rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 10 Signs of the effects of macro factors on the share of informal employment in the labor force, by country group and study</u>**

|Source|||Thi|s study||||Fial|ova and Schn|eider,2011||Perryet al.,2|007<br>Loayza et al. 2009|Loayza/R|igolini 2006|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent var.<br>(%labor force)|Empl<br>legal|oyees wit<br>contract|hout|Infor<br>self-e|mal<br>mployed||No pension<br>contribution|No legal<br>temporar|contract or<br>ycontract|Sel|f-employed<br>(S/E)|No pension<br>contribution|S/E<br>No pension<br>contribution<br>S/E|Self-e|mployed|
|Coverage|E&S|W&N|EUR|<br>E&S|<br>W&N|EUR|EUR17|EUR25|W&N&S|E<br>EUR25|W&N&S E|World|World|World|DC|
|# countries|14|12|27|14|12|27|17|25|16|9<br>25|16<br>9|60|60<br>70<br>57|42|18|
|# obs|35|34|67|35|34|67|17|49|31|18<br>49|31<br>18|60|60<br>70<br>57|525|205|
|_EPL_2 _|+|+||+||0||||<br>|<br>0|||||
|_GOV_EFFECT _|<sup>a</sup>|<sup>a</sup>||0<sup>a</sup>|<sup>a</sup>|||||||0<sup>a</sup>|<sup>a</sup>|||
|_GOV_EXPsq_||()<sup>b</sup>||||||||||||0||
|_REG_QUAL_||||||||0|0|0<br>|<br>0|||||
|_CORR_CONTR_||||||||||0<br>|<br>0|<sup>b</sup>|<sup>b</sup>|||
|_SAT_GOV_||<sup>b</sup>|||<sup>b</sup>|||||||||||
|_RULE_LAW_|<sup>b</sup>|||<sup>b</sup>||||||||<sup>c</sup>|<sup>c</sup><br><br>|||
|_INVEST_FREE_|<sup>a</sup>|||<sup>a</sup>||||||||||||
|_TRADE_FREE_|<sup>a</sup>|||0<sup>a</sup>||||||||||||
|_BUS_FREE_||<sup>a</sup>|||<sup>a</sup><br>||0|0||<br>0|0<br>0||<br><br>|||
|_MON_FREE_||<sup>b</sup>|||0<sup>b</sup>|||||||||||
|_FIS_FREE_|||||||||0|<br>|<br>|||||
|_TAX_WEDGE _|+<sup>b</sup>|||0<sup>b</sup>|||||0|<br>|<br>|||||
|_TAX_RATE_|+<sup>c,d</sup>|||||||||||||||
|_MIN_WAGE _|+||+|+|+|+|0|0|0|<br>0|0<br>0|||||
|_NO_MINWAGE _|||||+|0||||||||||
|_GDP_PC_|+||+|+||0||||<br>|<br>|||||
|_GROWTH_|+|+|+|+|0|+||||||||||
|_UNION_||+|+||+|||||<br>|<br>|||||
|_LMP_ACTIVE _|<sup>c</sup>|||||<sup>a</sup>||0|0|0<br>|0<br>0|||||
|_LMP_PASSIVE _|<sup>d</sup>|+|0<sup>a</sup>|||<sup>a</sup>||0|0|0<br>|0<br>0|||||
|_UBSA _||||||||||||||||
|_GINI_|+<sup>c,d</sup>||+|||+||||||<sup>d</sup>||||
|_MINORITY_|+<sup>c,d</sup>|||||||||||||||
|_IMMIGR_|+<sup>a,b</sup>||+|0|0|||||||||||

_Notes:_ For the coverage, the following notation is used: E, S, W, N –Eastern, Southern, Western and Northern Europe, respectively; EUR – Europe (countries as in Table 1, excl. Cyprus, Israel, and Switzerland); EUR17 – 11 „old Europe‖ countries and 6 NMS; EUR25 – same as EUR, excl. Russia and Ukraine; DC –developing countries. Explanatory variables: _REG_QUAL_ and _CORR_CONTR_ – indicators of Regulatory Quality and Control of Corruption, respectively, from the Worldwide Governance Indicators (see Kaufmann et al. 2010; World Bank 2010); _FIS_FREE_ (Fiscal Freedom) _-_ the Heritage Foundation (2010) measure of the tax burden, which is inversely related to top tax rates and to tax revenue. Other variables – see Table A1. Loayza et al. and Loayza/Rigolini use other versions of _RULE_LAW_ and _BUS_FREE_ ; Loayza et al. control also for average years of 49
high or very high. About _55_ to 60 percent of companies said that the technological level of a CONALEP education is high or very high, with large public companies giving the lowest ranking (46 percent).

# VI. **Data**

_The_ CONALEP Graduate Tracer Surveys This paper re-evaluates CONALEP's effectiveness using the CONALEP graduate tracer surveys conducted in 1994 and 1998.5 The first CONALEP graduate tracer survey was conducted in February 1994 (CONALEP, 1994) on the basis of a random sample of 1500 former CONALEP students who graduated between June 1991 and June 1993. The surveyed graduates were selected to represent the profile of the graduates in each of the three years in terms of all 13 major occupational groups of careers and the six geographical regions of the country. However, the sample is dominated by 1992 graduates who comprise 50 percent of the sample; 1991 and 1993 graduates each represent 25 percent. The sample selection is probabilistic and statistically representative of the universe of graduates in each cohort. For each graduate (M), two substitutes were chosen from the same career and school (S and T).

**Table 3. Distribution of the 1994 Sample by Cohort**

|**Graduation Year**<br>**Cohort**|**Planned Selection**|**%**|**Actual Selection**|**%**|
|---|---|---|---|---|
|**1991**|**375**|**25**|**346**|**24.7**|
|**1992**|750|_50_|704|50.3|
|**1993**|**375**|**25**|**349**|**24.9**|
|**Total**|**1500**|**100**|**1399**|**100**|

_Source:_ CONALEP (1994).

5 A third CONALEP graduate tracer survey was conducted in January of 2001. The data are expected by mid-2001.

22
product varieties within that sector. Thus, V,” is the full set of varieties exported in sector s, taking account of all exporting countries and all time periods. Average world trade values by product variety across all years (pi’q'”) are used to create the sum. While the denominator is invariant by exporter and time, the numerator is not. It consists of the sum of world average trade values in product varieties shipped by exporter x at time t. The use of world average trade values ensures that variation in the numerator—and in A, itself—is due only to changes in x’s variety set. This measure therefore has the important advantage of allowing consistent comparisons of product variety to be made across years and countries.

To implement this approach empirically, I use 8-digit import data from the European Union for the years 1995-2003.'3 In line with availability of standards data, I calculate A for three sectors: textiles (HS chapters 50-60), clothing (chapters 61-63), and footwear (chapter 64). Prior to calculation, I exclude from the dataset all observations relating to internal trade amongst EU-15 members, as well as product codes without verbal description which correspond to residual categories covering confidential or otherwise unclassified flows. For the world average trade value <u>pi’qj”,</u> I take the average over the sample period of import values for the EU-15 (treated as a single entity). Table 2 provides some basic descriptive statistics for this variety measure, broken down by sector and year. (Full results are available on request.) The median variety measure in the clothing and footwear sectors (Ameq = 0.2 to 0.3) is noticeably higher than in textiles (Ajneq < 0.1). However, the range in each case is very wide, running from just a little above zero to 0.8 or 0.9. The fact that the median is so low within this range suggests that most countries export a relatively modest range of varieties in these three sectors, but that a few countries export a very wide range.

In terms of the rank ordering of countries by variety, results are broadly sensible: China, Turkey, India, and a number of countries in Central and Eastern Europe appear at the top of the list for clothing and footwear, while highly industrialized countries like Switzerland and the United States arrive in the lead for the more capital intensive textiles sector. The presence of the United States and Switzerland amongst the leading countries in clothing and footwear suggests that the trade data <u>~ BThese data are freely available through the</u> Eurostat website (http://fd.comext.eurostat.cec.eu.int/xtweb/).

16
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# **1 Introduction**

After a long period of historically low inflation, the post-pandemic recovery and the war in Ukraine resulted in a sharp rise in consumer prices in early 2022, particularly in food and energy (World <u>Bank, 2022a). In Europe, the annual median inflation reached 12 percent in October 2022, a level</u> not recorded since the 1990s. The increase in the expense to purchase goods and services necessary to maintain standards of living, that is, the cost of living, is now the most pressing concern for 93% of Europeans (European Parliament 2022).

The reasons for concern are clear. High inflation has a negative impact on real incomes. Rising food prices and energy bills, increasing mortgages and rents, and higher service prices are eroding the standards of living of a large part of the European population. Households previously considered well-protected might become vulnerable to various income shocks that could push them into poverty. During the first half of 2022, real wages declined by 2.4 percent in the European Union, 3.3 percent in Eastern Europe, and wage growth slowed in Central and Western Asia <u>(Belser et al. 2022). About 60% of European workers now have lower real incomes than they did</u> before the pandemic (UN 2022).

These estimates of the impact of inflation on real incomes are based on each country’s official consumer price index (CPI), which typically measures the change in prices of a fixed consumption basket of goods and services. The actual consumption basket of each household may differ from the CPI basket. When price variations are small, this difference may not matter. But when price variations are high or uneven – that is, when prices of some goods or services increase more than the prices of others – changes in the price of the CPI basket may be very different from changes in the price of households’ actual consumption baskets. Under these circumstances, the CPI may be a poor indicator for measuring a household’s COL.

In contexts of high or uneven inflation, the changes in the cost-of-living across households might be heterogeneous: increases in the price of a given household’s consumption basket may be different from that of the CPI, and they may be different from the price changes of other households’ consumption baskets. The burden of inflation may be unequally spread across households. These differences have clear policy implications, as many public programs targeted at vulnerable populations use CPI-based inflation adjustments, which may not represent the actual changes in the cost-of-living of targeted populations (Schultze and Mackie 2002).

2
Improving the quality of prenatal care has a low marginal cost and relatively high payoff for providers. To increase the quality of prenatal care, providers did not need to go into the community to motivate women to change their behavior; it was sufficient for providers to change their own practices, with the women that came in for prenatal care anyways. And the facility’s quality index _Q_ provides high-powered incentives, as the quality index is a scaling factor that is applied to all payments for all of the facility’s services, and not only to payment of prenatal care services.

In addition, elements of prenatal care quality entered the payment formula both as _Ujit_ , and within the quality index. For instance, P4P was associated with an increase in tetanus toxoid vaccines delivered during prenatal care visits. The tetanus vaccine is priced moderately at $0.46, but also contributes the multiplicative _Q_ index and requires relatively low effort on the part of the provider as it is mostly in the control of the provider.

Second, as predicted, we find mixed effects on the services with some increasing and others unaffected. The services that increased were those with higher prices as well as more in the provider’s control and less in the patient’s control. While institutional delivery had of the highest payment rate of $4.59, institutional deliveries require mothers (families) to decide to plan to deliver in an institution and come in when it is time. Qualitative evidence from field visits suggests that in order to capture this rent, facilities paid community health workers to locate women in the late stages of pregnancy and then encourage and assist them to deliver at the facility. The health centers’ strategy of teaming up with community health workers likely had higher chances of success because the community health workers have ongoing relationships with mothers.

Well-child care also responded strongly to the P4P scheme, even though its unit payment is relatively low at $0.18 per visit. However, the payment rate jumps substantially to $2.03 if the provider identifies a malnourished child and gets them into treatment. This becomes a very strong incentive since over half the children in Rwanda were stunted in 2005 (Institut National de la Statistique du Rwanda and ORC Macro 2006). Similar to institutional deliveries, facilities employ a strategy of working with community health workers to get infants and small children into care. In addition, since the P4P scheme led to higher rates of institutional delivery, treatment facilities would have more complete lists of infants and children in their books than comparison facilities making follow-up easier and cheaper. For the same reason, treatment facilities also had higher direct access to new mothers to try to convince them to come in for well-baby care.

21
Overall, the sensitivity analysis underscores the key insight of our paper: current overlapping policies can significantly reduce the cost disparity between China’s DEPS and CAT largely for the period of 2020-2035. This conclusion remains valid across a range of parameter settings, although the extent to which overlapping policies interact with China’s ETS is influenced by these settings.

# **Appendix References**

Aguiar, A., Chepeliev, M., Corong, E. L., McDougall, R., & Van Der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10. Journal of Global Economic Analysis, 4(1), 1-27.

- Chen, Y. H. H., Paltsev, S., Reilly, J., Morris, J., Karplus, V., Gurgel, A., Winchester, N., Kishimoto, P., Blanc, E., & Babiker, M. (2017). The MIT Economic Projection and Policy Analysis (EPPA) Model: Version 5. MIT Joint Program on the Science and Policy of Global Change, Technical Note 16, 1-34.

- CEC. (2019). Analysis of Trading Information in the National Electricity Market. Beijing: China Electricity Council.

Cossa, P. F. (2004). Uncertainty Analysis of the Cost of Climate Policies. PhD Thesis, Massachusetts Institute of Technology.

- Goulder, L. H., Long, X., Qu, C., & Zhang, D. (2023). China’s nationwide CO2 emissions trading system: A general equilibrium assessment (NBER working paper No. w31809). Cambridge, MA: National Bureau of Economic Research.

- He, X., & Reiner, D. (2016). Electricity Demand and Basic Needs: Empirical Evidence from China’s Households. Energy Policy, 90(March), 212–21.

- Herd, R. (2020). Estimating Capital Formation and Capital Stock by Economic Sector in China: The Implications for Productivity Growth. World Bank, Washington, DC.

- Hertel, T., Hummels, D., Ivanic, M., & Keeney, R. (2007). How Confident Can We Be of CGEBased Assessments of Free Trade Agreements? Economic Modelling, 24(4), 611–35.

- Hertel, T., & van der Mensbrugghe, D. (2019). Chapter 14: Behavioral Parameters (Center for Global Trade Analysis). Purdue University, West Lafayette, IN: Global Trade Analysis Project (GTAP).

66
Government of Moldova, 2004. Policy and Poverty Impact Report. Ministry of Economy and Trade. _Mimeo_ .

Government of Moldova, 2005. Medium Term Expenditure Framework 2006-2008. Ministry of Finance. _Mimeo_ .

Imai, Kosuke, King, Gary, Stuart, Elizabeth, 2006. The Balance Test Fallacy in Matching Methods for Causal Inference. _Wired at: polmeth.wustl.edu/_ Lokshin, Michael, and Ravallion, Martin, 2000. Welfare impacts of the 1998 financial crisis in Russia and the response of the public safety net. _The Economics of Transition 8, 2, 269-295_ .

Leuven, Edwin and Sianesi, Barbara, 2003. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. _Wired at: http://ideas.repec.org/c/boc/bocode/s432001.html_ <u>.</u> Lokshin, Michael, and Yemtsov, Ruslan, 2003. Evaluating the impact of infrastructure rehabilitation projects on household welfare in rural Georgia. _World Bank Policy Research Working Paper 3155_ .

Milanovic, Branko, 2000. Social transfers and social assistance: An empirical analysis using Latvian household survey data. _World Bank Policy Research Working Paper 2328_ .

Okrasa,Wlodzimierz, 1999a. The dynamics of poverty and the effectiveness of Poland’s safety net (1993-1996). _World Bank Policy Research Working Paper 2221_ .

Okrasa, Wlodzimierz, 1999b. Who avoids and who escapes from poverty during the transition? Evidence from polish panel data, 1993-96. _World Bank Policy Research Working Paper 2218_ .

Ravallion, Martin, Galasso, Emanuela, Lazo, Teodoro, and Philipp, Ernesto, 2005. What can exparticipants reveal about a program’s impact? _Journal of Human Resources 40, 1, 208-230_ . Ravallion, Martin, van de Walle, Dominique, and Gautam, Madhur, 1995. Testing a social safety net. _Journal of Public Economics 57, 175-199_ .

Signoret, Josè E., 2003. The Moldova hbs: A note on the panel sample. _Mimeo_ .

17
# 3 Relati--e Price Estimation

- Although I do not observe Ai and ai a directly, I can estimate them with cross-country regresgions performed for each good i. The data used for estimation are dra''n from the last phase of the Incomes Comparison Project, which consists of goods prices relative to the United States for 151 traded and nontraded goods based on detailed price and expenditure data from a cross section of 57 countries in 1980 (Kravis, Heston and Summers, 1982; United Nations, 1986). In order to ensure the plausibility of the assumption that a, is the same across countries, I aggregate the original 151 traded a.' nontraded goods to a sam1ple of 16 traded goods and one nontraded good (see table 1)4. Prices were comiputed for these 16 traded goods and two nontraded goods (rent and services/construction) as the expenditure-weighted averages of the original 151 prices. Extensive efforts have been made in producing these data to control for cross-country differences in quality such that prices are comnpared for the same good across countries.

## **3.1** bias from the impact of intervention **on nontraded prices**

If the dollar price of nontraded goods in country j is influenced by the average policy distortion **cj,** then estimating equation 10 using nontraded prices as an explanatory variable wiU bias the estimates of **ai** - a. The relationship between nontraded prices and average tariffs and subsidies in long run equilibrium is described in a detailed model in the appendix, but will be summarized here. An increase in the average import tariff or export subsidy shifts production towards the

> _4edIlc_ ation andl health care werr d ropped fro:mi esti iiationi to avoidl **the lifficiilties** of _cross-coo o_ rv qualit, cotmparisoins **in** these gooks.

11
10 actual intra-sectoral structure of distortions to domestic prices of tradable farm goods. They show also that, if one is willing to assume that domestic price elasticities of supply (demand) are equal across farm commodities, then the only information needed to estimate the WRI, in addition to the NRAs and CTEs _,_ is the share of each commodity in the domestic value of farm production (consumption) at undistorted prices.

To obtain dollar values of farmer assistance and consumer taxation, we have taken the country authors’ NRA estimates and multiplied them by the gross value of production at undistorted prices to obtain an estimate in US dollars of the direct gross subsidy equivalent of assistance to farmers (GSE). These GSE values are calculated in constant dollars, and are also expressed on a per-farm-worker basis. Likewise a value of the consumer transfer is derived from the CTE, by assuming the consumption value is the gross value of production at undistorted prices divided by the self-sufficiency ratio for each product (production divided by consumption, derived from national volume data or the Food and Agriculture Organization’s (FAO’s) commodity balance sheets). These transfer values are helpful for generating an estimate of the contribution of each policy instrument to the overall NRA, and the trade data that provide the self-sufficiency ratio helped each country author attach a trade status to each product each year (bearing in mind also the likely impact of the NRAs and CTEs on the observed self-sufficiency ratio).

Once each farm industry is classified either as import-competing, or a producer of exportables, or as producing a non-tradable, it is possible to generate for a given year the weighted average NRAs for the two different groups of tradable farm industries. They can then be used to generate an agricultural trade bias index defined as: where NRAag m and NRAag x are the average NRAs for the import-competing and exportable parts of the agricultural sector (their weighted average being NRAagt ). This index has a value of zero when the import-competing and export sub-sectors are equally assisted, and its lower bound approaches -1 in the most extreme case of an anti-trade policy bias.

Part of the anti-trade bias in developing countries in the past was the result of government intervention in the domestic market for foreign currency. The most common arrangement was a dual exchange rate, whereby exporters had to sell part or all of their foreign currency to the government at a low price. This effectively taxed and thus