rafmacalaba/datause-encoder-data huggingface dataset:
# (OB.2) Evidence on Potential Coresidency Bias

## (OB.2.1) Are the Census Estimates of Mobility CEFs Substantially Biased?

Recent evidence shows that sample truncation due to nonrandomly missing children biases the estimated slope parameter (called IGRC in the literature) downward in a linear educational mobility model, see Emran et al. (2018) and Azam and Bhatt (2015). To understand whether the estimates from the census data are substantially biased, we take advantage of the fact that the IFLS collected information on the nonresident children. If the estimates from census data are substantially biased downward, the estimates of relative mobility would be substantially dierent from those based on IFLS. Table B.6 below presents the estimates of the linear (see columns 1 and 2) and quadratic (see columns 4 and 5) mobility models for the census and IFLS data (including IFLS-East). The evidence suggests that the census estimates do not suer from any substantial biases. For example, for years of schooling, the IGRC estimates are 0.489 (census) and 0.522 (IFLS) for girls, and 0.433 (census) and 0.455 (IFLS) for boys. Thus the estimated intergenerational persistence in the census data is smaller in magnitude which is consistent with the ndings of Emran et al. (2018), but the magnitude of the downward bias is small. The estimates for primary completion similarly suggest small to moderate bias.<sup>1</sup> The evidence on the quadratic mobility model (for years of schooling and primary completion) also suggests that, in general, the census estimates are moderately biased downward, except for the case of sons' years of schooling for which the biases are somewhat larger.

## (OB.2.2) Inverse Probability Weighting for Correction of Coresidency Bias

Given the evidence that the estimates of the mobility models using census are in general close to the IFLS estimates, it seems unlikely that our conclusions about the causal eects of Inpres schools can be driven by coresidency bias. But one can argue that we do not know the direction of bias for the causal eects from a priori considerations, and additional evidence would be helpful. To address any remaining concerns, we use inverse probability weighting to correct for possible biases in our estimates of the causal eects of Inpres. Nicoletti and Francesconi (2006) provides evidence in favor of IPW for correcting the coresidency bias in intergenerational mobility analysis relative to Heckman selection correction.

> 1For son's primary completion, the estimated slope parameter is identical across census and IFLS data.

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Note also that education has a large and highly significant impact - increasing educational levels by one year would, other things equal, increase profits by almost C$ 2,500. In line with our hypothesis, 'marginal returns to land-attached investment are higher than those to mobile capital, shifting resources from the latter towards the former will increase overall economic efficiency. This suggests that, in addition to enhancing overall levels of investment, the higher level of tenure security brought about by land titling in Nicaragua will also lead to a more optimal balance in the mix of investment between moveable and attached goods.

Taken together, these two results provide a strong justification for programs of titling and registration from an economic perspective, supporting what had already emerged from the descriptive analysis. In addition, they are consistent with producers' desire for full registration of land ownership, rather than merely award of an agrarian reform title, as evidenced earlier in the descriptive analysis. In the Nicaraguan context. which can be taken to be representative of situations with high levels of endemic tenure insecurity, producers need a legally clear title rather than an intermediate substitute. Only having a legally valid title seems to prompt them to increase their level of investment. This suggests that, by falling short of full registration, the government's program may have foregone considerable gains in terms of investment and the higher levels of production and welfare this would have implied. To exarrmine the extent to which such lack of investment was sub-optimal from an economic perspective, we tum to the results from the land price regression as discussed earlier.

# **4.2 Tenure security and land values**

Results for the estimation of the land price regression using OLS, as well as random effects and fixed effects techniques, are reported in table 7. Note first that the results empirically confirm that presence of correlation between unobservables and the right hand side variables in the regression. Moving from the OLS to the fixed effects regression, the coefficient on the value of land improvements decreases continuously, from 6.1% in the OLS regression to 2.3% in the equation with household fixed effects. This suggests that more productive households are more likely to undertake land improvements. As it can not distinguish between the two effects, estimation via OLS would result in serious overestimation of the impact of such investment. The same is true for land registration, the coefficient of which decreases from 37% to 29% as one moves from OLS to fixed effects. Indeed, performing a Hausman test confirms the presence of correlation between Z and the household specific elements of the error term; the X2statistic of 129.9 leads to a clear rejection of the hypothesis of no correlation between Z and s, thus indicating that the fixed effect estimates are more appropriate. In interpreting the results, we will therefore focus on these coefficients.

17
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In 1995, revenues generated by construction of housing units amounted to MDH 2,200 million, whereas revenues from taxes on existing units amounted to MDH 4,259 million. It is apparent that housing construction is highly taxed in comparison to existing units.

<u>Table 4 : The Housing Tax System in Morocco – Financial Masses – 1995</u>

|_Category_||_Tax revenues_||
|---|---|---|---|
||_Flows_|_Stocks_|_Total_|
|Corporate Tax|350|-|350|
|Income Tax – Rental income|280|276|556|
|Income Tax – Deductibility of mortgage interests|-|-|-|
|VAT|949|-|949|
|Tax on Capital Gains|95|378|473|
|Solidarity Tax|-|350|350|
|Registration Fees|185|742|927|
|StampDuty|10|40|50|
|Total|1869|1786|3655|
|“Patente” (business tax)|-|833|833|
|Taxe Urbaine|-|391|391|
|Taxe d’édilité|-|1214|1214|
|Total|0|2438|2438|
|Tax on vacant land|-|25|25|
|Tax on land division operations|9|-|9|
|Tax on “lotissement”|46|-|46|
|Construction Tax|213|-|213|
|Publicland occupation fee|63|-|63|
|Total|331|25|356|
|**Grand Total**|**2200**|**4249**|**6449**|

Source : Etude PLURAM INTERNATIONAL-ALGOE-PROMOCONSULT, Ministry of Housing, Morocco (1999), except numbers with a *: Source, World Bank.

Table 5 presents subsidy breakdowns for the housing sector through tax cuts, direct subsidies, and land subsidies to producers, with the relevant estimated expenditures. Although the general principles underlying the PLURAM-ALGOE-PROMOCONSULT study to calculate expenditures on subsidies are consistent with those mentioned above, the exact methods used vary from one subsidy to another.<sup>44</sup> As underlined in the previous chapter, estimating tax expenditures requires consensus on the definition of “normal” tax levels.

> 44 The evaluations rely primarily on common sense, rather than technically involved mathematics. Most evaluations involve no more than a “proportion rule”, the difficulty being to locate reliable  information to assess the quantity to be estimated.

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# **Appendix Table A3b: Impact on Individual Planning Practices (ITT)**

||(1)|(2)|(3)|
|---|---|---|---|
||Reviewed Financial<br>Performance to Identify<br>Areas of Improvement|Compared Target vs.<br>Actual Monthly Sales|Anticipated Budget for<br>Upcoming Business Costs|
|Assigned Handbook|0.007|0.034|0.037|
||(0.033)|(0.035)|(0.023)|
|Assigned Handbook & Movie|0.035|0.052|0.037|
||(0.033)|(0.034)|(0.023)|
|Assigned Handbook & Assistance|0.041|0.015|0.040*|
||(0.032)|(0.033)|(0.023)|
|Assigned All Three|0.065**|0.081**|0.056**|
||(0.032)|(0.034)|(0.023)|
|R-squared|0.106|0.130|0.185|
|N|2204|2204|2204|
|Dependent Variable Mean in Control Group|0.642|0.418|0.146|
|F-test (p-value): Book = Book & Movie|0.396|0.613|0.971|
|F-test (p-value): Book = Book & Assistance|0.298|0.578|0.918|
|F-test (p-value): Book = All Three|0.074|0.180|0.445|
|F-test (p-value): Book & Movie = Book & Assistance|0.869|0.277|0.887|
|F-test(p-value): Book & Move = All Three|0.359|0.402|0.413|

**Notes:** This table presents impact analysis (Intent-to-Treat) for individual planning practices across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).  Details on business practices can be found in Appendix Table A10.
# **I. Introduction**

There has been substantial work in better understanding the role of fiscal policy in Poland. Some of the highlights are work looking at the labor incentive effects of tax reforms (Morawski and Myck, 2010; Myck et al, 2013) and analyzing the combined effect of direct taxes, pensions, social security contributions, and social benefits (De Agostini et al 2014; Paulus, 2015; Brzeziński, 2015). There are well known country-level microsimulation models using the Household Budget Survey that have separately undertaken microsimulation analysis of specific reforms including changes in family benefits (Myck 2015a); Value Added Taxes (Myck, 2015b); and others. Existing work has shown the dominant role of pensions in overall incidence (Paulus, 2015) and the relatively small role of policy in reducing inequality (Myck and Najsztub, forthcoming).

Despite the large literature on the tax-benefit system in Poland, most of the existing analysis that has aimed to combine the impacts of taxes and spending has not included indirect taxation in the overall analysis, despite the fact that it makes up about half of total tax collections. Similarly, most studies have not included the impact of spending on education and health, despite the fact that they make up more than a third of total social spending and are an important component in terms of improving equality of opportunities across the population.

This paper aims to present a more comprehensive analysis of the impact of fiscal policy on inequality and poverty in Poland relative to earlier studies. Building on existing work done in Poland, the analysis covers the impact of the contributory pension system, direct taxes and transfers, the impact of value added and excise taxes, as well as the impact of education and health spending. The analysis assesses the progressiveness of each fiscal instrument and its contribution to reducing poverty and inequality. The approach follows the Commitment to Equity (CEQ) methodology (Lustig and Higgins, 2013), allowing comparisons between Poland with other countries where the CEQ methodology has been applied.<sup>2</sup> The analysis is built on the 2014 Polish Household Budget Survey (HBS) collected by the Central Statistical Office of Poland (GUS), and data from National Income Accounts and public finance accounts from the Ministry of Finance for 2014. Although most of the analysis follows a partial equilibrium approach, we use the 2010 input-output matrix is used to capture the second-round effects of excise taxes on intermediate goods, such as fuel, since these can be important as they are passed through to prices of all other goods. In terms of coverage of components of fiscal policy, the analysis includes 62 percent of tax revenue and 51 percent of government spending, in line with other CEQ studies. The analysis does not cover the corporate profit tax and VAT paid by government or institutional consumption as these are difficult to assign to individual households

> 2 For more details, see <u>http://www.commitmentoequity.org .</u>

2
Chinese history.

The _Likin_ system should have raised local fiscal capacity. Facing the brutalities of the war, safety would have become especially valuable for the gentry class in the Taiping Armycontrolled areas, and local gentry and merchants would have been more likely to help with collecting and complying with higher _Likin_ taxes. Moreover, once the tax apparatus is established, future marginal costs of tax collection are lowered. Then local officials and warlords have had a strong vested interest in defending _Likin_ collection. Thus, higher Likin revenues in Taiping Army-controlled areas would have persisted.

_The War-Induced State-Capacity Hypothesis._ The level of Likin is persistently higher in the Taiping Army-controlled areas than elsewhere.

What are the long-term effects of _Likin_ ? If acting only as taxation **,** it should have negative long-term effects. Heavy transit taxes, such as _Likin_ , hinder inter-regional trade, encourage autarky, and impose large burdens on producers and traders.<sup>23</sup> Moreover, _Likin_ could increase rent-seeking resource misallocation (Acemoglu 2010). Strengthening local fiscal capacity entails temptations regarding the wielding of power and struggles to obtain power, which may result in further distortion in resource allocation, as the period of warlords in early twentieth century testifies.

_The Likin-as-taxation hypothesis_ . Due to the incentive effects of _Likin_ , post-war population growth should be lower in the areas with higher _Likin_ burdens _._ Moreover, the longterm development there should also be worse.

The perspective of _Likin as state capacity_ , by contrast, implies positive long-term effects. State capacity refers to the government’s ability to implement a range of policies, including raising revenues (Tilly 1985; Brewer 1989; Levi 1988), and supporting market development (Besley and Persson 2010). Recent literature has emphasized the critical role of state capacity for long-term development. Fiscal capacity is presumed to be a key prerequisite for development, expanding the extent of the market, enabling the development of modern infrastructure and other market-supporting institutions, and increasing the state’s incentives to develop the economy due to a higher stake (Besley and Persson 2013). State capacity is viewed as a key ingredient of the “East Asian Miracle” (Johnson 1982; Amsden 1989; Wade 1990), and the lack of it is viewed as contributing to economic failures of some African and Latin American countries (Herbst 2000, Centeno 2002). The case of state capacity’s importance is

> 23 According to the Second Historical Archives of China (1996, Vol. 1, p. 83), “in 1900s a cargo shipped from Zhenjiang to Shanghai with a distance of about 240 km in between would have to go through seven Likin stations and at each checkpoint pay the Likin tax rate of about 5%, implying a total transit tax rate of 35%.”

19
For years before 1985 direct information about rural - unemployment is nonex-itent apart from the 1973-74 surveys. One way of approaching the problem is to accept both urban labor force surveys and SPO estimates at face value, assuming that both talk about unemployment in the same sense and -from them compute the implicit rural unemployment for the periods 1967-69 and 1982-85 when only urban LFSs were conducted and the implicit urban unemployment for 1973-74 when only rural LFSs were conducced. Since possibly the SPO notion of unemployment is wider than that of unemployed seeking employment, I have in Tables VI.Ap.1.1 and 2 computed adjusted unemployment rates on a wider definition, including some unemployed not seeking employment. Information about such unemployment has been collezted by all the Turkish LFSs.<sup>3</sup> As already argued, it makes sense to count both categories as unemployed in the labor force and if rural LFSs were to be conducted for the winter season the latter category might take on some real importance although landless rural labor seems to be a relatively small group in Turkey.As it is, the adjusted unemployment rates of the LFSs are not spectacular'y higher than the unadjusted ones. Taking into account the coverage of the LFSs as measured by population, we find on this basis an implicit urban unemployment rate of 10.5 percent for 1973-74 and implicit rural unemployment rates of 5.3 percent for 1967-69 and 10.7 percent for 1982-85, average. The implicit urban rate for 1973-74 is simply not credible. The implicit rural rates are too

- 14 -
data sources, we estimate the parameters of the model by maximum likelihood. Specifically, the contribution of the data for country c to the log-likelihood function is given by: (13) lnL(α, β, σ|y(c)) ∝ln|BΣB+ ββ<sup>′</sup> | + (y(c) −α)′(BΣB + ββ)<sup>−1</sup> (y(c) −α) Summing across all countries and maximizing with respect to the parameters delivers maximum likelihood estimates of α(k) , β(k) , and σ<sup>2</sup> (k) for the representative data sources.<sup>23</sup>

In the second step, we estimate the parameters for non-representative sources covering subgroups of countries with different geographical or income group coverage using a different approach. This is because we cannot assume that the unobserved distribution of governance has the same mean and standard deviation in these subgroups as in the global sample. To address the problem, in the second step we interpret Equation (1) as a linear regression and substitute unobserved governance g(c) with an estimate of governance based on the representative data sources, ĝ(c) = g(c) + u(c) where u(c) represents the measurement error in our estimate of governance based on representative data sources. Because the right-hand-side variable in this regression is measured with error, the slope coefficient will suffer from attenuation bias, i.e. for non-representative data source k , the probability limit of the slope coefficient of a regression of y(c, k) on ĝ(c) is plimβ̂(k) = β(k)<sup>V[g(c)]</sup> ~~.~~ However, V[ĝ(c)] because we know the margin of error of the estimate of governance based on the representative data V[g(c)] sources, we can use this to estimate the ratio and then use this to adjust the slope coefficient to V[ĝ(c)] obtain a consistent estimate β̂̂(k) = β̂(k)<sup>V[g(c)]</sup> . Finally, with this adjusted estimate of β(k) in hand, V[g(c)] we can obtain estimates of α(k) and σ(k) as the mean and standard deviation of y(c, k) −β̂̂(k)ĝ(c) .

These two steps give us estimates of the parameters of the UCM for all data sources. We then insert these estimates into Equations (2) and (3) to obtain our final estimates and standard deviations of governance for each country. These estimates by construction have a mean and standard deviation close to zero and one, respectively. For ease of interpretation, as a final step we standardize the indicators and standard errors so that the reported estimates of governance have exactly mean zero and standard deviation of one across countries in every time period.

> 23 This is equivalent to maximum likelihood estimation of a principal components model.  In fact, we implement the calculations in Stata using the command for estimation of principal components models.  Details can be found in the reproducibility package for the WGI available here.

53
to crowding-in effects of firms in sectors (Farole and Winkler, 2015) or to technology transfer or domestic competition (Alfaro and Chen, 2019). Our results suggest that the spillover happens because--to survive in the fierce domestic competition fueled by FDI faced by a firm—other firms in the sector have a greater incentive to improve productivity and thus to become more competitive. To establish this argument, we leverage firm-level data from the World Bank Enterprise Survey. We first show that sectoral FDI has a positive impact on firm-level productivity, and this impact is ameliorated by the GVC participation of a firm. Second, we show that the effect of FDI on a firm’s propensity to do research and development (R&D) is intensified if the firm has RELATIVELY lower GVC participation.

We use a similar empirical specification for the firm-level regression, except we can distinguish the locality further, from country to city using WBES. We use _i_ to denote city, _j_ firm, _s_ sector, and _t_ year. The regression specification follows lnyijst = α0 + α1lnFDIist × GVCijt + α2lnFDIist + α3GVCijst + γi + θj + CX + τt+εijt(3) where the outcome variables yijst represent labor productivity; GVCijt is GVC participation of city _i_ firm _j_ sector _s_ year _t_ ; _FDIist_ is FDI at time t at the sector-city level.<sup>5</sup> The control variables’ vector **_X_** include the number of skilled workers of a firm in the previous year, foreign ownership share,<sup>6</sup> foreign technology adoption, firm size, sector type, and years of experience of top managers. γi represents city fixed effects; θjdenotes sector fixed effects; τt controls for year fixed effects, while εijt is the error term. Firm’s GVC participation here is defined as the average of its intermediates’ import share and the exporting intensity. The export intensity, in turn, is defined as the ratio of exports over sales.

Table 11 shows that sectoral FDI has a positive effect on firm-level labor productivity, and this effect is weakened by the GVC participation of the firm. This somewhat mirrors the

> 5 For the measurement of FDI here, we tried both number of FDI projects and the dollar value because the fDi Markets FDI project data is more accurate for recent years than the dollar-value FDI data, which is more of an estimate for recent years. The results are qualitatively similar. Moreover, our current data goes to 2017 so revisions have likely ameliorated this problem at the time of writing.

> 6 This control variable is important because it captures the direct firm-level effect of being the recipient of foreign financing—and at the same time whether the firm is mostly a foreign-owned affiliate (share over 50 percent) or a local firm. Recall that the key result of our regressions is the effect of the indirect ‘spillover’ effects. Direct financing to one firm in that city/sector, for worker training or machines for example, is thus captured by the foreign ownership share variable.