rafmacalaba/datause-encoder-data huggingface dataset:
This study also provides exogenous variation to correct for the potential endogeneity of regional public expenditure. The variation comes from the use of lagged regional mineral extraction multiplied by non-predictable mineral price changes, which I refer to hereafter as unanticipated mining revenues. The instrument is a function of natural resource extractions in each political region of Peru during 2001 and the change in the world prices of these resources between 2002 and 2006. This instrument has a strong impact on public spending because it is directly related to the amount of natural resource royalties that regional governments receive each year. The instrument that I use is the improvement in health input prices, which are typically used in the health economic literature (Schultz 2004), because it is not altered by individual regional migration. Any omitted factors need to fluctuate within regions over time in the same non-linear way as unanticipated revenues in the mining sector to have a confounding effect on child malnutrition. Two possibilities are that (1) regional income increases with increases in unanticipated mining revenues and (2) health outcomes worsen with increases in these revenues. I show that both concerns are unsubstantiated. It is also possible that regional governments changed their patterns of expenditure in response to the amount of royalties received, spending more in sectors that directly influence child health. I verify that changes in the composition of expenditures are not responsible for the differentiated impact of public expenditure.

In addition, this paper differs from the existing literature because it analyzes the potential barriers that diminish the effectiveness of public expenditure. I find that public expenditure is ineffective and even harmful in rural areas, possibly due to lower quantity and quality of public services in these areas. In urban areas, public spending does have the ability to improve

> 2 Source: 2004–6 ENDES.

3
# **Table 7: Wage Equation Results (2003-2007)**

||**#1**|**#2**<br>**(Both**|**#3**|**#4**|**#5**|
|---|---|---|---|---|---|
||**(Both sexes)**|<br>**sexes,**<br>|**(Both sexes,**<br>**2007)**|**(All men)**|**(All women)**|
|**De**|**pendent Varia**|**2003)**<br>**ble = ln(Dai**|**ly Wage)**|||
|**Personal Attributes**||||||
|Gender|-0.25|-0.26|-0.27|||
||(0.003)***|(0.006)***|(0.007)***|||
|Experience|0.03<br>|0.03<br>|0.03<br>|0.03<br>|0.03<br>|
||(0.000)***|(0.001)***|(0.001)***|(0.000)***|(0.001)***|
|exp * exp (/10,000)|-0.04|-0.04|-0.04|-0.04|-0.04|
||(0.001)***|(0.001)***|(0.002)***|(0.001)***|(0.001)***|
|**_Education Dummies_**||||||
|_Elementary_|0.11|0.10|0.09|0.13|0.07|
||(0.004)***|(0.008)***|(0.010)***|(0.005)***|(0.009)***|
|_High School_|0.40|0.37|0.40|0.37|0.46|
||(0005)***|(0009)***|(0011)***|(0005)***|(0010)***|
|_College_|.<br>1.01<br>(0.006)***|.<br>0.94<br>(0.013)***|.<br>1.02<br>(0.014)***|.<br>0.86<br>(0.008)***|.<br>1.14<br>(0.010)***|
|**Sector (Agricultural as reference)**<br>Manufacturing|0.38|0.42|0.37|0.41|0.31|
||(0.004)***|(0.007)***|(0.008)***|(0.004)***|(0.009)***|
|Services|0.17|0.24|0.16|0.30|0.01|
||(0.004)***|(0.007)***|(0.009)***|(0.005)***|(0.007)|
|**Occupation (Worker/laborer as refere**|**nce)**|||||
|Officials, managers, executives|0.50|0.52|0.45|0.44|0.58|
||(0.008)***|(0.017)***|(0.018)***|(0.010)***|(0.012)***|
|Professionals|0.42|0.47|0.36|0.33|0.45|
||(0.005)***|(0.010)***|(0.011)***|(0.007)***|(0.007)***|
|**Region dummies (NCR as reference)**||||||
|Luzon|-0.199<br>|-0.165<br>|-0.218<br>|-0.182<br>|-0.211<br>|
||(0.004)***|(0.008)***|(0.008)***|(0.004)***|(0.006)***|
|Visayas|-0.413|-0.362|-0.45|-0.397|-0.422|
||(0.004)***|(0.009)***|(0.010)***|(0.005)***|(0.007)***|
|Mindanao|-0.38|-0.324|-0.427|-0.359|-0.387|
||(0.004)***|(0.009)***|(0.010)***|(0.005)***|(0.007)***|
|**Year dummies (2003 as reference)**||||||
|2004|-0.016|||-0.026|-0.005|
||(0.004)***|||(0.004)***|(0.006)|
|2005|-0.073<br>***|||-0.056<br>***|-0.097<br>***|
||(0.004)|||(0.005)|(0.007)|
|2006|-0.092|||-0.081|-0.11|
||(0.004)***|||(0.005)***|(0.007)***|
|2007|0069|||006|0087|
||-.<br>(0.004)***|||-.<br>(0.005)***|-.<br>(0.006)***|
|Constant|4.40|4.35|4.39|4.32|4.26|
||(0.007)***|(0.013)***|(0.015)***|(0.008)***|(0.012)***|
|Number of observations|156167|34380|30806|93242|64289|
|Adjusted R-squared|0.55|0.56|0.53|0.52|0.58|

_Note:_ Robust standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

Wage differences across regions suggest some segmentation in the labor market. Individuals 11
instrument is based on an argument that past exposure to climate-related disasters is correlated with beliefs about climate action by affecting people’s perceptions of the risks of experiencing similar disasters in the future. Having been affected by disasters in the past may make an individual more likely to support policies aimed at limiting the impacts of such disasters, but this increased willingness to pay operates through changed perceived risks of such events happening in the future.

In <u>tables 6</u> and <u>7 we report marginal effects of age in regressions without the perceived risks</u> variable (Column 1), with the “perceived risks for self” variable added (Column 2) and “perceived risks for children” added in column 4, and 2SLS estimates in which the perceived risks for self are instrumented with past exposure to climate disasters (Column 3) and with perceived risks for children instrumented with past exposure in Column 5. In all specifications, the tests reject the null of exogeneity and pass the weak instrument tests.<sup>14</sup> Subject to the validity of the assumptions underlying the instrumentation strategy, the age gradient in beliefs that climate change action should be a government priority remains positive when the model includes perceived future risks to one’s self. The gradient becomes smaller but remains statistically significant when the model includes perceived risks for children. IV estimates do not change the significance of the age gradient in willingness to pay additional taxes or give a part of one’s income to combat climate change when risks to one’s self are instrumented with past exposure to climate disasters. However, the correlation between age and willingness to pay becomes negative when perceived risks to children are instrumented with past exposure.

# _Alternative framing of climate action questions_

So far, the willingness to pay for climate action results were based on a question that implies that the money would go to “combating climate change” without specifying what combating climate change means. The LiTS survey includes three questions on willingness to pay for specific climaterelated outcomes: _reducing or preventing pollution by improving the quality of air or water or dealing with waste/sewage_ , fighting _global warming or the greenhouse effect_ , and preventing _the loss of plant or animal species or biodiversity_ . These questions allow us to test the sensitivity of the age gradient to the framing of the question related to willingness to pay for climate change action.

> 14 We cannot perform overidentification tests as we only have one instrument per endogenous variable.

20
# **Figure 8: Impacts on participants' poverty in R1**

_Cumulative distributions of consumption with and without earnings from public works_ <!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>Poverty line<br>without net earnings from public works<br>with net earnings from public works<br>Poverty impact graph<br>0<br>-.01<br>-.02<br>-.03<br>-.04<br>Poverty line<br>1000 5000 6988 10000<br>1000 5000 6988 10000<br>cumulative density<br>Impact on poverty (change in CDF)<br><!-- End of picture text --> _Note_ : A smoothing parameter of 0.3 is used for all figures in this paper. Poverty impact graph is estimated as the difference between the cumulative distribution functions of consumption with and without the program. _Source:_ Authors’ estimates based on BREGS Survey.

32
**Figure 4: Revisions to the PPP $ value of national poverty lines**

<!-- Start of picture text --> 360<br>320<br>280<br>240<br>200<br>160<br>120<br>80<br>40<br>0<br>0 40 80 120 160 200 240 280 320 360<br>Poverty line at 1993 PPP ($/month)<br>Poverty line at 2005 PPP ($/month)<br><!-- End of picture text -->

**Figure 5: Densities of national poverty lines at 1993 and 2005 PPP**

<!-- Start of picture text --> .012<br>2005 PPP<br>.010<br>1993 PPP<br>1993 PPP in 2005 US prices<br>.008<br>.006<br>.004<br>.002<br>.000<br>0 100 200 300 400 500<br>Density<br><!-- End of picture text --> 27
simultaneity--has been unable to distinguish between the expected positive effect of imports on productivity growth in the long run and the fact that imports are drawn to low productivity sectors where a country does not have an international advantage. Second, the observed relationships could also be explained by the well-known pro-cyclical nature of productivity growth: productivity growth tends to be higher when output is growing, and falls during recessions or low-growth periods. Corsequently, if greater import penetration is accompanied by a contraction of domestic industry, it is not surprising th:Nt productivity growth also falls.

One paper which has been able to convincingly address this simultaneity problem is Kaufman (1991). Using micro-level data on the rate of return to World Bank investment projects, Kaufman finds that a range of policies--including trade and exchange rate policies--significantly affect the rate of return to projects. A more open policy environment can double the rate of return to investment, even after controlling for general economic trends, such as GDP growth.

One difficulty in measuring the impact of trade policies on growth is that trade policy itself may be a function of other variables, including growth. Studies that have tried to identify the causal relationship between GDP growth and growth in exports or imports have i,+d mixed results (See Section IV in Table 1).

This brief review of the literature on openness and economic growth reveals twe important considerations. First, 8
# **3. Data and Results**

Our data source is Guatemala’s Living Standards Measurement Survey (ENCOVI) from the year 2000. Guatemala’s National Statistics Institute carried out interviews for the nationally representative ENCOVI with 7,276 households, including 37,771 individuals of all ages from urban and rural areas. It is also one of Latin America’s only surveys designed to be statistically representative of indigenous and nonindigenous people. Regrettably, we do not separately consider household work and paid work because the ENCOVI data reports a negligible share of children engaging in paid work, which raises estimation issues. We therefore collapse child laborers engaging in household work and paid work into one category.

Figure 2 illustrates that though child labor rates have fallen for both indigenous and non-indigenous populations, non-indigenous rates have fallen at a quicker rate. The lack of change in child labor among indigenous Guatemalans partly supports the positive stigma argument, but it may also reflect low socioeconomic status (and reliance on child labor) over time. We are unable to further investigate the role of socioeconomic status on changing child labor and education patters across cohorts because the ENCOVI data does not have information on childhood socioeconomic status of adults. Accordingly, we focus on children for the remainder of this paper.

Table 2 presents descriptive statistics for all Guatemalan children in the 7 to 14 age-group. The rate of combining child labor and schooling—the most preferred activity in the positive stigma model—are 8.1 percent and 2.6 percent for indigenous and nonindigenous children. The rates of only schooling for indigenous and non-indigenous children are 63.3 percent and 81.5 percent. The rates of only working for indigenous and 5
<!-- Start of picture text --> foe] Daily wage by region: 2003-07<br>S —— Manila -—--—-—- Luzon<br>seceesenene Visayas —-—-- Mindanao<br>A<br>8 ab<br>| JAA<br>83] i} sant<br>i=Bo] oeyp oT.<br>So) ye MN<br>i; we<br>4 I<br>° = ==. .<br>0 200 400 600 800 1000<br>Daily Wage in 2000 PHP<br><!-- End of picture text -->

<!-- Start of picture text --> Daily wage by region (Luzon): 2003-07<br>6 AA —— Manila -——-—-—- locos Region<br>: i seseeeeeeee Cagayan Valley — —-- Central Luzon<br>© Pi ——-— CALABARZON —------ MIMAROPA<br>8 : a cee Bicol weeee= Caraga<br>ge] IN<br>8 i \y {\<br>Es 3M3 %<br>s& VASO<br>Ae<br>J Rapa SS<br>°<br>0 200 400 600 800 1000<br>Daily VWage in 2000 PHP<br><!-- End of picture text --> <!-- Start of picture text --> 8 Daily wage by region (Visayas): 2003-07<br>2 — Western Visayas ----- Central Visayas<br>stesseeeeee Eastern Visayas<br>2] lyn oa<br>oS A VA<br>| uy dt<br>2Cc Foy:<br>33—_o waV1\ 4 1<br>o ‘4<br>N \<br>=} j =e<br>“| ;gy ‘oa<br>i Sy<br>4 2 so me<br>0 200 400 600 800 100€<br>Daily Wage in 2000 PHP<br><!-- End of picture text -->

<!-- Start of picture text --> 8© Daily wage by region (Mindanao): 2003-07<br>S —— Zamboanga 9 ———-—- Northern Mindanao<br>j strseeseene——-— DavaoCordillera ——-—-—-- "> AutonomousSOCCSKSARGENRegion<br>g| $<br>fa} Ihe y<br>fel A<br>cs> HaeWF PYAR<br>==Sa) Wii VMoe H'<br>5 iON 4<br>N / ¥,<br>=) 3 oo<br>é M7. \<br>Ss ~<br>sae Comata<br>0 200 400 : 600 800 1000<br>Daily Wage in 2000 PHP<br><!-- End of picture text -->
will have to be paid more than one working on a single-storey building to compensate him for a the greater probability of dying on the job.

A second approach is based on observing the behavior of consumers to see how much they are willing to pay in exchange for safety features, such as automobile seat belts or safer vehicles, to lower the risk of death. The third approach differs from the first two approaches, which are based on observing actual behavior, and instead is based on survey responses to hypothetical questions about the willingness to pay for a lower risk of death or disability. There are a number of problems with the stated-preference approach that could introduce bias in VSL estimates, but these methods have improved substantially over the years. The VSL estimates used in our study are based on the first approach.

There have been some conceptual and implementation-related critiques of VSL. [For a fuller discussion, see a working paper by Grüne-Yanoff ~ (http://www.infra.kth.se/ <u>gryne/VLS061120.pdf). Arguments against VSL relate to</u> specific measurement practices and particular contexts of applications and do not rule out the use of VSL as an instrument for policy evaluation. The include a) problems with the practice of using a uniform VSL for all contexts – for instance a VSL that is measured in the context of work-related risks may not be applicable to a context of diet-related risks to health; b) commonly used measurement approaches make it difficult to interpret the resulting risk-wage tradeoff function; and c) risk preferences are correlated with risk exposures leading to overstated VSLs.] A number of studies and meta-analyses have been published on VSLs (Viscusi 1993; Miller 2000). For the purposes of this study, we anchor VSLs to a central estimate of $6.1 million in 2004 dollars as recommended by the U.S. Environmental Protection Agency after an extensive analysis of the theoretical and empirical literature (U.S. Environmental Protection Agency (USEPA) 2000).

# **Relationship of VSL to GDP**

Since hedonic wage studies have not been conducted in most low-income countries, a method known as benefits-transfer has been used to translate VSL estimates developed in 11
Table 10. Economy on transportation of Turkish exports to Azerbaijan, 1999 data

||_Tran_|_sp._|_Value,_|_Transp_<br>|_. costs,_<br>|_Econom_|_y, 000 $_|
|---|---|---|---|---|---|---|---|
||_costs,_<br>U|_% of_<br>V||_00_|_0 $_|||
|_Commodities_|_Min_|_Max_|_000$_|**_Min_**|_Max_|**_Min_**|_Max_|
|**Total**|||**73601.7**|**5223.7**|**16035.1**|**522.4**|**1603.5**|
|Equipment for TV and|_5_|10|10913.2|545.7|1091.3|54.6|109.1|
|radio transmission||||||||
|Electric power|||6973.9|N/A|N/A|N/A|N/A|
|Sugar and granulated|10|25|5492.2|549.2|1373.1|54.9|137.3|
|sugar||||||||
|Iron structures and parts|10|50|4661.6|466.2|2330.8|46.6|233.1|
|Equipment for wheat|10|50|4213.1|421.3|2106.6|42.1|210.7|
|processing||||||||
|Wheat flour|10|25|3512.6|351.3|878.2|35.1|87.8|
|Refiigerators and freezers|5|10|3371.9|168.6|337.2|16.9|33.7|
|Aluminum structures and|10|50|3060.8|306.1|1530.4|30.6|153.0|
|parts||||||||
|Package equipment|_5_|10|2887.4|144.4|288.7|14.4|28.9|
|Synthetic fabrics|_5_|10|2708.9|135.4|270.9|13.5|27.1|
|Margarine|10|25|2543.2|254.3|635.8|25.4|63.6|
|Furniture and parts|_5_|10|2330.6|116.5|233.1|11.7|23.3|
|<br>Birds eggs in shell|10|25|2077.8|207.8|519.5|20.8|51.9|
|Plastic construction|10|50|1978.8|197.9|989.4|19.8|98.9|
|comm.||||||||
|Synthetic washing|10|25|1851.7|185.2|462.9|18.5|46.3|
|preparation||||||||
|Other sweets|10|25|1515.4|151.5|378.9|15.2|37.9|
|Sunflower oil|10|25|1444.3|144.4|361.1|14.4|36.1|
|Confectioneryproducts|10|25|1187.5|118.8|296.9|11.9|29.7|
|<br>Paper products for|5|10|<br>1178|<br>58.9|117.8|<br>5.9|11.8|
|housing||||||||
|Ventilators|_5_|10|1172.5|<br>58.6|117.3|<br>5.9|11.7|
|Cars- new|_5_|10|1094.8|<br>54.7|109.5|<br>_5.5_|10.9|
|Soap|10|25|959|95.9|239.8|9.6|24.0|
|<br>Water pumps|_5_|10|<br>921.5|46.1|92.2|<br>4.6|9.2|
|Other conditioners|5|10|<br>867.4|43.4|86.7|<br>4.3|8.7|
|Doors wood|10|50|867|86.7|433.5|8.7|43.4|
|Cosmetics|5|10|<br>822.5|41.1|82.3|<br>4.1|8.2|
|Tea|10|25|730.6|73.1|182.7|<br>7.3|18.3|
|Macaroni products|10|25|<br>535|<br>53.5|133.8|<br>5.4|13.4|
|Cooling compressors|5|10|<br>512.6|<br>25.6|51.3|<br>2.6|5.1|
|<br>Potatoes|10|25|<br>450|45.0|112.5|<br>4.5|11.3|
|Fruits and vegetables juice|<br>10|25|<br>447.1|44.7|111.8|<br>4.5|11.2|
|Garlic and onion|10|25|<br>318.8|<br>31.9|79.7|<br>3.2|8.0|

28
**Table 13:  Firm-level outcomes of city upgrading and city capacity for incumbents using an alternative measure of capacity**

|**_Incumbent_**<br>**_Firms_**|Log main<br>operating<br>revenue|Log main<br>operating<br>cost|Log main<br>operating<br>profit|Log # of<br>employees|Log labor<br>vocational|Log wage<br>per<br>employee|Log<br>export|
|---|---|---|---|---|---|---|---|
|**City**|-0.606**|-0.560*|-0.642***|-0.457**|-0.496|-0.340***|-0.734|
||(0.301)|(0.306)|(0.234)|(0.228)|(0.302)|(0.114)|(0.530)|
|**Capacity**|-0.00236|-0.00171|0.00730|0.00318|-0.00427|0.000172|-0.0136|
||(0.00674)|(0.00672)|(0.00841)|(0.00387)|(0.00519)|(0.00173)|(0.00899)|
|**City ×**<br>**Capacity**|**16.22***|**14.70**|**16.29****|**9.629**|**12.46**|**9.621*****|**16.91**|
||(8.977)|(9.032)|(7.336)|(6.678)|(8.363)|(3.185)|(16.13)|
|**Constant**|11.07***|10.64***|9.474***|6.081***|2.779***|8.356***|8.822***|
||(0.682)|(0.658)|(0.776)|(0.201)|(0.190)|(0.504)|(0.917)|
|Observations|32,517|32,428|21,283|18,193|3,892|17,496|4,759|
|R-squared|0.325|0.330|0.225|0.240|0.218|0.252|0.180|
|Block FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry**×**<br>Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Standard errors are in parentheses and clustered at county-level.

3. Analysis is restricted to firms opened before 1997.

4. Vocational labor data is only available in 2004.

Similarly, SOEs are less likely to outperform private firms in cities with high capacity when an alternative measure of city capacity is used.

18
It is important to notice that the industry considered in these fixed effects is at the 4-digits NAICS, a more aggregate level with respect to the 6-digits level of disaggregation of FDI.<sup>25</sup> This is done in order to be able to include a variable that captures the level of headquarter intensity of the sector, defined as � in equation 1. In particular, we measure � as the ratio between total capital expenditures and total wage at the industry level using data from the Annual Survey of Manufactures in 2007 provided by the U.S. Census Bureau and we introduce a dummy equal to one if � is above the average in the regressions.<sup>26</sup> Country-sector-year fixed effects are used to control for time variant country specific factors such as domestic policies that might affect the location and control decisions of parent firms. Together with the variables capturing the remoteness of the destination country, these fixed effects control for the multilateral trade resistance.<sup>27</sup> Finally, in all regressions standard errors are clustered at the 6-digits sector level.

Table 3 reports the estimated OLS coefficients on the impact of deep integration on FDI using different combinations of year-, country-sector and country-sector-year fixed effects.<sup>28</sup> The results in column 2 show that having a trade agreement is associated with higher levels of FDI. Signing a trade agreement corresponds to an increase in FDI of 54 percent. The depth of an agreement is positively associated with foreign direct investment. Columns 3 to 8 report the coefficients for different measures of depth, namely the number of provisions, ���5 and ���10 indexes. In particular, column 4 shows that including one additional provision in the agreement is associated with an increase in FDI of 1.82 percent. The coefficients of depth when measured by the ���5 index (columns 5 and 6) are very similar to the coefficients of the ���10 index (columns 7 and 8): an increase of one percent in ���5 (���10) is associated with an increase of vertical FDI by 0.48 (0.44) percent.

> 25 The results are actually robust to the inclusion of fixed effects at the 6-digits.

> 26 The same data from the ASM have been used in Nunn and Trefler (2012) where they examine the importance of the relative contractibility of headquarter services and supplier inputs.

> 27 An alternative way to control for general equilibrium effects in a similar context is presented in Egger et al (2011). Their estimation strategy deals also with endogeneity of trade agreements and the presence of numerous zeros in the bilateral trade data matrix.

> 28 The results presented in the paper focus only on the positive flows of FDI. When using the pseudo-Poisson maximum likelihood (PPML) methodology proposed by Santos-Silva and Tenreyro (2006) to take into account zero flows, the coefficient of PTA becomes non-significant while the number of provision is still positive and significant.

20