rafmacalaba/datause-encoder-data huggingface dataset:
significant or whether they are more likely to be the result of random sampling variation. This is why we again resort to regression analysis, which allows us to formally explore hypotheses about average trade responses. Our dependent variable in this analysis is the percentage increase in predicted trade volumes for a 50-percent fall in trade costs. As explanatory variables we consider whether the firm in question is based in Brazzaville (as opposed to being based in Kinshasa), whether it is formally registered, whether it is a local manufacturing firm (as opposed to be a pure importer or exporter), whether the trade cost considered is a fall in transport costs or in administrative costs (as opposed to a reduction in customs delays), and whether the trade flow in question is an export (as opposed to an import). In addition to these binary variables, we consider the size of the firm (in hundreds of workers) as a continuous regressor. Since firm size is not known for all sample firms, we show regressions with and without inclusion of that variable.

_Table 9: Predicted Trade Expansion – Regression Estimates_

||_percentage inc_<br>|_Dependen_<br>_rease of Xpredict_<br>|_t variable =_<br>_edfor 50-percentfal_<br>|_l in trade costs_<br>|
|---|---|---|---|---|
|_Independent variables:_|_X = total inter_<br>|_national trade_<br>|_X = Kinshasa ↔_<br>|_Brazzaville trade_<br>|
|Brazzaville-based firm|-1.7<br>_14.0_|-3.9<br>_14.1_|37.7**<br>_17.2_|60.1**<br>_24.9_|
|Formal firm|25.6**<br>_12.44_|13.5<br>_14.2 _|8.2<br>_19.1 _|8.4<br>_20.7 _|
|Manufacturing firm|-3.5<br>_12.7 _|-4.8<br>_11.4 _|133.0***<br>_25.2 _|67.6**<br>_31.5 _|
|Fall in transport cost|26.5**<br>_11.6_|33.7***<br>_12.1_|||
|Fall in admin. trade cost|31.6***<br>_11.9_|34.4***<br>_11.2_|||
|Exports|37.9**<br>_17.7_|39.7**<br>_18.4_|||
|Firm size|0.2<br>1.1||-5.3***<br>1.5||
|Kinshasa**→**Brazzaville|||5.1<br>_14.5_|-5.9<br>_21.6_|
|Constant|-13.5<br>_20.0_|-3.5<br>_20.1_|2.3<br>_20.0_|-4.5<br>_22.7_|
|R<sup>2</sup>|0.11|0.12|0.59|0.18|
|_N_|129|138|26|27|

_Notes_ : firm size in terms of hundreds of workers; heteroskedasticity consistent standard errors in italics; */**/***: significant at the 90/95/99% level

**We again find that a general fall in trade costs would stimulate exports more than it would stimulate imports.** The first two columns of Table 9 report regression results for total international trade, i.e. primarily overseas trade via Matadi and Pointe Noire. The coefficient on _Exports_ , a binary variable set to one for predicted exports and to zero for predicted imports, shows that 50-percent reduction in external trade costs would stimulate Congolese exports up to 40 percentage points more than it would increase the volume of imports. With exports being more elastic relative to trade costs than imports, trade liberalization would serve not only to increase the volume of international trade but also to improve the trade balance of DRC and RC. The regression estimates furthermore

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<!-- Start of picture text --> &<br>£ ‘ ‘<br>ee<br>40% H<br>|current Demand: 855Mm3 |SEDAPAL 2040 Demand Projection: 1.125 Mm3<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->
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# <u>Figure 12: Trends in catastrophic expenditures (10% threshold) in selected countries</u>

<!-- Start of picture text --> 35%<br>30%<br>Georgia<br>25%<br>20%<br>15%<br>10%<br>Mexico<br>5%<br>USA<br>Indonesia<br>0%<br>1980 1990 2000 2010 2020<br><!-- End of picture text --> Figure 13 shows median incidence rates of catastrophic health expenditures by income quintile for 35 upper middle-income and high-income countries. On average, the lower income quintiles are over twice as likely to experience catastrophic health expenditures than the top income quintile. The inequalities across consumption quintiles (not shown here) look quite different reflecting the point made earlier about households borrowing and dissaving to finance health expenses, making them look relatively well off in consumption terms.
the other core subjects of English and mathematics. Figure 8 depicts the evolution of the level of QAT score variation by subject. While the level of QAT score variation in Urdu is not always higher than those for the other subjects, there was a major spike in the level of its variation between QAT 5 and QAT 6: Urdu score variation increased by 61% and 57% for phase-3 and phase-4 program schools, respectively. Thus, it appears that the score reweighting amplified the effect of the spike in Urdu score variation on total QAT score variation. We however cannot provide an explanation for the observed spike in Urdu score variation.

How do these test score variance decomposition estimates compare to estimates from other recent studies from the South Asia region? Our estimates of inter-school differences appear to lie on the lower end of the range of available estimates. For example, similar to our findings, on the lower end, Goyal and Saiens (2009), using 2007 data from a nationally-representative sample of grade-2 and grade-4 students from Bhutan, find that 26–41% of the total variation in test scores across tested grades and subjects is explained by variation between schools. Andrabi et al. (2007) find that, in a sample of grade-3 students in private and public schools in rural Punjab, Pakistan, roughly 50% of total variation is explained by variation between schools. On the higher end, although the statistic is not reported, Saiens (2009) finds that, in a sample of public schools from rural Sindh, Pakistan, the majority of the variation in test scores for grade-3 and grade-5 students is explained by variation between schools.

# **6. Pre-program correlates of learning dynamics**

In this section, we examine what factors are associated with learning levels and changes in program schools by essentially estimating school effectiveness regressions. The factors that we examine are basic school-level characteristics captured at the program application stage (i.e., preprogram) for phase-3 and phase-4 program schools such as school size, number of teachers, maximum and minimum teacher salaries, number of classrooms, level, gender type, location, registration status, and mean SLQAT score. Learning is measured in terms of the school-level mean QAT score in the core subjects of mathematics, English, and Urdu. The conditional relationships between these factors and learning (changes) are investigated via simple pooled ordinary least squares (OLS) regression as well as via fixed-effects (FE) and random-effects (RE) regressions which take advantage of the panel structure of the data (multiple test rounds over schools) to control for heterogeneity at the school level.

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in detail the implications of relaxing some of these assumptions in the present model. We readily concede that relaxing these assumptions could alter our conclusions, but they would not facilitate our assessment of the rules-ofthumb in a clean and simple environment.

An important component of any practical trade liberalization package is the way in which the revenue effects of the policy are treated. We exploit the ability of a 'simulation laboratory" to control for these effects by adopting an explicit replacement tax such that government revenue remains constant. We allow the value addJ.d tax or a lump-sum tax serve as replacement taxes for any changes in revenue. In the absence of any other changes in policy the first tax effects distortionary replacements, whereas the lump-sum tax is non-distortionary in our model (there is no labor-leisure choice). In the case of Turkey we find that the value added tax is an excellent 'real-world' alternative to the theorists' lump-sum replacement tax, in the sense that it has a relatively small marginal excess burden for the range of revenue replacements required here.

In Section 2 we outline the model that has been developed, including the procedures used to empirically estimate the model to the Turkish economy using 1985 input-output data and 1989 protection data.<sup>3</sup> In Section 3 we report the results of our policy simulations. Finally, in Section 4 we draw our conclusions for policy.

# 2. A SMALL OPEN ECONOMfY MODEL

# 2.1 General Model Structure

Our Small Open Economy (SOE) model is designed for trade policy analysis with a large number of sectors. The model is a "generic' general equilibrium model of a single economy along the lines of de Melo and Tarr [ 19921. The distinguishing feature of the model is that it effects a simple closure with respect to foreign tmade such that the economy experiences no terms-of-trade effects.

> 3 Formal details of the algebraic structure are presented in Appendix A. Appendices B and C provide additional details about recent developments in the tade regime in Turkey and the specific estimates used in our model.

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Households with 3 working members have income of IQD 250,000 / $211 more per month than households with no working members (Figure 9). Female-headed households, on average, have a much lower average monthly income, at IQD 85,631 / $72. By contrast, the mean income for male-headed households is IQD 156,781 / $132 (not shown).

In general, households have expenditures that are greater than their incomes (Figure 10). Average total monthly expenditure for the sample is IQD 467,665 / $393, significantly greater than the average income of IQD 137,041, which translates to an average per capita monthly consumption of IQD 60,422 / $ 51. The gap between income and expenditure is partly explained by debt repayments (not included in the per capita consumption calculation), which do occur even among very low-income households, and the reliance on gifted in-kind food and non-food items and humanitarian aid to meet basic needs, in place of spending. The sample has relatively large debt – average of IQD 2,481,099 / $2,077. Much of the debt households accrue is to meet basic needs: more than four out of five (84 percent) resort to buying basic goods on credit as a coping strategy.

_Figure 4: Per capita income and consumption (IQD_ <!-- Start of picture text --> 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000<br>Per Capita Income (IQD) Per Capita Consumption (IQD)<br>Data Source : CLCI Legacy Database.<br><!-- End of picture text --> Temporary unemployment among this sample is high: 72 percent of households reported no member of the household having worked in the previous 30 days. This ranges from 59 percent in Falluja to 93 percent in Tikrit. Among female-headed households, this increases to a mean of 86 percent. The number remained high over the period of assessment and changes inversely with per capita consumption (Figure 11). In terms of main sources of income, 31 percent of the households in the sample reported a temporary work, 22 percent state their community, friends, or family, and 16 percent reported loans. The remaining 24 17
**50%.** Based on administrative tax data, previous research estimated the share of minimum wage earners in Romania in 2020 – 2021 and described their profile (Robayo-Abril, Zamfir, and Wroński, 2024). According to our results, based on the tax data, more than 25% of all employees earn minimum wage. The proportion of minimum wage earners varies across branches of the economy and regions of the country. The share of minimum wage earners is exceptionally high in micro enterprises (less than ten employees), where 67% of all employees are minimum wage earners. In construction, accommodation, and food services, more than 50% of employees earn only the minimum wage; in the transport and storage sector, this share is over 40%. The proportion of minimum wage earners is higher in low-income regions. The share of minimum wage earners is highest among the youngest and oldest employees.

**However, the estimated share of minimum-wage earners in tax data may be subject to measurement error due to tax evasion and unreported income; in particular, it may be biased upwards.** In particular, workers and employers may underreport their actual earnings to avoid paying taxes or to receive government benefits. This behavior could lead to an upward bias in estimating the share of minimum wage earners in the tax data. Moreover, envelope payments, typically made in cash and not reported to tax authorities, are more prevalent among workers in low-wage occupations, further complicating the measurement of the share of minimum-wage earners based on tax data. It is theoretically possible that some employees earn minimum wage formally and more informally, e.g., by receiving envelope payments. To assess the reliability of the estimate of the share of minimum wage earners based on tax data, we compare the share of minimum wage earners in survey data, tax data, and imputed survey data. We define minimum wage earners the same as Robayo-Abril, Zamfir & Wroński (2024); we cover separate minimum wage for the construction sector, and we do not cover the separate minimum wage for employees with higher education.<sup>16</sup> Due to the significant gap in the share of part-time workers in

> 16 Thus, we define minimum wage workers as those earning between 95% of the minimum wage for employees without secondary education and 105% of the minimum wage for employees with higher education and those who earn between 95% and 105% of the minimum wage for the construction sector. Robayo-Abril, Zamfir, and Wronski (2024) describe the institutional setting of minimum wage legislation in Romania in detail.

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# **Table 6**

# **Correlates of Participation in Employer-Based Pension**

Dependent Variable: Participates in Employer-Based Pension Probit Marginal Effects

|Controls|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Age|0.027***<br>(0.005)|0.032***<br>(0.005)|0.031***<br>(0.005)|0.032***<br>(0.005)|0.033***<br>(0.005)|0.033***<br>(0.005)|
|Age-Squared|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|
|Years of Schooling|0.030***|0.025***|0.021***|0.026***|0.026***|0.026***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Post-Secondary Education|0.046**|0.010|0.010|0.007|0.008|0.006|
||(0.019)|(0.021)|(0.021)|(0.021)|(0.021)|(0.021)|
|Married|0.047***|0.025|0.023|0.024|0.027|0.028|
||(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|
|Female|-0.022**|-0.012|-0.010|-0.011|-0.036**|-0.036**|
||(0.011)|(0.012)|(0.012)|(0.012)|(0.015)|(0.015)|
|Urban Migrant w/ Other Urban|-0.365***|-0.311***|-0.312***|-0.310***|-0.333***|-0.307***|
|<br>Hukou|(0.017)|(0.019)|(0.020)|(0.035)|(0.025)|(0.044)|
|Rural Hukou Migrant|-0.538***<br>(0.012)|-0.502***<br>(0.015)|-0.496***<br>(0.015)|-0.474***<br>(0.024)|-0.534***<br>(0.018)|-0.477***<br>(0.029)|
|Year 2010|0.056***<br>(0.012)|0.092***<br>(0.012)|0.105***<br>(0.013)|0.102***<br>(0.015)|0.091***<br>(0.012)|0.102***<br>(0.015)|
|Female x Rural Migrant|||||0.074***<br>(0.025)|0.001<br>(0.046)|
|Female x Urban Migrant|||||0.044<br>(0.032)|-0.013<br>(0.064)|
|Urban Migrant x Year 2010||||-0.003||-0.038|
|||||(0.038)||(0.049)|
|Rural Migrant x Year 2010||||-0.045||-0.101**|
|||||(0.031)||(0.040)|
|Female x Rural x 2010||||||0.107**<br>(0.044)|
|Female x Urban x 2010||||||0.074<br>(0.063)|
|Has Labor Contract|||0.270***<br>(0.013)||||
|_Other Controls_|||||||
|Industry|No|Yes|Yes|Yes|Yes|Yes|
|Work-Unit Ownership|No|Yes|Yes|Yes|Yes|Yes|
|Average Participation|0.634|0.634|0.634|0.634|0.634|0.634|
|Observations|9,830|9,830|9,820|9,830|9,830|9,830|

Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes 9830 working age adults, aged 18-60, who are employed for a wage. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables. Models (2) through (6) control for 17 industries and 8 work unit ownership types.

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# **GRAPHS & FIGURES BASED ON THE RICS-AGSS DATA**

# **Figure 1: Localized Activities**

<!-- Start of picture text --> a) Where is your enterprise located?<br><!-- End of picture text --> <!-- Start of picture text --> In community<br>Same Woreda, different FA<br>Same FA, different community<br>Same Zone, different Woreda<br>Same Region, different Zone<br>Different Region<br>0 10 20 30 40 50 60 70<br><!-- End of picture text --> <!-- Start of picture text --> b) Who are your most important customers?<br>Local consumers or passers-by<br>Market<br>Traders<br>Others<br>NGOs<br>Cooperatives<br>Government<br>0 10 20 30 40 50<br><!-- End of picture text -->

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types of accompanying policies that respondents would be interested in, as it allows them to suggest that the government spend the resources saved from reducing subsidies on any policy they see fit. The design of these two questions about conditional support for subsidy reform is identical to the unconditional support question asked immediately beforehand, which asks about support for energy subsidy reform that results in a specific energy price rise. Thus, there is a clear point of comparison for these questions about unconditional and conditional support for subsidy reform.

# **3.1.3 Information treatments**

The information treatments included in this randomized survey experiment aimed to increase respondents’ knowledge about the negative impacts of energy subsidies on equity, efficiency, or the environment. Following the second section of the survey, respondents were randomly allocated to one of four groups: Group A received information about government spending on energy subsidies (efficiency treatment), Group B received information about the regressivity of energy subsidies (equity treatment), Group C received information about the negative environmental consequences of energy subsidies (environment treatment), and Group D received no information (the control group). All three treatments begin with the same information about the amount of money (in local currency units) that is spent by the government on subsidies each year in their country (sourced from the IEA [2023]), and the subsequent information is then tailored to each treatment.

The information that is shared with participants about the efficiency, equity, and environmental arguments in favor of reducing fossil fuel subsidies is similar to what is presented in the background section of this paper. The efficiency treatment provided respondents with information about the size of government spending on energy subsidies compared to spending in the health sector in their country (this information is sourced from World Bank [2023A] and is based on the latest available year for all countries, which is 2019). This information was selected to illustrate the relative cost of subsidies compared to another type of spending that many people are expected to care about. The equity treatment provided respondents 19
CKFORFGE - -59.66 - 4.542 Ql - 10.05 Q2 - 10.79 Q3 - 8.889 RPAFC (22.20) (3.175) (3.118) (3.145) (2.430) + 15.88 RRELFORC + 17.01 RSPFORC - 1.234 DRSI - 24.85 DMYPIK (10.17) (8.451) (.7232) (5.005) R2 = .704, **R**<sup>**2**</sup> - .623, DW 2.45, ay = 6.458, Data- 1978:2 - 1987:3 Estimated Equation for KGOVFGE KGOVFGE - .1277 - .3869 Ql + 1.389 Q2 + .39d5 Q3 + .5176 KGOVFGE(T-1) (.9858) (1.325) (1.336) (1.276) (.06948) + .07412 RSPFORPC + 18.39 DMYPIK + 38.84 RSPCHIGH (.0342) (2.992) (4.998) R<sup>2</sup> = .933, 2 = .924, DW - 1.49, ay - 3.397, Data - 1973:1 - 1987:3 Estimated Equation for RPAFC RPAFC = 2.370 - .05076 Q1 - .2600 02 - .3627 Q3 + .7008 RPAFC(T-1) (1.660) (.2062) (.2719) (.3452) (.08059) - .005093 KMKTFGE + .1781 EXR - .00376 RWPRDFG + .003661 RPMEAT (.002564) (.5768) (.OG207) (.003918) 2 = .901, R<sup>2</sup> = .885, DW = 1.95, CY - .3494, Data - 1973:1 - 1987:3 Estimated Equation for PAFSG PAFSG = 1.616 PAFC (.009685) R<sup>2</sup> = .968, R2 = .966, )W = .918, aY = .1904, Data - 1970:1 - 1987:3

# **Ouarterly Soybean De_mnd Block**

The soybean demand block contains equations for exports, crushings, and price with inventory determined implicitly by a supply-demand identity. The structure of the export equation is essentially the same as for feed grains. Crushings a:e determined by livestock numbers reflecting the feed use of soybean meal and consumer income reflecting demand for soybean oil.

19
Republic of Turkey Ministry of Family and Social Policy and The World Bank. 2017. Turkey’s Integrated Social Assistance System. Ankara, Turkey.

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<mark>United Nations High Commissioner for Refugees</mark> (UNHCR), 2020. Global Trends in Forced Displacement 2019.

Valli, E., Peterman, A., Hidrobo, M., 2019. Economic Transfers and Social Cohesion in a Refugee-Hosting Setting. J Dev Stud. 13;55(sup1):128–46.

World Food Program, 2008. Food Consumption Analysis: Calculation and Use of the Food Consumption Score in Food Security Analysis. Technical Guidance Sheet. Rome. WFP Vulnerability Assessment and Mapping Unit.

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World Food Program, 2020. Turkey Annual Country Report, Country Strategic Plan 2018-2019.

World Food Program and Turkish Red Crescent, 2019a. ESSN Task Force Izmir Meeting Minutes October 2019.

World Food Program and Turkish Red Crescent, 2019b. Refugees in Turkey: Livelihoods Survey Findings. Ankara, Turkey 37