rafmacalaba/data-use-mentions-extended huggingface dataset:
increasing the progressivity of the VAT (Bachas et al., 2020). While the PECS 2016/17 does not have a place of purchase variable to proxy households’ consumption informality, this study imputed average consumption informality by decile based on estimates from similar countries available in Bachas et al. (2020). We calculated the indirect effects of the VAT for the West Bank region using the 2017 Input-Output table and the Cost-Push Model (Inchauste and Jellema 2018, Inchauste et al. forthcoming).<sup>51</sup>

## **4.4.3. Excises**

**We model the fuel excises for gasoline, diesel, and kerosene** . Consistent with previous indirect taxes’ assumptions. We only simulate fuel excises in the West Bank, whereas we assume individuals in Gaza pay zero excises. The model of excises does not account for households’ consumption informality given that fuel products are typically not prone to tax evasion.

## **The simulation of fuel excises followed these steps:**

- (i) Mapped excise rates to relevant fuel products in the PECS 2016/17. Given data limitations, the model uses 2017 fuel excise rates from the Israeli Tax Authority<sup>52</sup> ; this is a plausible assumption since the excise rates from the Palestinian Territories and Israel are similar to avoid arbitrage between both countries.

- (ii) Prepared the Augmented IO Matrix for the West Bank<sup>53</sup> and mapped IO sectors (fuel products) to relevant excise rates. Also, all IO sectors were mapped to the associated final goods (COICOP codes) from the PECS.

- (iii) Excluded non-taxable categories such as self-production and gifts from the PECS 2016/17 consumption dataset.

- (iv) Calculated indirect effects of fuel excises based on the Augmented IO Matrix for the West Bank and the Cost-Push Model (Inchauste and Jellema, 2018). The model applies the price shock of fuel excises (
<mark>Table 2c: Transition Matrices - Synthetic vs. Actual Panel Data Peru 2008 and 2009</mark>

|Status in 2008 2009|Low|er Bou|nd Estim|ates|Truth|Upp|er Boun|d Estim|ates|
|---|---|---|---|---|---|---|---|---|---|
|,|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|
|Poor,Poor<br>|33.90<br>|31.72<br>|28.70<br>|29.01<br>|23.61<br>(21.89,25.31)|16.20<br>|17.64<br>|17.56<br>|18.57<br>|
|Poor,Non-poor<br>|1.26<br>|2.50<br>|4.87<br>|4.18<br>|9.74<br>(8.54,10.93)|20.32<br>|17.24<br>|17.25<br>|16.40<br>|
|Non-poor,Poor<br>|0.19<br>|2.38<br>|5.40<br>|5.09<br>|10.49<br>(9.25,11.72)|17.90<br>|16.46<br>|16.54<br>|15.53<br>|
|Non-poor,Non-poor<br>|64.65<br
invariant by definition, a necessary requirement for the method to work properly. Panel B of table 5 tests the robustness of results by replacing department of birth fixed effects by current place of residence fixed effects. Results show that the methodology works well when using current place of residence instead of retrospective data on place of birth; 95 percent true panel confidence interval of mobility lies within upper and lower bound estimates and results are similar to those in table 2c.

# _5.4.3. Sub-national Aggregates: Census vs. Household Survey_

Columns three and eight of table 2c presents estimates of mobility in Peru based on underlying models of consumption that include sub-national controls from the 2005 Census at the villagelevel (i.e., percentage of female head of households, percentage of head of households with primary education incomplete, percentage of head of households with secondary education incomplete, percentage of head of households with secondary education complete, percentage of households with electricity, and percentage of households with access to water), as well as their interaction with household time invariant characteristics. As in the case of department of birth, sub-national controls are of great value since they are time invariant. Panel C of table 5 tests the robustness of results by replacing Census variables at the village-level by potentially Census variables at the department level. Predictions are based on the third specification explained in section 3.4. The technique performs well when using Census variables at the department-level, 95 percent true panel confidence interval of mobility lies within upper and lower bound estimates. Results are also robust to the use of department-level controls from the survey instead of the Census (not shown). Taken together, these results suggest that we can directly use survey information to construct sub-national controls and interaction terms to increase precision of estimates.

18
1 is benchmarked against years in which the district receives a negative rainfall shock.

# **3.2 Rainfall Shocks and Non-Farm Enterprise Performance**

The following empirical specification is used to identify the causal impact of quasi-exogenous weather shocks on enterprise performance: – _FE_ in specification (3) denotes our battery of fixed effects namely district, quarter, survey-year, and 4-digit industry fixed effects. These control for time-invariant district and industry specific characteristics, and common time-specific shocks affecting enterprise performance. **X** includes district and enterprise-specific covariates. Enterprise covariates include a quadratic in enterprise age and other enterprise characteristics. District covariates include district literacy, financial infrastructure, monthly per capita consumption, labor force participation rate, employment in public and private enterprises and number of census towns in the district. With the exception of financial infrastructure – measured as bank branches per

> 5 We construct _PosShockdt_ and _NegShockdt_ using rainfall data from a 40-year period between 1975 and 2015.

> 6 The Simpson’s index is computed by summing the square of the share of area allocated to each crop, and subtracting the sum of squares from 1. Higher values indicate higher levels of crop diversification.

8
So far, attention has been focused upon _direct_ links. It is equally plausible however; that migrant networks exist between trading pairs which pertain to a country (of birth) _k_ , which is neither the importing ( _k≠i_ ) nor exporting nation ( _k≠j_ ) i.e. that a _third-party_ effect exists which is driving the observed coefficients.<sup>27</sup> Continuing from the discussion in Section 3, these third party effects can be modeled as ln(MIGki*MIGkj) where _i≠j_ and _k≠i and k≠j._ This is another important contribution of this paper, since despite only 68 countries being chosen for analysis; the comprehensive migration dataset permits thirdparty effects pertaining to potentially all (178) countries of the world to be included in estimation (see Equation 11).

# 11.

Where additionally, ς1 is a vector containing the coefficients for the interactions of the third-party effects. Table 8 again presents the statistically significant point estimates for the third-party effects estimated in [11] while both omitting and including τij. The estimates of the indirect effects of migrants are more stable in comparison with the existing literature, whether or not pair-wise fixed effects are included in the regression. Prior to the inclusion of τij, there are 30 positive and 29 negative point estimates; suggesting in comparison with direct effects third-party networks are more likely to divert trade.

It is not as immediately obvious why pair-wise fixed effects should be implemented when testing for third-party effects. Felbermayr, Jung and Toubal (2009) argue that third-party effects should be exogenous to bilateral trade flows between countries _i_ and _j_ , since the migrants pertain to countries _k_ .<sup>28</sup> However, if migrants from a country k are believed to foster trade between countries _i_ and _j_ then there might be unobserved bilateral factors between _i_ and _j_ , which encourage migrants from _k_ to select into migrating into those countries in the first place. In fact, controlling for pair-wise unobserved factors
**Table 6. Prevalence of disabilities at primary up to senior secondary level based on EMIS and Susenas**

|_Category_|**EMIS(e **<br>_All_<br>_disabilities_<br>_category_|**nrolled stud**<br>_Total_<br>_student_<br>_(disabled_<br>_and non-_<br>_disabled)_|**ents)**<br>_%_<br>_disabled_|_Visual_<br>_disability_|_Auditory_<br>_disability_|**SUSENAS(enrol**<br>_Motor/Sensory_<br>_disability_|**led student**<br>_With at_<br>_least_<br>_one_<br>_physical_<br>_disability_|**s)**<br>_Total_<br>_students_<br>_(disabled_<br>_and non-_<br>_disabled)_|_%_<br>_disabled_<br>_(at least_<br>_one_<br>_physical_<br>_disability)_|
|---|---|---|---|---|---|---|---|---|---|
|Primary|4,663|3,668,985|0.13%|24,730|19,486|32,991|66,752|27,678,335|0.24%|
|Junior<br>Secondary|1,902|3,167,870|0.06%|11,177|3,754|4,343|17,643|10,800,786|0.16%|
|Senior<br>Secondary|2,461|1,374,981|0.18%|6,057|1,788|2,314|9,
Although remittance flows are strong contributors to increased consumption and reduced poverty in origin countries, their magnitude has also raised questions about undesirable effects on the recipient economies. These issues are similar to those that emerge during periods of massive capital inflows. In particular, there is a concern that remittances could cause Dutch disease effects. In other words, the massive inflow of foreign currency could be associated with a real exchange rate appreciation and loss of international competitiveness, which, in turn, could lead to a decline in the production of manufactured and other tradable goods. Empirical studies looking at this question include include Acosta, Lartey, and Mandelman (2009), Lartey, Mandelman, and Acosta (2012), Amuedo-Dorantes and Pozo (2004), and Rajan and Subramanian (2005), all of which use cross-country data to document that real exchange appreciation follows remittance flows.

# **7 Institutions and social norms**

Finally, a thinner but rapidly evolving literature shows that temporary migration can impact origin countries’ institutions and social norms that matter for development. Foreign-educated individuals, for example may bring democratic change if they acquired education in democratic countries (Spilimbergo 2009; Mercier 2016). Using experimental data, Batista and Vicente (2011) show that emigration from Cabo Verde contributed to better governance at home. This is primarily driven by return migrants who lived in destination countries with good governance and democratic institutions. In Morocco, having a returnee from a Western European country in the household increases the demand for political and social change, after controlling for the double selection into emigration and return migration (Tuccio, Wahba, and Hamdouch 2019). Because better institutions usually translate into higher total factor productivity, such channels are partly captured by productivity responses. A positive impact of return migration on political participation rates and on electoral competitiveness has been found in Mali, which mainly stems from returnees from non-African countries (Chauvet and Mercier 2014). The impact of returnees on turnout was found to go beyond their own participation, and to affect electoral
_APoverty(w) - APoverty(w)_ **_=(_** _- ) *_ **_(rIfood_** - _ri_ **_non-food) *_** _(oRE/r)_ **_*_** _(P ty food no-food aREj ii_ In words, the difference in the estimate of poverty between two periods from using two different weights given to food in a price deflator is (a) the difference in the food share times (b) the difference in food and non-food inflation (which determines the change in measured inflation) times (c) how much real expenditures change due to a change in inflation times (d) how much the poverty rate changes for a distributionally neutral change in real expenditures. So, if we did nothing else besides use an estimate of inflation based on a food share of 70 percent versus using the CPI (with a food share of less than 40 percent) this would change estimates of the change in poverty between the two periods by approximately: _APoverty_ -0.3*(160-81)* 0.4*0.5 **_=_** 4.7 percentage points.

This is obviously an enormous change. Similarly, if only food price inflation was used versus an inflation rate calculated based on the food/non-food share of the bottom 30 percent in actual consumption (around 0.7) this would increase poverty rates by a similarly large amount.<sup>7</sup>

> _7_ Some have actually indirectly proposed poverty rates based on the price of rice alone, by using a poverty standard based on purchasing power over rice only.

11
together with banks, perform these functions.<sup>28</sup> Beck, Demirguc-Kunt and Levine (2000) construct a large cross-country panel database on the mixture of banks and financial markets across 150 countries.<sup>29</sup> Demirguc-Kunt and Levine (2001) construct measures of the comparative size and activity of stock markets and banks such as the ratio of Stock Market Liquidity (defined as the value of stock transactions as a share of national output) to Bank Credit (defined by the claims of the banking sector on the private sector as a share of GDP) and ratio of Market Capitalization (measured by the value of listed shares divided by GDP) to Bank Credit. An alternative measure focuses on the restrictions on bank activities with the idea that in financial systems where banks face fewer restrictions, they are potentially more powerful (e.g. Barth et al. 2001; Beck and Levine, 2002). Others have examined the percentage of assets of the ten largest banks in each country owned by the government as a measure of the extent to which the financial system is bank based (e.g. La Porta et al. 2002).

While the classification distinguishing between bank-based and market-based countries can vary, Beck and Levine (2002) show that these measures are highly correlated. Thus, bankbased financial systems also tend to be dominated by state-owned banks (e.g. Bangladesh, Pakistan, Costa Rica) and countries with no state ownership in the ten largest banks (e.g. Canada, Japan, U.K., U.S.) tend to be market based financial systems. Further, countries whose banking systems are dominated by state-owned banks also tend to impose more restrictions on their banks (e.g. Egypt, Bangladesh, and Israel). Demirguc-Kunt and Levine (2001) show that while both types of financial systems are more developed in richer countries, financial systems tend to become more market based as countries become richer. Further
_51_ assets the poor lose first, when coping with survival crises. The lack of these indicators makes it difficult to build up an understanding of the capabilities and resources of the poor - in terms of physical assets and human resource endowment. The survey should include some limited information on household expenditures, solely to provide the means to group households into approximate expenditure quintiles. One rather overwhelming lesson which the research team learned is the complexity, cost and time-consuming nature of analyzing two years of a National Consumer Survey. Expenditure coding was overwhelmingly large. With 1600 codes for household expenditure, one cannot do this type of research without questioning whether these codes could not be aggregated into 50100 key expenditure items, same as the items for which prices are collected.

Another key lesson for the team has been the fact that the Nigerian statistical system is in need of a major overhaul. A complete review of the system is crucial, not only for poverty monitoring, but for the basic information needed to understand welfare in Nigeria.

The data used in this study was particularly weak with respect to information on women. Gender analysis in the paper was limited because of the nature of the data set. In most surveys, and the present one is no exception, women are liable to be missing. This invisibility does not only refer to failure to count women, but also to the way questions are posed and coded. For example, in so far as it is true that men are more likely than women to be participants in the modern sector, then, a focus on that sector will exclude women. We recommend that the next surveys should consider these issues for improved policy-making and planning, and make a significant contribution to the gender dimensions of poverty. Data collection could focus on: (a) female resources: education, responsibility for child care, (b) female economic activity: time use, work actually done by women, whether paid or, more commonly, unpaid, and (c) female poverty: Finally there is need
1995. They also show that there were significant decreases in the share of products that are subject to quantitative restrictions which went down from 87% in 1987 to 45% in 1994. Goldberg et al. (2010b) explain that the tariff liberalization up until 1997 was unanticipated, not targeted toward specific industries, and they were free of political economy pressures. I obtain the data on tariff rates at four digit industry level from Topalova and Khandelwal (2011). They construct a database of annual tariff rates at 6-digit level according to Indian Trade Classification Harmonized System Code based on various publications of the Ministry of Finance. Then they match the products at 6-digit with 4-digit NIC codes using the concordance by Debroy and Santhanam (1993) to calculate average industry level tariffs. These industry-level output tariffs are combined with input-output transaction table from 1993-1994 to calculate the input tariffs. Controlling for input tariff rates is particularly important because Goldberg et al. (2010b) find that reduction of input tariff rates significantly increased innovation capacities of firms. Hence, the relationship between licensing reform and innovation can be biased if the effect of tariff reform is not controlled for.

Indian government also reduced the barriers to foreign direct investment in a group of industries after the balance of payments crisis in 1991. The reform allowed majority ownership rights to foreign firms. As a result of this reform, total foreign direct investment in India increased from around 250 million dollars in 1989 to around 2200 million dollars according to the numbers in UNCTAD’s FDI database. I use the data compiled by Topalova and Khandelwal (2011) which is a 4-digit industry level, time varying measure of openness to foreign direct investment. They obtain the data from the publications of Handbook of Industrial Statistics. The FDI measure takes a value of zero before 1991 in all industries when FDI was strictly controlled. After that year it shows the percentage of the industry that is opened to FDI. Foreign held companies are usually better at technology adoption and they are more innovative than domestic firms. They can
education, secondary education, post-secondary education, and college), occupation (i.e., paid employee, employer, self-employed, and others—informal work—including unpaid family workers and other categories), and residence areas (i.e., rural or urban).<sup>25</sup> A couple of remarks are in order for Figure 5. First, having no education achievement, being employed as informal worker, and living in rural residence are all characteristics that are positively associated with lower-than-average chances for upward mobility during the pre-Arab Spring period (i.e., the orange dots represent the transition percentages lying below the dashed line that represents the national average in panel A). These are also the same characteristics that are associated with having higher-than-average chances of downward mobility (panel B); while the opposite holds for the remaining characteristics.<sup>26</sup> Second, the two definitions of the welfare groups offer qualitatively similar results for the different population groups. This is consistent with the findings we have at the country level, where upward mobility is generally higher than downward mobility.

The variables in the Gallup Poll surveys are defined somewhat differently from those in the harmonized household survey data and we add another variable indicating whether an individual is a migrant (from another country) or a native resident. Figure 6 then plots the same type of graphs for the subjective well-being dynamics of. The results are qualitatively similar but perhaps more muted than those for the monetary measures in Figure 5. In this case too downward mobility is stronger than upward mobility but there is somewhat more variation by population group in the

> 25 We show the conditional, rather than the joint, probabilities in Figures 5, 6, and 7 since this helps us bring out more clearly the transition patterns for the different population groups. For example, a small percentage of the population with secondary or higher education are usually found in poverty or vulnerability in the first period to start with, consequently
Under the null hypothesis that the observed persistence is due solely to genetics, the economic mechanisms identified in the theory are unlikely to provide a coherent explanation of the pattern of mobility across countries. We use household income data from Chinese Household Income Project (CHIP 2002, CHIP 1995) and National Sample Survey (NSS 1993) of India to explore the mechanisms behind the pattern of relative mobility (the IGRCs) across farm and nonfarm households. The evidence suggests an important role for the economic forces (returns to education and occupational persistence) in explaining the pattern of educational persistence across farm and nonfarm households, both in rural India and rural China. The theory also suggests that we should observe changes in the intergenerational educational persistence in rural China for the younger generation because of the effects of reform on the relevant mechanisms such as higher returns to education in nonfarm occupations. In contrast to the separability observed for the older cohorts, we indeed find evidence of emerging complementarity between father’s education and nonfarm occupation in rural China for the educational attainment of the younger generation (18-28 years old in 2010).

The rest of the paper is organized as follows. Section 2 develops a model of intergenerational persistence with credit constraint that incorporates the salient features of farm vs. nonfarm households relevant for educational attainment. The next section describes the estimating equations derived from the theory and the empirical issues in understanding potential complementarity between parent’s education and nonfarm occupation in determining children’s schooling. Section 4 discusses the data and section 5 reports the main empirical estimates. The following section explores the evidence on the mechanisms identified by the theory underlying the observed pattern of slope (IGRC) and intercept estimates for the cohorts who went to school in the 1990s or earlier. Section 7 provides evidence on possible changes in the pattern of educational mobility in rural China for the younger generation (18-28 years old in 2010). The paper concludes with a summary of the main results from the theoretical and empirical analysis.

# **(2) A theory