rafmacalaba/datause-encoder-data huggingface dataset:
that informal financing almost always carries associated informal costs (i.e., costs not reflected in interest rates), such as informal gifts and implied obligations to assist lenders in the future, making them relatively less attractive to borrowers with formal options. Another reason for their greater reliance on formal loans is that better-educated people tend to have fewer siblings and a higher opportunity cost of their time, which would raise their relative costs of using informal financing. They also have higher-paying jobs in general, and likely steadier incomes, making them better candidates for bank financing. The overall effect is therefore that bettereducated residents rely more on formal financing.

Contrasts in the configurations and effectiveness of various social networks on access to finance shed light on how finance works for Chinese households. Rural households with an affiliation to the Communist Party are 10.2 percentage points more likely to have a bank loan (model 6). For urban households, incidence of affiliation with the Communist Party is relatively high (18%, in contrast to 9.5% in rural areas),<sup>20</sup> and party affiliation is not associated with a greater likelihood of having a bank loan.

While party membership is associated with greater use of bank loans in rural areas, it is not associated with more usage of informal loans. These pieces of evidence suggest that affiliation with political power can affect access to formal credit in some rural Chinese contexts, perhaps because state-owned banks have greater influence over credit markets in rural areas. The number of siblings, a proxy for the extent of private social networks, also influences informal financing. Each additional sibling is associated with 4.7 and 3.9 percentage point gains in the likelihood of informal borrowing in urban and rural areas, respectively (models 8 and 9). Thus, private social networks also play an important role in facilitating informal credit markets.

> 20 Figures based on CHFS data.

21
# **Table S18. Prevalence of food insecurity as it relates to health and financial concerns**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Concerned about|0.042|0.010|
|household health|(0.030)|(0.021)|
|Concerned about|0.206***|0.062***|
|household finances|(0.031)|(0.019)|
|Ethiopia|-0.170***|-0.093***|
||(0.023)|(0.018)|
|Uganda|-0.311***|-0.173***|
||(0.022)|(0.015)|
|Ethiopia-Uganda|0.000|0.000|
|Observations|6,893|6,893|
|R-squared<br>_Note_: Each column reports|0.089<br>results from a sing|0.051<br>le regression of the|

_Note_ : Each column reports results from a single regression of the prevalence of either moderate or severe food insecurity or just severe food insecurity. Food insecurity is measured using FIES. The FIES variable is regressed on indicators whether or not the respondent is concerned about the health impacts of COVID-19 and the financial impacts of COVID-19. Also included are indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi and in Uganda. Nigeria is not included because questions about concerns were not asked in the same rounds as questions about food insecurity. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

45
Table 2: Summary Statistics

||Mean|Std. Dev|Min|Max|
|---|---|---|---|---|
|ln(GDP/capita)|8.56|0.53|6.80|9.60|
|Inc-Ind (2005 ppp)|502|915|0|393,155|
|Inc-Fam (2005 ppp)|331|645|0|691,599|
|Members|4.47|2.15|1.00|43.00|
|Age|31.5|17.9|0.0|117.0|
|Male|0.57|0.49|0.00|1.00|
|Educ - High School|0.30|0.46|0.00|1.00|
|Educ - College|0.08|0.27|0.00|1.00|
|Migrant|0.29|0.45|0.00|1.00|
|Public Firm|0.09|0.28|0.00|1.00|
|Large Firm|0.23|0.42|0.00|1.00|
|Formal|0.35|0.48|0.00|1.00|
|Union|0.05|0.22|0.00|1.00|
|Urban|0.75|0.43|0.00|1.00|
|Self-Employed|0.20|0.40|0.00|1.00|
|Employer|0.03|0.18|0.00|1.00|
|Number of Obs.|8,475,013||||

_Notes_ : Authors’ calculations using SEDLAC data. Summary statistics include all observations, covering different years for countries as described in Table 1.

ables listed in the summary statistics are used as controls in the later regressions. It is also important to mention that 75% of the sample is located within urban areas. This is particularly the case since data for Argentina and most of the data for Uruguay are taken from surveys representative only for urban areas. Over a third of the workers have formal jobs, but over two-thirds of the workers are in small firms. The SEDLAC data defines a small firm as having less than five employees for most countries. For Bolivia (2001-2002), Chile (1996), the Dominican Republic, Panama, and Uruguay, the cutoff for a small firm is less than four employees. Note that formal jobs are highly correlated with large or public firms. While many workers with a formal job also have a job contract, and vice versa, the two concepts are not perfectly correlated. Also, _Edu − High_ , the indicator for completing high school, includes individuals that have completed college as well.

9
For the co-state variables we have Equations (7)-(12), which are derived from the two first order conditions with respect to the choice variables _c_ and<sup>_e_</sup> _p_<sup>and the four equations for the co-state variablesλ</sup> 1, λ 2, λ 3, and λ 4 , give us, together with the four state variable equations (2)-(5), a system of ten equations in ten variables { _c_ , _e p_ , _k_ , _h_ , _g_ , _b_ , λ 1, λ 2, λ 3, λ 4} .

Writing eqns. (7) and (8) as and setting the differential equations (2)-(5) as well as (9)-(12) equal to zero, we can obtain a stationary state { _c_ *, _e_ * _p_ , _k_ *, _h_ *, _g_ *, _b_ * λ 1*, λ *2, λ *3, λ *4} . Plugging these into the production function _y_ = _Ak_ α ( _u_ 1 _h_ ) β ( _v_ 1 _g_ ) γ , we can also obtain the per capita income, _y_ * , at the stationary state. Given our specification of human capital and its use, we can study the effect of the allocation of human capital on per capita income and the other macroeconomic variables. Furthermore, varying our parameter set<sup>_v_</sup> 1<sup>,</sup><sup>_v_</sup> 2<sup>and</sup><sup>_v_</sup> 3<sup>, we can explore the impact of different public expenditure</sup> compositions on the steady-state outcomes of per capita income and the other variables. A further effect on per capita income is predicted to be caused by a change of the parameter A and of foreign aid, if<sup>_i_</sup> _pf_ > 0 . Overall, from all of those comparative static studies we expect to obtain information on per capita income, consumption and the other macro variables.

Specifying all parameters, the following numerical stationary solution is obtained from necessary optimality conditions using the computer software _Mathematica_ :<sup>161718</sup>
|**Country**<br>This table presents c<br>EPFR data. TIC data i<br>residents in a country)<br>the absolute value of <br>by market capitalizati<br>Bloomberg, US Treasu|**Equity EPFR/TIC**<br>**Correlation**<br>**Country**<br>**Table 8**<br>**Correlation between TIC and EPFR data**<br>ountry correlations between capital inflows <br>s the absolute value of monthly inflows (purch<br>normalized by equity market capitalization o<br>monthly inflows into a country by global equ<br>on or total debt. * denotes significance at the <br>ry, BIS.|**Bond EPFR/TIC**<br>**Correlation**<br> <br> measures from TIC and<br>ases minus sales from US<br>r total debt. EPFR data if<br>ity/debt funds normalized<br> 1% level. Source: EPFR,|
|---|---|---|
|Argentina|0.4422*<br>Argentina|0.2211|
|Brazil|0.1846<br>Brazil|-0.0704|
|Chile|-0.03<br>Chile|0.0922|
|China|0.1475<br>China|-0.1972|
|Czech Republic|-0.0691<br>Hungary|0.1152|
|Egypt|-0.0892<br>Indonesia|-0.1646|
|Hungary|0.1475<br>Poland|0.1454|
|India|0.0839<br>Mexico|0.2493|
|Indonesia|-0.0055<br>Peru|-0.0794|
|Israel|0.2089<br>Philippines|0.0269|
|Korea|0.2517<br>Poland|0.0964|
|Malaysia|0.3057<br>Russia|-0.1464|
|Mexico|0.0716<br>South Africa|0.0873|
|Peru|0.2878<br>Turkey|0.1249|
|Philippines|0.1398<br>Venezuela|-0.0861|
|Poland|-0.0241||
|Russia|0.0383||
|South Africa|0.1092||
|Taiwan|0.0776||
|Thailand|0.4421*||
|Turkey|-0.0961||
# Table 2. Bond-Level Pricing OLS Regressions (Equation (2), excluding Chinese issuance)

Dependent Variable: Fixed yield-to-maturity of Bond Tranche

||Push||Inter-|Push||Inter-|Push||Inter-|Push||Inter-|Push|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Factor|_GDPPC_|action|Factor|_GROWTH_|action|Factor|_EXT_|action|Factor|_CAD_|action|Factor|_PCRED_|Interaction|
|_VIX_|-0.907|||0.654*|||0.860**|||0.946***|||1.390**|||
||(1.654)|||(0.334)|||(0.340)|||(0.328)|||(0.523)|||
|Pull Factor||-0.423|||-0.317**|||-0.00223|||0.103|||0.0153||
|||(0.792)|||(0.137)|||(0.00719)|||(0.108)|||(0.0233)||
|Interaction|||0.224|||0.0945**|||0.00371|||-0.0340|||-0.00814|
||||(0.188)|||(0.0412)|||(0.00232)|||(0.0362)|||(0.00805)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|
|_RISK_|-0.179|||0.893**|||0.937**|||1.076***|||1.277***|||
||(2.163)|||(0.381)|||(0.390)|||(0.375)|||(0.446)|||
|Pull Factor||0.155|||-0.0874**|||0.00516|||0.0264|||-0.00645||
|||(0.456)|||(0.0387)|||(0.00751)|||(0.0266)|||(0.00557)||
|Interaction|||0.154|||0.0713**|||0.00419**|||-0.0355|||-0.00396|
||||(0.251)|||(0.0277)|||(0.00204)|||(0.0264)|||(0.00655)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|
|_FED_|-0.832|||-0.640*|||-0.624*|||-0.694*|||-0.655*|||
||(1.844)|||(0.357)|||(0.362)|||(0.382)|||(0.389)|||
|Pull Factor||0.0478|||0.457|||0.0292|||-0.0677|||0.00858||
|||(2.794)|||(0.449)|||(0.0401)|||(0.355)|||(0.0408)||
|Interaction|||0.0141|||-0.0354|||-0.00152|||0.00531|||-0.00125|
||||(0.207)|||(0.0327)|||(0.00284)|||(0.0258)|||(0.00300)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|2,738|58<br>173|2,502|
|_LIBOR_|0.311|||0.294*|||0.286|||0.438***|||0.496**|||
||(1.380)|||(0.170)|||(0.174)|||(0.149)|||(0.236)|||
|Pull Factor||0.753|||-0.165**|||0.00290|||0.0571|||-0.0110||
|||(0.628)|||(0.0711)|||(0.00972)|||(0.0568)|||(0.0133)||
|Interaction|||0.0188|||0.0423*|||0.00396**|||-0.0188|||-0.000521|
||||(0.163)|||(0.0218)|||(0.00167)|||(0.0194)|||(0.00460)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||2,454|58<br>173|2,245|2,454|58<br>173|2,245|2,454|58<br>173|2,245|2,454|58<br>173|2,245|2,454|58<br>173|2,245|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Note: Obs. = Observations, #C = Number of Countries, #Id = Number of Industries, # Bd = Number of Bonds. R-squared for regressions with the _VIX_ , _RISK_ , and _FED_ = 0.65; R-squared for regressions with _LIBOR_ = 0.63.

41
**Table 5: Marginal Effects from Probit Regression Assessing Relationship between Survey Participation** **<u>and Respondent Characteristics</u>**

||**Model 1**|**Model 2**|**Model 3**|
|---|---|---|---|
|**Explanatory Variables**|Dependent<br>variable:<br>Participant<br>completed<br>|Dependent<br>variable:<br>Participant<br>completed all 4|Dependent<br>variable:<br>participant<br>completed 0|
||surveyin round|surveys|surveys|
|Survey Round: December|[reference]|||
|Survey Round: January|-0.0741***|||
|Survey Round: February|-0.156***|||
|SurveyRound: March|-0.198***|||
|Age: Below 20||[reference]||
|Age: 20-29|0.0872**|0.0578|-0.0275|
|Age: 30-39|0.118***|0.127**|-0.0474|
|Age: 40-49|0.143***|0.108|-0.0807***|
|Age: 50plus|0.141***|0.139*|-0.0492|
|Gender: Male||[reference]||
|Gender: Female|0.0973***|0.0336|-0.0745***|
|Language: Arabic||||
|Language: Dinka|-0.0168|-0.00764|-0.00198|
|Language: Nuer|0.0834|0.0525|0.0170|
|Language: English|-0.0115|-0.0258|-0.0209|
|Top-up value: 5 SDG||[reference]||
|Top-upvalue: 10 SDG|-0.0637***|-0.0274|0.0700***|
|Phone Type: Nokia||[reference]||
|Phone Type: Solar|-0.0252|-0.0433|0.00230|
|Alternative phone not available||[reference]||
|Alternativephone available|0.237***|0.188***|-0.145***|
|Tow of residence: JUBA||[reference]||
|Town of residence: AWEIL|0.0805|-0.0511|-0.0923***|
|Town of residence: BENTIU|-0.175**|-0.248***|-0.0829*|
|Town of residence: BOR|-0.0799|-0.212***|-0.00464|
|Town of residence: KWAJOK|-0.146*|-0.191***|0.0346|
|Town of residence: MALAKAL|0.0386|-0.0453|-0.1000***|
|Town of residence: RUMBEK|-0.0594|-0.0434|0.00538|
|Town of residence: TORIT|-0.116**|-0.236***|-0.0332|
|Town of residence: WAU|0.101*|0.0577|-0.0732**|
|Town of residence: YAMBIO|0.0987*|0.0471|-0.0849***|
|Observations|3,900|975|975|
|Number of households|975|975|975|

*** p<0.01, ** p<0.05, * p<0.1

Participants who completed all four survey rounds were more likely to be above the age of 30 and more likely to have access to an alternative phone. Gender, the type of phone, and the incentive value were not associated with the probability a respondent would complete all four rounds of the survey. The very strong geographical pattern associated with completing all four survey rounds suggests that the differential reliability of network coverage across the ten urban centers was a strong determinant of 14
3 profitable economic activities in order to evade government taxes. Estimates of the marginal costs associated with raising revenues from the least distortionary tax instrument in developed countries range from 32%-47% in the United States to 120% in Sweden. In developing countries, the marginal cost of public funds is likely to be even higher.<sup>4</sup> Based on these findings, it is probably safe to assume that for Mexico there is a deadweight loss of at least half a Peso imposed on the economy for each Peso raised in taxes.

One direct indication of high deadweight losses in Mexico's tax system is given by the low productivity of its value-added tax (VAT). The VAT productivity ratio -- defined as the ratio of actual VAT revenues over GDP divided by the average VAT rate -- is widely regarded as an important indicator of the efficiency of revenue generation. As shown in Loayza and Palacios (1997, Chart 16), Mexico has one of the lowest (trade-adjusted) VAT productivity ratios in the region as well as the OECD. This ratio averaged 0.23 in Mexico during 1992-94 (and declined to 0.15 during the 1995 crisis), compared to an unweighted average ratio of 0.28 for Latin America and the Caribbean. This figure is substantially below the ratios observed, for example, in Argentina (3 1%), Brazil (47%), Chile (0.39) and Colombia (0.30), or in developed countries, where they lie between 0.30 and 0.70.

# **B. The Evolution of Public Investment in Mexico**

Table 1 shows how public and private investment have evolved in Mexico since 1960. Public investment started out as a modest share of GDP in 1960, increased sharply in the 1970s, and then declined again in the 1980s and 1990s. This evolution reflects the evolution of economic strategies pursued in Mexico over this period: during the import substitution industrialization (ISI) strategy adopted in the 1960s, public investment focused on the development of key "strategic" industries, considered complementary to the overall industrialization effort; e.g., the electricity sector, railroads and irrigation systems. The rapid expansion of public investment in the 1970s, in turn, coincides with the indiscriminate proliferation and expansion of parastatals that accompanied the public sector-driven growth strategy implemented in the 1970s. This period is characterized by the expansion of various "white elephants", such as the public steel enterprise. The main boom in public investment spending, however, took place toward the end of the decade and focused on the petroleum sector, following the discovery of major oil deposits.

After peaking at 12 percent of GDP in 1981, Mexico's public investmnent experienced a progressive decline that continued until the mid- 1990s. At first, this decline was driven by the exigencies of fiscal retrenchment and the drying up of foreign borrowing opportunities in the aftermath of the 1982 crisis. Starting in the mid- 1 980s,

- 4These estimates are from Devarajan, Squire and Suthiwart-Narueput (1995), pg. 18, which also discusses various key factors that determine the marginal welfare cost of taxation.
ject to centralized policies and work in an environment with higher prevalence of incentives for performance. Principals report fewer problems with regard to teacher absenteeism, which squares with an explanation about a Confucian heritage culture.

**Schools** : Vietnam has a much lower level of economic development compared to the Dev7 countries, which is reflected in lower levels of educational attainment of parents and lower level of home possessions, including so called cultural possessions such as artwork and books. Also, comparatively more Vietnamese students go to school in villages and small towns, reflecting the national population distribution. Yet, two things are striking about schools - although schools have fewer computers compared to Dev7 countries, these computers are as likely as Dev7 countries to be connected to the internet. Also, indices regarding quality of school infrastructure and school educational resources are less deficient in Vietnam compared to Dev7, which is indicative of substantive investments in schools in the past few decades.

Overall, across these four domains of information, it seems likely that the PISA data set is able to detect significant cultural differences between Vietnam and Dev7 countries. There appears to be some influence of policy, looking at student achievement assessment and teacher incentives, and higher levels of centralized controls, but the effectiveness of such policies is also likely tied to cultural factors. Unlike the ‘World Values Survey’ the set of PISA instruments is not suited to clearly identify cultural differences, for instance through responses regarding beliefs, attitudes and practices defined specifically to discriminate between cultures. While mean differences provide interesting hints, they are essentially bi-variate correlations. In order to tell us more about the correlations, which ones are more important than others, and whether indeed some unobservable ‘Vietnamese culture’ variable may be a plausible explanation, we need to unravel the mystery further through a study of multi-variate correlations. We do this first by using the Fryer-Levitt approach.

# **3 Regression Approach I: Fryer-Levitt**

We are now ready to investigate the secret a bit further by deepening our analytical approach beyond a mere comparison of means. We adopt a simple methodology that is easy to understand and interpret. Our approach closely follows [Fryer and Levitt, 2004] who sought to explain the black-white achievement gap in the first two years of schooling for children in the United States. For the results presented in this section, we pool the student level data from Vietnam and Dev7 countries. Recall that Dev7 stands for the seven developing countries in the 2012 PISA dataset with a per capita income below the cut-off 19
# **1. Introduction**

COVID-19 has worsened gender inequalities in many countries around the world. In particular, the pandemic resulted in more job losses and labor income decreases for women than men (Dang and Nguyen, 2021; Alon _et al.._ , 2022). Yet, the existing literature focuses on richer countries and there are fewer studies for poorer countries, perhaps due to a shortage of nationally representative survey data that span the pandemic in the latter.<sup>1</sup> Furthermore, recent studies offer mixed evidence on the impacts of COVID-19 on gender gaps in employment for poorer countries.

Examining the pandemic effects in India, Deshpande (2022) finds that in August 2020, for women, the likelihood of being employed was 9 percentage points lower than for men, compared to April 2019, conditional on previous employment. However, by December 2020, gender gaps in employment were at the pre-pandemic December 2019 levels. Alfonsi _et al._ (2023) find that lockdowns imposed in Uganda reduced employment by 69% for women and by 45% for men, and the gap persisted 18 months after the onset of the pandemic. On the other hand, Dang, Nguyen, and Carletto (2023) analyze Viet Nam’s Labor Force Surveys spanning 2015 to 2020 and find negative pandemic effects on employment, temporary layoff rates, and employment quality, but this study does not find differential effects for women. Halim _et al._ (2023) even observe that the pandemic has reduced the gender gap in employment and participation for Indonesia.

We offer an early study on the pandemic impacts on the labor market in the Islamic Republic of Iran. The Islamic Republic of Iran offers an interesting, even unique, case study for several reasons. Female labor force participation (LFP) in the Islamic Republic of Iran is among the lowest

> 1 Most existing studies for developing countries rely on high-frequency phone surveys (Egger _et al._ , 2021; Khamis _et al._ , 2021; Mahmud and Riley, 2021; Krafft _et al._ , 2022; <mark>Bundervoet, Dávalos, and Garcia, 2022)</mark> , which can be prone to various data quality issues. These issues include low response rates or undercoverage and shorter questionnaires with fewer variables than the typical household survey, so typically do not allow for the rigorous and comprehensive analysis that can be implemented with nationally representative data (Miguel and Mobarak, 2022).

2