rafmacalaba/data-use-mentions-extended huggingface dataset:
Returning to the differences between Columns 1-3 and 4-6, we suspect that three factors are behind the weaker response on total activity compared to entry. First, our post-upgrades data come from 2005 and 2007, which is just at the end of the GQ upgrades that began in 2001. It takes time for activity to shift spatially, especially if there are agglomeration forces or similar with existing industry bases, and we are likely under-estimating the ultimate changes that may occur in the spatial distribution of Indian manufacturing as a consequence. By contrast, the entry margin—where location choices are being made at present—adjust much faster to the changing attractiveness of regions, and thus register sharper effects in the short- to medium-run. A second reason why total shifts in activity may be dampened in districts on the GQ network is that entrants may be displacing incumbent establishments from the districts. In fact, this competition is a key reason cited by proponents for infrastructure investments. Unfortunately, our data do not allow us to study the exit margin with sufficient detail to make accurate assessments.

Table 2b presents evidence on a third rationale that partly overlaps with the other two. Prior to the GQ project, there existed some infrastructure linking these cities. In a minority of cases, the existing roads did not even comprise the beginning of a highway network, and so the GQ project built highways where none existed before. In other cases, however, a basic highway existed that could be upgraded. Of the 70 districts lying near the GQ network, new highway stretches comprised some or all of the construction for 33 districts, while 37 districts experienced purely upgrade work.

In Table 2b, we split the 0-10 km interaction variable for these two types of interventions. The results are very interesting. Columns 4-8 show mostly similar entry and productivity consequences regardless of the initial roadwork’s condition. Columns 1-3, however, show distinct effects regarding total activity. Places that completely lacked a highway before GQ exhibit increases in aggregate activity. In these cases, the entry
|.0003**<br>(2.42)|.0003**<br>(2.45)|
|Dummy for Grande<br>Norte|-.131**<br>(-2.08)|.096<br>(1.60)|.161***<br>(2.68)|
|Pseudo R2|.0887|.0052|.0090|
|N|2204|2255|2255|
|Mean of target|199.9|199.8|200.0|
|Standard Deviation of<br>target|22.2|15.4|14.8|
|Range of target|130.1-300.4|148.1-264.5|150.2-259.9|
|Percentage of Schools<br>that met target in 2008|52.3%|53.1%|52.3%|

Looking at patterns of coefficients in probit estimations is one way of judging whether the probability of achieving the target is systematically related to the particular circumstances of the agents. However, there is some value to having a summary measure that could be used to judge whether there is any significant relation to the circumstances. A summary measure is readily available because the problem that is posed in judging the adequacy of the target is an exact parallel to the problem of determining whether opportunities are equally distributed in the population in the literature of equality of opportunity. The metric developed for the equality of opportunity, the human opportunity index, is an inequality-adjusted coverage rate, with the adjustment or penalty determined by the extent to which there is a deviation from randomness in who receives the opportunity and who does not. If there is no systematic relation between who gets the coverage and a set of circumstances, then there will be no difference between the estimated coverage rate and the human opportunity index. The relevant test, then, is a test of whether the penalty is
(ρ��0.46), GDP growth (ρ��0.10) and child mortality (ρ��0.24). Hence, we expect that the percentage of _suffering_ individuals is closely related to perceived hardship and grievances and hence has a direct effect on nonviolent resistance. In the regressions, we control for the percentage of people who report to be _struggling_ and therefore may also be experiencing considerable hardship.

_Table 1 Countries with the lowest and highest percentage of citizens who report to be 'suffering'_ according to the Gallup World Poll (2005-2015)

|Countries with lowest incide<br>|nce of suffering<br>|Countries with highest incidence of suffering<br><br>|
|---|---|---|
|Country|Suffering|Country<br>Suffering|
|Norway|1.2|Bulgaria<br>39.1|
|Denmark|1.3|Afghanistan<br>34.9|
|Canada|1.6|Burundi<br>34.5|
|Netherlands|1.7|Haiti<br>32.5|
|United Arab Emirates|1.8|South Sudan<br>32.5|
|New Zealand|1.8|Armenia<br>29.2|
|Switzerland|1.8|Tanzania<br>29.0|
|Sweden|1.8|Yemen,Rep.<br>28.5|
|Oman|2.0|Hungary<br>26.7|
|Luxembourg|2.0|Georgia<br>26.7|
|Australia|2.1|Serbia<br>25.9|
|Brazil|2.3|Ukraine<br>25.8|

Table 1 lists those countries with the lowest and highest percentages of suffering people.

Not surprisingly, most of the countries with the lowest percentage of _suffering_ individuals are high-income economies
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the small property exemption may be only a benefit to individuals holding wealth in this form.

An argument for exempting small properties is also made on administrative cost grounds. Taxes on low value property are said to cost more to administer than they can yield in revenue. This is less true than it would appear. Where property taxes are administered on the basis of a comprehensive fiscal cadastres, all properties, regardless of value, must be mapped, assigned an identiy Acation code, and valued sufficiently well to identify those qualifying for exemption. Exempting low value property thus only spares the costs of billing and collection.

Where small, low value properties constitute a large proportion of the real property assets in a city, tar administrators would be better advised to adopt extremely simple valuation methods for typical units, thus bringing them into the tax system at the lowest possible cost.

# Preferential Assessment Ratios for Home Ownership

Many countries permit preferential rates, or assessment ratios, to owner occupied residential property. The rationale for preferential treatment is either to encourage home-ownership, or on ability-to-pay grounds (the latter case reflecting the belief that because owner-occupied property does not generate rental income, owneroccupants are less able to pay recurrent property taxes). The merits of encouraging home ownership aside, it is unlikely that differentials have much impact on tenure decisions. This preferential treatment does, however, represent a subsidy to middle and upper income groups, which account for the bulk of owner occupants in LDC's.
Table 7 provides information on the importance of intra-industry trade in Central America based on the adjusted Grubel-Loyed intra-industry trade index.<sup>5</sup> This index can take values between 0 (no intra-industry trade) to 1 (all trade is intra-industry). There appears to be some importance of intra-industry trade within Central America, however, with the exception of Costa Rica (0.3) there is virtually no evidence of intra-industry trade with the United States. For El Salvador and Guatemala intra-industry trade appears to be quite high with Mexico and Brazil.

**Table 7. Intra-industry Trade**

||**Costa Rica**|**El Salvador**|**Guatemala**|**Honduras**|**Nicaragua**|**Argentina**|**Mexico **|**Canada**|**France**|
|---|---|---|---|---|---|---|---|---|---|
|**El Salvador**|0.36|||||||||
|**Guatemala**|0.38|0.45||||||||
|**Honduras**|0.40|0.27|0.33|||||||
|**Nicaragua**|0.34|0.15|0.21|0.15||||||
|**Mexico**|0.18|0.43|0.42|0.11|0.02|0.26||0.49|0.57|
|**Brazil**|0.08|0.43|0.51|0.03|0.28|0.39|0.51
laws and collateral registries can make the use of collateral more feasible, particularly the kind of lower value movable collateral that is more suited to microenterprise loans. Improving credit information sharing systems, and including microfinance providers, enables microfinance providers to make use of more efficient credit scoring technologies that allow them to lower costs, risks, and loan pricing.

**Regulated financial entities should be required to share credit information,** ideally through national credit bureaus rather than only through narrow microfinance information-sharing systems. Sharing borrower information is key to lowering costs and overcoming information constraints. By integrating microfinance borrower information into credit bureaus, the coverage and utility of data in the credit bureaus can be improved.  Financial institutions of all types can extend access to new customers, while managing risks and costs more effectively.   Government intervention or encouragement may be needed to ensure that pricing by monopolistic credit bureaus is accessible for microfinance institutions. Lower pricing for loans below a certain size, may be needed to avoid passing on disproportionately high costs to borrowers. The Moroccan

32
# **II.  Measuring Total Factor Productivity**

Measuring TFP generally requires an empirical specification of the production function (1). Because of data restrictions, it is often more practical to specify a _value added_ production function of the form, where _Y_ represents value added, _K_ represents capital inputs and _L_ represents labor inputs:<sup>2</sup> Measurement is usually based either on time series data or on cross-sectional data. While aggregate or firm-level data can be used for either type of analysis, time series analysis generally employs data on aggregates of firms and cross-sectional analysis usually employs data on individual firms.

_Growth accounting_ methods are used to identify the rate of growth in TFP in time series data by subtracting the effect of growth in inputs from growth in output. The residual is the growth rate of TFP: where _α_ and _β_ are the cost shares of _K_ and _L_ respectively. This type of measure is used by national statistical agencies to track productivity improvements through time.<sup>3</sup> It does not permit a quantitative partition of TFP into technology and efficiency improvements, since both types of improvements occur over the course of the time series and contribute to growth in TFP.

Cross-sectional analysis generally defines some index of relative TFP for each firm _i_ defined as:

> 2 Specification of this function depends on the assumption the original production function is separable into a “ _KL_ ” component and a “ _ME_ ” component. Separability implies that the substitution possibilities between _K_ and _L_ do not depend on the level of _M_ or _E_ . See Varian, 1992.

> 3 Actual growth accounting methods used by statistical agencies are more complex as they must control for changes in product mix among other factors. See Bureau of Labor Statistics, 2000.

3
20_._98|9_._23|50_._89|80_._35|
|Wheat|7_._00|||5_._48|8_._54|46_._09|67_._56|
|Wheat||0_._70||20_._98|9_._23|50_._93|80_._35|
|Wheat|||0_._05|24_._91|8_._14|53_._92|87_._59|
|Wheat||||24_._91|8_._15|53_._93|87_._59|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by BGS. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

31
the estimation that uses the two year average data, trade openness enters with a negative (but not statistically significant) sign in the lower growth and lower vulnerability (L) cluster. Thus, trade openness reduces the likelihood of being in this cluster relative to the reference cluster V (i.e., relative to the lower growth and higher vulnerability cluster). In contrast, trade openness enters with a positive (and statistically significant) sign in the higher growth and higher vulnerability cluster (H), which indicates that it increases the likelihood of being in

> 7 The simple two year average is performed for each of the variables; the first two-year average covers the period 1999-2000 and the last two-year average the period 2007-08. CA is performed on these average values.

9
26 economic liberalization; high tax burden; and macroeconomic instability). Consequently, countries like Georgia, Azerbaijan, and Ukraine exhibited very large unofficial economies by the 1990s, having experienced very rapid growth since the late 1 980s.

# _Nature of the Data and Implications for Measurement_

By definition, any measurement of the unofficial economy is subject to substantial margins of error. Further, errors stemming from the methodological assumptions made in our approach ought not be ruled out. Hence, it is important to not leave the reader with any false sense of precision concerning the numbers. Instead of accurate point estimates of unofficial economy, figures should be regarded as orders of magnitude, and each estimated share should be viewed as falling within a range. It should be noted that we have tried to maintain a conservative approach to the estimates. And that support for the methodology and empirical estimates is found from independent microsurvey data. Further, sensitivity analysis regarding reasonable ranges for the assumed initial unofficial economy share and for the elasticity of electricity consumption suggests that the margins of error are unlikely to be large enough to question the overall orders of magnitude and broad trends reported here.

The sheer orders of magnitude and variance in these unofficial economy estimates point to the need for furthering a country-specific understanding of the unofficial economies in the region. The lack of official recognition of these activities in most analyses implies that an implicit estimate of zero (or close to it) for the unofficial economy was chosen by default (or when analyzing trends, the "default" assumption is that the growth rates of the official and unofficial economies have moved in tandem--which is a fallacy in the FSU and CEE region). Hence, however imperfect the unofficial economy estimates may be initially, attempts at measuring and understanding the unofficial economy are bound to significantly improve measurement and understanding of the dynamics of the overall economy.

# _Implications for Policy_

Incorporating more fully the evolution of the unofficial economy into our understanding of the dynamics of the overall economy leads to some differences in approach to
regions using an in-depth methodology that combines contextual data, technical assessments, and secondary indicators.<sup>119</sup> For each year, a country is given an overall data set score (1–100) for the completeness of OGD data sets based on a standardized rubric<sup>120</sup> across a number of areas, including: 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. For the purposes of the present analysis, raw scores provided by the ODB were slightly remixed to obtain a certain operationalization of the variables based on the theoretical literature and conceptual framework.<sup>121</sup>

- **Accountability:** Since the research will focus on Sub-Saharan Africa, accountability data were obtained from the Mo Ibrahim Index of African Governance (IIAG), which has rated accountability in virtually every African country since 2001. The data set relies on an accountability index based on an inclusive list of sub-indicators, including those listed Annex 3. As noted in the theoretical literature, there are many definitions of accountability and there are likewise many different measures of what constitutes accountability across different international institutions’ indices. Accordingly, such an indexed variable—which draws on many of the principle accountability indexes including those produced by the World Bank, African Development Bank, Economist Intelligence Unit, Bertelsmann, the World Economic Forum, and others—ensures that the concept is broadly operationalized.<sup>122</sup>

- **Service Delivery:** Measuring service delivery can be difficult, as it has many different dimensions including access, quality, timeliness, price, efficacy, and satisfaction. For the sake of simplicity, service delivery will be operationalized in two dimensions: (i) provision of services and (ii) outcomes of services. The rationale for this is that provision (e.g.
atmospheric and land-soil variables such as temperature, winds, soil moisture and atmospheric ozone concentration.

Finally, the area of influence and intensity of the droughts in Mozambique during the period of analysis is measured using weather data from the African Flood and Drought Monitor (AFDM) system. This is an initiative developed by Princeton University that monitors and forecasts meteorological, agricultural and hydrological droughts for countries in Sub-Saharan Africa. AFDM data span the period 1950-2015 and are also available with a resolution of 0.25 by 0.25 degrees (Sheffield et al., 2014).

# _Cropping cycle data_

Agricultural production cycles are defined based on the temperature and moisture conditions suitable for crop growth across districts in Mozambique. The Harvest Choice Project developed by IFPRI provides information on the start and end dates of the growing cycle across countries in Sub-Saharan Africa with a 10km x 10km resolution. These data are derived from the Enhanced Vegetation Index (EVI) data set produced by the MODIS satellite images for the period 20012004. Since treatment is determined at the district level, we generate area-weighted averages of the start and end dates of the growing cycle for each of the districts in the sample.<sup>3</sup> Figure 1 shows the distribution in the number of days for the main cropping cycle across the country.

# **2.3 Weather Shocks**

After validating the occurrence and spatial position of the weather shocks listed in the EMDAT database using remote sensing data, we chose three extreme events based on their scale and timing with respect to the dates of the household surveys: 1) the floods that occurred between late 2006 and early 2007 along the Zambezi River Basin, 2) a category 3 cyclone (Jokwe) that struck northeastern Mozambique between March 8 and 18, 2008, and 3) a drought that affected parts of central and southern Mozambique between May 2005 to January 2006.

> 3 We use only the between-district variation in growing cycles, which is substantially larger than the variation within districts. The standard deviations for
ber 2019). Such variation in treatment time also applies in Gorontalo.

We performed DiD estimations separately for RPOS, TMD, and WS (West Manggarai) and RPOS, RPOS-Grab, and TMD (Gorontalo) to get a picture of POS impact on restaurant tax receipts. Likewise, we run separate estimation for WS (West Manggarai) and PAC Hotel and TMD (Gorontalo) on our hotel observations to get a sense of POS’ impact on hotel tax receipts.

We opted to run the estimation routine separately for each type of POS devices instead of a single estimation on multiple treatments sample set due to a number of fundamental reasons. Firstly, pooling all the treatment types into a single estimation apparently requires a stricter assumption, that is, the effect of at least one type of treatment needs to be homogenous across observations (Fricke, 2017).

In our case, the assumption requires, for example, the expected impact of RPOS distribution on supposedly TMD-targeted restaurants should be identical to RPOS’ impact on RPOS-targeted restaurants. Likewise, the expected effect of WS distribution among supposedly RPOS-targeted restaurants should be about similar to that of originally WStargeted restaurants, and so on. That is not a plausible assumption given the nonrandom treatment assignment in most policy interventions, including in our case. More often than not, choice of treatments and targeted treated observations are usually fixed considering the varying efficacy of different treatments on different treated observation groups. Furthermore, we also detected a strong tendency of behavioral variation among POS recipients of different sizes, scale, and technological mastery which again signals the weak foothold for such an identifying assumption.

Secondly, we do not have a clear _a priori_ view on the relative intensity among different POS types discussed in the paper. In other words, the POS types are not clearly ordered in terms of effect strength on the outcome–they are distinct, and each works differently. This prevents us to confidently infer from a single estimation with multiple treatments as a lower bound for ATETs magnitude, as has been laid out
controlled 56% of the fixed assets of industry, but produced only 33% of the gross output. The performance of state enterprises has improved over time so that in the aggregate they produce substantial profits. However, both the aggregate statistics and micro evidence show that state enterprises are less productive and profitable than private ones. A large survey of private and state firms in comparable sectors carried out by the World Bank in 2002 and 2003 finds that private firms have TFP about twice the level of state-owned ones in the same sector (Dollar, Hallward-Driemeier, and Mengistae 2005). The median pre-tax rate of return on assets was in the 30% range, far higher than that of state firms. The aggregate statistics show high profitability of private companies as well (after-tax rate of return on physical assets of 15% in 2003).

Both private and state firms in China have high rates of reinvestment of retained earnings (enterprise savings). Our interpretation of this is that private firms see a high rate of return and good expansion opportunities. In the case of public firms, however, we hypothesize that the high rate of reinvestment instead reflects poor governance. State steel firms in China, for example, are making a lot of money and reinvesting it in steel. Despite their overall profitability, most state firms have not paid any dividends to the government owners in over a decade. With better governance, it seems likely many SOE investments would not be made.

There is also a very high level of direct government investment from the budget. The government finances 4-5% of GDP in infrastructure investments from its budget, and also makes a roughly equivalent capital transfer to state enterprises in power, water, and transport. It is harder to measure the returns from this budget-financed investment, but it is certainly plausible that the returns to this are similar to those in the commercial state enterprise sector. Between state enterprises and the budget, the public sector finances something on the order of 20% of GDP in investment each year. Various
consistent with evidence on the US only by Bloom et al. (2021). Second, emerging technology jobs are highly concentrated in multinational firms and their supply chains. Around one third of all job postings related to these 29 frontier technologies in the 17 countries are posted by Fortune 500 companies and their buyers, suppliers, affiliates or innovation partners. Third, technology jobs spread faster to richer regions and countries, a finding consistent with the existing literature on cross-country technology diffusion. Finally, technology jobs also spread faster to closer regions and countries to the locations where the technologies emerged.

Given the concentration of emerging technology jobs in multinational firms and their supply chains, we next explore the role of pre-existing firm-to-firm linkages in the diffusion of these technologies. For each technology, we use global patents data to identify the regions where the technology emerged, finding that all of these technologies emerged from cities within the USA. We then identify firms that had ex ante linkages to a firm in the 1