rafmacalaba/data-use-mentions-extended huggingface dataset:
government procurement practices conducted by the World Economic Forum and reported annually in the _Global Competitiveness Report._ Table 4 presents the average responses (scored on a scale from one to seven) of business people to five questions about public procurement policies in 46 developing countries in 2001.<sup>13</sup> (The five questions are stated at the top of table 4.) For each of the five questions posed, business people perceive that 10 developing countries fall below the respective 46-country sample average; suggesting that these 10 countries are seen to have poor procurement practices across-the-board. The perception that state officials “favor well connected firms and individuals” is strong in Bolivia, the Dominican Republic, Ecuador, Guatemala, Honduras, and Zimbabwe (all of which score less than two on a scale of one to seven.) Furthermore, “irregular extra payments or bribes connected with public contracts/investment projects” appear to be more prevalent in Bangladesh, Bolivia, Nigeria, Paraguay, the Ukraine, and Zimbabwe (all of which score less than three) than elsewhere.

While only suggestive, the survey responses reveal there is considerable variation in the perceived even-handedness of procurement practices across countries.<sup>14</sup> This in turn may have implications for domestic and foreign firms’ investment decisions. Unfortunately, no information can be distilled from such surveys regarding the extent to which differences in underlying procurement laws and their implementation affect these aggregate perceptions, or how responsive the latter are to reforms. To the best of our knowledge, no one has attempted to answer this empirical question. The paucity of quantitative, cross-country analyses of national procurement practices is striking.

> 13 For comparison purposes, the mean values of responses in the OECD nations are also reported.

> 14 These survey responses have been criticised as subjective, as potentially biased by different cultural norms, and by the fact that few business people are familiar with the procurement policies in many jurisdictions—so raising the question as to how the survey’
Table 2 (Cont’d). Descriptive Statistics – Cox’s Bazar Panel Survey

||Average|Std. Deviation|Observations|
|---|---|---|---|
|_E. Health-related Outcomes_||||
|Suffered from diarrhea last month [Yes=1]|0.071|0.257|1,842|
|Suffered from fever last month [Yes=1]|0.493|0.500|3,374|
|Suffered from cough last month [Yes=1]|0.224|0.417|2,206|
|Health provider: UN|0.006|0.076|7,173|
|Health provider: NGO|0.017|0.130|7,173|
|Health provider: Privately run|0.788|0.409|7,173|
|Health provider: Religious group|0.005|0.071|7,173|
|After 2017: Improved overall health care [Yes=1]|0.784|0.411|4,344|
|After 2017: Improved treatment in health care [Yes=1]|0.683|0.465|2,954|
|After 2017: Improved health care safety [Yes=1]|0.608|0.488|2,388|
|After 2017: Improved health care accessibility[Yes=1]|0.698|0.459|3,102|
|_F. Education-related Outcomes_||||
|Student [=1]|0.577|0.494|7,950|
|Primary Education [=1]|0.768|0.422|1,749|
|Junior Secondary Education [=1]|0.292|0.455|1,156|
|Secondary Education [=1]|0.196
- Rossi, M., & Trucchi, S. (2016). Liquidity constraints and labor supply. European Economic Review, 87, 176-193.

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-

- Tangney, J. P., Baumeister, R. F., & Boone, A
# 3 Empirical Results

I exemplify the use of the method by estimating the responses of quantities consumed and agricultural wage income in rural Mexico. The case of Mexico is, in principle, a good case study because of the importance of the agricultural sector. Mexico is also close to the United States, and thus WTO reforms are likely to reach rural producers.

In Mexico, a good source of data is the Household Income and Expenditure National Surveys, ENIGH (Encuesta Nacional de Ingresos y Gastos de los Hogares). During the 1990s, the data were collected every other year; I use data from the 1994, 1996, 1998, and 2000 rounds. The surveys are representative of the population at the National level, and cover rural and urban areas. Since I am focusing on the impacts of price reforms on wage agricultural income, I use the rural modules only. Table 1 reports some summary statistics.

I begin with sample sizes and number of clusters c. The ENIGH is carried out at different weeks in different districts. To make the assumption of common prices in a given cluster c reasonable, I define them as district-week pairs. There are between 3306 and 5007 rural households in the samples. There are 1095 clusters in the pooled sample, approximately 275 in each ENIGH round. The number of households interviewed in each “cluster” is typically larger than in other surveys, with at least 20 households in each of them.

On the consumption side, expenditure data include food and non-food items. Since quantities are only reported for foodstuff, I estimate the model for the following six goods: corn, wheat, dairy, oils & fats, meat, and fruits & vegetables. These goods are aggregates of differentiated goods within each category. For instance, corn includes own-consumption, corn grain, and finely grinded corn; meat includes different types of beef, poultry and pork. These varieties are considered homogeneous goods. Consumers choose different varieties of good, but report expenditures and purchases of consumption aggregates (see Appendix 1
firms results in declines in productivity and economic growth. As a result, a rise in government debt is associated with a decline in corporate investment and R&D. Crowding-out of private investment due to high public debt is also shown in Huang, Pagano, and Panizza (2017), with data for China, and Panizza, Huang, Varghese (2018), with firm- and industry-level data across 69 countries. They find that the negative relationship between public debt and private investment reflected tightening credit constraints.

# 5. Debt: How much is too much?

Weighing these benefits and costs of debt, the literature has attempted to identify how much debt is “too much”—a threshold level of debt below which it is sustainable or not harmful to growth. A rich theoretical literature has focused on the interactions between governments, monetary authorities and private agents in response to numerous shocks. The empirical literature has estimated a wide range of threshold values that appear to be tipping points for adverse effects of debt.

> 7 For details about the links between government debt and interest rates, see Ardagna, Caselli, and Lane (2007); Codogno, Favero, and Missale (2003); Laubach (2009); Rubin, Orszag, and Sinai (2004).

12
# Voice and Punishment:

# A Global Survey Experiment on Tax Morale

Fredrik M. Sjoberg Jonathan Mellon Tiago Peixoto Johannes Hemker Lily L. Tsai 2019/7/3 Word count: 8,173 H26 - Tax Evasion and Avoidance; H41- Public Goods; H50 - Government expendituresGeneral Keywords: tax morale, cross-national experiment, retributive justice.

This paper is a product of the <mark>Digital Citizen Engagement</mark> Unit, <mark>Governance Global Practice D</mark> epartment. 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://econ.worldbank.org. The authors may be contacted at <u><mark>tpeixoto@worldbank.org</mark></u> .
dotted line C. The prevalence of malnutrition for B is the smallest followed by C and A. This argument is consistent with Deaton (2003). It is also similar to the argument made by Ravallion (1988) and Haddad and Kanbur (1990), though they dealt with welfare risk and intra-familiar distribution respectively.

To see if Figure 6 is reasonable, we also regressed average severity of malnutrition among _<u>P</u>_<sup>2</sup> malnourished children ( _i.e. P_<sup>0)on</sup><sup>_Y_ ̄andGE(0)aswellasprovincial-leveldummies.We</sup> found that the coefficient on GE(0) was neither significant for stunting nor underweight. Once we include the prevalence of malnutrition _P_<sup>0</sup> on the right hand side, GE(0) has a positive and significant coefficient for both malnutrition indicators as shown in Table 5. This means that, given the prevalence of malnutrition and the logarithmic mean of consumption, the average severity of malnutrition among malnourished children within the same province tends to be higher in communes with higher inequality. This is consistent with Figure 6, because the average severity among malnourished children tends to increase with increased level of inequality regardless of the location of the threshold X on the horizontal axis.

If the effect of consumption on the prevalence of malnutrition is linear, inequality should have no effect on _P_<sup>0</sup> of malnutrition. Hence, non-linear effects of consumption on nutrition are extremely important in understanding the relationship between consumption and malnutrition. This is particularly the case when we have only aggregate data. Prevalence is an important indicator, but we also need to look at the FGT measures with higher parameter values. DHS estimates are typically too aggregated to meaningfully investigate aggregate relationships. The small-area estimates of consumption and health are very helpful for exploring the relationship between poverty, inequality and malnutrition.

# **6.2
1=less than primary up to 8= graduate degree), feeling safe while home (1= very safe up to 4= very unsafe), child gender (0=male, 1=female), refugee status and child age (continuous in months) Individual linear regression analyses were run with each exposure variable (depression, anxiety, parenting stress) on the AIM-ECD sum score, measured as percentage correct (Table 8). The unadjusted model regressed the exposure on ECD for 3- to 6-year-olds controlling only for child age, while the adjusted regression analysis regressed the exposure on ECD for 3- to 6-year-olds controlling for food insecurity, flooding impact, fear of husband, asset index, maternal education, feeling safe when home after dark, child gender, refugee status, child age, and enrollment in early childhood education. A significant positive association (p<.05) of parenting stress on ECD for children aged 36-72 months was found in the unadjusted and adjusted models. A significant negative association was seen between maternal anxiety and ECD for 3- to 6-year-olds in the adjusted model, but not the unadjusted model. No association was seen between maternal depression and ECD in either the unadjusted or adjusted model. However, the coefficient in both instances is negatively signed.

**Table 8** _Individual Linear Regression Results of Parenting Stress, Depression, and Anxiety on ECD (36=_ _<u>72 months) Direct Assessment (n 635)</u>_

|Outcome|Predictor|Beta<br>Coefficient|Linearized<br>Standard Error|t-value|p-value|R-<br>Squared|
|---|---|---|---|---|---|---|
|ECD Unadjusted|Parenting Stress|0.007|0.002|3.560|<.001|.092|
||Depression|-0.001|0.002|-
conducted on a continental basis. Specifically, it is possible that food prices in Africa are biased upward by methodological issues, although it is difficult to substantiate such a claim. A more general problem with purchasing power parities is the challenge of finding common items to compare across countries. Exchange rate distortions may be

> problematic for this index, although data on black market premia on exchange rates suggest that most exchange rate distortions have declined markedly over time.

13
These sources of information combined with traditional data allow new approaches for empirical investigation. For example, Blumenstock et al. (2018) combine firms’ mobile phone and administrative data with geocoded violent events data to investigate how Afghanistan’s private sector reacts to insecurity. Using existing cell phone network data in this case was particularly fruitful because of the unsafe and violent context of the country.

In countries where data availability is constrained, or where data have not been collected, mobile phone metadata can provide important insights into population behavior and characteristics. This source of information not only reveals an individual’s intricate social network, but can reveal communications frequency, intensity, directionality, and timing; travel patterns; migration patterns and specific locations; and consumption and expenditure histories with much more frequency than traditional survey information.

Cell phone technology uses base transceiver stations to connect cell phones with the network, and ranges for stations vary from less than 1 km<sup>2</sup> in dense urban areas to more than 3 km in rural areas. Thus, mobile phone data permit a much more disaggregated geographic level of analysis compared to household surveys, which are limited to state or district levels (Soto et al. 2011). In theory, the frequency and locational advantages of mobile phone network data could be combined with new methods for real-time policy evaluation and population monitoring in remote, violent, and inaccessible regions.

# <u>Data requirements and pitfalls</u>

While some studies have shown that an individual’s footprint can predict her socioeconomic level, and therefore substitute for census or household data (Blumenstock, Cadamuro & On, 2015; Soto et al., 2011), most studies use network data to complement more traditional data sources. For example, Gutierrez, Krings, and Blondel (2013) created a predictive model to extract indicators of wealth, inequality, and segregation from mobile phone data. They suggest that network data sets represent a starting point to understand socioeconomic variables in countries with no
# **Panel G – 1994 Start Ages**

Start ages from the 1994 census are used to calculate all intensity measures. The timing of this survey is problematic in the sense that the MTI implementation had already begun at this time. This along with any anticipation of the forthcoming FPE program could alter the decision to enter school in 1994.

# **Panel H – Three Part Trend**

The district-specific linear trends are replaced with a set of district-specific trends that are allowed to change slope at two points, in 1978 and in 1987. On time entrants are partially treated beginning with the 1978 cohort, and fully treated beginning with the 1987 cohort.

# **Panel I – Regional Trends**

The district-specific linear trend is replaced with a region-specific linear trend.

# **Panel J – No Trends**

All trend variables are removed from the estimating equations.

# **Panel K – Only Zones in All Rounds of the DHS**

Data are restricted only to zones with observations in all three of the rounds of the DHS survey. This includes 25 of 30 zones in the non-MTI regions, and 48 of the 60 zones throughout Ethiopia.

# **Panel L – Zones with Fewer than 4,000 Organized Violence Deaths (1989 to 1991)**

Data from the four zones with the highest level of pre-independence violence, three of which are in the non-MTI sample, are removed from the sample. These zones contain more than 75 percent of all deaths included in the data over this time period.

# **Panel M – Zones with Fewer than 500 Organized Violence Deaths (1989 to 1991)**

Data from 13 zones with more than 500 deaths related to organized violence in the pre-independence period, eight of which are in the non-MTI sample, are removed from the sample. These zones contain more than 96 percent of all deaths included in the data over this time period.

# **Panel N – Zones without High Intensity of Famine (1985)**

Areas
In exploring the prospects for services-led development compared to the traditional export-led manufacturing model, the following is worth emphasizing. First, that services may “need” a manufacturing core to develop does not take away from the fact that many services, in turn, are vital inputs into the production of manufactured goods. This symbiotic relationship between the two sectors therefore requires a reexamination of the linear structural change process from agriculture to manufacturing and then services.

Second, productivity gains from resource reallocation will also happen within sectors, for which there is evidence of large heterogeneity across firms (McMillan and Rodrik, 2011; Bloom et al. 2010, Syverson 2011). Hsieh and Klenow (2009) find that between a third and half of the differences in manufacturing total factor productivity between China and India and the United States can be explained by the large number of inefficient firms. This dispersion in the productivity distribution of firms applies equally, if not more, to services. Using firm-level data from Portugal, for example, Dias et al. (2016) show that reducing misallocation in the service sector to manufacturing levels would boost aggregate value added by around 31 percent. The importance of productivity gains within sectors is underscored by China’s experience where reallocation accounted for only one-fourth of the productivity growth in the three decades between 1980 and 2010 (Herrendorf, Rogerson and Valentinyi 2013).

Third, firms are increasingly structured around the close interaction of ‘manufacturing’ and ‘services’, which makes it difficult to assign them exclusively to one sector. Manufacturing companies increasingly no longer sell only physical goods, but instead sell bundles including design, development, marketing, warranties and after-sales care, etc. Xerox, for example, has restructured itself into a ‘document solution’ company, offering technology advanced printer systems but also services like document managing and consulting; in fact, services represent around 40% of Xerox’s turnover and are soon expected to represent more than 50% (Benedettini et al. 2010). Similarly, many services firms
**|-0.448***|
||(0.060)|(0.058)|(0.063)|(0.067)|
|(QuangTri & Hue) _×_ post|-0.429***|-0.400***|-0.574***|-0.546***|
||(0.095)|(0.092)|(0.028)|(0.029)|
|Observations|7,991|7,991|2,090|2,090|
|R-squared|0.311|0.294|0.067|0.069|
|Month FE|Yes|Yes|Yes|Yes|
|Individual FE|No|No|Yes|Yes|
|Standard errors in parenth|eses, *** _p_|_<_0_._01, ** _p_|_<_0_._05, * _p <_|0_._1|

Note: this table shows the heterogeneous impact of Formosa on fishery income (monthly;’000 VND; logtransformed) in 2016, separately for the upstream (Ha Tinh & Quang Binh) and downstream (Quang Tri & Thua Thien-Hue) Formosa-affected regions. “Post” indicates May-2016 or after. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. Columns 1-2 report results using a sample consisting of all individuals identified as saltwater fishery workers (for Panel A) and workers in the control industries (for Panel B) before May-2016. Columns 3-4 report results using a sub-sample restricting to only individuals who were surveyed twice in the Labor Force Survey 2016 – before and after
, 1977). The weights for each dataset drawn from this pair wise comparison matrix are summarized in Table 3.

||**Annual**<br>**average of GHI**|**Renewable groundwater**<br>**resources**|**Aquifer**<br>**storage**|**Population**<br>**density**|**Static**<br>**water level**|**Aquifer**<br>**transmissivity**|
|---|---|---|---|---|---|---|
|**Annual average of GHI**|1|2|3|4|5|6|
|**Renewable groundwater**<br>**resources**|0.5|1|2|3|4|5|
|**Aquifer storage**|0.33|0.5|1|2|3|4|
|**Population density**|0.25|0.33|0.5|1|2|3|
|**Static water level**|0.2|0.25|0.33|0.5|1|2|
|**Aquifer transmissivity**|0.16|0.2|0.25|0.33|0.5|1|

_Table 2 - Pair wise comparison matrix for the risk of over exploitation of groundwater resources._

|**Dataset**|**Weight**|
|---|---|
|Annual average of GHI|38 %|
|Renewable groundwater resources|25 %|
|Aquifer storage|16 %|
|Population density|10 %|
|Static water level|6.5 %|
|
# **Annex: Data**

Most of the data utilized in this study come from a series of Encuesta de Ingresos y Gastos de los Hogares (ENIGH), collected by the Instituto Nacional de Estadistica Geografica e Informatica (INEGI), and conducted in the third quarters of 1989, 1992, 1994, 1996, 1998 and 2000. ENIGH is a rich household survey built for the purpose of measuring the consumption and earnings of Mexican households. Even if the size of the ENIGH has varied from year to year, and its questionnaire updated from survey to survey, the conceptual framework remains the same. This ensures that ENIGH’s results are comparable across years. The survey is stratified according to urban and rural location. The sampling is done to assure that households are representative of geographic clusters with probability of being included proportional to cluster size. Most of the analyses (specifically the welfare calculations) are based on the 2000 survey, which has a sample of about 10,000 households.

Trade data was obtained by the UN COMTRADE database. Tariff data was obtained from the TRAINS UNCTAD database. Further data comes from other national account statistics and from the World Bank WDI database.

A key input in the paper is represented by the price changes estimated by the GTAP model. The GTAP model (Hertel, 1997) is a standard multi-region applied general equilibrium model. It has perfectly competitive markets, constant returns to scale technology, and a supply-side that emphasizes the role of inter-sectoral factor mobility in the determination of sectoral output. Product differentiation between imports and domestic goods, and among imports by region of origin, allows for two-way trade in each product category, depending upon the ease of substitution between products from 28