rafmacalaba/datause-encoder-data huggingface dataset:
Survey (HIES) (2000 and 2005) of Bangladesh.<sup>1</sup> The regional gaps in the welfare in our empirical analysis are measured by the di ff erence in the distribution of log of real per capita consumption expenditure between regions. These regional di ff erences in the living standards are then decomposed into a ‘sorting’ e ff ect arising due to di ff erences in the observed household characteristics, and a returns e ff ect resulting from the di ff erences in the rates of return to those characteristics.

Bangladesh provides an excellent case to study the roles of di ff erent factors in explaining the spatial di ff erences in returns for several reasons. First, there are no administrative restrictions on migration in Bangladesh. As much of the Bangladesh’s population share the same ethnicity, religion and language, there are no serious ethnic or cultural barriers to internal migration. Despite the absence of serious barriers to labor mobility, Ravallion and Wodon (1999) has shown that both sorting and returns e ff ects are important in explaining average regional gaps in welfare in Bangladesh.<sup>2</sup> Second, the capital city Dhaka and the main port city Chittagong have emerged as two growth centers in the country, dominating both the urbanization process and economic growth. The country is sliced into three pieces by two major Asian rivers, the Ganges and the Brahmaputra. The natural border de fi ned by these two rivers allows us to de fi ne two regions in terms of their access to Dhaka and Chittagong without relying on potentially endogenous factors such as travel time to these centers. Speci fi cally, we de fi ne an integrated (I) region consisting of areas which are geographically contiguous to either the Dhaka or Chittagong metropolitan areas.<sup>3</sup> The rest of the country constitutes the less integrated (LI) region.<sup>4</sup> The natural border created by the rivers hinders movement of goods and people across the I and LI regions.

> 1 Nguyen et al.(2007) applied this technique to separate out the contribution of covariates and that of returns to urban-rural inequality in Vietnam.

> 2 Ravallion and Wodon (1999) used data from HIES 1988-89 and 1990-91 and carried out decomposition exercise based on the mean regressions. Our paper advances the understanding of the spatial di ff erences in welfare in several important ways. By using the quantile regression approach, we allow household attributes to have di ff erent marginal e ff ects depending on a household’s position in income distribution. Moreover, instead of capturing spatial e ff ects using district dummies, we de fi ne regions in terms of di ff erences in infrastructure endowments and natural borders. Finally, we also attempt to evaluate the contribution of di ff erent factors to the persistence of spatial di ff erence in rates of returns.

> 3 I region thus lies to the North of the Ganges and East of the Brahmaputra rivers.

> 4 The LI region accounts for the territory that lies to the West of the Brahmaputra (Rajshahi Division) and South of the Ganges (Barisal and Khulna divisions, and a small part of Dhaka Division).

3
sectors and sub-sectors. This suggests that the sectoral pattern of growth does not matter for poverty (that is, whether growth is concentrated in the primary, secondary or tertiary sector is unrelated to poverty changes). Table 4 shows the estimation results for the three sector case.

|**Table 3: Pove**|**rty Changes a**<br>|**nd the Emplo**<br>|**yment/Producti**<br>|**vity Intensity**<br>|**of Growth**<br>||
|---|---|---|---|---|---|---|
||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non Upper<br>Middle<br>Income|(4)<br>Non<br>LAC|(5)<br>Positive<br>Growth|(6)<br>Negative<br>Growth|
|Inverse of dependency|||||||
|A/N|-2.895|-2.287|1.709|-8.124|1.395|-37.11|
||(-0.59)|(-0.48)|(0.24)|(-1.29)|(0.29)|(-1.70)|
|Value added per|||||||
|worker Y/E|-3.612***|-3.686***|-1.762|-4.391**|-3.828***|-8.137|
||(-3.95)|(-4.26)|(-1.24)|(-2.76)|(-3.35)|(-1.55)|
|Employment rate E/A|-4.604**|-4.783**|-0.690|-6.905**|-3.413**|-14.48**|
||(-2.59)|(-2.64)|(-0.37)|(-2.36)|(-2.47)|(-2.34)|
|Change in Gini (%)||2.586***|1.543***|2.341***|1.719***|4.217|
|||(2.84)|(3.59)|(3.26)|(3.42)|(1.33)|
|Constant|0.144|0.146|-0.0263|0.302*|0.102|0.205|
||(1.37)|(1.45)|(-0.25)|(1.76)|(1.04)|(0.53)|
|Observations|104|104|63|41|73|31|
|AdjustedR-squared|0.127|0.233|0.128|0.270|0.207|0.192|
|*p<.10,**p<.05,***|p<.01.T-statis|ticsinparenth|eses.||||

We now turn to the sectoral employment intensity of growth by estimating Equation 5. As mentioned previously,<sup>_~~e~~_</sup> _s_<sup>reflects how much of the observed aggregate growth can be</sup> linked to increases in employment in sector _s_ , and ~~ω~~ _s_ corresponds to the amount of growth that can be linked to changes in productivity in sector _s_ . Again we perform the estimation for seven and three sector disaggregations. We cannot reject equality of coefficients across _sub-_ sectors in the seven sector case, so we only report the estimation for the three sector disaggregation. Table 5 illustrates the results. Employment-intensive growth in the secondary sector is negatively and robustly correlated with poverty reduction, except in recessions. Employment-intensive growth in agriculture is correlated with increases in poverty, except in recessions and in non LAC countries. Even so, the coefficient is significant only in non upper middle income countries. Employmentintensive growth in the tertiary sector is negatively correlated with poverty reduction for the upper middle income countries, and positively correlated (though not significantly) in the poorest countries. Productivity-intensive growth, however, does not seem to be robustly correlated with poverty reduction.

19
_Page 8_ Figure 7: Growth Rate of Central Government Expenditure on Education Real Per capita: 1980-85 <!-- Start of picture text --> 20<br>5<br>0-<br>-15<br>Brazil Chit CRica Mex Urug Ven Chin Kor Talw Fin Italy Nor L Am Asla Eur<br><!-- End of picture text --> <!-- Start of picture text --> Sources IMF<br><!-- End of picture text --> Figure 8: Growth Rate of Central Government Expenditure on Health Real Per capita: 1980-85 <!-- Start of picture text --> 15-<br>0<br>.5<br>-10<br>Argon Chll CRila Mex Urug Van Chin Kor Taiw Thai Fln Italy L Am Asia Eur<br><!-- End of picture text --> source: IMF
# X. Annex Tables

## **<u>Table 15: DID Regression (1)</u>**

||Distance t<br>Populated|<br>o:<br> area|<br>Municipal|<br>capital|Hospital||School||
|---|---|---|---|---|---|---|---|---|
|D|-0.215|(0.198)|0.454|(0.066) ***|0.338|(0.074) ***|-0.125|(0.113)|
|T|-0.967|(0.213) ***|-0.275|(0.111) **|-0.192|(0.124)|-0.112|(0.149)|
|D*T|-0.369|(0.289)|-0.357|(0.152) **|-0.184|(0.165)|0.600|(0.190) ***|
|lnHH size|-0.145|(0.192)|0.290|(0.114) **|0.207|(0.118) *|0.001|(0.131)|
|Adult share|0.408|(0.407)|0.251|(0.217)|0.122|(0.229)|0.682|(0.265) ***|
|HH head sex: Male|0.534|(0.211) **|-0.271|(0.133) **|-0.285|(0.142) **|-0.390|(0.147) ***|
|HH head education: Elementary|0.335|(0.170) **|0.139|(0.090)|-0.048|(0.092)|-0.149|(0.110)|
|HH head education: Secondary|0.011|(0.298)|0.108|(0.163)|0.041|(0.173)|0.093|(0.188)|
|HH head education: University|-0.054|(0.643)|0.411|(0.333)|0.110|(0.373)|0.101|(0.409)|
|Appliance ownership: Refrigerator|0.297|(0.243)|0.070|(0.138)|0.011|(0.132)|0.200|(0.153)|
|Appliance ownership: Color TV|0.086|(0.228)|0.001|(0.128)|-0.149|(0.127)|-0.300|(0.144) **|
|Appliance ownership: Radio|0.064|(0.153)|-0.005|(0.079)|-0.040|(0.085)|0.033|(0.097)|
|Appliance ownership: Gas stove|0.351|(0.216) *|0.074|(0.114)|-0.011|(0.107)|0.174|(0.136)|
|Appliance ownership: Washing|||||||||
|machine|-0.441|(0.208) **|-0.322|(0.118) ***|-0.383|(0.138) ***|-0.070|(0.137)|
|Gas use for cooking|-0.339|(0.192) *|0.087|(0.104)|-0.102|(0.125)|-0.253|(0.124) **|
|Power use for lighting|-0.876|(0.242) ***|-0.377|(0.129) ***|-0.342|(0.130) ***|-0.575|(0.148) ***|
|Tap water for cooking|0.752|(0.158) ***|0.131|(0.087)|0.066|(0.093)|-0.557|(0.101) ***|
|Constant|-0.348|(0.608)|2.503|(0.335)***|2.948|(0.370)***|0.444|(0.415)|
|Obs.|2064||2064||2064||2064||
|R-squared|0.063||0.059||0.054||0.075||
|F-stat|8.85||9.09||7.91||10.37||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

34
# Table la

Water discharge characteristics in 1997

|**Average discharges (kg/year)**||
|---|---|
|TSS|47,861<br>(530)|
|COD<br>._____________________________|48,591<br>___ (626)|
|**Average concentration (mg/l)**||
|TSS<br>.___<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _|99<br>(50 3 )|
|COD|280<br>(507)|
|**Proportion of firms paying levy**(%)|45|

Table lb Air discharge characteristics in 1997

|**Average discharges (ton/year)**||
|---|---|
|TSP|136<br>(640)|
|**Average concentration (mg/m3)**||
|TSP|572<br>(211)|
|**Proportion of firms paying levy**(%)|23|

In 1997, the 640 enterprises were the object of 5287 inspections, most of them (99.4%) performed by the Zhenjiang Environmental Protection Bureau. These enterprises were also the object of 78 water related complaints, and 163 air related complaints.

# 4.2 Results

Given the presence of endogenous variables in the pollution equation (equation 2), the general method of moments (GMM) is an appropriate method of estimation to obtain consistent and efficient estimates. Thus, the GMM is applied to equation (3). The instruments being used are _(Q, V, P, V, P, Z)_ 16 where the matrix V consists of X,

- l6 See Ahn and Schmidt (1995) for a thorough discussion on instruments.

16
# **References**

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Iqbal, F., (2006), Sustaining Gains in Poverty Reduction and Human Development in the Middle East and <u>North Africa, World Bank, Washington D.C.</u> Iqbal, F, and Kiendrebeogo, Y., 2016. “The Determinants of Child Mortality Reduction in the Middle East and North Africa.” _Middle East Development Journal_ , 8:2, 230-247.

Iqbal, F. and Kiendrebeogo, Y., 2015. “Public Spending and Education Attainment in the Middle East and North Africa.” _Review of Middle East Economics and Finance_ , 11:2, 99-118.

21
# 4.2 Income Tax Compliance

We start by analyzing the impact of the emails on income tax compliance non-parametrically. Figure 2 shows the rate of income tax ling and payment over time by treatment status, with the start of the experiment indicated by a vertical line. The left (right) column refers to rms with (without) third-party information. While pre-intervention trends in the treatment and control groups were nearly identical for all outcomes, a positive treatment eect on ling and payment emerged sharply at the start of the experiment. This eect stabilized by about ve weeks after the experiment start date, and did not decrease thereafter.<sup>25</sup> This conrms that the emails generated additional tax payments rather than just bringing forward payments that rms would have made anyways. After 15 weeks of the intervention, the ling rate for information-covered rms reached 32.5% for those sent the baseline email, and 34.2% for those sent the email with examples of third-party information, relative to 11.5% for the control group.<sup>26</sup> The dierence between the two treatment eects is statistically signicant at the 10% level. The payment rate was 4.7% for those sent the baseline email, 5.3% for those sent the email with examples of third-party information, and 1.7% for the control group, though the dierence between the two treatment groups is not signicant.

For rms not covered by third-party information, the shape of the ling and payment response relative to the control group is similar, with the exception of any signicant dierence between the two treatments. Emphasizing the use of third-party information, in addition to highlighting deterrence measures, thus did not enhance compliance among rms not covered by an information trail, which suggests that these rms did not expect the tax authority to possess any third-party reports about them. After 15 weeks of the intervention, the proportion of treated rms ling an income tax declaration reached 19%, relative to 3.9% for the control group. The rate of payment among rms not covered by third-party information was below 1%, as most rms declare a net liability of zero, but the payment rate was still signicantly higher for the treatment group.

To consider a larger number of outcomes and control for covariates, we report probit, OLS, and PPML estimates in Tables 4 and 5 for the two sub-experiments. Table 4 presents estimates of the treatment eect at 15 weeks for rms covered by third-party information. The rst three columns report extensive-margin responses on income tax ling, reporting a positive net liability, and making

> 25This is true also when considering a longer post-intervention period (results available upon request).

> 26The fact that over 60% of the treatment group do not le in response to the (strong) enforcement message might partially be attributable to the fact that not all taxpayers received and opened the email. It is unfortunately not possible to identify the taxpayers who did not receive or open the email.

18
Table 1

# EXP tDITURE ON VOCATIONAL AND TECHNICAL EDUCATION AS PERCENT OF CENTRAL GOVERNMENT RECURRENT EXPENDITURE ON ALL SECONDARY EDUCATION, CIRCA 1980

|Country/region|Percent|
|---|---|
|Peru|27|
|Latin America and the<br>Caribbean (22)|25|
|Africa (22)|18|
|Asia (18)|16|
|Europe (13)|34|

Note: In parentheses, the number of countries in each regional sample.

Source: Unesco (1e6).

Because the evidence on returns is mixed, conclusions with regard to rates of return are also mixed. Of the studies that report both costs and returns and then calculate a rate of return, a higher rate for investment in secondary VTE is reported for Brazil (20 percent vs 16 percent, Psacharopoulos 1987) and for Venzuela (26 percent vs 20 percent, Psacharopoulos and Steier 1987). Comparable rates to secondary VTE and general education are reported for Colombia (Psacharopoulos 1984) and for Cyprus and Taiwan (Psacharopoulos 1985).

Past studies comparing VTE and general education have focused on secondary education almost exclusively. This study of Peru will be one of the first to extend the comparison to tertiary education.

6
_33_ _Economics_ , 127(2):753–792. JPAL (2017). Increasing test score performance. _Retrieved from: https://www.povertyactionlab.org/policy-lessons/education/increasing-testscore-performance_ .

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- NEC (2005). National education census. Technical report, Pakistan Bureau of Statistics.

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- Sacerdote, B. (2011). Peer e↵ects in education: How might they work, how big are they and how much do we know thus far? volume 3, chapter 04, pages 249–277. Elsevier, 1 edition.

- Udry, C. and Anagol, S. (2006). The return to capital in ghana. _American Economic Review_ , 96(2):388–393.
Since in section 5.1 we established that both the fed funds rate and the output gap had little discernible effect on the exchange rate, we focus on shocks to copper prices, money and the exchange rate itself.<sup>4</sup> <!-- Start of picture text --> 0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>-0.3<br>-0.35 Copper Money Exchange rate<br>0 1 2 3 4 5 6 7 8<br><!-- End of picture text --> _Figure 7: Pass through to consumer prices for different shocks_

|**Horizon**|**0**|**1**|**4**|**8**|
|---|---|---|---|---|
|Copper|0.06|-0.02|-0.03|-0.04|
|Money|-0.03|-0.14|-0.21|-0.22|
|Exchangerate|-0.02|-0.03|-0.05|-0.05|

_Table 2: Pass through for different shocks and time horizons_ Figure 7 plots the results. First, note that for a shock to the exchange rate, the effect is even slightly lower than that reported in section 5.1. This is because a shock that raises the exchange rate by 1% at impact still raises it in the subsequent periods; effectively, Figure 7 refers to smaller exchange rate shocks in all periods after impact.

A 1% increase in the exchange rate caused by a shock to copper prices comes with an increase in consumer prices of 0.05%, implying a negative pass-through at impact of about -5%. This may reflect aggregate demand effects related to the stimulus to the copper industry (increased employment, rents). The effect is reversed in later periods, with a pass through between 5% and 8% in the following quarters.

Finally, monetary shocks are associated with by far the strongest pass-though: exchange rate fluctuations that are caused by a monetary shock are estimated to translate into consumer prices with an ERPT of about 25% after three quarters, with a lasting effect of about 23%. This is particularly relevant in the view of the results from the historical variance decomposition in section 5.2, which indicate that, while the variation of the exchange rate has markedly declined overall, it has recently increasingly been driven by monetary shocks. The remaining exchange rate fluctuations may therefore be disproportionately associated with consumer prices. Note however that this is more than offset by the smaller amplitude of the fluctuations in general.

> 4 The latter may seem redundant, as conceptually it is very similar to the exchange rate pass-through already quantified above. Because of calculation in each period, and because an exchange rate shock that raises it by 1% in the first period may (and does) have a different effect in the second period, it is not identical.

19
Understanding the scale and geographic scope of spillovers is an important piece in the overall growth puzzle. For example, one reason why cross-country growth spillovers might be localized is because spillovers of knowledge between countries are also localized. This may be the case if knowledge is embodied in intermediate goods and trade in such goods is more probable between countries which are geographically proximate<sup>1</sup> or which share a history of strong trading relations (Coe and Helpman, 1995, pp 861863). Growth theory suggests that these trading partners will form so-called convergence clubs with economic growth correlated across neighboring countries (see, _inter alia_ , Grossman and Helpman, 1991, Ertur and Koch, 2005, 2007). This might help to explain why successive waves of economic development have tended to be confined within relatively well-defined geographic regions.

With localized cross-country growth spillovers, not only will growth miracles tend to be spatially correlated, but so too will growth disasters. This might form part of the explanation for SSA's growth failure, both in absolute terms and relative to other developing parts of the world, in recent decades. It might also account for the large and statistically significant Africa dummy that has been a recurring finding in the empirical growth literature (Easterly and Levine, 1998). In that case, a coordinated effort between SSA nations to stimulate domestic economic growth will have a multiplier effect stemming from the neighborhood interactions implied by the existence of localized spillovers ( _ibid._ , pp 136-137).

> 1 Empirical evidence from the estimation of gravity models of trade provides strong support for the hypothesis that the strength of trade between two countries is inversely related to distance (see, for example, Brakman _et al_ ., 2009, chapter 1).

2