rafmacalaba/data-use-mentions-extended huggingface dataset:
## **Figure 6 Services trade restrictiveness index, 2018**

(simple average of STRI across 22 service sectors) <!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Latvia Netherlands Czech Rep. Germany Ireland UK Australia Lithuania Denmark Japan Portugal Chile Spain New Zealand Canada Sweden Finland Slovak Rep. Luxembourg France Estonia US Slovenia Costa Rica Austria Greece Norway Hungary Belgium Colombia Poland Italy South Africa Korea Switzerland Malaysia Brazil Israel Turkey Mexico Iceland China Russia Indonesia India<br><!-- End of picture text --> Source: OECD; authors’ calculations. Note: A higher STRI indicates a more restrictive regime.

# **2. Factors behind the recent slowdown in productivity growth**

The post-global financial crisis slowdown in labor productivity growth in China was driven by a significant deceleration in TFP growth from 2.8 percent per year in the decade before the global financial crisis to just 0.7 percent afterwards. The growth contribution of physical capital accumulation increased over that period.

## **Figure 7 The recent widespread decline in TFP growth**

(annual average TFP growth in 2010–17 (y-axis) versus 2000–07, percent) <!-- Start of picture text --> CHN<br>-10 -5 0 5 10<br>2000-2007 annual average TFP growth-5<br>10<br>5<br>0<br>-5<br>2010-2017 annual average TFP growth<br>-10<br><!-- End of picture text --> A slowdown in TFP growth has been observed in many advanced and developing countries since the 2008 global crisis (Ball, CHN 2014; Foda, 2016; Adler et al., 2017; Ollivaud and Turner, 2014). In the PWT 9.1 data, world
||adult. As net wage data for 2023 are not yet available, a comparison using 2022 data indicates that the value of the<br>minimum consumption basket exceeds the net salary at the economy level (3,801 RON) in 2022, except for<br>Bucharest and Cluj, all other NUTS 3 regions had an average net wage lower than the value of the consumption<br>basket.|
|---|---|
|c)|**Prices for the goods included in the basket were not provided by the National Institute of Statistics. Still,**<br>**they were collected in the five cities from supermarkets and markets (for food, household, and personal**<br>**hygiene products).**Prices collected from the five cities were also used for other products and services (housing,<br>transportation, cultural activities, etc.).|
|d) <br>e)<br>f)|**The periodic update of the minimum consumption basket for a decent living does not involve indexing**<br>**with the inflation rate, as it is a consumption basket with different weights compared to the standard**<br>**basket considered by the National Institute of Statistics for calculating the Consumer Price Index.**<br>Updating the minimum consumption basket is complex and needs to be more predictable. The**National Institute**<br>**of Statistics**does not provide price indices by categories of goods with this granularity. Additionally, other<br>methods of indexing are applied to specific expense categories.<br>**Equivalence Scales.**For calculating the value of the consumption basket for families other than the reference<br>one, higher equivalence scales than those used by the OECD and the European Commission were used, leading<br>to a potential overestimation of the consumption
- Klimek, P., Yegorov, Y., Hanel, R., and Thurner, S. (2012). Statistical detection of systematic election irregularities. _Proceedings of the National Academy of Sciences_ , 109(41):16469–16473.

- Lehmann, M. C. (2025). Migration and informational autocracy. _Journal of Population Economics_ , 38(1):12.

- Li, X., Li, D., Xu, H., and Wu, C. (2017). Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during the Syrian Civil War. _International Journal of Remote Sensing_ , 38(21):5934–5951.

- Li, X., Zhou, Y., Zhao, M., and Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Scientific Data_ , 7(1):168.

- Loiko, S. (2011). Russians are leaving the country in droves. _Los Angeles Times_ .

- Martinez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates? _Journal of Political Economy_ , 130(10):2731–2769.

- Melki, M., Rapoport, H., Spolaore, E., and Wacziarg, R. (2024). Cultural remittances and modern fertility. Technical report, National Bureau of Economic Research, Cambridge, MA.

- Michalopoulos, S., Murard, E., Papaioannou, E., and Sakalli, S. O. (2025). Uprootedness, human capital, and skill transferability. Technical report, National Bureau of Economic Research.

- Migraci ́on Colombia (2023). Migration flow records 2012–2022.

- MODIS Land Cover (2023). MODIS land cover type yearly global 500m.

- Morales-Arilla, J. (2021). Autocrats in crisis mode:
. We find that the elasticity with respect to work city size shrinks from 0.052 to 0.021 and becomes only marginally significant, while the elasticity with respect to college city size remains relatively high at 0.071 and the elasticity with respect to high school city size becomes almost zero. Thus, agglomeration effects seem to arise from college city size rather than work city size.<sup>8</sup>

> 7It is worth noting that for many workers we observe wage data more than one year (usually two or three years). In all estimations we restrict the analysis to the last observed wage (often the second or third year after entering the labor market), to avoid capturing an internship spell that takes place immediately upon graduating from college. Given the reduced number of annual observations per worker and the sizeable number of workers with only one wage observation, we cannot include worker fixed effects in our estimations.

> 8We are aware that when we include simultaneously the three location variables, the coefficients on city sizes are estimated exclusively on the basis of migrants. It is not our purpose to provide causal estimates for each of these city sizes. At this point, we want to underscore the relatively larger effect that college city size has on wages. To unfold potential drivers for this large magnitude, we will later analyze location trajectories of migrants, with special emphasis on those who move for work to bigger and smaller cities.

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areas, while others will be predominantly in rural areas. By limiting the substitutability of rural and urban labor in each sector, we are able to proxy the economic effect of geographically distributed production activity. Thus, if trade liberalization boosts the demand for goods that are predominantly produced in urban areas, then urban wages will rise, relative to rural wages, and migration will be encouraged. Of course we would ideally model the geographic distribution of industrial activity, but unfortunately the data do not exist to support this type of split.

All commodity and non-labor factor markets are assumed to clear at market prices. With the exception of the farm/non-farm labor supply decision, labor is assumed to be mobile across sectors, but rural-urban migration is subjected to the direct and indirect transactions costs discussed above, so the unskilled and semi-skilled rural wages are equated to the comparable urban wages, less transactions costs. Capital is assumed to be partially mobile, reflecting differences in the marketability of capital goods across sector.

In order to look at the impacts of labor mobility over time, the model has a simple recursive dynamic structure. Dynamics in the model originate from accumulation of productive factors and productivity changes. The model is benchmarked on China’s 1997 data and is solved for subsequent years from 1998 to 2007. We turn now to the details of the baseline scenario.

# **4.  Base Case Projections and Simulation Design**

_Base Case Scenario:_ Our base case scenario is purposely defined as being without China’s WTO accession. This is because we seek to explore separately the impact of labor market distortions and WTO accession, thereafter examining how this recent opening of the economy interacts with existing labor market distortions. In the base case, GDP grows at an average rate of about 7.8%, and life expectancy rises from 70.3 to 74.0 years of age. Urban and rural fertility from WTO accession at the lowest value for this elasticity.

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and a large number of children aged zero to five years.

The study sample is composed of 305 villages. Before baseline, villages were randomly assigned to the control group or one of the three treatment groups: CPS (T1); CPS and D2D (T2); or CPS, D2D, and HBP (T3). Randomization was stratified to obtain a sample for which treatment is balanced within each of the 13 provinces of our sample.<sup>15</sup> The design is summarized in Table 1.

> 11 Therefore, our cost measures are not directly comparable to studies that base their costeffectiveness analyses on programmatic costs only (e.g. Brinkman et al. (2017))

> 12A full discussion of cost estimates can be found in the SIEF impact evaluation report (Berkes et al., 2019).

> 13The cost estimates reported here are based on a model using mid-range estimates for unobserved costs. Under all models, costs per child at SPS were 50%—126% higher than at CPS.

> 14This is probably an overestimation of the IPS cost since IPS cater to more students and the training costs were higher for CPS than they were for IPS.

> 15The randomization was performed with a list of 310 eligible villages provided by the government. Of these, 60 were assigned to the control group, 123 to T1, 63 to T2, and 64 to T3. Unfortunately, the randomization list contained erroneous village names and five of them were duplicated or could not be identified after the randomization even after substantial effort by MoEYS and the data collection firm. Therefore, the total number of villages decreased to 305. We treated this dropout as random and did not replace the villages. The randomization list also contained villages for which a CPS teacher was no longer available or for which no land could be secured for CPS construction. Therefore, only 91% of treatment group villages received a CPS. Since these factors are potentially endogenous, we do not treat these as random.
economy. This is similar to the method used by James Thurlow (2004). In addition, Autonomous Energy Efficiency Improvement (AEEI) in the CGE model is considered in this study, and is assumed to be 1% per year following the common assumptions in CGE models (Wing, I. S., 2008). Since the available social accounting matrix (SAM) is for 2012, our base year is 2012. If the model has to adopt the projected growth rate of GDP (e.g., projected by the government), it is done through adjustments in total factor productivities (TFPs).

# **2.7.1 Labor supply growth**

Labor supply in different time periods is described as: where _lgow_ is the annual growth rate of labor supply.

# **2.7.2 TFP increase**

In the model, TFP increase is represented by the change of the technology parameter in the second tier of the CES production function.

Where _tgrow_ is annual growth rate of TFP.

# **2.7.3 Capital Accumulation**

We adopted the method used by James Thurlow (2004) to describe the capital accumulation in different time periods. In the model, total capital supply is endogenous in a given time period and the total available capital is determined by the previous time period’s capital stock and new investment. In this model, the new capital stock resulting from previous investment is allocated across sectors in proportion to each sector’s share in aggregate capital income, and these proportions are adjusted by the ratio of each sector’s profit rate to the average profit rate for the whole economy.

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costs.<sup>30</sup> However, the fact that providers almost never violated the rules of the vouchers in _DV_ , but did in _PV_ , suggests that gatekeeping costs matter in our context.

A final question is whether differences between _PV_ and _DV_ reflect differential rates of patients “stockpiling” ACTs for future use. Differences between _PV_ and _DV_ due to stockpiling would be consistent with patientdriven demand, but may not occur in a long-run equilibrium where subsidies are permanent. We check for stockpiling using our home survey data, where we cycled through the list of all medications purchased at the clinic and asked if the patient was currently taking the medication.<sup>31</sup> Appendix Table B13, column 2, shows that 94 percent of individuals who purchased ACTs for simple malaria at the clinic reported taking ACTs during the home survey, with no significant differences by voucher condition. Barring the possibility of poor self-reporting, this suggests that stockpiling is not a significant driver of the higher purchase rates in _PV_ relative to _DV_ .

**Hawthorne and Experimenter Demand Effects.** It is also important to ask how our results might be influenced by Hawthorne or experimenter demand effects. This could be especially relevant for doctor behavior; although all clinics were formally told that the study was not a performance review and that all data would be kept strictly confidential, clinics were informed that the study was being conducted in collaboration with the National Malaria Control Program and the National Directorate of Health. Clinics also knew that the pre-intervention training was part of the study; this training emphasized the importance of adhering to official Malian policy when treating malaria, meaning that only patients with confirmed positive test results should receive an antimalarial prescription. We therefore expect Hawthorne effects to have increased the likelihood of providers adhering to official treatment guidelines.

Since treatments rotated within clinic, this would impact behavior in _all_ treatment arms, which would bias us away from finding _PV_ - _DV_ differences in outcomes. Consistent with this (and the hypothesis that Hawthorne
of child care to retain female workers. Most of these initiatives are small in scale and patchy, compared to the vast needs for child care. SEWA and Mobile Crèche have long highlighted the needs of especially the poorest informal women in large urban centers – women who work on construction sites, street vendors, domestic workers, and others who work in taxing manual jobs, for low wages. As yet however, there is not a similar lobby for the needs of educated women who may want to work, but cannot.

Micro studies shine more granular light on women’s child rearing and domestic responsibilities and their influence on urban women’s labor market decisions. Qualitative research based on interviews with professional women, confirms accounts of women placing their reproductive and caregiving roles above their jobs and careers, where domestic help is hard to find and the employer does not provide child care facilities or flexible work options (Agarwal et al. 2012; Buddhapriya 2009; Valk and Srinivasan 2011). Sudarshan and Bhattacharya (2009), based on a household survey in urban Delhi in 2006 found that 60 percent of respondents had quit work after childbirth, and around 57 percent of working respondents felt that women should stop working when their children are young. Among women who withdrew from the formal labor market, the most common reason was that household work had become too burdensome. Both working and nonworking women reported neglect of children and conflict over domestic chores as the two factors that most constrained women’s employment.

Yet many women, not just the poorest, have to walk a fine balance between market work and child care, and while a growing feminist discourse is drawing attention to this, empirical studies are limited. A study in urban areas of Uttar Pradesh and Gujarat, found that women from certain communities would forego wage work, saying it was mostly due to family responsibilities and social norms (IAMR and ILO 2013). On average, working women spent 4–6 hours a day either attending to household duties or taking care of
on labor markets: “flexibilization” of the labor market, changes in the minimum

> 26 This is a three-way variable: there could be a pro-openness reform, anti-openness reform, or no change. The calculation is based on Free the World  (FTW) measure of trade taxes: if they decrease sufficiently, a country is deemed a reformed over the entire five-year period (FTW data are available at five-year itervals). “Sufficiently” is defined as reduction in trade taxes/trade ratio by more than 1⁄2 of the world standard deviation in that variable. In other words, if world’s standard deviation of trade taxes/trade variable is 5 percent, than if country’s trade taxes/trade ratio goes down by 5 percentage points, it is deemed a proopenness reformer. The opposite is the case for anti-openness reforms. Free the World data are available at <u>www.freetheworld.com. The report is issued by the Fraser Institute, Vancouver (see http://www.fraserinstitute.ca/shared/readmore.asp?sNav=nr&id=537).</u>

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* , 2, 259-94.

Lemieux T., B. W. MacLeod and D. Parent. (2009), ‘Performance Pay and Wage Inequality,’ _The Quarterly Journal of Economics_ , **<mark>124</mark>** <mark>, 1, 1-49.</mark> Lipsey, R. E. (2004), ‘Home and Host Country Effects of Foreign Direct Investment,’ in R. E. Baldwin and L. A. Winters (eds.), _Challenges to Globalization: Analyzing the Economics_ , 333379. Chicago and London: University of Chicago Press.

Lipsey, R. E. and F. Sjöholm. (2004), ‘Foreign Direct Investment, Education, and Wages in Indonesian Manufacturing,’ _Journal of Development Economics_ , **73** , 1, 415-422.

Lipsey, R. E. and F. Sjöholm. (2006), ‘Foreign Firms and Indonesian Manufacturing Wages: An Analysis with Panel Data,’ _Economic Development and Cultural Change_ , **55** , 1, 201-221.

Madani, D. (1999), ‘A Review of the Role and Impact of Export Processing Zones,’ _World Bank Policy Research Working Paper_ , **2238** .

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DSSI initiative, granting temporary payment suspensions to many developing countries, but as for other creditors, these agreements did not reduce the nominal repayment burden. Even prior to 2020, we find that almost none of the distressed debt restructurings with Chinese policy banks and other state-owned entities involves face value debt reduction.<sup>2</sup> The typical deal involves a lengthening of maturities or grace periods. This “kicking the can down the road” is reminiscent of Western commercial banks in the 1980s, which long opposed face value debt reduction and only agreed to deeper debt relief as part of the US-led Brady deal initiative in the early 1990s. China’s current approach to crisis resolution also resembles the blueprint of the Paris Club, mainly in the 1980s and 1990s, when it opposed deep debt relief and rescheduled the same debts again and again, thus contributing to the “lost decades” in many low income countries, featuring low growth and unresolved debt overhang.

# **2. Credit events on Chinese overseas debts, 2000-2021: The database**

We build a comprehensive database of the external debt credit events of developing and emerging market sovereigns. We move beyond the usual focus on sovereign bonds and account for debt restructurings with bilateral creditors and, in particular, with Chinese lenders.

Due to the lack of official data, it is challenging to track credit events involving Chinese loans. Since Chinese creditors are (mostly) state-owned, their restructurings are not monitored by the major global credit rating agencies. China is not a member of the Paris Club or the OECD and does not report on lending and restructuring activities. We are not aware of an international financial institution that provides comprehensive data on defaults or restructurings on Chinese loans. In this void, researchers have stepped in to provide data hand-coded from official announcements, press reports and other sources.

This paper creates a data set on credit events on Chinese loans based on multiple sources created by researchers. The starting point was our own preliminary work in Horn et al
sup> <mark>FD > 0) + ε Here, FD</mark> dd <mark>r in a 10-year flood as indicated through the Fathom flood</mark> dd dd<sup><mark>(</mark></sup> <mark>maps, β</mark> 0<sup><mark>is the average speed difference on routes that are unlikely to be flooded and</mark></sup> <mark>β</mark> 1<sup><mark>is the additional</mark></sup> dd<sup><mark>is the maximum flood depth on route</mark></sup> <mark>speed difference on routes likely to be flooded. As before p-values for β</mark> 0<sup><mark>and</mark></sup> <mark>β</mark> 1<sup><mark>were calculated using the</mark></sup> <mark>bootstrap methodology, allowing for potential correlation within the error term ε</mark> _<mark>Results</mark>_ dd<sup><mark>.</mark></sup> <mark>Results of the matching procedure outlined previously are presented in T</mark> able 4 <mark>. They indicate that travel speeds of different public transport modes are lower during wet conditions than during dry conditions. The</mark> 13
consumption expenditures. An important question then is: How do households adjust their consumption due to higher energy prices? While there is no evidence of price elasticity of demand for Armenia, as is the case for most transition economies. However, a post gas hike survey of residents of multi-apartment buildings shows that the use of gas-based heating declined in 2010 given the sharp increase of gas tariffs for small consumers (EDRC, 2011). According to this survey, while gas remained the dominant heating source, the share of households relying on it decreased from 72.5% during the 2008-2009 heating season to about 69.8% during the 20102011 heating season. There was increased use of electricity and firewood for heating during the same period. In the following section, the impact of current gas price hike is simulated for different household groups, under a range of assumptions about price elasticity of demand.

# **3. Welfare Impact of the Gas Price Hike**

Gas is an important fuel for heating and cooking, as well as an input into the production of many goods and services (see Table 5). The gas price increase can therefore influence the cost of the basic consumption basket and the price of other alternative energy sources such as electricity and fuelwood. Ideally the welfare impact should be analyzed in a general equilibrium framework that takes into account such linkages. However, general equilibrium modeling is data intensive and sensitive to a number of assumptions. In this paper, the analysis is necessarily limited to the _direct_ impact of the gas price increase on household welfare.

## **_Methodology and data_**

The 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010. The ILCS collects data from nearly 8,000 Armenian households surveyed year round. It is based on a representative sample of about 2,000 households surveyed each quarter. It contains detailed information on household composition, energy use, income, public and private transfers, expenditure, saving and debt,
declined over the last several decades, it has risen every year between 2015 and 2019 (FAO, 2020a) and has almost certainly risen disastrously during the COVID-19 pandemic (FAO, 2020b; Laborde et al., 2020; Smith and Wesselbaum, 2020) and more recently the war in Ukraine. This would have important implications on societal trust – both vertical and horizontal dimensions of trust. In 2014, the Food and Agriculture Organization’s (FAO) Voices of the Hungry project developed an experiential measure of food insecurity called the Food Insecurity Experience Scale (FIES), and contracted Gallup, Inc. to use its Gallup World Poll (GWP) to collect data in over 150 countries (Cafiero et al., 2018). The rich individual-level data in the GWP combined with asking individuals directly about their experiences and behaviors related to their access to food in the FIES module, provides an unprecedented opportunity to examine the relationship between food insecurity and trust globally.

2