rafmacalaba/data-use-mentions-extended huggingface dataset:
the firm site selection issue. Imagine a case where firms update their beliefs about location j’s future weather shocks as the climate scientists are predicting that location j faces medium term extreme weather risk. Forward looking firms will start to migrate to relatively safer locations over time. In this case, an econometrician who estimates equation (1) should be concerned that the firms who continue to locate in location j are not a representative sample of firms. One selection hypothesis would be to posit that better managers might locate in such areas if they have a risk adaptation edge. In this case, they can rent cheap land and still be productive. Another selection hypothesis would posit that worse managers will choose to locate in the riskiest areas because avoiding risk is a normal good.

In estimating equation (1), the researcher does not observe the costs that the firm has incurred to offset extreme weather. Such a regression does estimate the marginal cost to the firm of being exposed to extreme weather. If the firm has incurred upfront costs to adapt, then the econometrician is likely to estimate smaller marginal coefficients in equation (1). In an economy where firms are anticipating more extreme weather, firms will invest more in adaptation and the econometrician will actually estimate a smaller marginal damage coefficient over time.

The climate damage estimate literature relies on observational data as extreme weather provides the variation. Adaptation scholars can rarely implement field experiments here. A field experiment might consist of randomly distributing risk report cards to firms to educate them about the risks their geographic location faces. If firms trust the information source and were unaware of the risk, then this “new news” may affect their adaptation investment. Survey research and observational data on subsequent output can be used to study whether such an information nudge accelerates adaptation investment and lowers the marginal damage (B1 in equation 1) for the treated firms. Another field experiment research design would be to randomly assign different subsidies to firms for purchasing adaptation products. This price variation would trace out the demand curve
real-time traffic anomalies that arise from accidents, roadworks, and other disruptive events. Similar approaches have been used to develop an intelligent interface platform in Dublin (Daly et al., 2013) and a smartphone application in the United Kingdom (see Hampson et al., 2017) that provide real-time information on traffic congestion sources. In the case of the United a mobile known as Kingdom, application Twitraffic provides real-time traffic information from users’ tweets. On average, Twitraffic is believed to detect traffic incidents about 7.1 minutes before they are picked up by the UK government’s highway agency data (Hampson et al., 2017).

# C. Urban Sensing Technology

In addition to mobile and social sensors, credit cards, smart cards in public transit, retail scanners, and digital toll systems, are increasingly being used as “urban sensors” (Blazquez and Domenech, 2018). Data generated through the urban sensors can be used to measure broad patterns of urban mobility and to some extent traffic flows and congestion.

Transponders at toll plazas can be used to measure personal and commercial vehicle flows along a road segment (Kitchin, 2014), and eventually track variations in economic activities. For example, Askitas and Zimmermann (2013) explore data from an electronic toll collection system in Germany to generate a monthly Toll Index, which they use to nowcast business cycles. Smart cards in public transit, such as SmarTrip in the Washington metropolitan area and Oyster in London, can be used to track individual travel behavior, and generate useful information on economic and social outcomes. It is possible, for example, to characterize a cardholder’s commuting trajectory while generating information on his/her place of residence and employment status by combining smart cards with credit card data (Wang and Moriarty, 2018). Smart cards also allow for granular analyses of transport behavior. As an example, Tao et al. (2014) explore a large smart card database to examine the spatial-temporal dynamics of Bus Rapid Transit (BRT)
reflect the actual situation on the ground, and have the most direct effect on accountability, irrespective of the different dimensions of OGD that they may cover. These include the aggregate score for data on maps, land ownership, census, government budget, government spending, company registration, legislation, public transport, international trade, health, education, crime statistics, environment statistics, election results, and contracts. Conversely, in the regression specification related to the relationship between OGD and service delivery, the Open Data Barometer Raw Scores were used **_only in selected data dimensions._** Based on the literature, for governments to make informed policy, budgetary, and service delivery decisions based on data, data on sector performance are needed as well as data on budget allocation and execution. To be sure, the results would be skewed if for instance a country had a high degree of public transport data, but a low level of health sector data—and the aggregated levels of data openness were used. Accordingly, this analysis re-constructs the OGD variables as follows: Health OGD: quality of health sector data (50% weight), budget data (25% weight), spending data (25% weight). OGD related to the health sector typically includes data on Mortality and survival rates; Levels of vaccination; Levels of access to health care; Health care outcomes for particular groups; Patient satisfaction with health services; Waiting times for medical treatment; and Spend per admission. Education OGD: quality of education sector data (50% weight), budget data (25% weight), spending data (25% weight). OGD related to the education sector typically includes data on Test scores for pupils in national examinations (not only rates of approvals); School attendance rates; and Teacher attendance rates.

> 122 Importantly, in order to reduce the chance for a distorted correlation between OGD and accountability, three sub-indicators that are particularly endogenous were removed and the index was recalculated (see annex 2 for
# **Endogeneity concerns**

Extending the baseline model by including additional control variables controls for any omitted variable bias and accounts for potential endogeneity concerns. These additional control variables are formal and informal labor productivity, elevation and night lights emission. First, informal labor productivity denotes the productivity of informal businesses and strives to control for a potential endogeneity stemming from more productive businesses implementing more digital technologies (see top rows in Table 4). The effects of distance on different types of digital technologies are in line with the baseline results. In particular, the results indicate statistically significant negative effects of distance on the adoption of various digital technologies, suggesting that the baseline results are not driven by the potential common factor that more productive businesses are more likely to adopt digital technologies. The middle rows show results when formal labor productivity, which is the productivity of the closest formal firm, is included. Formal labor productivity controls for the fact that spillovers might be driven by informal businesses being close to very productive formal firms. The results remain robust to the baseline.

**Table 4: Baseline Model with Labor Productivity**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm|-0.567*|-0.749***|-0.187***|-0.196***|-0.196|
||(0.314)|(0.154)|(0.009)|(0.036)|(0.405)|
|Informal Labor Productivity (log)|0.056|0.093|0.174***|0.184***
typically goes fishing alone or accompanied by another household member or relative. The beach-landing fiber boat is a relatively new technology, with the first such boats appearing in Tamil Nadu in the mid-1990s. Fiber boats typically have a crew of four, some of which can be relatives of the boat owner and the rest are laborer-fishermen. Every crew member earns a daily minimum wage and a percentage of the value of catches.

On a typical day, boats leave the shore around 1 am and land back at the market place on the beach between 7 and 11 in the morning. There, local fish auctioneers and staff from the fishermen association market the catches to a group of buyers, which comprise local traders as well as agents of nation-wide operating fish-processing companies. Prices are fixed in advance every week depending on the season and variety of fish, so most of the variation in the value of catches on a day to day basis comes from quantity and variety rather than price. Fish that are too small under international legal minimum size standards are sold at the local market at a discount.

Our data come from three different sources. Boat owner characteristics for all 272 boat owners come from a household survey conducted in November 2007. We then fielded our methodological experiment in July 2009 to elicit expectations about August catches. Actual daily catches for August then come from the hand-written records kept by both the fishermen association and the auctioneers. Daily records for five individuals show that they did not go fishing in August, as they decided to work as laborers for large mechanized boats. In addition, we do not have daily catch data for two boat owners because the auctioneer they work for refused to share his records. Two individuals also refused to answer the expectations survey. As a result we have expectations data for 270 boat owners, and realizations of August catches daily for 263 boat owners.

Table 1 provides some basic demographic characteristics of the sample of boat owners. All boat owners are male, with an average age of 41 years. Mean years of
The model uses data from the input-output (IO) table which provides more details about intermediate demand between sectors and the composition of final demand across sectors. The IO table maps the different intermediate products (notably energy projects) used to produce the goods and services that comprise final demand. By using the technical coefficients from this table (how many units of electricity and coal are needed to produce a unit of transportation) and time series data for final demand and aggregate sectors, we are able to generate pseudo time series for the intermediate demands of each sector (assuming that the technology of production is constant over time).

The technical relationships between elements of final demand and sectors of production that are summarized by the IO table are illustrated in the following diagram, which focuses on a single sector i.

Government Households ii,II ii,CC ii ii,ττ ααii,GGGG αα II αα CC VVAA + αα ττ<sup>KKIIDD</sup> Foreign countries Sector i Other sectors ii,XX ii,IIKKDDSS SS αα XX αα IICCTT ii,MM ii,IIKKD DD αα MM αα IICCTT Effectively, one unit of demand for exports will imply units of final demand for the output of sector I, which in turn would generate units of intermediate demand for the output of each sector j. In ii,XX αα this way each component of expenditure GDP is shared out to individual sectors. E.g. the exports of sector ii,XX jj,ii are written as the share of total exports, given by αα ∗ αα . It is important that = 1 , such that all of exports are shared out. Imports are given by . The intermediate goods supplied to and demanded from ii,XX ii ii,XX ii the other sectors is denoted as and , respectively. In addition, sector αα ∑αα supplies government MM consumption , investment goods and consumption goods . Value added ( ) and indirect taxes ( ) SS DD IICCTT IICCTT ii correspond to the value of capital and labor
chapters.

Chapter III discussed two methods of adjusting the covered part of output in terms of prices of the other country, X(QB * pM ) and M y y c (Qy * py)c to total gross value of output. In practice we prefer the method which assumes the price relationships for the covered part of output to be representative for uncovered output as well (see chapter III).

Table 4.4 presents the gross output quantity comparison calculated both in terms of pesos and cruzeiros. The Brazilian figures at national prices are directly derived from the Censo Industrial, and for Mexico from the Resumen General (see also table 4.1). The figures in country B at prices of country N are derived by adjusting the matched figure by the inverse coverage ratio for country B; for country M at prices of country B by using the coverage ratio of country M (compare with formula 3.5a and 3.5b in chapter III). Table 4.4 also shows the corresponding ratios of Mexican to Brazilian output.
Sudan during wave 4 in 2017 rather than inflation between waves 1 and 2 in 2016. The coefficients for the regression model for waves 1 and 2 are generally not statistically significant, while those for the regression for waves 1 and 4 are significant with the initial specifications above. This is consistent with the inflation dynamics between waves 1 and 2 and waves 1 and 4 and changes in welfare indicators (see Figure A16).

# **4. CONCLUSIONS AND POLICY RECOMMENDATIONS**

The high inflation in South Sudan continues to put many households under extreme financial stress, as they face increased prices without compensatory increases in income. This paper contributes to the available empirical evidence on micro-level impacts of inflation by analyzing the impact of high inflation on household livelihoods in urban South Sudan. We use panel data collected during the High Frequency South Sudan Survey (HFSSS) waves in 2015, 2016 and 2017. Increasing resilience to high inflation would allow to shift the inflation crisis management paradigm from a humanitarian to a development approach.

Breaking down inflation by food price inflation and non-food price inflation reveals that the latter increased drastically more than the former during this period. Although there have been significant increases in both food and non-food prices, the observed high inflation in South Sudan is mostly driven by the escalation of non-food prices. Inflation negatively impacted various household livelihood indicators related to poverty, education, labor, hunger and perceptions of welfare.

Inflation had a strong negative impact on urban poverty between 2015 and 2017, mainly driven by the increase of non-food prices. The loss of purchasing power of wages and salaries has driven many of the South Sudanese residing in urban areas into poverty. Continuous increases in inflation will only worsen the already high poverty situation. Addressing the issue of high inflation must be at the center of efforts to achieve stability the economy and reduce poverty in South Sudan. In addition, higher education has a key role for poverty reduction because the impact of inflation on consumption is significantly lower for households whose heads do have university education.

Food price
20-24<br>15-19 15-19<br>10-14 10-14<br>5-9 5-9<br>Under 5 Under 5<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Males Females Males Females<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text --> Source: Cai, Giles and Wang (2009). Historical population pyramids are calculated from the National Bureau of Statistics, 2005 One Percent Population Sample (2007), China Statistical Press. Yao, Xinwu and Yin Hua, China Normally Used Population Dataset, China Population Press (1994).

30
Finally, on the sectoral dimension, we focus the analysis on the four major sectors, which comprise each economy, including Agriculture, Mining, Manufacturing and Services. In addition, given the importance of the manufacturing sector for the CEFTA economies and for robustness, we also obtain partial estimates for eleven manufacturing sectors, which we label broadly as Chemicals, Electronics, Food, Machines, Metals, Minerals, Rubber, Textiles, Transport, Wood, and Other.

We perform the estimation (partial equilibrium) analysis with the original ITPD-E data without any adjustments.<sup>16</sup> In order to perform the general equilibrium (GE) counterfactual analysis in Section 4.2, we employ the original data for international trade flows from ITPD-E, and we impute missing observations for domestic trade, which are needed to balance the data for the GE analysis. To fill in the missing values for domestic trade, we proceed in three steps. First, we construct the ratio of total exports to internal sales for all of the original industries in ITPD-E for which data are available. Importantly, the original ITPD-E allows us to construct such ratios for each of the CEFTA countries in our sample for at least some of the industries within each of the main 14 sectors that we employ in our analysis (11 manufacturing sectors, plus Agriculture, Mining, and Services). Second, we construct an average ratio for each of the main 14 sectors and we use these ratios in combination with data on total exports to fill in the missing intra-national trade values at the most disaggregated industry level. Third, we aggregate the resulting balanced dataset to the 14 sectors that we employ in our analysis. Since data for services trade are very patchy in the latest years in ITPD-E, to construct domestic trade for services, we employ average data over the years 2010 to 2015 and we repeat the above steps with these data for the services sector.

We also employ several other data sets in addition to ITPD-E. Data on the standard gravity covariates
vector (r, -1). Finally, for the case where the largest root in the SURP, process exceeds unity but is less than 1+r (1< A < (1+r)), (35) holds if and only if SURP, and B,1 are cointegrated. In this case, the cointegrating vector need not be (r,- 1). The later is called the "Trehan-Waish cointegration test" below.

Trehan and Walsh (1991) applied their cointegration test to U.S. debt and primary deficits over the period 1964-1984, the same period employed in Hamilton and Flavin (1984). Their statistical tests indicate that SURP, is stationary (i.e., 0 < X < 1), but B, is not. Therefore they conclude that the government budget process is not sustainable. This result is consistent with the conclusion in Kremer's (1988) reconsideration of the Hamilton-Flavin analysis.

The government temporal budget constraint (3) depends only on SURP, not its decomposition into, say, non-interest government spending G and tax revenue T. By decomposing SURP in this way, however, it is possible to determine the restriction that the PVC imposes on the time series properties of government spending and revenue. Hakkio and Rush (1991) propose testing the PVC in (34) by checking whether the government expenditure _inclusive_ of interest payment G, + rtBt, is cointegrated with T, and whether the cointegration vector is (1,-1). The validity of their test depends on several auxiliary assumptions: (i) rt is stationary witl. unconditional mean r and (ii) both G, + (r,-r)B,-l and T, follow unit root processes (i.e. the A = I case above).

Hakkio and Rush apply their test to U.S. data for the period 1950:11 to 1988:IV as well as the sub-samples: 1964:I-1988:
identified through a separate survey effort in Tanzania. The suggestive evidence that households purchase in small quantities in order to avoid social taxation—non-household members consuming a portion of their purchases—aligns with recent work on redistributive pressures in similar settings (Anderson and Baland, 2002; Platteau, 2006; Goldberg, 2016; Baland, Guirkinger and Mali, 2011; Alby, Auriol and Nguimkeu, 2013; Jakiela and Ozier, 2016; Squires, 2016).

> which we hold constant in calculating the value of foregone consumption. Hence, the expected financial savings over the course of an entire year is still in the neighborhood of 9%. Of course, the household might have other ways to reinvest that 9% savings and further improve its financial position.

3
Nevertheless, the bluntness of these indicators is suggested by reference back to the microdata-based Kakwani index results for personal taxes for IDA countries in section 3. Ghana and Nicaragua appear more progressive than Tanzania and Uganda in Table 1, particularly in terms of the direct-to-indirect tax ratio. In contrast, results from section 3, albeit for different years, suggest that direct personal income taxes in both Tanzania and Uganda are significantly more progressive than in Ghana or Nicaragua (with Kakwani indices of 0.52 and 0.47 vs 0.29 and 0.31, respectively). That said, the low average tax rate effect for Uganda is consistent in both analyses, suggesting low redistributive impact. Meanwhile, Honduras exhibits comparatively low progressivity in both analyses. These comparisons emphasize the merit in presenting a range of metrics where possible for each country.

## 5.3 Pros and cons of aggregate tax revenue-based metrics

There appear clear benefits to the calculation of these summary metrics, which enable the impact of either all direct taxes, or of personal and corporate taxes (including tax revenue from capital income) to be incorporated. They are available for a large number of IDA countries, at low cost, and in most cases with only a time lag of around two years. However, they remain blunt indicators as they do not distinguish between taxes paid by poor and rich households and instead rely on strong assumptions regarding the incidence of different tax types. For example, if a country adopts a flat rate PIT system, the resulting lack of progressivity would be missed by these aggregate metrics.<sup>33</sup> Furthermore, there are data quality concerns in a number of countries, and while the time lags are generally small, for some countries the lags are large enough for them not to be useful in terms of the desired monitoring exercise for IDA20. If they are to be used, they should be complemented by additional indicators where possible, particularly of the progressivity of the PIT.

# 6 Measuring tax progressivity in
internet access and connectivity as a driver of poverty reduction and economic prosperity.<sup>4</sup> By showing that mobile broadband coverage can in fact reduce poverty and improve consumer welfare, this study provides important evidence on the benefits of connectivity that policy makers can consider when seeking to achieve universal internet access.

The rest of this paper is structured as follows. Section 2 describes the data sources while section 3 presents the estimation strategy. Section 4 presents the results, and section 5 concludes.

# **2. Data**

Our analysis relies on the overlap of two data sets for Nigeria: the General Household Survey (GHS) and the geographical coverage of mobile networks. Linking the location and the chronology in these datasets allows us to determine the treatment of interest for our analysis i.e. the availability of mobile broadband at the exact household’s location.

# General Household Survey Data

Our analysis uses three waves of the General Household Survey (GHS) of Nigeria (2010/11, 2012/13, and 2015/16) which is a nationally representative longitudinal survey.<sup>5</sup> The GHS is a dedicated living standards survey which provides detailed information on households’ consumption as well as other key demographic and socio-economic indicators of their members including education, labor market outcomes and access to the internet. The GHS also includes a community survey which provides data on prices from local markets as well as on community characteristics. In the initial round of the GHS survey (2010/11) 4,916 households completed interviews. The GHS had minimum attrition as most households interviewed in wave 1 were re-interviewed in the second wave ( _N_ =4,851) and the third wave ( _N_ =4,581).<sup>6</sup> Each wave includes two visits to each household during the autumn months (post-planting) and the spring months (post-harvesting).<sup>7</sup> Some important factors such as labor market