rafmacalaba/datause-encoder-data huggingface dataset:
- <u>`indicators():`</u> The `indicators` option is used to request the indicators to be estimated from the simulated vectors of welfare. The list of possible indicators is:

- The set of Foster Greer Thorbeck indexes (Foster, Greer, and Thorbeck 1984) _FGT_ 0, _FGT_ 1, and _FGT_ 2 ; also known as poverty headcount, poverty gap, and poverty severity respectively.

- The set of inequality indexes: Gini, and Generalized Entropy Index with _α_ = 0 _,_ 1 _,_ 2

- Set of Atkinson indexes

- `aggids():` The `aggids` option indicates the different aggregation levels for which the indicators are to be obtained, values placed here tell the command how many digits to the left to move to get the indicators at that level. Using the same hierarchical id specified in the `area` option, AAMMEEE, if the user specifies 0, 3, 5, and 7, it would lead to aggregates at each of the levels E, M, A and the national level.

- <u>`results():`</u> The `results` option specifies the path and filename for users to save as a txt file the results the analysis specified in the `indicators` option.

- `allmata:` The `allmata` option skips use of the plugin and does all poverty calculations in Mata.

# **6.3.1 Running the Second Stage (Simulation)**

The second stage of the command takes considerably longer to run, depending on the size of the target dataset and the number of simulations requested. The command for the second stage would look like the following: ``` ` ́` ́` ́ .saesimpovmapyxvar[aw=hhw],area(localityid_n)uniqid(hhid)psu(thepsu)zvar(zvar)///
>eta(normal)epsilon(normal)varest(h3)lnypwcensus(p_hhsize_hh)///
>vce(ell)rep(100)seed(89546)ydump(`sfiles ́)res(`result ́)addvars(pline///
>pline2rdefrdef2rentdefrdef_rentroom2rdef_rentall2)///
>matin(`matadata ́)col(10)bootebeststage(second)aggids(0)indicators(fgt0)///
>plines(5291.52)
note:p_ageomittedbecauseofcollinearity Note:Dependentvariableinlogarithmicform WARNING:76observationsremovedduetolessthan3observationsinthecluster.
``` ``` OLSmodel: ```

|`lny`|`Coef.`|`Std. Err.`|`z`|`P>|z|`|`[95% Conf.`|`Interval]`|
|---|---|---|---|---|---|---|
|`dw_bath`|`.1397767`|`.0200267`|`6.98`|`0.000`|`.100525`|`.1790283`|
|`dw_btype_d3`|`.0496587`|`.0211568`|`2.35`|`0.019`|`.0081921`|`.0911253`|
|`edu_postsec`|`.0816588`|`.0883387`|`0.92`|`0.355`|`-.0914818`|`.2547994`|
|`edu_prim`|`-.8848284`|`.1744707`|`-5.07`|`0.000`|`-1.226785`|`-.5428722`|
|`j_firm_access1`|`.0382874`|`.0129973`|`2.95`|`0.003`|`.0128132`|`.0637616`|
|`jud_erate`|`.8928555`|`.2215139`|`4.03`|`0.000`|`.4586962`|`1.327015`|
|`jud_occup_el_sh`|`-1.333666`|`.2481222`|`-5.38`|`0.000`|`-1.819977`|`-.8473555`|
|`p_activity_1_sh`|`-.204576`|`.0306396`|`-6.68`|`0.000`|`-.2646285`|`-.1445235`|
|`p_age`|`.0134131`|`.0023387`|`5.74`|`0.000`|`.0088293`|`.0179968`|
|`p_age2`|`-.0000788`|`.0000238`|`-3.31`|`0.001`|`-.0001255`|`-.0000321`|
|`p_ecstat_2_sh`|`-.2178931`|`.0840416`|`-2.59`|`0.010`|`-.3826117`|`-.0531745`|
|`p_ecstat_4_sh`|`.4529504`|`.0297431`|`15.23`|`0.000`|`.394655`|`.5112458`|
|`p_ecstat_hh_2`|`-.1803367`|`.0690036`|`-2.61`|`0.009`|`-.3155813`|`-.0450921`|

21
Table A.8: Effects of mining in a sub-sample of countries when controlling for living close to a road.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.104***|0.031**|0.001|0.018|
||(0.039)|(0.016)|(0.026)|(0.033)|
|inactive (20 km)|0.196***|0.001|-0.008|0.139***|
||(0.046)|(0.019)|(0.022)|(0.022)|
|road within 50 km|-0.004|-0.001|-0.018***|0.025***|
||(0.007)|(0.001)|(0.004)|(0.009)|
|Observations|151,355|151,355|151,355|151,355|
|R-squared|0.169|0.049|0.146|0.318|
|F test: active-inactive=0|2.330|1.417|0.0692|9.245|
|p value|0.127|0.234|0.793|0.00237|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. *** p<0.01, ** p<0.05, * p<0.1 Table A.9: Effects of mining intensity on our main outcomes.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.018*|0.015***|0.007|-0.019|
||(0.010)|(0.005)|(0.008)|(0.013)|
|inactive (20 km)|0.080***|-0.000|-0.014|0.065***|
||(0.020)|(0.006)|(0.015)|(0.024)|
|intensity|0.009**|0.006***|-0.007***|0.012***|
||(0.004)|(0.001)|(0.002)|(0.004)|
|Observations|518,368|518,368|518,368|518,368|
|R-squared|0.198|0.092|0.142|0.354|
|F test: active+2*intensity-inactive=0|3.800|11.83|0.143|4.714|
|p value|0.0513|0.000583|0.706|0.0299|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Intensity is a count variable for the number of active mines that *** ** * are near. p<0.01, p<0.05, p<0.1 36
system involving the use of ex ante production contracts. The model showed that ex post permits were used to create competition among farmers to increase cane quality and, hence, the probability of receiving a permit. The model showed that the ex post permit system increased the processor’s profits and farmers’ costs, but it did not increase farmers’ price under the price-floor policy.

The theoretical model also predicted that as the expected difference between the floor price and the price in the unregulated gur market increases, farmers have a greater incentive to invest in costly quality-enhancing production practices. Finally, the model predicted that private factories in AP utilizing ex post permits receive cane of higher quality than comparable factories, such as cooperatives, that utilize an alternative procurement model analogous to ex ante contracts.

These hypotheses were tested and not rejected using primary data from a survey of 205 cane farmers and secondary data as needed. Specifically, cane farmers in areas served by private factories had significantly higher per-unit production costs than their counterparts in cooperative factory areas. This cost difference increased in the gap between the price floor and the unregulated market price for cane utilized to manufacture gur. Finally, private factories received cane of significantly higher quality than their cooperative counterparts.

Higher overall cane quality is achieved through the use of ex post permits. However, this analysis suggests that the permit system reduces the overall efficiency of the sugar sector because the value of the extra sugar yielded to private factories from higher-quality cane is less than the farmers’ incremental costs of producing it. Only the processor benefits from this system, whereas farmers face price uncertainty and bear the extra costs of quality enhancement without any commensurate gain in the price they receive.

30
In 1993-2003, the shift of labor into higher-productivity sectors contributed on average only 0.8 percentage point to overall productivity growth of 7.7 percent per year. On the other hand, “organic” productivity growth in industry, contributed a spectacular 5.2 percentage point to _overall_ productivity growth (2/3 of the total).

Concerns about the quality of China’s macroeconomic data warrant caution. However, as illustrated in the Annex, data issues are unlikely to change the conclusions of the paper materially.

Table 2. China: sectors' contribution to overall labor productivity growth (1978-2004) (average annual increase, in percent)

||1978-93|1993-2004|
|---|---|---|
|Labor productivity 1/|6.7|7.7|
|From shifts in employment b/t sectors|1.2|0.8|
|From organic productivity increase in sectors|5.5|7.0|
|Agriculture|1.0|0.5|
|industry|2.7|5.2|
|Services|1.4|1.1|

_Sources: NBS, and authors' estimates._ 1/ Artithmetic average, implying small differences with geometric averages used in the rest of the paper.

The important roles of capital and industry in China’s economy are of course related. Industry requires more physical capital per worker than the service sector, and, accordingly, the share of income distributed to workers is lower in industry than in the services sector. Indeed, countries where industry is more important tend to invest more.

The growth pattern described above generated very high GDP growth for China with only moderate urban employment growth, strikingly at odds with the theoretical expectation and most analyses/assumptions of what has happened in China. Indeed, the persistence and widening of large sectoral labor productivity gaps and the lower-than-expected flow of labor in the presence of those gaps points to market imperfections and/or government intervention. In the absence of market imperfections and government intervention, (i) more investment would be expected to take place in sectors other than industry, where the true marginal return to capital should be much higher than in industry; and (ii) labor should flow into industry where labor productivity is much higher than in other sectors.

The current pattern of growth and resulting consequences for resource intensity and income inequality could be linked to the growth strategy the government has pursued in the past. The government has subsidized and favored industry and investment over the services sector and domestic consumption, especially at the local level, maybe partly because of incentives created by the system guiding promotion of local officials and the tax system, including the productionbased value added tax. For example:

- Industrialization was promoted by keeping the prices of energy, electricity, utilities (including water), and land low and by not having sufficiently strict environmental regulations or enforcement. While several energy subsidies have been removed, cheap

8
<!-- Start of picture text --> Inflows, all countries Outflows, all countries<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>2000 2005 2010 2015 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> Inflows, advanced countries Outflows, advanced countries<br>10 10<br><!-- End of picture text --> <!-- Start of picture text --> 8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>2000 2005 2010 2015 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> Inflows, emerging countries Outflows, emerging countriesging countriesing countriesg countries countries<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>2000 2005 2010 2015 2000 2005 2010 2015<br><!-- End of picture text --> Outflows, emerging countriesging countriesing countriesg countries countries Figure 6: Average over the indicated set of countries (excluding Ireland) of the standard deviation of capital flows, as percent of trend GDP, explained by the factors, estimated over 20-year overlapping windows ending in the indicated in the x-axis. Two-standard error bars obtained year through country block bootstrap with 10000 replications.

37
participant welfare; and Section 5 concludes.

# **1.1 WWS Program Description**

During 2008-10, as the global financial crisis unfolded in Latvia, labor market conditions worsened and unemployment reached historic highs. A large and increasing proportion of registered unemployed people was not receiving unemployment benefits, or was receiving very low benefit amounts (Hazans, 2012). In September 2009, as a safety net response, the Government of Latvia implemented an emergency public works program (WWS) with financial support from the European Social Fund (ESF) and technical assistance from the World Bank. The goal of the WWS program was to reduce the severity of the social consequences of the global financial crisis on Latvians through temporary labor-intensive job creation. Central government expenditures on the WWS program amounted to about LVL 8.0 ($16) million in 2009; LVL 27 ($54) million in 2010; and LVL 20 ($40) million in 2011 (Government of Latvia, 2011).<sup>7</sup> During 2010 and 2011, Government expenditures amounted to about 0.25 percent of GDP, or 2.0 to 2.5 times expenditures on the main poverty-targeted social assistance program. i.e. the Guaranteed Minimum Income (GMI) program (World Bank, 2010). Government administrative data show that the WWS program created more than 110,000 temporary jobs over 2009-11.<sup>8</sup> All registered unemployed people who were not receiving unemployment benefits were eligible to participate in the WWS program and opportunities were provided on a firstcome, first-served basis.<sup>9</sup> The WWS program participants were eligible to participate up to six months with a two week minimum requirement. There was no limit to the number of

> 7On March 31, 2011 the exchange rate was: e1.00: US$1.42: LVL 0.704

> 8There is significant variation in the duration of participation in the WWS program, with some participants completing a few days and others completing the allocated six months before returning to the program to perform another period of participation.

> 9Registered unemployed could also chose WWS instead of unemployment benefits if unemployment benefits were less than the WWS stipend.

5
_GDPit_ : GDP per capita (constant 2000 $US).

_it_ _POPit_ : Rate of population growth or age dependency ratio as a share of the total population.

_TRADEit_ : Trade openness (measured as ratio of exports plus imports of goods and services to GDP).

_AGRit_ : Agricultural value added.

_CORRit_ : Corruption index.

_BUREAU it_ : Bureaucracy quality.

The underlying hypothesis of the specification is that the tax or fiscal revenue capacity of a country is determined not only by economic factors but also by key demographic and institutional characteristics. In particular, high corruption, high population growth rates, and high age dependency ratios tend to depress the taxable capacity of a country, other things being equal.

There are two major sets of the independent variables tested. The first part consists of traditional supply side factors, including GDP per capita, population growth rate, international trade, and agricultural value added as a fraction of GDP. The data are mainly obtained from the World Bank’s WDI (World Bank 2006). The second part includes the proxies for the institutional setting of a country. To test the robustness of the institutional impact on a country’s taxable income, two alternatives are used: the corruption index and the bureaucratic quality scores. The indexes are obtained from the Institutional Country Risk Guide (ICRG). Annex 2 presents descriptions and data sources for each variable used in the model.

_GDP per capita._ GDP per capita is included in the regression as a proxy for the level of development of a country. In our analysis, GDP per capita is measured in constant 2000 USD. As a higher level of income typically correlates with a greater demand for public goods and services, and higher income increases the overall ability to pay in a society, one should expect higher tax payment and collection (Bahl 1971;Fox, et al. 2005). By analyzing the recent worldwide trend and pattern of revenue collection, we also demonstrate that richer countries tend to collect more revenues, and similarly, countries tend to collect more revenues as they become more affluent (Section II). One would expect the sign of the coefficient on GDP per capita in the regression to be positive _Population growth rate and age dependency ratio._ To test the impact of demographic characteristics on a country’s taxable income, we use two alternatives, specifically population growth rate and age dependency ratio. Bird et al. (2004) suggest that as the rate of population growth increases, the tax system may lag behind in its ability to capture new taxpayers. This problem is more pronounced when a country has weak tax administration capacity. Thus, the population growth rate is expected to be negatively related to the tax capacity. An alternative approach to measure the impact of the demographic feature on the taxable capacity is to use the age dependency ratio indicator. Consistent to the World Bank WDI definition, this indicator is the ratio of the dependents – people younger than 15 or older than 64—to the working age population—those in the productive age

- 7 -
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particular aspect of governance is better than in the world as a whole (e.g. BERI, which covers primarily developed countries). Suppose further that this source tends to score countries relative to each other, so that the worst (best) country in the sample receives the lowest (highest) possible score? of zero (one). This would be reflected in a relatively high value of ,8(k), since relatively small differences in true govemance are magnified into relatively large differences in observed scores.

## **The Conditional Distribution of Governance**

Our objective is to summarize our knowledge about governance in each country j by the distribution of governance conditional on the observed data in country j. This task is greatly simplified by assuming that both g() and the disturbances so,k) are jointly normally distributed. In this case, go) and y(,k), k=1,...,Ko) are jointly normal, and the conditional distribution of g() given the data is also normal, with mean and variance given by:.

# (3) VE[(g)ly(j)] = 1y- c

where y() is a Kj)xl vector which stacks the K(j) data points for country j, a is the corresponding K()xl vector of a(k)s, B and , are K&)xK() diagonal matrices with the corresponding ,B(k)s and _a,(k)_<sup>_2_</sup> _s_ on the diagonal, and t is a K()xl vector of ones. We refer to the conditional mean in (2) as the estimated value of that aspect of govemance in country j. With a slight abuse of terminology, we refer to an interval from the (o/2)t percentile to the (1-8/2)th percentile of the conditional distribution of **go)** as an o-percent "confidence interval" around this estimate, and we refer to the square root of the conditional variance in (2) as the "standard error' of this estimate.<sup>5</sup>

> 5 This framework has a distinctly Bayesian interpretation. The distribution of go) conditional on the observed data yj) can be viewed a posterior distrlibution, and the mean of this distribution as an estimator of g() would be justified as a point estimate of go) by a quadratic expected posterior loss function. Similarly, the "confidence interval' is analogous to a Bayesian highest posterior density interval.

11
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30