rafmacalaba/datause-encoder-data huggingface dataset:
# **_Annex C:  Projecting out‐of‐pocket expenditures_**

Future values of OOP spending per capita are needed as input parameters into the demand model projections to predict future values of physicians per 1,000 population. Based on historical data for total health expenditures per capita (PPP constant 2011 international $) and the percentage of total health expenditures spent OOP, we calculate the OOP health expenditures per capita for each country from 1995 to 2013 based on the following formula for each country _c_ in year _t_ :

-

- (Eq 8) _OOPPCct_ = _% total health expenditures spent OOPct Total health expenditure per capitact_

For 27 countries where health expenditure information was completely missing, year‐specific mean OOP spending per capita by region and income group were substituted. These countries were as follows: American Samoa, Aruba, Bermuda, Cayman Islands, Channel Islands, Curacao, Faeroe Islands, French Polynesia, Greenland, Guam, Hong Kong SAR, China, Isle of Man, Democratic People’s Republic of Korea, Kosovo, Liechtenstein, Macao, New Caledonia, Northern Mariana, Puerto Rico, Sint Maarten, Somalia, St. Martin, Taiwan, China, Turks and Caicos, Virgin Islands, West Bank and Gaza, and Zimbabwe.

To select the appropriate model, the data was split into two parts:

1. Initialization data set included data for years 1995 – 2008.

2. Testing data set included data for years 2009 – 2013 (except for OOPPC variable, which was set to missing).

To project future values of OOP spending per capita, we tested eight models with the initialization data set:

- 1) For each country _c_ , a moving average for 18 prior periods without weights was estimated:

(Eq 9) ������� � ��� ∑����� ������,��� A total of 18 previous periods was chosen to maximize the number of prior year observations possible to base projections off of (e.g. the projection for 2030 needs at least 17 lags to use prior information from 2013).

- 2) For each country _c_ , a moving average for 14 prior periods with weights was estimated, with the more distant observations progressively receiving smaller weights:

(Eq 10) ������� � ���� ∑������18 ���������,���

- 3) For each country _c_ , double‐exponential smoothing over 14 prior periods was applied:

36
4

**Tiered Pricing:** This is a multi-rate volumetric method, in which water rates vary as the amount of water consumed exceeds certain threshold values.

**Two part tariff:** Involves charging irrigators a constant marginal price per unit of watef purchased (volumetric marginal cost pricing) and a fixed annual (or admission) charge for the right to purchase the water. The admission charge is the same for all farmers. This pricing method has been advocated, and practiced, in situations where a public utility produces with marginal cost below average cost and must cover total costs (variable and fixed).

**Betterment levy:** Water fees are charged per unit area, based on the increase in land value accruing from the provision of irrigation.

**Water markets:** In some developed economies, markets for water or water rights have been formed and determine water prices.

Bos and Walters (1990) investigated farmers representing 12.2 million hectares (1 hectare = 10 dunams _ 2.5 acres) of irrigated farms world wide and found that in more than 60% of the cases, water charges are levied on per unit area basis. In less than 15% of the irrigation projects, water is charged based on a combination of per unit area and volumetric. In about 25 % of the cases studied, the charging method is volumetric.

# _2.2. Pricing practices in developing countries_

The information in this section has been collected based on many sources. Some of the information may be out dated both in terms of price levels, and in terms of water charge system.

Armenia: The Armenian irrigation system was designed to serve big collective farms. It is based mainly on supply of surface water, and is amended in some places by tube wells that are operated by individual farmers. Presently, energy cost for pumping irrigation water is fully subsidized. The pricing system intends to recover O&M costs by per area charges. However in 1992, the collection rate of the water charges was only 27% (World Bank, 1994a).

China: Most Chinese irrigation facilities are publicly owned. Large and medium irrigation projects are managed by government water organizations. Small projects are generally owned by local, collective farmers' organizations. Methods used to charge for
effect of labor instrumented using this two-stage least squares specification on outcomes of adults is not statically significant. This result suggests that the instruments do not affect the outcomes of interest through any channels other than child labor, which is consistent with the behavior of instruments that satisfy the exclusion restriction.

# _VI.3 Robustness: magnitude of the shock_

Finally, we exploit cross-sectional variation in the size of shocks to test whether our second stage results are driven by large shocks or whether we obtain similar results for smaller shocks. As discussed in Section IV.2, we are more confident that the exclusion restriction required for a valid instrumental variable (namely that the instrument affects the outcome only through the endogenous variable) will be satisfied for small shocks, since these are less likely to have direct long lasting effects on household outcomes. In Table 9, we re-estimate our results, using three alternative definitions of the shock. We set the shock magnitude thresholds as shocks that result in a loss of 5, 10 and 20 percent of the crop and excluding those individuals who experienced shocks larger than the respective thresholds. For instance, at the 20 percent threshold we exclude 69 individuals from our original sample who experienced a crop loss greater than 20 percent. We find that the magnitude of the impact of child labor on the outcomes does not change substantially with magnitude of shock, suggesting that our findings are not driven by long-lasting effects of larger shocks and that the exclusion restriction is satisfied.

# **VII. Conclusion and Future Research**

In this paper we investigated the impact of child labor on education and labor market outcomes using panel data from the Kagera region of Tanzania. Building on our previous work, 21
namely that incumbent exporters benefit from their insider status in existing markets-products. Tariffs show up insignificant at both margins of trade.

**Table 11**

**Firm-Level Export Growth and the Role of Export Costs along the Margins of Trade**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|gr_rupees|gr_rupees|gr_rupees|gr_rupees|gr_rupee|sgr_rupees|
|foreign demand,log-change(ldnet_imp)|0.0371***|0.0291***|0.0297***|0.0291***|-0.0514**|-0.0462*|
||(0.00703)|(0.00701)|(0.00699)|(0.00701)|(0.0240)|(0.0245)|
|# days customsprocedures,change|-0.198|-0.0372|13.23***|-0.0371|-0.0149|13.23***|
||(0.352)|(0.358)|(1.120)|(0.358)|(0.356)|(1.118)|
|mfn tariff rate|0.0317|-0.0446|-0.0489|-0.0437|-0.0454|-0.0455|
||(0.0472)|(0.0501)|(0.0501)|(0.0507)|(0.0502)|(0.0508)|
|complexitytariff regime(sd_mfn_hs4)|-0.125**|-0.0756|-0.0721|-0.0982|-0.0753|-0.154|
||(0.0619)|(0.0505)|(0.0501)|(0.132)|(0.0504)|(0.123)|
|bilateral nominal exchange rate,change|0.0814***|0.0890***|0.0925***|0.0890***|0.0890***|0.0923***|
||(0.00879)|(0.00882)|(0.00882)|(0.00880)|(0.00884)|(0.00884)|
|preferential trade agreements(PTA)|0.0151***|0.0167***|0.0162***|0.0167***|0.0170***|0.0133***|
||(0.00262)|(0.00281)|(0.00288)|(0.00281)|(0.00284)|(0.00348)|
|Extensive margin||0.851***|1.053***|0.850***|0.839***|1.038***|
|||(0.0322)|(0.0330)|(0.0327)|(0.0320)|(0.0333)|
|Ext*change # days customsprocedures|||-16.49***|||-16.45***|
||||(1.208)|||(1.207)|
|Ext*complexitytariff regime||||0.0259||0.0918|
|||||(0.140)||(0.125)|
|Ext*foreign demand|||||0.103***|0.0969***|
||||||(0.0309)|(0.0312)|
|Constant|1.978***|1.128***|0.925***|1.128***|1.133***|0.934***|
||(0.00269)|(0.0325)|(0.0328)|(0.0330)|(0.0323)|(0.0331)|
|Observations|673687|673687|673687|673687|673687|673687|
|R-squared|0.022|0.059|0.066|0.059|0.059|0.066|
|Robust standard errors inparentheses|||||||
|***p<0.01,**p<0.05,*p<0.1|||||||

_Note: Firm-country-product year level OLS regression results. Robust standard errors adjusted at the six-digit HS level are in parentheses. Time-varying sector fixed effects are for two-digit HS codes. Dependent variable is the mid-point growth rate. Regressions cover the period 2002-2010. *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. Coefficients for fixed effects are suppressed from table of results._ _Source: Authors’ calculations_ In order to disentangle the effect of trade costs along the different dimension of the extensive margin of trade, presents the estimation of our equation and decomposes the extensive margin 44
per capita measures (i.e. an increase of 100 refugees per 100,000 residents). We look at the largest increase within eight-year windows. Therefore, if a region has consecutive years of growth of at least 10,000 refugees, we select the year with largest absolute change as the event.<sup>25</sup> Since we study a period of 14 years, a few regions are included twice that have events more than four years apart. The empirical strategy is therefore at the region-event level.

We present multiple specifications that allow for different threats to identification. Our first model compares the periods before and after events, within regions with an event. We report the specification that pools the four years in the post period (which mirrors a regression discontinuity design) in the tables and the event study specification that includes binary variables for leads and lags (each of the years until or since the event) in the figures. In most specifications we include year fixed effects that control for trends across the sample.<sup>26</sup> Formally, we estimate the following equation using only eventually-treated regions: where _i_ denotes individuals, _r_ sub-national regions, and _t_ years. Our outcome, _Attitudesirt_ , comes from questions asked of all GWP respondents about their views on whether _"the city or area where they live a good place or not a good place to live for immigrants from other countries"_ . Responses are coded as dummy variables, with one representing a positive answer and zero otherwise.

_Wavert_ denotes our treatment variable, which takes a value of 1 if the region _r_ received a large wave of refugees in period _t_ or at any earlier period as discussed above. _Rrt_ is a vector containing two control variables at the region-year level: the Inverse Hyperbolic Sine (IHS) transformed total population of region _r_ at period _t_ , and the IHS transformed refugee population in the same region and period. _Xirt_

> 25We are working on additional definitions, including examining the first event in a window, as well as defining an event across consecutive years, to better accurately capture the pre-post spirit of the idea.

> 26We also include specifications without year fixed effects given the recent concerns raised by De Chaisemartin and d’Haultfoeuille (2020) and others.

16
engaged in informal activities that may not be captured by standard labor modules.<sup>9</sup> In this regard, our paper is the first of its kind to rigorously demonstrate how survey methods can increase the reporting of employment and work of young males compared to older adults and deliver evidence that can inform data collection efforts to ensure that women’s and youths’ labor activities are well documented and represented statistically.

Second, we provide suggestive evidence on how gender norms and prevalence of informality can explain why and how some survey methods are more effective at addressing mismeasurement of gender- and age-based labor gaps. Studies have documented that occupational segregation by sex, which is rooted in gender norms (Franck & Olsson, 2014; Muller & Sousa, 2020) and persists over time, contributes greatly to gender wage gaps (World Bank, 2011; Borrowman & Klasen, 2020). At the same time, the prevalence of informal work to which the respondent is exposed can bias the respondent’s reporting of labor indicators. Despite these findings, studies have yet to demonstrate how norms related to occupational segregation by sex and the prevalence of informality influence how effective the different survey methods are. Thus, another novel and noteworthy contribution of our paper is its evidence for how exposure to gender norms related to housework and the prevalence of informality in the respondent’s community along with preliminary participation in alternative survey methods can have heterogeneous effects on women’s and youths’ reporting of labor indicators.

# **2. Study Design**

## **2.1 Randomized Methodological Survey Experiment**

We designed and implemented a survey experiment to test whether different survey methods capture work activities that respondents would not report when responding to a standard labor module.<sup>10</sup> In particular, we focus on women and youths’ reporting of employment and work compared to men and adults, respectively, within the context of a household survey. We conducted this experiment in four stages described below.

> 9 To our knowledge, only one study shows how response fatigue can have differential effects on the reporting of productive activities by gender and age (Ambler et al., 2021). The authors, however, do not study the impacts of any specific survey method on the reporting of labor outcomes.

> 10 The standard labor module follows the guidelines of the 19th International Conference of Labor Statisticians (ICLS) and collects respondent labor data for the previous 7 days.

6
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qualification-based systems; and (3) the more general discussion about what would constitute a robust program of civil service reform and the level of political appetite for its implementation. Following is a brief review of the ABSM analysis on the third issue, the broader civil service reform agenda.

**Framing the question.** Although the Government of Timor-Leste had articulated its commitment to improving public administration and service delivery, comprehensive civil service reform can be difficult to achieve anywhere. Well-intentioned reforms can easily be derailed by weak capacity, overlapping and ill-defined policies, feeble coordination across ministries and agencies, a fragile political coalition, and stakeholders’ worries about what might happen to their own pay levels, positions, and recruitment and promotion opportunities. The ABSM analysis focused on the potential scope of support for wide-ranging reform and attempted to identify the major coalitions that might form on this issue. In Timor-Leste’s social and political order—characterized by political schisms and corresponding patronage networks borne of a violent struggle for independence— merit-oriented civil service reforms were highly contentious. Given the country’s need to elevate government performance to achieve tangible results on service delivery as a means to political stability, the stakes were particularly high.

**Specifying the issue.** The process of arraying the varied elements of a comprehensive civil service reform package along a linear continuum proved complicated. The team divided the overall agenda into three levels of cumulative reform, building from 0 to 100 (Figure 5).

**Figure 5:  Policy Steps in Timor-Leste’s Civil Service Reform Agenda**

<!-- Start of picture text --> 100 – Independent oversight of merit-based civil service regime<br>95 – Merit-based civil service regime implemented<br>80 – Robust establishment management system implemented & resourced<br>75 – Govt-wide pay & grading system implemented & adequately resourced<br>55 – Legislative basis for civil service institutional responsibilities<br>45 – Senior executive cadre defined, recruited, & adequately resourced<br>40 – Civil service management framework approved<br>30 – Technical analysis, including job evaluation & comparative pay<br>10 – Pay & grading pilots initiated<br>5 – Provisional pay & grading package approved in decree law<br>0 – Status quo<br><!-- End of picture text --> The first level focused on the low-hanging fruit: temporary pay relief, pay and grading pilots, and technical analysis. The second level covered the design of a new system: approving a new framework for civil service management, creating a senior executive cadre, and defining the legal basis for institutional mandates regarding the civil service. The last—and most politically difficult—level 9
in _Green_ zones.<sup>16</sup> Throughout the paper, we use this color scheme as the treatment across Indian districts. In particular, each treated firm is located in a _Red_ or _Orange_ district, while each control firm is located in a _Green_ district.

**Physical and cultural distance.** We use different measures of _distance_ , which we include as controls in our empirical results. The measures of geographic distance between districts calculate the length of the shortest distance between district centroids. The measure of linguistic distance between Indian districts is from Kone et al. (2018) using the commonly used ethnolinguistic fractionalization (EFL) index (Mira, 1964). This index measures the probability of two randomly chosen individuals from different districts speaking the same language.

**Other controls.** We control for different firm and district-level time-varying variables such as data on the monthly number of cases, deaths, and recoveries from Covid-19 for all of India at the district level from Covid India. For each firm, we construct two variables that measure the firm’s exposure to global demand and supply shocks that vary at the product and country level, following Hummels et al. (2014). The construction of these exposure variables is described in detail in online data Appendix C.

**Summary statistics.** We present some key summary statistics from our firm-to-firm trade dataset in Table A1 and Figure A1. They report the unique numbers of sellers and buyers, total sales (in million rupees), and total number of transactions during the months of January-March, April-June, and July-September for both 2019 and 2020. The most noticeable pattern is the large drop in all variables in 2020 compared to 2019, particularly during the April-June period, which coincided with the lockdown policies. The total value of sales and the number of transactions fell by almost 60% during April-June of 2020 compared to 2019. For reference, the fall in the value of sales was only 25% after the strict centralized lockdown was over (July-September) and only 15 _._ 6% before the lockdown (January-March) compared to the corresponding months in 2019.

The characteristics of the network, including measures of network in-degree (i.e., number of sellers per buyer) and outdegree (i.e., number of buyers per seller), are in Tables A2 and A3. Similar to other production network datasets, our network is sparse. In terms of network indegree, for 4-digit HS products, the median buyer has 2 suppliers per product but 6 suppliers on average with a standard deviation of 17. In terms of network outdegree, the median seller has 5 buyers

> 16See the Economic Times. On April 30, one _Red_ zone district was reclassified to the _Green_ zone, but we maintain the initial classification as it is likely to be more exogenous.

8
**Figure 8.  Stable oil and gas revenue while government non-oil and gas revenue grows faster**

<!-- Start of picture text --> 1,000<br>Total central gov't revenues<br>Non-oil and gas central gas revenues<br>Oil and gas central gov't revenues<br>800<br>600<br>400<br>200<br>0<br>1994 1997 2000 2003 2006<br><!-- End of picture text --> Note: Deflated by the implicit GDP deflator. Projected 2008 deflator growth of 12 percent. _Source: Ministry of Finance, various APBN data and reports, World Bank staff calculations._

**Table 3. Oil and gas revenues relative to domestic revenues** _<u>Rupiah trillion</u>_

||**%**|**2003**|**%**|**2006**|**%**|**2007**|**%**|**2008***|**%**|
|---|---|---|---|---|---|---|---|---|---|
|**I. Domestic Revenue**|**100%**|**340.9**|**100%**|**636.2**|**100%**|**706.1**|**100%**|**892.0**|**100%**|
|**1. Tax revenues**|**56%**|**242.0**|**71%**|**409.2**|**64%**|**491.0**|**70%**|**609.2**|**68%**|
|Income tax|28%|115.0|34%|208.8|33%|238.4|34%|305.0|34%|
|- Non-Oil and Gas|19%|96.1|28%|165.6|26%|194.4|28%|251.4|28%|
|- Oil and Gas|9%|19.0|6%|43.2|7%|44.0|6%|53.6|6%|
|**2. Non Tax receipts**|**44%**|**98.9**|**29%**|**227.0**|**36%**|**215.1**|**30%**|**282.8**|**32%**|
|Natural Resources|37%|67.5|20%|167.5|26%|132.9|19%|192.8|22%|
|- Oil and Gas|32%|61.5|18%|158.1|25%|124.8|18%|182.9|21%|
|i. Oil|25%|43.0|13%|125.1|20%|93.6|13%|149.1|17%|
|ii. Gas|8%|18.5|5%|32.9|5%|31.2|4%|33.8|4%|

Note: *2008 revised budget (APBN-P) _Source: World Bank calculations based on Ministry of Finance publications._

# _Non-tax oil and gas revenues: distributing production between the producer and the state_

The vast majority of Indonesia’s oil and gas output has been extracted under contracts with private investors. The state-owned oil company, Pertamina, produced only 5 percent of total output independently in 2004, the most recent year for which data are available. Private contractors share their revenues with the government through revenue-sharing agreements. These are based on Net Operating Income (NOI), or “profit oil”, which is the amount of oil and gas production in excess of the amount needed to cover the costs of production (“cost oil”), not including any production-related government taxes and charges. Such taxes and charges include the central government’s corporate income tax, interest dividend tax, royalties, and Pertamina’s retention fee. They also include local taxes (land and building taxes), since there are a 8
<!-- Start of picture text --> 25,000 ee =Ss . a... raceenssecussseey<br>3<br>£<br>8 |<br>3 |<br>g | |<br>oO<br>Ke)<br>®<br>= —<br>z<br>2011. 2012) «2013. 2014. 2015) 2016) 2017-2018 »3= 2019 2020-2021 = 2022<br>[559 _Istoéen [51 Jugoistocen | Jugozapaden [559 Pelagoniski<br>(1) Poloski [551 Severoistocen (1 Skopski _— Vardarski<br><!-- End of picture text -->

<!-- Start of picture text --> x|<br>§—<br>a ———<br>£5= 304 me i ve me a ve ee me ms ——. me<br>a<br>£<br>ro 204 ves i vs ves ves — ot os vise vse<br>2011. «2012-2013, 2014. 2015) 2016 )=— 2017) 2018 )3=s 2019 2020. 2021 = 2022<br>[55 Istoéen [51 Jugoistoéen |) Jugozapaden 145) Pelagoniski<br>(1 Poloski [0 Severoistocen {1 Skopski |. Vardarski<br><!-- End of picture text -->
# (table 2 continued)

|7. Clothbna|||
|---|---|---|
|7.1 clothing|26,020||
|7.2 shoes|6,270||
|7.3 belts, bags and similar|355||
|8. Per6onal<br>Needs|||
|8.1 shaving<br>materials,<br>deodorants<br>and similar|<br>4,840||
|8.2 items for the house|128||
|8.3 legal and other services|411||
|Total in prices of March 1982|215,133|41,863|
|(25Z expansion<br>factor for extreme-poverty)|<br>-|52,328|
|Total in prices of January 1984|635,512|151,753|
|Total per trimester<br>(*.25)|158,878|37,938|
|Total per capita (*1/4.9)-|||
|Poverty lines|32.424|7.742|

Source: For the line of moderate poverty Coplamar (1983); for the line of extreme-poverty own construction.
# Figure 4. Health care spending: Baseline case

<!-- Start of picture text --> 12<br>10<br>%fGOPf<br>8 /_<br>7<br>6 -<br>5 II I<br>96197 00/01 10/11 20/21 30/31 40/41 50/51<br><!-- End of picture text --> Figure 5. NZS spending: Baseline case <!-- Start of picture text --> 10<br>9-<br>8<br>%ofGDP<br>7<br>6<br>5<br>96/97 00101 10/11 20/21 30/31 40141 50/51<br><!-- End of picture text --> 14