rafmacalaba/data-use-mentions-extended huggingface dataset:
_tice_ ) contained only the personalized information (the owner’s name and a photograph of the property) and the compliance instructions. Finally, the notice for the third treatment group (the _penalty notice_ ) had no personalized information but included the information on penalties. Appendix Figure A1 provides images of the notices.

Due to the 2014 Ebola outbreak, field activities were suspended following the property enumeration. In March 2017, using the information from the property enumeration list, the LRA prepared the different notices for property owners according to their randomized group. Delivery agents were provided with “address books” containing the photograph and location of each property to hand-deliver the notices. If the property owner was not present, the notice was handed to the resident to deliver to the property owner.

Given the three-year lag between the enumeration process and the delivery of the notices, there was some attrition in the sample. The team successfully delivered notices to 90 percent of the property owners in the sample, and this rate was not significantly different across the treatment and control groups.<sup>15</sup>

# **4.2 Data and Empirical Specification**

The data for this study come from two main sources: the baseline enumeration data and administrative records on tax payments. The baseline enumeration data include the property block and number (using a numbering system put in place by a pilot project of the postal office), the community name, the name and phone number of the resident with whom the enumeration survey was completed and his or her relationship to the property owner, the name and phone number of the property owner(s) (if they were not the survey respondents), and property characteristics, such as the number of rooms and bathrooms, the presence of a porch and/or garage, building materials, and whether the property is residential or commercial. The enumerators also measured and recorded the exterior dimensions of the property using sketches and took photographs of the exterior. The LRA determined property values using a
common factor captures fluctuations in global economic activity and represents the world business cycle. I include the estimated common factor and oil market variables (the real price of oil, U.S. oil consumption, and U.S and non-U.S. oil production) in a factor-augmented VAR model. Exploiting a set of sign and elasticity restrictions, I identify structural shocks, including U.S. energy efficiency and U.S. oil supply shocks.

My empirical results support previous findings in the oil market literature and provide additional evidence of the effects of U.S. energy efficiency and U.S. oil supply shocks. For example, I find that U.S. energy efficiency shocks have a larger impact on global output and the real price of oil than U.S. oil supply shocks. I reaffirm the key result from Kilian (2009) that it is important to disentangle the underlying drivers of oil price movements. The impulse responses indicate that both shocks decrease real price of oil and generate a favorable effect on global output. However, U.S. energy efficiency shocks decrease total world oil production and consumption, whereas U.S. oil supply shocks have the opposite effect. Moreover, results from a set of FAVAR models augmented with country GDP data indicate that the GDP implications of U.S. energy shocks are heterogenous across net oil-importing and net-oil exporting countries.

Historical decompositions indicate that the importance of U.S. energy efficiency and U.S. oil supply shocks has varied over time. For the period 2010–2019, positive U.S. oil supply shocks, led by productivity gains in the U.S. shale sector, resulted in an increase of 0.27 percentage points in global GDP growth. During the same period, a series of negative energy efficiency shocks decreased global GDP growth by 0.19 percentage points. The latter results are most prominent during the second shale boom, 2017–2019, when negative energy efficiency shocks cancelled out the
standards as measured in surveys, and inequality has been increasing on average (populationweighted). Of course, we could also get to any given target poverty rate without these conditions, as long as we achieve sufficiently higher growth rates in average household consumption.

Applying the same World Bank growth forecasts used in Table 1 under these more ambitious assumptions about the distribution of the gains from growth I find that this scenario would get us to a poverty rate of 11% by 2017 and 8% by 2022.<sup>26</sup> Table 2 gives the calculations. Notice that this would generate a marked concentration of extreme poverty in Sub-Saharan Africa, which will account for 72% of the poor by 2022.

> 25  Regressing the log of the income-gap ratio (PG index divided by headcount index) on the year one obtains projections of 0.282 for 2017 and 0.271 for 2022. The projected PG indices are then obtained by applying these projections to the prior linear projections for the headcount index.

> 26 This is based on the same forecasts for growth rates underlying Table 1, but extended for an extra two years, but under the more ambitious distributional assumptions.

10
estimate that China's share of the world capital stock as of 2000 is about 8 percent. We choose units such that the world capital stock to one in the theory. We therefore set (1⋅ η) a*=0.08, or a=1.15 and a*=0.4 implying that the rest of the world has per capita wealth that is roughly three times higher than in China.

> σ * φ ⋅ σ We next calibrate production risk by equating V = and V = with the

> η 1− η

standard deviation of rest-of-world and Chinese real per capita GDP growth. For this calculation we define the rest of the world as the 98 countries in the PWT6.1 with continuous annual data on real GDP per capita between 1960 and 2000. The standard deviation of real per capita GDP growth for the world excluding China over this period is 1.4 percent, so we set V=0.014 which implies σ=0.013. For China we focus on the post1978 period where the standard deviation of real per capita GDP growth was 3.7. We set V*=.037 which implies φ=1.32. For sovereign risk we follow KLSV and set α=0.03, who argue that this roughly corresponds to the frequency of episodes of widespread default over the past 150 years.

We also need to calibrate China's relative productivity advantage or disadvantage, π. We use a very simple cross-country development accounting exercise to retrieve π from the data. Assuming a common Cobb-Douglas production function, the share, A and A* are total factor productivity, and K/L and K*/L* are capital stocks per worker. In the theory, the ratio of the expected marginal product of capital in North corresponds to κ=0.75. This can be justified by interpreting K as a broad concept of capital. We next retrieve estimates of A and A* as residuals from this production function,
# **1 Introduction**

In 2015, in correspondence of the Paris Agreement, the Government of the Dominican Republic (DR) submitted its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change.<sup>1</sup> In its NDC, the Dominican Republic has pledged to reduce its greenhouse gas (GHG) emissions by 27% by 2030, relative to the levels recorded in 2010. Despite the effort to meet this commitment,<sup>2</sup> the DR is still an economy which relies heavily on fossil-fuel sources.<sup>3</sup> In similar contexts, countries have considered large-scale regulatory interventions, such as the introduction of a carbon tax.<sup>4</sup> The regulation would provide a strong price incentive for firms to reduce consumption of fossil-fuels and switch to lower carbon alternatives. While contributing to reducing emissions, the introduction of a carbon tax would also affect energy costs, which indicates that it would also have an impact on the allocation of factor inputs across firms.

In this paper, we analyze the impact of a hypothetical carbon tax on aggregate total factor productivity (TFP) through the resource allocation channel. We begin by developing a model in which a carbon tax affects firms’ consumption of energy inputs. In this model, the carbon tax is levied on firms’ total emissions from the use of electricity and fuel in the production process. Consequently, the introduction of this tax alters the cost of energy inputs, thereby changing firms’ demand for all factor inputs, modifying their emission intensities, and affecting aggregate TFP through resource reallocation.<sup>5</sup> Utilizing data from all formal firms in the Dominican Republic between 2009 and 2018, we calibrate the model at the sector level and quantify changes in firm-level emission intensities and aggregate sector TFP under two carbon tax scenarios. Our findings indicate that a carbon tax is more effective when levied on fuels rather than electricity. Additionally,
politically connected. At the same time, the advantage of using stock market data is that it avoids being reliant on the inevitably subjective expert assessments that formed the basis for the Castle ranking of connectedness. Second, it is likely that there are other privately held politically connected firms that are not part of large conglomerates. This measure therefore likely underestimates the prevalence and importance of political connections. This issue is compounded by the fact that only firms that appear in the 1997 manufacturing survey are potentially identified as politically connected; firms that enjoyed connections but exited before 1997 (or lost their connections before 1997) are never identified as being connected in our sample. We also do not know what happened to connections of firms after Suharto’s resignation or whether new connections were formed.

These measurement issues could impact our estimates; by construction the methodology selects overreactive firms, which creates a risk of correlated measurement errors (firms wrongly identified as connected are also those which for some other reason are more likely to be sensitive to political changes while those wrongly identified as non-connected are more likely to be less sensitive to political changes), which could bias upwards the impact of Suharto’s fall. By contrast, if measurement errors are not correlated, this could bias estimates towards zero. To limit these potential biases we primarily focus on firms with family connections, for whom there is no risk that aberrant share price responses result in misclassification and correlated errors.

The number of economically active politically connected firms increased from 191 in 1993 to a peak of 245 in 2000, before decreasing to 178 in 2009. No exit is recorded between 1997 and 2000, the crisis years.<sup>24</sup> As information on connected firms is most accurate in 1997 and 1996 is the last pre-crisis year, in our industry-level analysis we proxy connections with the Suharto regime by the average of the sum of the market shares of politically connected firms within a given five-digit industry in 1996 and 1997. Construction of this de facto time-invariant industry-level presence of politically
proximity from one another could affect their FDI attractiveness. Similar to past studies, the interest of this type of research is to provide some empirical guidance to local policymakers to help them overcome some of the obstacles that regions are facing in attracting FDI inflows.

The empirical analysis presented in the next section will be divided into interrelated parts. We investigate in the first part the significance of some of the determining factors of FDI inflows to various governorates of the country. This analysis is complemented in the second part by a spatial analysis of the distribution of the FDI across various regions and potential agglomeration and spillover effects.

# **4. 1. Presentation of variables**

In line with past research, the dependent variable in our model is the FDI which measures the inflows in millions of Tunisian dinars of incoming foreign direct investments to each governorate over the period 2008-2018 (FIPA 2019, 2017, 2016, 2015, 2014).

The potential determinants of these inflows are captured by the following economic and institutional factors:

- _Unemployment rate by governorate_ : The abundance of human resources in a region is often considered as key incentives for foreign investors who are looking for low production costs and skilled labor force (INS 2019, ITCEQ 2016, ONEQ 2013).

- _Regional GDP_ : This variable captures the productive capacity of a region. In the absence of data on regional GDP in Tunisia, we decided to use as a proxy, the regional share of consumption of high and medium voltage electricity (INS 2019, 2016).

- _Number of private companies per governorate (EPV)_ : This indicator reflects the density and dynamic of the local industrial network. Foreign actors may choose to invest in a region to leverage new business relationships, to get closer to sources of raw materials and/or to have rapid access to the services and resources necessary for production (INS 2019, 2016, 2013).

- _Number of regional development zones (ZDR):_ To attract foreign investors and promote the industrial development of certain regions,
Behind this average, however, are again large crosscountry variations. While only 1.3% of firms have an overdraft facility and 3.1% a loan in Guinea-Bissau, 87.5% and 74.5%, respectively, do so in Chile.

We also use information from the Enterprise Surveys to control for firm-level characteristics that might affects a firm’s ease of access to financial products. In particular, we construct dummy variables for firm size (small, up to 19 employees; medium, 20-99 employees; large, 100 or more employees), being a subsidiary, and being publicly listed, and control for the percentage of the firm owned by private foreign owners and the percentage of a firm owned by the state, as well as the firm’s age. The descriptive statistics in Table 1 show that 47.4% of all firms are small, 34.3% are medium-sized and 18.3% large. 13% are subsidiaries of other firms, 8
may also be welfare reducing. It should be noted that the usefulness of the concept of natural trading partners to characterize trading blocs that are welfare-improving is not without controversy. Bhagwati and Panagariya (1996) argue that these results are highly sensitive to functional forms, and provide examples in which it is more desirable to form a link with more distant country.

2Frankel, Stein, and Wei (1995)) use a gravity model to explain trade patterns and find evidence that the volume of trade between countries is negatively related to the distance between them, which is consistent with a role for transport costs. Furthermore, they include dummy variables for regional groupings as a proxy for the effect of preferences, and find some significant coefficients. In contrast, Amjadi and Winters (1997) construct a direct measure of transport cost from trade data for Mercosur countries. They find that transport costs average approximately 6% of intra-Mercosur trade for Brazil and Argentina and 12% of trade with the rest of the world (excluding Chile). They point out that the differential of approximately 6% between within bloc and outside bloc trade is well below the differential found by Frankel, Stein and Wei to represent natural trading blocs in their simulations.

1
!" )  and non-­‐exporters  (!! ),  separately.  The  responsiveness  of  average  wages  in  firms  serving  only 18  The  real  exchange  rate  series  for  Brazil  is  constructed  in  Muendler  (2003)  and  is  available  at _<u>http://www.econ.ucsd.edu/muendler/html/brazil.html#brazdata.</u>_ 19  Since  the  overall  impact  of  the  time-­‐varying,  economy-­‐wide  RER  is  absorbed  by  the  region-­‐specific year  effects  ( _relative_ to  domestic  firms.  We  also  conduct  robustness  checks  using  industry-­‐specific  exchange  rates.  δ _tr_ ),  we  can  only  separately  identify  the  effect  of  the  RER  changes  on  exporting  firms 20  We  define  the  firm’s  occupational  skill  composition  as  the  share  of  the  firm’s  workforce  in  four occupational  categories:  unskilled  blue  collar,  skilled  blue  collar,  other  white  collar,  and  professional and  managerial  workers.  Unskilled  blue-­‐collar  workers  are  the  omitted  category.

21  We  define  the  firm’s  age  composition  as  the  share  of  the  firm’s  workforce  in  six  age  categories: youth  (15-­‐17),  adolescent  (18-­‐24),  nascent  career  (25-­‐29),  early  career  (30-­‐39),  peak  career  (40-­‐49), and  late  career  (50-­‐64).  Youth  workers  are  the  omitted  category.

22  We  define  the  firm’s  educational  skill  composition  as  the  share  of  the  firm  in  three  education categories:  less  than  high  school,  at  least  high  school,  and  more  than  high  school.  Less  than  high school  is  the  omitted  category.

23  By  including  both  the  educational  and  the  occupational  skill  composition  of  the  firm,  we  are  able  to allow  for  the  possibility  that  firms  use  increasingly  higher-­‐skilled  individuals  (as  defined  by education)  in  lower-­‐skilled  occupations.  See  Muendler  (2008)
syndrome in population-based studies, we conclude that it is plausible to assume that high parity, especially when they are closely spaced, results in substantial maternal morbidity, other things being equal. This morbidity may range from nutritional deficiency states, to a greater likelihood of some life-threatening conditions, e.g., hemorrhage, toxemia, sepsis and abortion, and uterine prolapse. It follows - - that family planning fewer and better spaced pregnancies is likely to reduce maternal morbidity, as well as maternal mortality, and contribute importantly to the health and wellbeing of women in the childbearing years.

# II. HEALTH EFFECTS OF FERTILITY CONTROL

Family planning is generally far safer than childbearing for women in developing countries, as Table 1 demonstrates. We will summarize only briefly the main health effects of contraception, since a separate paper on contraceptive safety has been prepared for the Conference. Contraceptive methods generally are safer and free from substantial risk of complications compared to other drugs and surgical procedures, as well as to childbearing (Gray, 1986). Comparisons with other drugs or common remedies are complicated, however, by the fact that individual contraceptive methods may be used for relatively long periods for preventive rather than curative effect. Antibiotics or anti-malarials, by contrast, are used episodically, usually in the presence of serious disease. Comparisons with childbearing
**Table A7.2 Twitter Reform Sentiment and Intensity and FDI Inflows into the GCC**

|Dependent Variable:|FDI Inflows (% of GDP)|
|---|---|
|Twitter Reform Intensity (t)|-0.222<br>(0.665)<br>|
|Twitter Reform Sentiment (t)|0.403<sup>**</sup>|
||(0.119)|
|Twitter Reform Intensity X Sentiment (t)|-0.894<br>(0.597)|
|Twitter Reform Intensity (t-1)|-1.479|
||(1.082)|
|Twitter Reform Sentiment (t-1)|-0.478|
||(0.543)|
|Twitter Reform Intensity X Sentiment (t-1)|0.647|
||(0.765)|
|GDP Growth (t)|0.0816|
||(0.137)|
|Inflation Rate (t)|0.437|
||(0.292)|
|Trade Openness (t)|0.0930|
||(0.0685)|
|Institutional Quality (t)|0.628|
||(3.686)|
|Country FE||
|Year FE||
|R2|0.663|
|Mean of Dependent Variable|1.753|
|Number of Countries|6|
|Observations|46|

Note: This table presents the relationship between GCC Twitter reform intensity and reform sentiment and FDI net inflows to the GCC. Coverage includes the six GCC countries between 2011 to 2019. Twitter intensity and sentiment are derived from Arabic tweets, and the measures are normalized to have unit variance. Trade openness is measured as the value of exports plus imports as a percentage of GDP. Institutional quality is the average of six measures from the World Governance Indicators database.
With respect to out-of-pocket spending on services, among villages included in the population surveys with a voucher client who delivered most recently at a private facility, the proportion of respondents who paid for the services declined by 44 percentage points between 2008 and 2010–11 (table 5). In villages with no voucher client at follow-up, the proportion of women who delivered their most recent babies at a private facility and paid for the services declined by only 11 percentage points between 2008 and 2010–11. The decline in the proportion of women paying for delivery in private facilities was therefore greater—by 33 percentage points—in villages with a voucher client than in villages with no voucher client at follow-up. The difference-in-differences estimate from the multilevel logit model shows that the decline was significantly greater in villages with a voucher client, compared with villages with no voucher client.

Table 5 Percentage of Women Who Paid for the Delivery of the Most Recent Birth and Difference-in-Differences Estimates in Changes over Time between Villages with and Villages without a Voucher Client Resident in 2010–11

|Services|Vouch<br>resident<br>at fol<br>(|er client<br>in village<br>low-up<br>%)|No vou<br>resident<br>at fol<br>(|cher client<br>in village<br>low-up<br>%)|Differen<br>differe|ce-in-<br>nces|
|---|---|---|---|---|---|---|
||2008|2010–11|2008|2010–11|Percentage<br>points<sup>a</sup>|Odds<br>ratios<sup>b</sup>|
|Paid-for delivery<br>at|||||||
|Private facility|98|54|97|86|33|0.1*|
|
. ―Meso-scale modelling of runoff and water balances using remote sensing and other GIS data,‖ _Hydrological Sciences -Journal- des Sciences Hydrologiques_ , 39(2): 121-142.

- Scipal, K., C. Scheffler, and W. Wagner. 2005. ―Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing,‖ _Hydrological Earth System Science Discussion_ , 2: 417–448.

29
rural areas important inter-household transfers may not be observed because they take the form of labor input into family farming (Lee and Xiao, 1998).<sup>3</sup> Evidence from the 2005 one percent population sample, which is highlighted in Table 1, shows the sharp differences between rural and urban elderly in their sources of financial support. Where a substantial share of the urban population relies on pension income, fewer than five percent of rural residents over 60 reported that pensions are their most important source of financial support. Indeed, the rural elderly rely significantly more on support from family members, 54 percent, and also quite striking, 38 percent of the rural population over 60 relies primarily on their own labor income for support.

When we examine sources of support by gender in Table 1, we note that family support is relatively more important for women over 60, who tend to live longer, and labor income remains more important for men. Of women over 60, 69 percent report financial support from family members as their most important source of support and only 28 percent report that labor income is most important. By contrast, 49 percent of men over 60 report that their own labor income remains their most important source of support, and only 39 percent report support from family members. When distinguishing the importance of pension by gender in rural areas, a significantly higher share of men (8.1 percent) than women (1.3 percent) report pension income to be their most significant source of financial support. While the gap between men and women would appear to be significant, it reflects historical differences between genders in employment in local government and the military, both of which may provide some pension support.

Neither the nationally representative statistics that we calculate from the 2005 one percent population sample, nor the World Bank Poverty Assessment (Chaudhuri and Datt, 2009)

> 3Using other methods, Cameron and Cobb-Clark (2008) do not find evidence that transfers to parents respond to low parent income in