rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5:** Income and Institutional Trust (heterogeneity)

||President<br>(1)|Municipal Gov.<br>(2)|Trust in<br>National Leg.<br>(3)|Judicial Syst.<br>(4)|Media<br>(5)|Church<br>(6)|
|---|---|---|---|---|---|---|
|||Panel A:<br>|Individuals Livin<br>|g in Rural Are<br>|as<br>||
|NTL Density at Municipality (in logs)|0.092***|0.001|0.051|0.265***|0.109***|-0.000|
||[0.035]|[0.029]|[0.032]|[0.070]|[0.023]|[0.037]|
|Observations|3,187|4,508|4,362|4,425|3,911|4,495|
|Number of Municipalities/Waves|75/6|101/8|101/8|101/8|101/7|101/7|
|R-squared|0.114|0.045|0.042|0.065|0.055|0.113|
|||Panel B: I|ndividuals Livin|g in Urban Are|as<br>||
|NTL Density at Municipality (in logs)|0.031|0.097**|0.030|-0.037|0.057|-0.049|
||[0.040]|[0.047]|[0.032]|[0.080]|[0.037]|[0.042]|
|Observations|5,564|7,523|7,464|7,439|6,560|7,457|
|Number of Municipalities/Waves|85/6|112/8|112/8|112/8|112/7|112/7|
|R-squared|0.135|0.051|0.077|0.086|0.078|0.051|
||Panel<br>|C: Individuals Liv<br>|ing in Municipa<br>|lities with Wea<br>|k State Capa<br>|city<br>|
|NTL Density at Municipality (in logs)|0.094***|0.031|0.036|0.187**|0.098***|0.014|
||[0.029]|[0.024]|[0.028]|[0.073]|[0.021]|[0.030]|
|Observations|3,082|4,323|4,209|4,237|3,756|4,302|
|Number of Municipalities/Waves|47/6|64/8|64/8|64/8|64/7|64/8|
|R-squared|0.104|0.036|0.055|0.074|0.055|0.074|
||Panel|D: Individuals Liv|ing in Municipal|ities with Stron|g State Cap|acity|
|NTL Density at Municipality (in logs)|0.000|0.005|0.000|0.133|0.089**|-0.063|
||[0.037]|[0.051]|[0.041]|[0.095]|[0.040]|[0.045]|
|Observations|5,669|7,708|7,617|7,627|6,715|7,650|
|Number of Municipalities/Waves|41/6|50/8|50/8|50/8|50/7|50/8|
|R-squared|0.126|0.044|0.064|0.086|0.074|0.046|

Notes: All regressions include individual-level controls (gender, age, age squared, years of schooling, unemployment status, and small town dummy), homicide rates, FODES transfers in previous periods, municipality and year fixed effects. Robust standard errors clustered at the municipality-year level in brackets. FODES refers to Development Municipal Fund Economic and Social transfers. A municipality is defined as weak (strong) state capacity if the average share of household with access to electricity and water services is below (above) 80 percent.Statistical significance: *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

at confidence in the judicial system (see columns 4 in Panel A and C). Further, most of the income impact on trust in mass media is explained by individuals living in rural areas (see columns 5 in Panel A). On the contrary, the positive association between income and trust in the municipal government comes from individuals living in urban areas (column 2 in Panel B). There does not seem to exist an important heterogeneity in the association of income and trust in the Catholic Church.

## _4.2 LATINOBAR OMETERO Analysis (2004-2018)_<sup>_ ́_</sup>

Table 6 presents the main results on the relationship between income and a set of attitudinal variables in Latinobar ́ometro. Specifically, I look at perceptions of fairness in income distribution 18
do these increase as the result of one extra connection? We compiled annual data on the number of connections and the administrative and network maintenance costs from Umeme’s annual reports. We convert these costs to 2012 prices based on the Uganda Manufacturing PPI in July each year. Figure 8 shows the relationship between these annual costs and the number of customers. We use the slope of the regression line as our estimate of the variable component of costs. This slope is equal to 8,650 Ugandan shillings, or US$41 per connection per year.

# **5 Results**

## **5.1 Fuel demand model**

Fuel demand is estimated separately for kerosene and electricity (Table 2). Each observation is an individual household’s consumption of the fuel in one month, for a household who has chosen that fuel for lighting.<sup>22</sup> For example, all households with an electricity connection are assumed to use electricity for lighting, so they will not be included in the kerosene estimation even if they report using kerosene. The primary specification pools repeated observations for individual households in the panel.

Fuel price has a negative e↵ect on the consumption of kerosene and electricity (Columns 1 and 4) that is statistically significant for kerosene. The kerosene coefficient is smaller for the regression that includes a linear time trend (Columns 2). The coefficients on log price in Columns 2 and 5 imply price elasticities of -0.24 for kerosene and -0.14 for electricity. These are consistent with previous estimates in the literature. Similarly, household total expenditure has a significant positive e↵ect on kerosene and electricity consumption, at least over the range of expenditures observed for most households in the data.

For kerosene, the coefficient on log price reduces in magnitude from -0.24 in the pooled model to -0.03 in the fixed e↵ects model (Columns 3). This result is consistent with the results of Miller and Alberini (2016), who use data for the United States to show that consumers are found to be more price inelastic for models that include fixed e↵ects. For electricity, the coefficients on log price are not significantly di↵erent from zero, and there is an imprecise positive coefficient in the fixed e↵ects model (Column 6).

The signs of the other variables in the model are as expected. Kerosene and electricity

> 22Where available, fuel consumption quantities are those reported by households. If quantity data are not available, we calculate the consumption quantity as the household’s reported expenditure on the fuel in the previous month, divided by the regional price of the fuel in the month before the interview.

15
**Figure 4. MENA Public Health Spending as a Percentage of Total Government Spending (FY05– FY14 averages)**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> Source: WDI 2017.<br><!-- End of picture text --> The largest share of the public spending in this sector goes to the compensation of employees, however. Since FY09, the share of compensation of employees in total health spending has grown from 44.6 percent to 60.8 percent in FY15 at the expense of investments in health (which mostly include the construction of new hospitals and health facilities) and purchase of goods and services (which cover purchase of medical supplies and equipment and the maintenance of hospitals and medical equipment). Both of these items have each decreased by a little over 7 percentage points in the same period.

Two aspects of the health sector are important to note. The first point relates to the funding of the sector. The spending on budget as described above represents only about half of spending on public health services and hospitals.<sup>14</sup> Since the amount allocated in the budget is too little to cover the expenses of the sector, hospitals, health facilities, and health units have created other ways to fund the sector. They have three streams of income outside the amount allocated in the budget:

1. _Special funds_ are a revenue stream that is not represented in the official budget, although governed by ministerial decree. These funds come from four main sources:

- a. The private wing inside the hospitals is for people who require special services or who want a specific doctor; they pay a fee for the services provided to them. This fee is smaller than the private sector but greater than the public-sector fee.

- b. Small fees that all citizens pay when they are at a hospital, tickets to visit sick relatives, and small fees for x-rays or blood tests: a portion of the revenue from these fees goes into the special funds.

- c. Donations from private citizens go directly into the special funds.

- d. Revenues also come from the Citizens Treatment at the Expense of the State program.<sup>15</sup>

> 14 The spending figures relate to both contributory (for employees whose employers contribute to the health system) and noncontributory health systems.

15 The program is affiliated with and operated by the Ministry of Health and Population, though not completely autonomous as the Health Insurance Organization and of a smaller scale (1.9 million in 2008/09). A special discretionary fund, the program constitutes a safety net to cover the uninsured (and poor) population and help pay for a certain package of services (with services provided in Egypt or abroad).

12
better access to subsidized health (high _δ_<sup>ˆ</sup> 3) worsen the worker’s bargaining position, which leads to a lower wage in the bargaining process.

# **3.4 Optimal Decision Rules and Reservation Productivities**

Optimal decision rules are characterized by a reservation value property. Reservation productivities are obtained by: The Nash surplus sharing rule guarantees mutual agreement between the worker and the firm. Notice that _RIU_ ( _y_ )= _RUI_ ( _y_ )= _RFI_ ( _y_ ). On the other hand _RUF_ ( _y_ ) = _RFU_ ( _y_ ) since when the match breaks, the formal sector firm has to pay a severance cost.

The corresponding reservation wages can be obtained through the Nash bargaining wage rule which maps productivities into wages, conditioning on type.

## **3.4.1 Reservation Productivity,** _RFU_ ( _y_ )

A match in the formal sector is continued if and only if it is in the mutual interest of the worker and the firm to do so, so a necessary condition for match continuation is that the joint surplus must be non-negative. In equilibrium, the surplus is an increasing function of _x_ ,<sup>47</sup> and by definition _RFU_ ( _y_ ) is the threshold productivity above which the joint surplus is never negative. Therefore, _RFU_ ( _y_ ) is defined by the zero surplus condition: Using the sharing rule combined with the free entry condition, I get: Substitution and integration by parts gives:

> 47See Appendix 6.1 for the surplus analytical expression.

17
among firms—between the firm and its supplier and its customers—and such relationships are often based on personal networks or access to power. How well male versus female business leaders would perform would depend on their access to such key contacts and personal networks.

The literature suggests that women have less access to personal and professional networks and spend less time networking (Goetz 2007). For instance, young female lawyers have been found to spend significantly less time in professional networking (Azmat and Ferrer 2017). Based on representative population surveys in 17 countries, women are found to have less extensive networks (i.e., personally less likely to know someone who started a business) (Koellinger et al. 2013). This is not surprising: typically women spend more time in household chores and networking as parents, which takes away time from professional networking. Indeed, recent research suggests that even in the United States, wives frequently conform to the gender norm of not out-earning husbands, and when wives do out-earn their husbands, they instead spend more time doing household chores to maintain intrahousehold harmony (Bertrand, Kamenica and Pan 2015).

This lack of professional and business networks does not augur well for the potential positive effect of inter-firm trade credit relationships. Trade credit is a key source to finance firms in developing countries, especially where access to bank credit is limited (Fisman and Love 2003). Trade credit relationship needs strong trust built on personal relationship between upstream-downstream trading partners, which requires strong relationships. Or trade credit relationship requires strong relationship with privileged firms such as state-owned enterprises which have better access to bank finance (Cull et al. 2009). Furthermore, in societies where women are discriminated against, some social clubs do not admit women, again rendering women in networking disadvantage. Moreover, trade credit relationship also implies financial risks of default. Women’s relative apprehension of risks again would discourage the use of trade credit relationship. The lack of networking and the apprehension of trade credit risks thus put women business leaders at a disadvantage in environments where trade credit relationships are important, such as in environments of financial constraints. For instance, limited use of trade credit relationship could put female business leaders at a disadvantage in funding firm expansion and productive investments, 10
# **_Linkages between Manufacturing Industries and Services Sectors_**

The next question in our analysis is how to aggregate these sector-specific indices into a single index of services reform. Given that some services are likely to be more important for manufacturing industries than others, and that this dependence may vary across different manufacturing industries, an unweighted average of services sector indices is unlikely to be an appropriate measure of the potential impact of upstream services liberalization on the performance of manufacturing firms. Instead, we use information on the intensity with which services inputs are used in the production of a given manufacturing sector. In particular, we weight each of the reform indices for the four major services sectors (banking, telecom, transport and insurance) by the proportion _αjk_ of inputs sourced by the manufacturing sector _j_ from the services sector _k_ to create the index of services reform: where _αjk_ is based on the input-output matrix pertaining to 1993, the first year of our sample.<sup>14</sup> Data from a national input-output matrix contain information about the average inter-industry sourcing behavior of firms in a given sector of the economy. For an individual firm, the actual reliance on a given services sector may be somewhat different, but even if such information were available at the level of each individual firm (which it is not), such data would risk being endogenous to the performance of the firm, which would defeat our purpose. By using average information, we lose some precision in measuring the reliance of firms on services inputs, but we can be less concerned about the endogeneity of this measure. The fact that we use sourcing information from the 1993 input-output matrix should further minimize the scope for endogeneity even at the level of the average firm in an industry.

> 14 The input-output matrix includes 66 manufacturing sectors and 16 services sectors. The manufacturing sectors were aggregated to 38 sectors at which sector-specific price deflators were available. The services sectors include:  construction, electricity, gas, water supply, railway transport services, other transport services, storage and warehousing, communication, trade, hotels and restaurants, banking, insurance, ownership of dwellings, education and research, medical and health, other services. Input shares are calculated relative to the total value of inputs sourced.  Banking services constitute on average 5% of all inputs, transport 4.4%, telecommunications 1.6% and insurance 1.4%. An alternative normalization, by gross output, leads to the same conclusions.

16
term Effects of Political Violence in Per ́u.” The <u>Journal</u> of Human Resources 47(4):991– 1022.

- Lind, Jo Thori, Karl Ove Moene and Fredrik Willumsen. 2014. “Opium for the Masses? Conflict-Induced Narcotics Production in Afghanistan.” The Review of Economics and Statistics 96(5):949–966.

- Londono, Alicia, Perla Romero and Germ ́an Casas. 2009. “The association between armed conflict, violence and mental health: A cross sectional study comparing two populations in Cundinamarca department, Colombia.” Conflict and health 6.

- Matti Sarvim ̈aki, Roope Uusitalo, Markus Jantti. 2009. “Long-Term Effects of Forced Migration.” Discussion Paper, IZA (4003).

- Mehran, Weeda. 2022. “The Evolution in the Taliban’s Media Strategy.” GW Program on Extremist .

**URL:** _https://extremism.gwu.edu/evolution-in-taliban-media-strategy_

- Mesa-Vieira, Cristina, Andreas D Haas, Diana Buitrago-Garcia, Zayne M Roa-Diaz, Beatrice Minder, Magda Gamba, Dante Salvador, Daniel Gomez, Meghann Lewis, Wendy C Gonzalez-Jaramillo, Aur ́elie Pahud de Mortanges, Chepkoech Buttia, Taulant Muka, Natalia and Oscar H Franco. 2022. “Mental health of with Trujillo migrants preto armed a review and The

- migration exposure conflict: systematic meta-analysis.” Lancet Public Health 7(5):469–481.

- Monitor, Internet. 2023. “Afghanistan.”.

**URL:** _https://thenetmonitor.org/country-profiles/afg_

- Moya, Andr ́es. 2018. “Violence, psychological trauma, and risk attitudes: Evidence from victims of violence in Colombia.” <u>Journal</u> of Development Economics 131:15–27.

- Murthy, Srinivasa and Rashmi Lakshminarayana. 2006. “Mental health consequences of war: a brief review of research findings.” World Psychiatry 5:25–30.

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- Block, Steven A. 2013. “The Post-Independence Decline and Rise of Crop Productivity in SubSaharan Africa: Measurement and Explanations.” _Oxford Economic Papers_ (March 23).

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- Christiaensen, Luc, Lionel Demery, and Jesper Kuhl. 2011. “The (evolving) Role of Agriculture in Poverty reduction—An Empirical Perspective.” _Journal of Development Economics_ 96 (2): 239–254.

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# Global Inflation Synchronization

Jongrim Ha, M. Ayhan Kose, and Franziska L. Ohnsorge<sup>*</sup> Keywords: Global inflation, synchronization, dynamic factor model, advanced economies, emerging markets, developing economies. JEL Classification: E31, E32, F42

> * Ha: World Bank, Development Prospects Group; jongrimha@worldbank.org. Kose: World Bank, Development Prospects Group; Brookings Institution; CEPR, and CAMA; akose@worldbank.org. Ohnsorge: World Bank, Development Prospects Group; CAMA; fohnsorge@worldbank.org. We appreciate Kevin Clinton, Selva Demiralp, Norbert Fiess, Hans Genberg, Stefan Gerlach, Graham Hacche, Alain Kabundi, Ergys Islamaj, Sergiy Kasyanenko, Marc Stocker, Christopher Towe, Collette Wheeler, Johannes Wiedemann, and seminar participants at many institutions for their comments. Xingyue Wang and Heqing Zhao provided excellent research assistance. We gratefully acknowledge financial support from the PHRD fund. The findings, interpretations and conclusions expressed in this paper will be entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
12 where bars denotes the mean of the variables. Subscripts _pub_ refers to the public sector while _pvt_ denotes the private (formal sector). The first component is the difference in the base wage (constant term) which is often understood as the premium or pure rent from being in the public sector. The second component is due to difference in human capital endowment of the workers. The third is due to the difference in private returns to the endowments and the final component is due to the difference in the selection terms.

Table 4 shows the decomposition results for Djibouti with and without conditioning on selection. Both methods clearly show that the most important factor determining a positive wage differential in favor of public sector wages is a wage premium or rent. The premium is much larger when characteristics that determine participation in both sectors are controlled, as captured in the second column.

**Table 4 Djibouti: Mean Log Wage Differential between Public and Private (formal) Sector**

||**OLS**|**Selectivity**<br>**Corrected**|
|---|---|---|
|Total log mean differential|0.41|0.04|
|Components attributable to:|||
|Wage premium|0.21|2.86|
|Human capital endowments|0.34|-0.06|
|Market returns|-0.15|-1.80|
|Selection||-0.95|
|Total unexplained differential|0.06|1.06|
|_Source: Authors' calculations based on wage regressions in Table 5._|||

Hence in Djibouti, public sector workers earn a rent, after controlling for other wage determinants. Terrell(1993) found in Haiti that public administration workers earned a sizeable rent. Similarly, Lindauer and Sabot (1983) found that the wage premium was the most important determinant of public-private wage differential in Tanzania. The positive rent earned by the public sector in Djibouti is more likely due to the legacy of French