rafmacalaba/datause-encoder-data huggingface dataset:
IMF’s Government Finance Statistics and International Financial Statistics. To account for varying degrees of capital market developments, we obtained the Financial Market Depth variable from the IMF’s Financial Development Index data set.

In the factor analysis in Section 4.7, we will compare extracted latent factors with observed global financial variables (see Figure 9, Appendix). We consider the following candidates.

1. Monthly _federal funds rate_ data are provided by the Federal Reserve Economic Data (FRED) database of the Federal Reserve Bank of St. Louis. It serves as a proxy for global liquidity conditions (Csonto et al., 2013). This is particularly relevant for exchange rate volatilities, for instance due to the US federal reserve large-scale asset purchase programs, which Gadanecz et al. (2018) demonstrate to have influenced local currency bond yields.

2. We include the _TED spread_ and the Chicago Board Options Exchange’s Volatility Index to control for global credit risk and volatility conditions (Hilscher et al., 2010).

3. To mirror equity market conditions in developed markets, we include end-of-month values of the _MSCI World Index_ .

4. For global bond market conditions, we also obtain end-of-month values of _J.P. Morgan Global Bond Index (GBI)_ for emerging markets and the European monetary union, which captures developments in local currencies.

5. Furthermore, we obtain end-of-month values of _J.P. Morgan Emerging Markets Bond Index (EMBI)_ suite to reflect bond market developments in foreign currencies.

# **3 Preliminary analysis**

When a country raises capital on bond markets, it needs to set the return on the bond. This return, or yield, will determine the market appetite for this debt issuance. To simplify and focus the subsequent discussion, we treat bond yields as the price of borrowing. Considering the importance of natural capital to the economy and thereby to its bonds, we ask: Is it worth investing into forest expansion, sustainable agriculture or national parks? Figure 5 suggests two answers.

If we follow the dashed lines, it seems that more natural capital either raises or does not affect borrowing costs of B- and A-rated issuers, respectively. This is the **between-country view** or longrun view. Countries richer in natural capital, such as Brazil or the Russian Federation, tend to have higher bond yields than countries that have less natural wealth, such as Bulgaria or Hungary. This view effectively ignores the changes over time and compares the average yield with the average natural capital across countries.

If we instead follow the dotted lines, we find the exact opposite. As countries grow richer in natural wealth, the cost of borrowing drops. This is the **within-country view** or short-run view. Rather than comparing countries with each other, the within view compares the countries with themselves at different points in time. For instance, Colombia in 2019 is wealthier in natural capital and has lower yields than it did in 2009. China or Malaysia, in contrast, moved sideways, indicating that their yields were largely unaffected by changes in their natural capital.

10
GIR for _REER_ and _GR_ are presented in Figures 7 and 8. Overall, these responses are in line with previous empirical and theoretical results in the literature. The appreciation following an increase in interest rates is consistent with a standard model of exchange rate determination based on uncovered interest rate parity. The appreciation of _REER_ in response to higher _GR_ and _YGAP_ is in line with the Balassa-Samuelson effect and our stylized model of an open economy (Annex 1). Moreover, an exogenous improvement in the current account balance is associated with a depreciation of the real exchange rate in conformity with the results noted by Obstfeld and Rogoff (2000) in the case where monetary policy places a substantial weight on price stability as in the Eurozone. Real interest rate increases and _REER_ appreciation have, unsurprisingly, negative effects on economic growth.

**Figure 7:** Responses of the _REER_ to one standard deviation shocks for **Eurozone countries [1975-2011]**

<!-- Start of picture text --> R shock CA shock GR shock YGAP shock<br>0.5<br>0.9<br>0 0 .5<br>0<br>-0.5 -0.5 -0.5 -0.1<br>1 5 1 5 1 5 1 5<br><!-- End of picture text --> _Note._ The y-axis measures deviations from the baseline, while the x-axis represents years after shock.

**Figure 8:** Responses of the _GR_ to one standard deviation shocks for **Eurozone countries [19752011]**

<!-- Start of picture text --> R shock REER shock YGAP shock CA shock<br>1<br>0 1 0.5<br>0<br>-1 -0.5 -1 -0.5<br>1 5 1 5 1 5 1 5<br><!-- End of picture text --> _Note._ The y-axis measures deviations from the baseline, while the x-axis represents years after shock.

Table 1 reports the forecast error GVD for the current account over periods of two and four years ahead. Two general comments on the results (also valid for the alternative specifications considered below) are in order: First, external balance fluctuations seem to be explained mainly by its own shocks — a result commonly found in VAR studies of the determinants of endogenous time series variables. Second, the variance decomposition results do not seem to be very sensitive to the different horizons considered, implying that no shocks seem to gain relative stronger weight in explaining _CA_ fluctuations over time. More specific to this estimation, _GR_ and 15
agreements covering fewer than 10 policy areas dominated up to the late 1990s), an increasing share of PTAs over time has tended to cover a larger number of policy areas, suggesting a deepening of trade agreements.

Figure 1: Number of legally enforceable provisions in PTAs notified and in force, December 2015 <!-- Start of picture text --> 25 300<br>Cumulative<br>250<br>20<br>200<br>15<br>150<br>10<br>100<br>5<br>50<br>0 0<br>More than 20 Between 10 and 20 Less than 10 Cumulative<br>Number of Agreements<br>Cumulative Number of Agreements<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: Authors' calculations based on the Content of Deep Trade Agreements database.

Table 1 shows that there is an ordering in terms of which provisions are included in trade agreements with different values of depth. Specifically, we divide the agreements into three categories, based on the number of legally enforceable provisions and calculate the share of agreements that include each policy area. We find that policy areas included in shallower agreements (“Less than 10”), are at least as frequent in deeper agreements (cooperation on “statistics” is an exception). The majority of these agreements tend to cover tariffs and other border measures such as export taxes and customs. Competition policy is the only policy area outside the mandate of the WTO appearing in a majority of shallower PTAs. As agreements become deeper (“Between 10-20”), they increasingly extend their reach to a broader set of WTO + areas, including state aid, anti-dumping and countervailing measures. Finally, deeper agreements (“More than 20”) tend to cover areas related to intellectual property rights, movement of capital, and standards, in addition to the areas covered by shallower agreements.<sup>14</sup>

> 14 Figure A1 in Appendix A shows that recent agreements signed by the US and the EU include a larger number of areas than earlier agreements.  These new areas were often covered in other countries’ earlier PTAs, suggesting there may be learning from other countries’ PTAs.

7
types of labor. The reason is that as productivity of adults increase (a positive change in α ) the demand for child labor would also change, changing the wages of the children and thus potentially inducing a different level of child labor in the economy. In the specific example shown above we are restricting the technology so that there is no complementary between inputs.<sup>10</sup> Given this technology, the preferences of the parents interact with the nature of the technology in such a way that a simple restriction on preferences (similar to the one described above) would give us the result.<sup>11</sup> The result obtained in Basu and Van (1998) is consistent with ours, for the condition on preferences holds trivially in the case in which parents do not value leisure. The nature of unskilled labor markets in the Mexican rural setting allows us to test for this effect using the PROGRESA/OPORTUNIDADES database.

The nature of the available technology is thus a key determinant of child labor force participation in this simple static setting. Actually, it plays an even more important role than preferences of the parents, which under mild conditions would result in reduction of child labor as adult wages increase, even for parents who value their own leisure. Let us analyze in the following section whether this result holds in a dynamic setting.

# **3.2 Dynamic Setting with Purely Self-interested Parents**

The existence of child labor has dynamic economic implications, like those in terms of human capital investment and the evolution of aggregate human capital, with an effect on the growth possibilities for the economy as a whole. We follow the unitary model of the household. Jacoby and Skoufias (1997) used a dynamic setting, as they were interested in analyzing the use of children as a pool of assets as a consumption smoothing device in the absence of perfect credit and insurance markets. In that model it is assumed that parents care about the schooling of their children as such, in the form of a bequest of

> 10  Despite the fact that we can find economically meaningful ways to argue in favor of complementary (adults as supervisors or managers, for example), it does not seem to be extremely restrictive to think of productivity corrected perfect substitution between adult and child labor.

> 11 We use this example because it is the type of aggregation used in Basu and Van (1998).

10
weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

The labor market impacts of the war estimated using households’ exposure to battles within 20 km and 30 km are consistent with the DID findings reported earlier. An additional battle leads to about 0.5 percentage points reduction in participation in any livelihood activity (Table 11). The fall in economic activity was primarily due to a decrease in non-farm business and wage-earning activities. An additional battle is associated with 0.2 percentage points decrease in non-farm business activities and wage employment. There was, however, no impact on farm activities, signifying the resilience of the agriculture sector to the stresses of armed conflict.

**<u>Table 11: Armed conflict and livelihood activities</u>**

||(1)<br>Any<br>activity|(2)<br>Any<br>activity|(3)<br>Farm<br>activity|(4)<br>Farm<br>activity|(5)<br>Non-<br>farm<br>business|(6)<br>Non-<br>farm<br>business|(7)<br>Wage<br>related<br>activities|(8)<br>Wage<br>related<br>activities|
|---|---|---|---|---|---|---|---|---|
|Battles within 20 km|-0.005<sup>***</sup>||0.000||-0.002<sup>**</sup>||-0.002<sup>**</sup>||
||(0.002)||(0.001)||(0.001)||(0.001)||
|Battles within 30 km||-0.004<sup>***</sup>||0.001||-0.002<sup>***</sup>||-0.002<sup>***</sup>|
|||(0.001)||(0.000)||(0.001)||(0.001)|
|Household fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.012|0.012|0.013|0.013|0.004|0.005|0.004|0.005|
|Mean dep. var (pre-war)|0.801|0.801|0.313|0.313|0.241|0.241|0.417|0.417|
|No. observations|21704|21704|28247|28247|28247|28247|28247|28247|

Notes: The outcome variable in the first two columns is a dummy variable for participation in any economic activity. The outcome variable in the third and fourth columns stands for a dummy variable capturing participation in non-farm business activities while the dependent variable in the last two columns represents an indicator variable for engagement in wage-related activities. All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **5.4 Robustness Tests and Heterogeneity Analyses**

We run several empirical tests to prob the robustness of our results. Most importantly, our main estimations consider the rest of Ethiopia except Tigray as control group. However, households living in the highland regions (Amhara, Oromia, Tigray and SNNP) and lowland regions engage 36
Marcel Fafchamps, Jan Willem Gunning, Francis Teal, Simon Appleton, Bernard Gauthier, Abena Oduro, Remco Oostendorp, and Cathy Pattillo. 2000. “Rates of return on physical and human capital in Africa’s manufacturing sector.” _Economic Development and Cultural Change_ 48(4), 801–827 Butler, Alexander W., and Jess Cornaggia, 2009, “Does access to finance improve productivity?

Evidence from a natural experiment,” _Journal of Financial Economics_ Forthcoming.

Caselli, F., and J. Feyrer. 2007. “The Marginal Product of Capital,” _Quarterly Journal of Economics_ 122 (2): 535-568 Chawanote, Chayanee and Christopher B. Barrett. 2012. “Non-farm Occupational and Earnings Dynamics in Rural Thailand” Poster prepared for presentation at the Agricultural & Applied Economics Association’s 2011, AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania.

Cull, R., Xu, L.C., 2005. “Institutions, ownership, and finance: The determinants of profit reinvestment among Chinese firms.” _Journal of Financial Economics_ 77, 117–146. de Mel, Suresh; David J. McKenzie; and Christopher Woodruff. 2009. “Measuring Microenterprise Profits: Don’t Ask How the Sausage Is Made,” _Journal of Development Economics_ 88: 19–31.

de Mel, S., D. McKenzie, and C. Woodruff (2008). “Returns to capital in microenterprises: Evidence from a field experiment”. _Quarterly Journal of Economics_ , 123, 1329-1372.

Bhattacharya, Debapriya. (1996) ‘The Emerging Pattern of Rural Non-farm Sector in Bangladesh’. _The Bangladesh Development Studies,_ Volume XXIV, BIDS, Dhaka.

Estudillo, Jonna P., Tomoya Matsumoto, Hayat Chowdhury Zia Uddin, Nandika S. Kumanayake, Keijiro Otsuka. 2012. “Labor Markets, Occupational Choice, and Rural Poverty in Selected Countries in Asia and Sub-Saharan Africa,” Background paper for the World Development Report.

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Table 13: Impact of 3G/4G access on total consumption by group (fixed effects models)

||Female|Headed|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||No|Yes|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.013|-0.025|0.043|-0.034|0.026|0.011|
||(0.024)|(0.035)|(0.027)|(0.027)|(0.028)|(0.029)|
|Medium or strong 3G/4G|0.067***|0.010|0.094***|0.057*|0.060**|0.040|
|for one year|(0.025)|(0.034)|(0.028)|(0.031)|(0.028)|(0.030)|
|<br>Medium or strong 3G/4G|<br>0.089***|<br>0.010|<br>0.153***|<br>0.119***|<br>0.053*|<br>0.049|
|for two years or more|(0.024)|(0.035)|(0.027)|(0.028)|(0.027)|(0.031)|
|Medium or strong 3G/4G|0.093***|0.034|0.188***|0.122***|0.027|0.040|
|for three years or more|(0.025)|(0.037)|(0.030)|(0.027)|(0.027)|(0.032)|
|# of observations|10,772|2,959|7,246|6,297|6,747|6,985|
||Age House|hold Head|Localit|yType|Edu|cation|
|||50 or|||Primary|Less than|
||Under 50|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.013|0.024|0.015|0.004|-0.004|0.041|
||(0.029)|(0.025)|(0.025)|(0.041)|(0.025)|(0.045)|
|Medium or strong 3G/4G|0.051*|0.053**|0.069**|0.023|0.069***|0.035|
|<br>for one year|(0.030)|(0.027)|(0.028)|(0.038)|(0.025)|(0.043)|
|Medium or strong 3G/4G|0.087***|0.055**|0.115***|-0.007|0.088***|0.021|
|<br>for two years or more|(0.027)|(0.026)|(0.028)|(0.035)|(0.024)|(0.045)|
|Medium or strong 3G/4G|0.084***|0.078***|0.169***|-0.062*|0.081***|0.095**|
|for three years or more|(0.028)|(0.029)|(0.028)|(0.038)|(0.025)|(0.048)|
|# of observations|6,738|6,574|9,442|4,290|8,645|2,076|
||Lite|racy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|0.033|-0.012|-0.010|0.019|||
||(0.035)|(0.027)|(0.026)|(0.028)|||
|Medium or strong 3G/4G|0.047|0.060**|0.055**|0.055**|||
|<br>for one year|(0.036)|(0.027)|(0.027)|(0.027)|||
|Medium or strong 3G/4G|0.090***|0.063**|0.062**|0.085***|||
|<br>for two years or more|(0.035)|(0.026)|(0.026)|(0.027)|||
|<br>Medium or strong 3G/4G|<br>0.151***|<br>0.064**|<br>0.090***|<br>0.078***|||
|<br>for three years or more|(0.039)|(0.026)|(0.027)|(0.029)|||
|# of observations|4,976|8,587|6,423|7,309|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

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it should correct only those differences due to sampling factors - in other words, the part of the observed decline which reflects an underlying reality should be retained in the data.

The following procedures for weighting the sample to correct for the household size bias fulfill these requirements. First, it is assumed that the distribution of households obtained under the revised sampling frame and listing procedures (i.e. for 1987-88) is the true distribution, and that the distribution obtained under the original sampling procedures needs to be corrected. Second, two distributions are generated with the four years of data: Pool I data consist of all observations obtained under the original procedures -- that is, data sets 1 - 8 (see Tables 3 and 4). Pool II data comprise the remaining sets (that is 9 - 14). These distributions were generated for each of five strata in the survey (Abidjan, Other Urban, East Forest, West Forest and Savannah) and are reproduced in Annex I. Third, household-size weights are computed as the ratio of the frequencies in Pool I and Pool II for each size class (households are distributed across 18 size classes). These ratios are also reported in Annex I. To illustrate how these weights are then applied to re-weight the observations in data sets 1 - 8, Tables 5 and 6 report the pre- and post-weighted distributions of households in East Forest in 1985.

For the country as a whole, the household-size (H-S) weights reduce mean household size to 6.51, 6.25 and 6.20 in 1985, 1986 and 1987 resyectively (Table 7). For 1985 and 1986 (in which the whole sample was subjected to H-S weights), this reduction amounts to over 18 percent of the mean computed without the H-S weights. Since only a half of the sample in 1987 is subjected to H-S weights, the reduction in the mean is only 9 percent.

This effect of the H-S weights, however, is not evenly spread throughout the country (Table 7). The upward bias in mean household size appears to have been more of a problem in East and West Forest, and in urban areas other than Abidjan. The order of magnitude of the correction implied by the weights is certainly not trivial -- mean household size is reduced by as much as a quarter in West Forest and Other Urban areas. Savannah and 11
the case groupings were often more costly. Providers believed that the intensity of resource use was underestimated for more complicated medical cases.

In 2009, PPTP groups were abandoned and formal diagnosis-related groups (DRGs) were introduced, known locally as _dijagnostičko terapijske skupine_ (DTS). DRGs, which have much more refined case-groupings, are the most effective system of classification in health care and are used in most European countries, the United States, and Australia. DRG classifies cases of acute care into groups requiring similar consumption of hospital resources and that have similar clinical characteristics. Unlike PPTP, DTS has 671 groups of therapeutic-diagnostic procedures, which means that it is much more accurate in the pricing process. An example of the groups for cataract procedures is shown in Figure 2. Croatia’s DTS system is based on the Australian Refined Diagnosis Related Groups (AR-DRG) version 5.1, but modified for the Croatian context.<sup>1</sup> DTS was first piloted in four hospitals beginning in February 2006. As of April 2007, it was introduced in all Croatian hospitals, initially running in tandem with the existing billing system. During this period, HZZO actively worked with hospitals to ensure the appropriateness and quality of coding. One of the greatest challenges with the introduction of the Australian system in Croatia was the difference in DRG/DTS costing between the two countries. The original AR-DRG system used Australian data on resource use, clinical practice, and hospital billing. A second challenge was related to a form of ―gaming‖ known as ―code creep‖ in DRG systems, i.e. coding patients as having more serious or complicated conditions than they actually have. In Australia six different mechanisms are now employed to reduce this risk of upcoding<sup>2</sup> .

Using data over 10 years, this study examines the efficiency and quality impacts across three provider payment systems in Croatia: (1) the original fee-for-service (FFS); (2) broad case-based groupings under the PPTP system, and (3) DRG case-based groupings under DTS, which are in effect today.<sup>3</sup> The main objective is to examine the impact of introducing case-based payment systems, both broad (PPTP) and more detailed (DTS). Since

> 1 Like Croatia, several other European countries (e.g., Ireland, Romania, Germany and Slovenia) also have decided to import and modify the AR-DRG system, and not to develop their own. There are different versions of the DRG system in the world, namely: NorDRG (Denmark, Finland, Iceland, Norway, Sweden); AR-DRG (Australia); DPC (Japan); CMG (Canada); HCFA/CMS (USA); HBCs (Hungary); DBC (Netherlands); HRG (UK); G-DRG (Germany); T-DRG (Turkey); GHM (France); LDF (Austria) 2 Steinbusch PJ, Oostenbrink JB, Zuurbier JJ, Schaepkens FJ. The risk of upcoding in casemix systems: A comparative study. Health Policy 2007; 81(2-3):289-99

> 3 Through the rest of the paper, the term DRG will be used to refer to detailed case-based groupings such as DTS or other DRG systems in the world. The term case-based payment systems will refer to both PPTP and DTS.

3