the credit growth, both of the overall portfolio and of the loans to households. During this one-year period, the annual growth rate of the overall portfolio decreased by almost 40 percent, while the loans to households were reduced by 54 percent. At the same time, the liquid-to-total-assets ratio was been elevated by more than half from its lowest level at the end of the first quarter of 2009.<sup>36</sup> In addition to the undertaken measures, it should be emphasized that the credit growth slowdown was also a result of the negative impact of the financial crisis over the domestic economy. The worsening of the real sector performances, the decline of the disposable income of households, and the higher uncertainty had influenced the lending practices of banks. The banks strengthened their credit-granting criteria, which has additionally decreased the amount of loans to the real sector.

From the analyzed variables, the only indicator that has a diverse path is the share of the nonperforming loans (NPL ratio). This ratio experienced its lowest level right in the middle of the crisis period (third quarter of 2008) and its peak in the post-crisis period (10.6 percent at September 2010). The main reason for this behavior of the amount of NPLs is associated with the maturity of the banks’ credit portfolio. Namely, due to the credit growth, the newly extended

> 36 This is a starting date of the dynamic for achieving the prescribed liquidity ratios.

26
# **Tables**

Table 1: Category of property and statutory tax rates (STR)

|**Category**|**N° Prop**|**erties**|**STR**|**by yea**|**r(%)**|
|---|---|---|---|---|---|
||N|%|2018|2019|2020|
|Residential|109,729|82.8|0.30|0.50|0.69|
|Residential with commerce/factory|10,073|7.6|0.45|0.75|1.04|
|Commercial|7,425|5.6|0.70|1.18|1.68|
|Empty lot|1,166|0.9|0.70|1.27|1.84|
|Industrial|3,699|2.8|0.70|1.22|1.77|
|Civil entities|220|0.2|0.10|0.17|0.24|
|Religious entities|170|0.1|0.10|0.17|0.24|
|Wholesale establishments|1|0.0|0.45|0.76|1.10|

_Note:_ This table reports statutory tax rates by category of property as defined in the tax code ( _Ordenanza Impositiva_ ) every year. The first two columns display the number of properties in 2018. The tax base used to calculate the monthly fee corresponds to the assessed value in 2018 provided by the provincial tax authority (ARBA). Despite an annual inflation of 47% in 2018, 54% in 2019, and 36% in 2020, this base remained unadjusted. To compensate, the municipality increased the tax rates.

27
comparison of the cost effectiveness of uniform provision might be to cash transfer programs.<sup>10</sup> The per pupil cost of inducing an additional year of schooling with the conditional cash transfer program PROGRESA (subsequently _Oportunidades_ and _Prospera_ ) in Mexico was $3,333, and the cost was $1,429 for a conditional cash transfer program in Malawi (Dhaliwal et al. 2013). Both of these are significantly higher than the cost of an additional year of schooling through uniform provision. Thus, in some contexts, uniforms may be an effective way to boost short-term educational participation. But these results suggest that such efforts may have limited impact over the long run.

To place our results in the context of other results of uniform distribution programs, Hidalgo et al. (2013) examine the impact of providing uniforms on attendance in Ecuador using school-level randomized assignment of free uniforms. They find that announcing a free school uniform program had a negative impact on attendance. School uniforms were distributed in only 63 percent of the schools that were told that they would get them, thus the authors hypothesize that this negative impact could have been generated by creating false expectations of free distribution, or also by a sunk cost effect – that is, those in the control group, who pay for their children’s school uniforms, may feel more committed to the school than parents whose children get the uniforms for free (the treated) and therefore do not allow their children to miss classes easily. Significant differences in institutional environments between Kenya and Ecuador might explain the divergence in results. Ecuador is wealthier. The net enrollment of children in primary school in Ecuador is about 95 percent, while in Kenya it is about 82 percent (World Bank 2014).<sup>11</sup> The value of a free uniform on a marginal parent’s decision to send the child to school might be weightier in Kenya. Two additional potential explanations could be either negative peer effects from

> 10 Since children need clothing, one potential spill-over from a uniform transfer program might be to younger siblings, as the initial recipients grow out of their donated uniform. We find no significant impacts on school attendance for younger siblings of beneficiaries (results not reported).

> 11 This uses the most recent year of available data for each country, 2009 for Kenya and 2012 for Ecuador. (Ecuador’s net enrollment rate in 2009 was very similar to its rate in 2012.)

18
Finally, in testing for the consequences of employing the estimation method of instrumental variables (test of the endogeneity or exogeneity of regressors), one acknowledges the trade-off between a possibly biased and inconsistent OLS estimator and the higher asymptotic variance of the IV estimator. The test employed here is the Durbin (1954); Wu (1973) and Hausman (1978)’s version of the Hausman statistic, using the OLS estimate of the error variance, as opposed to the IV estimate.<sup>8</sup>

# **The Poverty Decomposition Model**

We use the Foster, Greer and Thorberke (1984) (therafter called FGT) poverty model to decompose SSCBT into various poverty statuses. The FGT class is based on the normalized gap _gi_ = ( _z − yi_ ) _/z_ of a poor person _i_ , which is the income shortfall expressed as a share of the poverty line ( _z_ ). Viewing _gi_<sup>_α_asthemeasureofindividualpovertyfora</sup> poor person, and 0 as the respective measure for non-poor persons, _Pα_ = (1 _/_ ( _n_ )) _gi_<sup>_α_is the</sup> average poverty in the given population. The procedure entails estimating the Incidence of individual Poverty ( _α_ = 0), the depth ( _α_ = 1) and severity ( _α_ = 2) of poverty among SSCBT. Income changes resulting from adoption of CLUSTER trading approach and changes in the number of SSCBT and depth of poverty were also estimated.

# **The interval regression for wilingness to pay(WTP)**

Willingness to pay (WTP) is a key component of consumer demand. It helps to set the prices at a level that allows firms to be sustainable over time by maximizing profits and customer satisfaction. In the case of the SSCBT studied, a monthly contribution set by the CLUSTER is paid by the members to unsure its sustainability in the future. Given this, we want to study the variables (especially the gender, as women are the core component of SSCBT) that determine the SSCBT’s WTP for the sustainability of the CLUSTERS over time. As the values surveyed on the WTP for the sustainability of the cluster are not continuous but consist of intervals and censoring observations. The Interval Regression Approach is the main technique to tackle such a data structure (Jin-Long and Zhang Yi, 2012; Louinord et al., 2013; Riccioli et al., 2019). The interval regression model assumes that WTP is a function of a series of explanatory variables. Thus, we assume the following function: where WTP represents people’s true WTP level; _xi_ represents a vector of independent variables, b is the corresponding vector of estimated parameter and _ξi_ is an error term

> 8Under the null hypothesis both estimates are consistent, but the OLS estimate is more efficient. Furthermore, the chosen flavor of the test has the additional advantage of performing better when the instruments are weak (Baum et al., 2003; Staiger and Stock, 1997).

12
aims to fill this gap by assessing the impact of geographic proximity between formal and informal firms on ICT adoption by informal businesses.

Using a unique set of geocoded data from the 2019 World Bank Enterprise Surveys in Zambia allows to examine these ICT spillovers from formal to informal businesses.<sup>4</sup> Geographic proximity is measured as the distance of an informal business to its closest formal firm. Informal businesses operating closer to formal firms have greater exposure to the formal firms’ business activities, their customer base and network, and potential value chain linkages. This geographic proximity reflects the benefits of these complementary forces.

The results suggest that geographic proximity to formal firms is associated with a significantly higher likelihood of digital adoption by informal businesses. The finding holds for various types of digital technologies, ranging from computers, tablets, and cell phones to mobile money transactions, and is robust to different assumptions of informality and geographic distance. These effects vary by the firm owner’s level of education, and firm age. Informal businesses run by more educated owners or younger in age—benefit more from potential technology spillovers of neighboring formal firms. In addition, we find that the role of geographical proximity in generating spillovers in ICT use can be explained by greater competition in the local market and by learning from formal businesses.

This paper contributes to the literature on the crucial role of geographic proximity for technology adoption and productivity advancement. For instance, Carboni (2013) applies a spatial autoregressive model to manufacturing firms in Italy and finds that geographical and industry proximity have positive effects on firms’ ICT investments. Hjort and Poulsen (2019) exploit the gradual arrival of submarine internet cables on the coast to show the positive impact of internet access on employment, firm entry, and productivity in Africa. Using a similar approach, Houngbonon et al. (2022) show a positive effect of access to high-speed internet on innovation at the firm level. Combining information on the GPS location of firms with the location of the internet backbone in Senegal, Cirera et al. (2022) explore the contribution of digital infrastructure to the adoption of technologies through the effects of the proximity to better quality internet service.

> 4 As shown in the following section, the level of digital adoption in Zambia is notably higher in formal firms than in informal businesses. Given the differences, it is assumed that technological spillovers pass from formal firms to informal businesses.

3
two observations are consistent with (i) development promoting outward FDI in “normal times”, especially in financially vulnerable sectors; and (ii) a severe tightening of credit conditions during the 2008-2010 global financial crisis, with the magnitude of the associated fall of outward FDI being relatively larger in the financially vulnerable sectors of countries characterized by high financial development.

Figure 1: <!-- Start of picture text --> 2003 2004 2005 2006 2007 2008 2009 2010<br>Year<br>3<br>2<br>1<br>0<br>Coefficient of the effect of FD on RFDI<br>−1<br><!-- End of picture text --> Note: interval.

The next section presents our empirical results where we investigate in depth the effects of a tightening of credit conditions on the relative volume of outward FDI in financially vulnerable sectors in more financially developed countries. This amounts to estimating the difference in the value of γ1 pre- and post-crisis in a model with additional controls.

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people to a total of 986 million or a 6.8 percent increase from the previous year. There are other ways of interpreting this absolute increase. For instance the true impact of the price increase was perhaps larger, as without the price shock, undernourishment could have declined below the 2007 baseline in 2008. Consider the 5 percent price increase as the counterfactual, for instance. The impact of the 2008 price increase would then have to be compared, not with the baseline figures for 2007 but the undernourishment under this counterfactual which is 902.2 million. Computed this way, one could argue the impact was as large as 9.1 percent. Moreover, Annex Table 2 shows that the choice of the price-calorie elasticity also has a bearing on our estimates. For instance if we use a lower price elasticity of calorie demand of -0.4, our estimate for the impact of the food price increase is 42 million people compared to 2007. Looking at absolute numbers in various regions, the biggest increases are likely to have been in Asia and Sub-Saharan Africa, while in proportional terms, the hardest hit regions were Latin America and Caribbean and Middle East and North Africa.

# **3.2 Impact of the Global Economic Slowdown on Undernourishment**

We use our model to obtain some estimates of the effect of the global economic slowdown on undernourishment. In Table 4, we show the regional growth forecasts for 2009 made by the World Bank before and after the true scale of the global economic crisis had become apparent. In June 2008 when the Global Development Finance 2008 was published, financial markets were already starting to show signs of stress but the sheer size of the upheaval that was to follow was largely unanticipated. The spike in food and energy prices seemed more pressing issues at the time. Therefore the GDF concluded at the time that “developing countries’ growth is easing but is still robust” (GDF, 2008). We use the growth predictions made in the 2008 GDF for 2009 as a baseline measure of economic growth that would have been realized had financial markets not imploded in the last quarter of 2008. We also have revised estimates of 2009 growth from the January 2010 Global Economic Prospects publication. We use a measure of the difference between these revised estimates and the growth predicted in 2008 to assess the impact of this growth shock on undernourishment.

The Newly Independent States in Europe and Central Asia and Latin America and Caribbean regions bore the biggest brunt of this growth shock. Whereas they were previously on course to grow at 5.4 percent and 4.3 percent respectively, the latest 2009 growth forecasts show them to
# APPLYING TAX POLICY MODELS IN COUNTRY ECONOMIC **WORK:** BANGLADESH, CHINA, INDIA

# Henrik Dahl and Pradeep Mitra

# SUMMARY

General principles can guide the design of the overall contours of a tax reform package to a considerable extent. Quantitative analysis, or a model, can however provide a useful supplement when it comes to examining specific proposals with respect to the two important dimensions of applied tax analysis, viz., revenue estimation and tax incidence. This paper draws attention to some of the ways in which tax policy models developed by PRE's Public Economics Division in the course of country economic work have been used to shed light on these questions.

The first part of the paper uses a tax policy model for Bangladesh to illustrate how the revenue and incidence effects of a tax reform proposal of the kind that can arise in country economic work may be analyzed. To that end the model is asked the question: what is the necessary increase in ad valorem excise taxes on a particular sector that will raise an additional 1 percent of total indirect tax revenue? This is done for each sector of the economy. Results are presented as to how the burden of tax increas2s in each sector is distributed across different rural and urban socio-economic groups and the impact on such variables as the consumer price index and the trade deficit. Since each sector is examined from the point of view of its effectiveness in contributing the same amount of revenue, the above results can be compared across sectors to allow a judgment to be made as to which of them are better candidates for increased taxation in an overall reform package.