rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 10<br>Recursive plots, ECM for total wages (including GDP)<br>± 2"S. E.-_~ ±CP2 =<br>LTot._ _ = .. .. i... _,.._,...<br>- ~~~~~~~~~~~~~~~~~~~~~~~~~~~.5_-4 ......... . ...<br>1989 1985 199D l995 198D 1985 1999 1995<br>DLCPI= LGDPcso_=<br>i Z.. E ± 2S.E.=<br>± _z.......... ,._.......... ,3 _ .2 i i .... . -----..... ..<br>D_ . _._, . ............ . _ %,..=<br>,~~~~~~~~~~~~~~~~~. 1 -. . --......-S._.._..<br>-2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. . . _. .... ....... . _._ ......._.__._<br>-42<br>1989 1985 1999 1995 1988 1985 l99D 1995<br>DL'GDPeso= _<br>.18_.*<br>~~~~~~~~~~~~~~~~~~......... ... --<br>.12 _<br>.86 _ . , -.........<br>D~~~~~~~~~~~~~~~ . . . . . . .. .. . . . . .<br>1980 1985 l99D l99S<br><!-- End of picture text --> Expectations, measured by prices, marginally affect wages positively in the long run but not in the short run. Furthermore, productivity affects the wage determination process positively. These coefficients are estimated to be statistically significant and different from zero.

We find that the wage term (W_i) is statistically significant and the coefficient is negative, hinting at the existence of a long run relationship between GDP, prices, productivity, and employment. Furthermore, the hypothesis that the sum of ihe coefficients of w, u and p equals zero cannot be rejected (F(l, 21) = 4.27 [0.05]) and all variables are significantly different from zero. Hence, there exists a co-integrating vector involving all variables, and we can use the wage equation to obtain a long-run equilibrium solution. The solved long-run equation with zero autonomous growth, Av= AGDP = Ap = 0, is: w=7.8+0.3GDP+0.3p+O.It 24
8 (Europe). Specifically, the migration variable is constructed using three steps. The first step uses data from the 1990 and 2000 U.S. Population Censuses on the “place of birth for the foreign-born population.” While these data are disaggregated by country of birth for about 50 different labor-exporting countries, it is not at all clear whether all of these “foreign-born” people are, in fact international migrants. For example, a person born in Mexico and brought to the United States as an infant would probably not consider himself as a migrant. Moreover, it is also not clear how many of those who enter the United States illegally are, in fact, included in the “foreign-born” population figures. As some observers have suggested, the U.S. Census data may be grossly undercounting the actual migrant population that is living – legally or illegally – in the United States.<sup>16</sup> The second step in calculating the migration variable is to estimate the number of “foreign born” living in the Organization for Economic Cooperation and Development (OECD) (Europe), excluding North America and Asia.<sup>17</sup> Unfortunately, the OECD (Europe) data are not as detailed as the U.S. Census data, and differ from the United States data in several key ways. Most basically, the OECD (Europe) data use a different way of classifying immigrants. Since United States-born children of immigrants have US citizenship, the United States defines an immigrant as a person who was born abroad to non-US citizens. Most OECD (Europe) countries, however, follow an ethnicity-based definition of immigration status. This method classifies a person on the basis of the ethnicity of the parent, rather than on place of birth. Thus, a child of Turkish parents born in Germany is typically classified as an immigrant. This different way of classifying immigrants has the net effect of increasing the stock of immigrants in any particular OECD (Europe) country, and perhaps biasing our estimates by including a
3 policy and temporary wage and price controls) to wipe out hyperinflation while simultaneously carrying out trade liberalization. Chile is an example of the economic success story one would like to ..ee everywhere (in spite of a difficult period of adjustment and a delicate transition from authoritarian rule to democracy). Mexico illustrates how the prospects of regional integration with a stable and developed neighbor can help cement impressive stabilization and liberalization policies. And Uruguay illustrates the difficulty of carrying out trade (and other) reforms in an economy with a long tradition of rent seeking activities.

At the outset of any exercise on lessons to be drawn from the Latin American experience with trade liberalization, a word of caution is in order. The region is known for its many policy reversals over the years. Therefore all the encouraging signs that are voiced in this paper should be interpreted cautiously, especially for Argentina, where reforms have only been in effect for three years. Also, unresolved losses of the banking system are significant in all five countries and could pose a threat for the success of the ongoing reforms. So could the loss of competitiveness through real exchange rate appreciation for the countries that have used the exchange rate to reduce inflation.

# 2. Trade and Growth in Latin America: A Comparative Perspective

It is now widely accepted that, controlling for other factors, more open economies - in the - sense of less distortions in their foreign trade regimes grow faster.' More open economies both discriminate less against tradable activities than closed economies, and within tradable activities, they T'he evidence of higher growth for open economies based on country studies and on crosssection regression analysis is overwhelming. For a recent entry point in that literature, see e.g. Dollar (1992) and references therein.
only 3.6 percent, because the US$2 per day poverty-income elasticity is significantly lower than the US$1 per day poverty-income elasticity.

These poverty impacts from full merchandise trade reform are significantly lower than past estimates. For example in the 2002 GEP, the number of poor people living on a US$1 per day was estimated to fall by 110 million, and the number living on US$2 per day to fall by 320 million (World Bank 2002). The new numbers reflect three changes in increasing significance. The first is the change in the baseline poverty forecast. The baseline US$1 per day poverty forecast is currently 622 million, compared to 734 million in the 2004 GEP (World Bank 2004). Using the 2004 GEP forecast would raise those lifted out of poverty to 38 million and 80 million, respectively, for US$1 per day and US$2 per day poverty lines from the current estimate of 32 million and 66 million. The second change is the new estimate of the change in the food wage. Both the unweighted and population-weighted estimates of the food wage impacts from global reform are reduced by 50 percent compared to the previous results.<sup>29</sup> This is largely due to the role of the new tariff database (particularly preferences and the policy reforms that are now subsumed in the baseline). If the previous estimates of the change in the food wage are used, the reductions in poverty would rise to 63 million and 190 million, respectively. The third change reflects the use of region- and indicator-specific income poverty elasticities.<sup>30</sup> Using the old

> 29. Earlier studies with the Linkage model found an estimated impact of roughly an 8 percent increase in the food wage (averaged over all developing countries). The new impact is roughly a 4 percent increase. The largest changes occur in China and the Middle East and North Africa, the regions with the most significant revision in tariffs. For China, the revision is largely due to the baseline changes, that is, WTO accession commitments. In the case of the Middle East and North Africa, it reflects a significant revision of the base-year tariffs, in part influenced by preferences.

> 30. Earlier studies with the Linkage model assumed a uniform poverty elasticity of 2.0 across regions and for both the US$1 per day and US$2 per day poverty lines. The assumption of the uniform poverty elasticity was maintained mainly to keep results comparable across studies despite newer estimates showing the relative variation across regions. For the US$1 per day index, the range over regions is 0.9 to 3.0. For the US$2 per day index, the range is 0.5 to 2.0. The average over regions is declining over time because those regions with high elasticities also tend to have high growth; therefore poverty becomes more and more concentrated in regions with relative low poverty elasticities with respect to income growth.

21
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**Figure 3. Distribution of imputed log of consumption**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>m9<br>m9<br>m8<br>m8<br>m7<br>m7<br>m6<br>m6<br>m5<br>m5<br>m4<br>m4<br>m3<br>m3<br>m2<br>m2<br>m1<br>m1<br>5th10th 25th 50th 75th 90th95th 5th10th 25th 50th 75th 90th 95th<br>Percentile Percentile<br>Nigeria 2012/13 - 2018/19 Tanzania 2019/20-2020/21<br>m9 m9<br>m8 m8<br>m7 m7<br>m6 m6<br>m5 m5<br>m4 m4<br>m3 m3<br>m2 m2<br>m1 m1<br>5th10th 25th 50th 75th 90th95th 5th10th 25th 50th 75th 90th 95th<br>Percentile Percentile<br>Vietnam 2014-2016<br>m9<br>m8<br>m7<br>m6<br>m5<br>m4<br>m3<br>m2<br>m1<br>5th 10th 25th 50th 75th 90th95th<br>Percentile<br><!-- End of picture text --> 42
transferred directly to citizens, and then having the state tax citizens to finance public spending.

We build the case in three steps. In section 2, we show that high levels of oil revenues are associated with low levels of transparency in public budgets and efficiency in public spending. In section 3, we demonstrate empirically that without taxation of citizens, accountability of public spending is necessarily limited and without government accountability vis-à-vis citizens, public spending efficiency is likely to remain low. We conclude that transferring oil revenues to citizens and taxing them is one way of improving public spending efficiency. To see how this proposal could be implemented, in section 4, we present various schemes to redistribute oil revenues to citizens. In section 5, we present some concluding remarks and areas for future research.

# **2. The relationship among oil, accountability and poor outcomes of public spending**

Low levels of budget transparency in oil-dependent countries are common and may lead to poor management of resource wealth over the medium to long term. Countries such as Sudan, the Democratic Republic of Congo, and Equatorial Guinea score 0 out of 100 on the Open Budget Index 2008 (Heuty et al. 2009)<sup>3</sup> .

The problem is exacerbated by the fact that public spending per capita in oilrich countries is much higher than in non-oil economies (see Figures 1 and 2). Not only are oil exports associated with higher public spending levels but the association is even higher in the case of large oil reserves (over 20 billion barrels). Large reserves

> 3 One of the main drivers of conflict in Sudan has been the historical concentration of wealth and power in the central government in the North, at the expense of the poor majority in the rest of the country. Since 2003, the country has been undergoing an oil and gas boom, accounting for an estimated $2 billion in annual revenues, or nearly 70 percent of the country’s exports. Despite the fact that the 2005 peace accord in Sudan mandated disclosure of the amount of oil revenues, neither the government in Khartoum nor that in Southern Sudan have provided reliable information, leading to suspicion that the money has been used for non-civilian purposes, which threatens the stability of the agreement (Heuty et al. 2009).

4
lights studies, which primarily employ country-specific time trends as robustness tests.

The goal of the error-measurement framework is to compute an optimal _composite estimate_ � � _y_ of output using both reported GDP _z_ and the signal-derived proxy _z_ . The weights _λg_ on GDP which yield this optimal composite are, Say the variance of GDP measurement errors _σz,g_<sup>2arerelativelylargeforonegradecompared</sup> to another. Then, the weight on GDP, _λg_ , intuitively falls relative to the weight on the proxy, 1 _− λg_ . And yet, while intuitive, the model is subject to a fundamental identification problems which prevents _λg_ from being calculated. We now describe the problem, and our solution.

# **4.2 Objective Identification by Instrumental Variables**

The identification problem may be summarized as follows. Given there are _G_ distinct categories of data quality grades, then the data supplies 2 + _G_ moments.

And yet, there are 3+ _G_ parameters which must be estimated to compute composite estimates: _β, σy_<sup>2</sup><sup>_,σ_</sup> _x_<sup>2</sup><sup>_,σ_</sup> _z,_<sup>2</sup> 1<sup>_,. . . ,σ_</sup> _z,G_<sup>2.Thus,themodelisunder-identified,andoneadditionalrestriction</sup> must be imposed. Henderson et al. (2012)’s approach is to fix the signal-to-noise ratio for one group using a-priori assumptions, which without loss of generality we may assume to be grade _σ_<sup>2</sup> A: _φA_ = _σy_<sup>2</sup> + _σyz,A_<sup>2.Relatedly,ChenandNordhaus(2011)’sapproachistofix</sup><sup>_σ_</sup> _z,A_<sup>2directly.</sup> We advance a data-driven approach which allows the analyst to estimate the model without imposing subjective assumptions. Note that for signal _x_ we may write, If we were to try to estimate the above relationship by OLS, there would be bias, due to the correlation of _z_ with _εz,g_ .<sup>11</sup> However, we are now working in the context of not just one, but two satellite signals, NO2 and night lights, and there is good reason to believe the measurement errors in either are uncorrelated. As we will explain, many of the sources of measurement error

> 11This is exactly similar to the bias in the estimated proxy discussed previously (Equation 3).

14
**Table S2.3. Proportion of choices that violate each archetype**

||All|**Burning**|**Stealing**|**Giving**|
|---|---|---|---|---|
|**A. UK 2014**|%|%|%|%|
|U.Selfish|40%|18%|58%|**8%**|
|U.Efficient|54%|18%|52%|99%|
|U.Altruist|23%|18%|0%|99%|
|U.Invidious|44%|38%|58%|8%|
|U.Warm glow|23%|18%|0%|**99%**|
|U.Inequal. Averse|46%|41%|53%|29%|
|N.observations|720|144|432|144|
|**B. Kenya**|%|%|%|%|
|U.Selfish|44%|13%|58%|**30%**|
|U.Efficient|51%|13%|54%|79%|
|U.Altruist|18%|13%|0%|79%|
|U.Invidious|49%|40%|58%|30%|
|U.Warm glow|18%|13%|0%|**79%**|
|U.Inequal. Averse|49%|44%|57%|32%|
|N.observations|990|198|594|198|
|**C. Uganda**|%|%|%|%|
|U.Selfish|53%|36%|61%|**48%**|
|U.Efficient|61%|36%|65%|74%|
|U.Altruist|22%|36%|0%|74%|
|U.Invidious|54%|41%|61%|48%|
|U.Warm glow|22%|36%|0%|**74%**|
|U.Inequal. Averse|51%|56%|55%|33%|
|N.observations|810|162|486|162|

Source: Authors analysis based on data described in the text.
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- [35] Phillips, P. C. B., and Perron, P. (1988). Testing for a Unit Root in Time Series Regression. _Biometrika,_ 75, 335–346.

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- [39] Steinbuks, J., de Wit, J., Kochnakyan A. and Foster, V. (2017). Forecasting Electricity Demand: An Aid for Practitioners. _Live Wire_ 2017/1. World Bank, Washington, DC.

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20
during the crisis is smaller than during the period of calm. Generally, for most countries the difference in coefficients between the two periods under study is not statistically significant. Thus, no definitive conclusions should be drawn from the results in Table 4.

To gain more conclusive insights on the effect of growth on employment generation in times of crisis, the analysis was refined to focus on s sub-set of the sample: the Philippines, Korea, and Taiwan, China. These are the countries with particularly weak OLC estimates in Table 4. They are also countries for which more fine-grained data are available for a more thorough analysis.

One factor that may conceal the overall effect of growth on employment is that jobs in agriculture may respond to growth differently. For one, to the extent that agriculture is not commercial, overall economic activity in a country affects farm employment less. Moreover, accordingly to the Lewis model, farmers move away from their land for job opportunities and higher wages in the ‘modern’ sectors (Lewis 1954). Yet when the economy slows or contracts, the opposite may happen where workers move back to the rural areas when they are laid off in the modern sector.

In all of East Asia, the percent of the labor force employed in agriculture has fallen. Yet there still exists considerable variation across the region. In the Philippines, around 40 percent of employment is still in the primary sector, while it is less than ten percent in Korea and Taiwan, China (Figure 5). Just inspecting growth and employment data – separated by employment in agriculture and non-agriculture visually, reveals an interesting relationship. Indeed, non-agricultural employment tends to be more in sync with growth than agricultural employment (Figure 5 through 8). There also appears evidence that agricultural employment is countercyclical, especially in times of crisis.

**Figure 5:** Employment in agriculture as a percentage of total employment

<!-- Start of picture text --> 60 Philippines<br>Korea, Rep.<br>50<br>Taiwan, China<br>40<br>30<br>20<br>10<br>0<br>1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

**Figure 6:** Employment gap and output gap: South Korea

<!-- Start of picture text --> 3 0.06<br>2 0.04<br>0.02<br>1<br>0<br>0<br>-0.02<br>-1<br>-0.04<br>-2<br>-0.06<br>Employment: Agriculture<br>-3 -0.08<br>Employment: Non-Agriculture<br>-4 Output (RHS) -0.1<br>1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1<br><!-- End of picture text --> _Source:_ Figure 5: WDI; Figure 6 Figure 1: See Table 1.

9
# Figure (3.1)

## VARIANCE RATIO STATISTICS OF k-DIFFERENCES OF RER AND ITS FUNDAMENTALS (Equation 3.12')

<!-- Start of picture text --> RER PX RER<br>1.40 C20<br>1.20<br>1.00<br>0.60~~~~~~~~~~~~~~~~~~~0<br>0.40~~~~~~~~~~~~~4<br>0.900''oo 0.20~~~~~~~~~~~~~~~~04<br>0.00<br>0.a a 4* 1 3 4 3 4 6 a 4<br>RER dRERA- PX dPX PM + PM<br>TX TM DCY<br>1.20 20 { {20<br>*0.0 O" OA.00<br>OOJ ' \OJ0\ 0.40<br>020, 0.20 _ 020<br>0A|,.0.00 a 2 a 4 O." 1 2 a 3 4 0.00 1 2 S 3 4 6<br>-TX +TX TM - TM - oCY + aDC<br><!-- End of picture text -->
# **Table 1: Household Structures in Vietnam**

||Nationa|llyRepre|sentative|Sample||Study|Sample||
|---|---|---|---|---|---|---|---|---|
||1993||1998||1993||1998||
||-1|-2|-3|-4|-5|-6|-7|-8|
||mean|s.e.|mean|s.e.|mean|s.e.|mean|s.e.|
|HH variables|||||||||
|# Sample Households|4799||5999||2255||2375||
|# Communes (PSU)|150||194||115||115||
|Household Size|5.91|0.09|5.48|0.06|6.55|0.11|5.95|0.08|
|# Persons 0-5|0.87|0.03|0.57|0.02|0.88|0.04|0.56|0.03|
|# Persons 6-15|1.61|0.04|1.46|0.04|2.34|0.03|2.14|0.04|
|# Persons 16-24|1.05|0.03|1.00|0.03|1.00|0.04|0.93|0.04|
|# Persons 25-64|2.11|0.04|2.12|0.02|2.09|0.03|2.09|0.02|
|# Persons 65+|0.28|0.01|0.33|0.01|0.23|0.01|0.24|0.01|
|Variables for Children 6-15|||||||||
|Mother Present|0.96|0.00|0.96|0.00|0.96|0.00|0.97|0.00|
|Father Present|0.88|0.01|0.91|0.01|0.89|0.01|0.92|0.01|
|Both Parents Present|0.87|0.01|0.89|0.01|0.88|0.01|0.90|0.01|
|# Co-Resident Siblings|3.17|0.07|2.63|0.07|3.30|0.07|2.71|0.08|
|Fraction Brothers|0.50|0.01|0.51|0.01|0.50|0.01|0.51|0.01|
|Fraction Younger|0.48|0.01|0.45|0.01|0.49|0.01|0.45|0.01|

All means are weighted to reflect sample design. All standard errors are corrected for primary sampling unit (psu) clustering. The study sample is restricted to rural households with children 6-15 in communes that are visited in both rounds of the VLSS.
Our finding of a significant relationship between socialist entrenchment, endowment with natural resources and initial political openness is robust to controlling for other factors that might have impacted initial political openness and to using alternative indicators of initial political openness and of endowments with natural resources. There is no evidence that FSU countries experienced a stronger role of communists in post-communist political life (column 2). Years under Socialism, on the other hand, enters significantly at the 3% level, suggesting that the distinction between the 11 original countries constituting the Soviet Union and the other transition economies is more important for the political structure after transition began than having been a member of the Soviet Union or not. Column 3 shows that political openness at the beginning of transition did not vary with the degree of ethnic fractionalization, while Years under Socialism and Initial Raw Exports still enter significantly at the 7% level. Landlocked countries did not start the transition process with significantly more or less political openness and controlling for landlocked economies does not affect our findings (column 4). Similarly, close economic links with other transition economies, measured by the Trade Share with CMEA partners relative to GDP in 1990 does not affect our results. Interestingly, a larger trade share with CMEA partners is associated with more political openness in 1992 (column 5). The level of education, measured by enrollment in tertiary education, is not significantly related with political openness in 1992 and does not change our results (column 6). Column 7 shows initial raw exports in GDP can explain cross-country variation in political openness in 1992 across the 14 FSU countries in our sample. Our results are thus not only driven by the difference in Years under Socialism, but also by different endowments with raw materials.<sup>18</sup> Using gas reserves per

> 17 There have been nine governments in Poland, seven in Estonia, and five in Hungary during the 1990s (World Bank 2002).

> 18 When we rerun the regression without the Baltic States and Moldova, Initial Raw Exports still enters negatively and significantly with a p-value of 10.1%.

22