rafmacalaba/data-use-mentions-extended huggingface dataset:
is a dummy variable indicating recorded natural disasters in The Chronicles in DDFFPPFFPprefectureFFDDii,tt _i_ in year _t_ , which includes droughts, flooding, locusts, and earthquakes. P P and are defined as before. represents the prefecture fixed effects; , year fixed effectstt . In columns 1 and 2 of Panel A in Table 3, neither XXii μμii P P nor P θθPtt × R R P is tt tt ii significant, suggesting that natural conditions did not deteriorate in the warlord period, and that FFRRF prefectural border ruggedness was not associated with obvious changes in local natural conditions.

We also conduct three other robustness checks in Table 3. First, a small number of prefectures (i.e., six) did not directly neighbor with prefectures controlled by other warlords— then their border ruggedness could have little military implication for them. In general, given the frequent changes of territories during that era, our prior is that even inner prefectures’ border ruggedness matters since they could become outer prefectures some months or years later. This is why in the base scenario we include the border ruggedness of all prefectures. After excluding all six of such prefectures in column 3, we obtain similar results as in our baseline regression.

Second, wars could interrupt agricultural and business activities and lead to famines. To exclude the impact of wars, we delete the observations with wars happening in the observation prefecture-year. The coefficient of P P × R R P is -0.096, similar as before. When tt ii the prefectures were surrounded by less rugged borders, warlords, war or not, still did less to FFRRF prevent famines or provide famine relief for their subjects.

Third, we consider the sensitivity of our results when taking into account famine severity. In our baseline regressions we consider famine incidence. But famines differ in severity, and more severe famines might reflect more serious governance failure. Do our key results still hold if famine severity is considered? Because the data set does not provide a precise scale of famines, we
s WEO database. Specifically, we control for the magnitude of international trade, by including the log of imports and exports, market size and potential demand in source countries, including GDP and GDP per capita, GDP growth, and measures to control for the overall governance environment.<sup>31</sup> To account for resource prices, we include the price of oil, agricultural raw material, and a metal price index. Finally, we include the lagged value of FDI in the FDI equation. Due to the long timeframe analyzed in these equations, we are less concerned about bias introduced through the dynamic structure of one equation (Nickel, 1970). However, we further examine the issue as part of our robustness checks by substituting the stock of FDI with FDI inflows, so that the dynamic nature of the equation is removed.

Since disturbance terms in the FDI and tax revenue equation are potentially correlated, we estimate the system of equations in a seemingly unrelated regression (SUR) framework (see Zellner, 1962; Zellner and Huang, 1962; and Zellner, 1963). Specifically, we first estimate system (9) equation-by-equation to obtain estimates of the cross-equation correlation of residuals. Subsequently, we use this information to compute the system’s covariance matrix and re-estimate the equations jointly in a generalized least squares approach. We thus improve estimation efficiency over the simpler OLS. We report Newey-West type standard 31 Control of corruption and governance effectiveness.
31 percentage points per year.<sup>20</sup> Note that imports and exports also grew rapidly in both periods, but the 1990s stand out in the rapid growth of investment and value added. For instance, capital grew by over 13.1 percent per year and value added by 10.4 percent so that the capital/value added ratio increased by 2.7 percent annually.

The share equations derived demonstrate the relative importance of output growth, capital, imports and exports in demand for skills. Table 6 reports the result of estimating equation (5) using the national data for 76 “4 digit” (ISIC)<sup>21</sup> industries. The left four columns of results report separate estimates for each period, revealing the contrast between periods. In 1984-89 growth in 20
|
||(.079)||(.069)|
|1|-<br>-.135***|-|-.130***|
||(.031)||(.031)|
|2|-<br>-.022|-|-.065*|
||(.043)||(.039)|
|3|-<br>-.222***|-|-.210***|
||(.062)||(.060)|
|4|-<br>-.070|-|-.134**|
||(.065)||(.056)|
|5|-<br>-.148***|-|-.164***|
||(.056)||(.054)|
|6|-<br>-.049|-|-.027|
||(.057)||(.056)|
|7|-<br>-.054|-|.028|
||(.056)||(.055)|
|Observations|3416|7637||

Source: EL.STAT, Quarterly Labor Force Survey-LFS (2017 – 2020, 1<sup>st</sup> quarter).

Notes: Reported estimates are average marginal effects drawn from a probit model (interaction effects model) and correspond to the post 2020Q1 period (additive effect). Based on the ILO definition of unemployment, the dependent variable takes the value of 1 if the individual is considered to be unemployed and 0 otherwise. Sample includes all individuals (aged 25-54) who have stopped working during the last 8 quarters (2 years). All models include controls for gender, age, country of birth, education, region and sector of economic activity of the last job
# **<mark>The Global Survey of Public Servants: A Foundation for Research on</mark>**

# **<mark>Public Servants around the World</mark>**<sup>**1**</sup>

<mark>Christian Schuster</mark> <mark>`r` Kim Sass Mikkelsen</mark> `r` <mark>Daniel Rogger</mark> <mark>`r` Francis Fukuyama</mark> `r` <mark>Zahid</mark> <mark>Hasnain</mark> <mark>`r` Dinsha Mistree</mark> `r` <mark>Jan Meyer-Sahling</mark> <mark>`r` Katherine Bersch</mark> <mark>`r` Kerenssa Kay</mark><sup>2</sup> JEL classifications: C83, D73, H7, H83 Keywords: Survey Methods, Bureaucracy, Administrative Processes in Public Organizations, Corruption, Public Administration, State and Local Government

> 1 Certified random author order. The authors gratefully acknowledge excellent research assistance from Robert Lipinski, Ayesha Khurshid, and Srishti Gupta, as well as financial support for the underlying data collection from the World Bank’s i2i and DIME initiatives and Governance Global Practice, UKAID, the European Commission, the University College London (UCL) Global Engagement Fund, Nottingham and Stanford. Further contributors to the Global Survey of Public Servants are listed at https://www.globalsurveyofpublicservants.org/surveyteam. 2 **Schuster** : University College London; **Mikkelsen:** Roskilde University; **Rogger, Kay, Hasnain** : World Bank; **Fukuyama, Mistree** : Stanford University; **Meyer-Sahling** : University of Nottingham; **Bersch:** Davidson College.
While the belief shock on knowledge externalities did not affect valuations as anticipated, it is possible to exploit the richness of the elicitation data to shed some light on what drives farmers’ valuations for others. To do so, I let four parsimonious model selection algorithms choose which variables out of 16 best explain _wij_ . The results of this exercise are displayed in Figure 5. The 16 variables I feed to the algorithms are: true plot distance, perceived plot distance, home distance, whether _i_ knows _j_ , whether _i_ visits (or receives visits from) _j_ , whether _i_ and _j_ are relative, whether _i_ and _j_ socialize, whether _i_ borrows (or lends) money from _j_ , whether _i_ borrows (or lends) goods from _j_ , whether _i_ receives (or gives) advice to _j_ , whether they speak about agriculture, whether they pray together (see Appendix F for variable definitions). These variables measure the extent of social connection between two individuals in different life domains, and are identical to the measures used by Banerjee et al. (2013) to quantify social connection between Indian villagers (but adapted to the Ugandan context). The model selection algorithms used in this exercise are three Lasso (adaptive, plugin, and minimum BIC) and Forward Stepwise. I adopt the following procedure: i) standardize regressors; ii) residualize both _wij_ and each regressor on fixed effects for _i_ and _j_ , such that the remaining variation is specific to the _ij_ dyad; iii) run the algorithms such that each selects the model with the highest explanatory power; iv) regress _wij_ on the four selected models. Figure 5 shows the coefficient sizes of regressors in each selected model, expressed in standard deviations. Whether farmers know each other is the predictor with the largest coefficient and is picked up by three out of four selection algorithms; it is followed by receiving advice from, paying visits to, and lending money to the other farmer, which are picked up by all four algorithms. Socializing also matters but
Although not based on a representative sample, Table 3.3 enriches the sectoral picture by showing data from the FGD study. HEs are categorized according to three major non-agriculture sectors (manufacturing, trading, and services) on the basis of the participants‟ description of their activities. Not surprisingly, a substantial number of HEs are engaged in some form of trade. Next in importance are manufacturing enterprises. This sectoral structure is in part a reflection of the ease of entry into informal trading (see Box 3.1), but also of the constraints HEs faced in undertaking production and other activities requiring electricity, fixed premises, labor, and skills.

**Table ‎3.3:  Types of HE Activities, by Sector, 2006**

|Trading||Manufacturing||Services||
|---|---|---|---|---|---|
|_Activity_|_Incidence_|_Activity_|_Incidence_|_Activity_|_Incidence_|
|Sellingroasted/fried cashew nuts|2|Tailoring|8|Motorcycle repair|2|
|Sellingwater|3|Makingsnacks|2|Video shows|1|
|Sellingsugarcanejuice|1|Restaurant|18|Decorations|1|
|Sellingbelts for clothing|1|Food vending|4|Women‟s hair saloon|2|
|Sellingsocks and wallets|1|Juice makingand selling|1|Motor vehicle mechanics|<br>1|
|Sellingfruits and vegetables|9|Masonry|1|Transport(_bodaboda_)|3|
|Dealer in used clothes|7|Carpentry|2|Pharmacy|2|
|Selling cellular phone SIM cards<br>and air-time vouchers|4|De-husking cashew nuts|1|IT equipment and video<br>cassette hiring|3|
|Sellingcookingstoves<br>|1|Foodprocessing|2|||
|A small shop/_genge_<sup>_7_</sup>|20|Local brew making|1|||
## Policy Research Working Paper 1712

# **Abstract**

This study investigates gender disparities in the tax burden in Addis Ababa, Ethiopia, using data on 2,320 taxpayers for 2011 and 2012. A quantile regression analysis is employed to control for firm characteristics such as sector, size, and age. The results show that women-owned businesses are more likely to operate in low-profit sectors and report lower sales and tax liabilities than men-owned businesses. However, women-owned businesses pay as much as men-owned businesses in taxes, suggesting that they are subject to a higher effective tax rate. This, in turn, may lead to women-owned businesses exiting the tax net at a higher rate. These findings suggest that gender disparities in tax compliance are not simply due to differences in firm characteristics but may also be due to biases in tax declaration and enforcement processes.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aambel@worldbank.org ; w.firew@gmail.com. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy
) for birth year and birth order and for _Firstborn girl_ and state FE in columns (1)-(4). The vector _Xijt_ comprises mother’s age at birth and, except in column (5), household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Data: NFHS. *** 1%, ** 5%, * 10%.

41
to government.

We initially present the government guarantee mechanism in its most general form, with a non-specific “target function” for each partner in the concession. The paper then discusses in greater detail three specific mechanisms: ensuring an exchange rate level, with the government guaranteeing a specific inflation-adjusted exchange rate; a guarantee of the net present value at the beginning of the franchise; and a guarantee of equal returns among (national and non-national) partners, while not committing to a specific return. Note that in all three instances, the mechanism is presented as an option, in principle at zero cost: at the contractual outset of the concession, the investor decides whether she wants protection or not, and what share of the project is to be insured. The mechanism is also adaptable to countries with limited regulatory resources, insofar as the tests to establish if the partner supplying capital in foreign currency should enjoy additional concession time (the “test function”) can be conducted in notional (or proxy) values, thereby reducing information requirements from the concession to the government, and thereby minimizing the probability of (bilateral) opportunistic behavior, while eventually facilitating court decisions.

Finally, empirical tests use data from a real world 25-year highway concession and exchange rates from 8 countries – Mexico, Peru, Chile, Brazil, South Africa, Mozambique, Indonesia and the Philippines – to show that the variations in concession length resulting from a hypothetical guarantee are relatively small under alternative functions. Given the baseline of 25 years, it is striking that the adjustment is less than 4 years (that is, 16% of the project’s length) for all countries. It is equally noteworthy that in six of the sixteen simulations per2
categorized non‐work activities as “work” (Campanelli, Rothgeb, and Martin, 1989).<sup>12</sup> A 1991 experiment to evaluate the CPS questionnaire revision used direct screening questions and vignettes for unreported work and found that both the sequence of questions as well as their wording influenced respondent interpretation of work and affected the employment statistics generated (Martin and Polivka, 1995). Specifically, and of interest for our setting, the study found that using direct screening questions helped in detecting under‐reporting of work related to household business or farm, as well as underreporting of teenage work. These concerns are highly relevant for developing countries, where much work activity is informal and risks to go undetected in a survey. This may be particularly true for female work, and several scholars have expressed concerns about the underreporting and undervaluing of women’s work when using common survey methods to collect employment data (Anker, 1983; Dixon‐Mueller and Anker, 1988; Charmes, 1998; Mata Greenwood, 2000).<sup>13</sup> In previous work, we illustrate that this is also relevant in the Tanzanian setting when obtaining labor statistics. Using a short questionnaire, without screening questions, seems to induce individuals to adopt a broad definition of employment, which tends to include domestic duties. But even after reclassifying these – to obtain the correct ILO classification, the short module results in lower female employment rates, higher working hours for both men and women who are working, and lower rates of wage work (Bardasi et al 2011).

Surveys with a labor content can be categorized into two groups: those making use of multiple detailed questions to find out whether the respondent is economically active and participating in the labor force during the reference period (typically the last seven days), and those using one question only to determine this. To illustrate the importance of this survey design feature in recently implemented surveys, Table 1 provides an overview of surveys with a labor content
**Table 10** . **Changes in Self-Reported Food Insecurity from 2007 to 2008**

||**Prevalence of food insecurity (%)**<br>**Population of food insecure (millions)**|
|---|---|
||_48 developing countries (43.3% of developing world population)_|
|2007|29.33%<br>821.4|
|2008|29.28%<br>820.1|
||−0.05 percentage points<br>−1.3 million|
||_47 developing countries excluding India (23.3% of developing world population)_|
|2007|31.51%<br>532.8|
|2008|34.04%<br>575.9|
||2.53 percentage points<br>43.1 million|

_Source_ : Author’s calculations from GWP data (Gallup 2011) .

Although we have explored validity issues in previous sections, another relevant question is whether the GWP results are supported by any other survey evidence. One other reasonably large survey of developing countries that was conducted before and during the crisis is the Afrobarometer survey. A recent working paper by Verpoorten, Arora and Swinnen (2011) explores trends in an Afrobarometer indicator that pertains to a very similar question to the one asked in the GWP and finds a 3-percentage-point increase in food insecurity in urban Africa from 2005 to 2008 and a 2-percentage-point increase in rural Africa. Thus, the overall picture of some deterioration in food insecurity in Africa is common across both the GWP and Afrobarometer surveys. Second, and perhaps most important, the most recent World Bank estimates of poverty trends also suggest that global poverty fell between 2005 and 2008 on every continent (World Bank 2012). Third, the FAO (2012) has revised its estimates of large increases in global hunger in 2009. The most recent estimates show a relatively steady
**Figure 12: Survival of Russian Exports by Category, 2002–12**

<!-- Start of picture text --> 0 5 10<br>Analysis Time<br>Primary Products Others Resource Based<br>Low Tech High Tech Medium Tech<br>.6<br>.4<br>probability<br>.2<br>0<br><!-- End of picture text --> _Source_ : COMTRADE data.

Note: Analysis time is in years, with year 0 = 2002 and year 11 = 2012.

0
- Page, J., Plaza, S., 2006. Migration remittances and development: A review of global evidence. J Afr Econ 15, 245–336.

- Pandey, N., Pal, A., et al., 2020. Impact of digital surge during covid-19 pandemic: A viewpoint on research and practice. International Journal of Information Management 55, 102171.

- Ratha, D., De, S., Plaza, S., Schuettler, K., Shaw, W., Wyss, H., Yi, S., 2016. Migration and development brief April 2016: Migration and remittances—recent developments and outlook. The World Bank, Washington, D.C.

- Ratha, D., et al., 2007. Leveraging remittances for development. Policy Brief 3.

- Reuters, 2020. Bank of America prev ́e contracci ́on del 8% para econom ́ıa M ́exico en 2020 — Reuters. URL: https://lta.reuters.com/article/economia-mexico-bofa-idLTAKBN21K1XF.

- Romano, P.P.O., 2015. Immigrants and cross-border workers in the US-Mexico border region. Frontera Norte 27, 5–34.

- Ruiz-Dur ́an, C., 2016. Mexico: Financial inclusion and literacy outlook, in: Aprea, C., Wuttke, E., Breuer, K., Koh, N.K., Davies, P., Greimel-Fuhrmann, B., Lopus, J.S. (Eds.), International handbook of financial literacy. Springer Singapore, Singapore, pp. 291–304.

- Shimizutani, S., Yamada, E., 2021. Resilience against the pandemic: The impact of Covid-19 on migration and household welfare in Tajikistan. Plos one 16, e0257469.

- Simpson, N.B., Sparber, C., 2020. Estimating the determinants of remittances originating from US households using CPS Data. East Econ J 46, 161
# <u>Calibrated model behavior</u>

Recall that the model is calibrated around a set of prices and quantities, together with two distributions of supply shocks that the model randomly draws on in simulation. Because of this structure, the mean values of the simulated series will be near their calibrated values. However, the variations in the simulated variables are affected by the sets of randomly drawn supply shocks. Because these distributions are not specified directly, we use the ex post variability in the simulated distributions of price, demand, supply and trade as to provide a validation check on the aggregate consequences of our ex ante parameter selections. With this as background, the simulated and historical coefficients of variation are reported in Table 3. As the table makes clear, orders of magnitude are similar between the historical and simulated distributions.

As emphasized in our earlier discussion of the link between storage and price expectations, all other things equal, low inventories should be associated with both higher expected price and more volatile price outcomes. This relationship is evident in Figure 3, which maps next-period simulated prices and expected prices against carry-in stock levels. Expected prices rise as carry-in inventories fall and the model-generated prices become more dispersed.

Before moving to a discussion of alternative policies, a couple of points should be made about trade and storage outcomes in the baseline model. First, given the large gap between production and demand in MENA, the simulated model never generates an outcome in which wheat is exported from the region. This is consistent with expectations since MENA has not been a net exporter of wheat in the last fifty years.

The second point has to do with speculative storage—that is, storage that is held in order to profit from expected temporal price changes. Data series, including the data reported in Figure 2, contain all forms of storage: speculative storage, public storage, and pipeline storage that is needed to keep shipping and processing operations running smoothly. It is impossible to isolate speculative storage in the data; however,
collected by the Center for Monitoring the Indian Economy (CMIE), a private data collection agency.

Using these alternative surveys, the remainder of this section reports summary statistics on recent trends in living standards.

# **2.1. Official data sources**

The SCSs on Education and Health are nationally representative surveys with a sample size of around 65,000 households in the earlier years, and around 100,000 in 2017/18. These surveys include a question on usual household consumption that is not comparable to the more comprehensive consumption aggregates produced for poverty estimation in the CES. The SCSs on Health and Education both show higher average consumption in 2017/18 than in previous waves.<sup>8</sup> Mean household consumption per capita appears larger in the CES than in SCS, for both urban and rural areas, although it is difficult to draw comparisons since the surveys were fielded in

> 7 Differences in the recall period of these different items led to different consumption aggregates over time. The 2011/12 survey included three different definitions of the aggregate: the so-called Uniform Reference Period (URP), Mixed Reference Period (MRP) and the Modified Mixed Reference Period (MMRP). The 2017/18 survey only included the MMRP. For details on the consumption aggregates, see http://mospi.nic.in/sites/default/files/publication_reports/KI-68th-HCE.pdf. Also see discussion in the Appendix. 8 We do not report the evolution of the consumption aggregate in the Periodic Labor Force Survey because there is no comparable survey before 2017. Comparing the PLFS (2017/18) and SCS Education (2014), Himanshu (2019) estimates that real consumption per capita declined by about 4 percent and 0.6 percent in rural and urban India, respectively.

4