rafmacalaba/datause-encoder-data huggingface dataset:
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# **5.4 Non-tariff measures are biased against the poor**

In addition to higher tariffs, the products produced by poor workers face a greater number of non-tariff measures. On average, the poorest (income deciles 1 and 2) face some 200 different types of NTMs while workers belonging to the top income decile face only 127 (72 less NTMs compared to the first decile). Unlike for tariffs, which consistently decline across the income deciles, the total number of NTMs for the poorer 5 deciles are not significantly different; the total count varying from 192 to 201. On the other hand, the number of NTMs falls significantly between the 9<sup>th</sup> and 10<sup>th</sup> deciles, with the latter facing 39 NTMs less than the former (Table 6).

Table 6. Total count of all types of NTMs faced, by income decile

|Income<br>Decile|Number of<br>NTMs<br>Faced|Duncan's<br>test|
|---|---|---|
|1|199|a b|
|2|201|a|
|3|201|a|
|4|192|b|
|5|194|a b|
|6|180|c|
|7|182|c|
|8|169|d|
|9|166|d|
|10|127|e|

*Using Duncan's test on the average count of NTMs faced. Averages tagged with the same letter of the alphabet are not significantly different.

# **5.5 Preliminary evidence that a coordination problem exists**

Higher tariffs on goods produced by poor workers likely reflects efforts by India’s trade partners to protect sectors where their own poor work. The same pattern can be seen in the tariffs on India’s imports, by matching India's own applied MFN tariffs in 2012 by sector to the income data from the survey. Goods produced by the richest workers (in the 10<sup>th</sup> decile) face a tariff of less than one-third the level on goods produced by the poorest workers (the lowest 2 deciles of the population—Table 7).

Table 7: India's import MFN applied tariffs in 2012 by income decile.

|Income<br>Decile|Applied MFN|
|---|---|
|1|35.6 c|
|2|39.0 a|
|3|37.5 b|
|4|35.3 c|
|5|33.8 d|
|6|29.9 e|

14
|Miscellaneous small activities X|-|1.217|0.869|
|---|---|---|---|
|Log running capital and value of enterprise||(10.62)||
|(Tk./year)||||
|Source of startup capital is own resource|0.905|0.947|0.797|
||(2.41)|(2.51)||
|Source of startup capital is loan from microcredit|1.148|1.161|0.055|
||(2.82)|(2.84)||
|Source of startup capital is loan from|1.207|1.303|0.004|
|commercial banks|(1.65)|(1.78)||
|Source of startup capital is loan from relatives|0.757|0.803|0.052|
|and friends|(1.82)|(1.93)||
|Other sources of startup capital|0.892|0.944|0.079|
||(2.23)|(2.35)||
|R<sup>2</sup>|0.316|0.319||
|F statistics for the model|F(45,|F (49,||
||2336)=23.93,|2332)=9.679||
||p>F=0.000|p> F=0.000||
|N|2,856|2,856||

Note: Level of FE is thana times year. For the estimation, data from 2000, 2005 and 2010 rounds of HIES are combined and then households from only those thanas that are common to all three rounds are kept. Figures in parentheses are t- statistics. In the activity list excluded category is the small miscellaneous activities, and among the sources of startup capitals excluded category is informal lenders. Regressions additionally include household variables (head’s age, sex and education, land and non-landholding), community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month). Sources: HIES surveys, 2000, 2005 and 2010.

**Table 12: Marginal return on enterprise investment**

|**Share of different sources (%)**|**Model 1**|**Model 2**|
|---|---|---|
|Manufacturing and processing activities|-|0.049**|
|Transport activities|-|0.027**|
|Trading activities|-|0.006**|
|Service activities|-|0.245**|
|Misc. Small activities|-|0.035**|

44
worse in more unequal communities, particularly when the distribution of power is concentrated within elites. A number of studies find a U-shaped relationship between inequality and project outcomes. The role of social heterogeneity is more complex to measure. However, most econometric studies that have attempted to devise measures of social fractionalization have shown that fractionalization tends to inhibit collective activity, but there is also qualitative evidence in the opposite direction.

Even in the most egalitarian societies, however, community involvement in choosing, constructing, and managing a public good will almost always be dominated by elites, who tend to be better educated, have fewer opportunity costs on their time, and therefore have the greatest net benefit from participation. It is not clear, however, that this always represents “capture,” in the sense of elites appropriating all the benefits from the public good. It may be useful to distinguish between extreme forms of capture, such as outright theft and corruption, and what might be called “benevolent capture.” However, when local cultures and systems of social organization result in tight control of community decisions by elites, malevolent forms of capture become likely. It is important therefore to understand what types of checks and balances are most effective in reducing capture and the systematic exclusion of the poor and of discriminated-against minorities. The problem in assessing elite capture is that there are no studies that look at an appropriate counterfactual. This remains an important area for future work.

Several case studies suggest that the success of participatory projects may also be affected by how well heterogeneity is managed, by what resources and strategies are used to bring communities together, and by how effectively differences are debated. The 55
29 essentially one destination, even if it possible to consider the inclusion of feeder services for other regions or connections opportunities. In many developing countries, the value of a road considered for private tolling depends on the existent of a supporting public network to enhance the commercial value of the private road. In many contracts in fact, concessionaire will demand the construction of this feeder road and the failure to do so is a reason for contract renegotiation (see Malaysia example). This is why the overall vision of an origin distribution important is generally important, even if it is true that it is significantly more important in an urban context.

Conceptually, an origin-destination matrix is almost always derived from a gravity model which can be justified in many ways and specified in almost as many forms. The oldest and best known is: and: - _Tij_ is traffic from i toj

- _Pi_ et _Pj_ are the potential users in i and j (e.g. population, number of firms)

- _dij_ is the distance between i and j

- a et b are parameters

Some of the problems of this model is that its does not address differences in socio-economic profiles or differences in the economic nature of the firms covered (industrial, service,...?).

In practice, since most estimates suggested that the value of a is I and for b around 2, a more general formula is: _7, = kO, Djf (co)_ and: - O is an "emission factor with origin i

- _Dj_ is an attraction factor by destination j

- -f is a decreasing function

- _Cij_ is the generalized cost of trips between i and j

An unconstrained version of this model allows a good estimate of the elasticity of demand to transport costs and deals with both trip generation and distribution. It is quite common for inter-urban traffic studies. But the model can be subject to various types of constraints (e.g. limiting the total traffic and some aspects of the traffic exogeneously or limiting total costs). The challenge is to specify a model that is well adapted to the situation to be analyzed. A wrong specification of this part of the model can be quite dramatic not only in terms of quality of
In order to create a counterfactual for household consumption, we first subtract the observed linear effect of the NDVI z-score on welfare. This removes the estimated effect of drought, leaving us with a demeaned welfare estimate _Y_<sup> ̄</sup> _icdp_ . Next, we draw a counterfactual NDVI z-score ( _z_ ( _NDV I_ ) _cdp,Y r_ ) based on observed NDVI z-scores in years _Y r ∼_ (2007 _,_ 2020), adding a random shock _ε ∼ N_ (0 _,_ 2) to allow for deviations from historical means and thereby multiply the set of plausible climate scenarios. This simulated NDVI z-score is added back to the demeaned welfare estimate in order to calculate the counterfactual welfare outcome. The simulation is repeated over 100 iterations The distribution of observed consumption _Y_<sup> ̄</sup> _icdp_<sup>_CF_is used to calculate the probability that a</sup> = household will be below the poverty line, defining it as vulnerable, where _V ulnerability P_ ( _Y_<sup>ˆ</sup> _icdp_<sup>_CF≤Pline_).Byconstruct,vulnerabilityisthereforeafunctionoftheprobability</sup> threshold for households. We posit a threshold of 25%, the welfare effect of a one in four year drought. We further explore a 20%, 10% and 5% threshold for robustness.<sup>34</sup> We present results using NDVI shocks in the paper. We get similar estimates with temperature shocks.

From the simulation, an estimated 61.8 percent of individuals would be pushed under the poverty line by a one in four year drought, compared to the 47.1 percent classified as poor in 2018 (see figure 15). Absent the effect of the 2018 drought, poverty would be 37.4 percent. Vulnerability climbs to 63.1 percent for a one in five year drought, 65.6 percent for a one in 10 year drought, and jumps to 98.6 percent for a one in 20 year drought. This last figure highlights how above a certain threshold of shock almost all households exhaust their coping capacities. Given that household must often resort to adverse coping strategies, such as selling assets or pulling children out of school, once a household falls into poverty it can be difficult for them to emerge from it. This highlights Afghans’ sensitivity to changing climatic conditions, which risk becoming more frequent and more severe over time.

> 34 By construct, a metric of vulnerability requires a probability threshold. While 50% illustrates the point where the expected consumption coincides with the poverty line (see i.e. Skoufias et al. (2021)), a lower threshold captures shock specific tail-end risks. For example, a 25% threshold illustrates the welfare effect of a one-in-four year moderate drought, a 10% threshold the welfare effects of a one in 10 year severe drought, and so forth.

18
comprehensive understanding of tax evasion. This paper is the first study linking administrative and survey data to investigate the issue of envelope wages and measure tax compliance in Romania.

**In the case of Romania, the following data sources were available for this study: administrative tax records, the EU-LFS (European Union Labor Force Survey), and the EU-SILC (European Union Statistics on Income and Living Conditions).** The administrative tax records collected by the Romanian Ministry of Public Finance included a full sample of monthly tax records for all employees from 2020 to 2021. The EU-LFS Additionally, the records for self-employed individuals were available for 2019 and 2020.<sup>2</sup> and EU-SILC, household survey data, were also available for this study. The EU-SILC data covered the years 2020-2021, which included the income years 2019 and 2020. The EU-LFS data covered the years 2019-2021. The EU-LFS and EU-SILC data were also valuable as they allowed for comparing estimates of reported and true incomes. Using a combination of administrative records and survey data, this study can better understand tax evasion in Romania.

**The administrative records are an essential and rich data source, providing detailed information on tax payments and potential estimates of reported income.** Brockemyer (2019) discusses the types of administrative data used in empirical economic research. She also debates the upsides and downsides of administrative tax data.  Our data is a tax register covering all employees who received labor income in 2020 – 2021. The value of income is reported by the third party – the employer. The tax administration extracted the de-identified data under the formal agreement. The tax administration also provided a detailed and comprehensive description of the tax forms and variables in the dataset. Tax administrative records cover the entire formal sector, providing a more comprehensive sample than survey data. The data provided detailed information on labor income and COVID-19 support programs. The monthly number of taxpayers varies between 6.9 million and 7.4 million. This data is less prone to selective non-reporting at the top of the income distribution and contains detailed information collected frequently. Electronic collection of tax data reduces errors, improving the data's accuracy. Tax data measures income variables with high precision. In contrast, survey data might be subject to self-

> 2 Though available, data on self-employees was not used for this analysis as the focus was on employees.

8
# Inflation Dynamics and Global Value Chains

François de Soyres & Sebastian Franco World Bank<sup>_∗_</sup> Keywords: inflation synchronization, global value chains. JEL classification: E16, E31, F1, F14, F15, F42, F62.

> _∗_ This is a background paper for the World Bank’s World Development Report 2020 "Global Value Chains: Trading for Development". We thank the WDR 2020 team for their comments. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank.
Figure 4: Dynamic Conditional Correlations of Maize Nominal Price Returns <!-- Start of picture text --> 0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(a) Crude oil (b) Coal (c) Natural gas<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(d) Soybean oil (e) Barley (f) Sorghum<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>−0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013 −0.21970 1978 1987 1996 2004 2013<br>(g) Rice (h) Wheat (i) Sugar<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>−0.21970 1978 1987 1996 2004 2013<br>(j) Coffee<br><!-- End of picture text --> Note. The solid thin lines plot the estimated dynamic conditional correlations (DCC), the solid thick lines represent their trends estimated by HP (Hodrick-Prescott) filter. The dashed lines are the estimated constant conditional correlations (CCC) based on Bollerslev (1990) and their corresponding 95% confidence intervals.

21
of a DSD from the average slope of all other DSDs in the district. According to the regression results, travel time increases signi...cantly with an increase in the average slope in a DSD relative to the surrounding DSDs. It is a powerful variable for explaining variations in travel time with a t statistic of 6.50. The Angrist-Pischke �<sup>2</sup> = 43:36 and F = 21:59 providing convincing evidence of the strength of the instruments in identifying the e¤ects of travel time.

For the interaction of LDO incidence and travel time, the interaction of the instruments has very high explanatory power, the t statistic is 10.05 in the ...rst stage regression (see column 3 in Table 3). The di¤erence of slope has a positive and signi...cant (at 1 percent) e¤ect. The Angrist-Pischke �<sup>2</sup> = 44:62 and F = 22:22, indicating that the instruments do not lack power in explaining variations in the interaction of LDO incidence and travel time. As additional evidence, we report the Kleibergen-Papp F statistic that test for weak identi...cation of the wage equation as a whole (see Table 4). The high values of Kleibergen-Paap F statistics in Table 4 shows that the wage equation does not su¤er from weak identi...cation.

The IV estimates of our parameters of interest are reported in Table 4 along with formal diagnostic test for the exclusion restrictions. An important point that comes across from the columns (3-5) in Table 4 is that the overidenti...cation tests cannot reject the null of valid instruments across the board; the P-value of Hansen’s J being consistently high (the lowest P-value is 0.32). The instruments thus comfortably satisfy formal tests of relevance and exclusion.

The ...rst column in Table 4 reports the main IV estimates corresponding to the instruments set discussed in Table 3. The ...rst thing to notice in column (1) is that the estimates of the coe¢ cients on LDO restrictions and its interaction with travel time have increased substantially in magnitude compared to the OLS estimates in Tables 3. This is not surprising given the evidence in Table 2 that the estimate of the LDO restrictions on wages in OLS regression is underestimated because of unobserved land productivity. The estimates of the parameters of our interest are signi...cant at 1 percent level.

The rest of the columns in Table 4 reports results from a set of robustness checks. The second column shows the results of a just identi...ed model where the set of instruments consists of (i) di¤erence in slope, (ii) interaction of district malaria with inland water in a DSD, and (iii) interaction of the above two instruments. The evidence from just identi...ed model may be useful 24
7 between urban and rural areas since the between group component of the decomposition increased over time, especially in 1995-96 for the lower poverty lines. Over time, the increase in the within group and between group components of the decomposition were of a similar order of magnitude.

# **HI Micro determinants of consumption, poverty, growth and inequality**

_III 1. Methods_ In the previous section, we have presented a big picture as to what happened to poverty and inequality in Bangladesh from 1983 to 1996. But what are the determinants of poverty and inequality, as well as of per capita consumption and growth in average consumption over time? To answer this question, a single set of regressions can be used for each one of the HES surveys. Following standard practice, we use the semi-log specification since per capita consumption (the dependant variable) is not normally distributed. These regressions in urban (U subscript) and rural (R subscript) areas are simply:

> Urban equation: Log yUi = **u'Xi** + £Ui (5.1) Rural equation: Log YRi = 1R'Xi + gRi (5.2)

Separate regressions are used for both sectors because the returns to household characteristics may differ between sectors. The dependent variables are the log welfare ratios (logs of nominal per capita consumption divided by the poverty line of the area in which the household lives). The independent variables are the same for both sectors. Apart from a constant, they include: (a) geographic location according to 17 districts (each household, whether urban or rural, lives in one of these districts); (b) household size variables: the number of babies, children, and adults, as well as the square of the number of babies, children, and adults; (c) other demographic and gender variables: the age of the household head and its square, the sex of the household head, and the family structure of the household, such as a head with a spouse, a head without a spouse but nevertheless married, a head without a spouse and single, or a head without a spouse and divorced or widowed; (d) education variables: the education level of the head