rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 6. Country correlations for CE with high capital account restrictions, versus CE with low capital account restrictions, 1980-2020 <!-- Start of picture text --> Low Capital Controls High Capital Controls<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>CE with Low Capital Controls = 0.33***<br>-0.2<br>CE with High Capital Controls = 0.51***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan Yemen Côte d'IvoireUkraine<br>NicaraguaEcuador Uruguay GuyanaLiberia Argentina<br>Rwanda Oman Congo, Rep.Trinidad and Tobago<br>Brazil Namibia Equatorial GuineaNigerBolivia KenyaZimbabwe FijiAngola Qatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Burundi Saudi Arabia Benin Turkmenistan MozambiqueChad Comoros The Gambia Colombia Tanzania LibyaCentral African RepublicGabon Iceland EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Islamic Rep.Madagascar<br>Kyrgyz RepublicRussian Federation Papua New Guinea Lao P.D.R. Cabo Verde Uganda Cameroon Indonesia Honduras Suriname TogoSeychelles<br>Bahrain Belize<br>Solomon Islands Iraq<br>Nigeria Peru<br>Armenia<br>Sierra Leone Kazakhstan Guinea-Bissau Mauritania Costa Rica Botswana<br>Guinea<br>Chile Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>United Arab Emirates<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<br>Canada<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high and low capital account restrictions. Based on the Chinn-Ito index of financial openness (where a
Africa*pre80*ln(Y)|-0.28**<br>|-0.44**<br>|-0.17**|
|Ln(Noeduc)<br>|-1.51**|-0.096**<br>||
|(Ln(Noeduc))<sup>2</sup>|0.27**<br>|0.033**<br>||
|Ln(secondary)|0.17**<br>|0.015<br>||
|Ln(RUW)|0.16**|0.32**|0.28**<br>|
|CivilRight*ln(RUW)|||-0.021**|
|Sivard|0.018|0.093||
|Adj. R<sup>2</sup>|.875<br>|.820<br>|.851<br>|
|N|166|99|303|

a. There were no countries classified as "Free"<sup>in Africa before 1980.</sup> Dependant variable is log of the level of urbanization.<sup>Notes: See Table 2.</sup> Thus where civil and political rights are low,<sup>urbanization should be much less affected</sup> by urban rural wage differentials. As a result,<sup>the convergence between rural and urban</sup> wages should proceed more slowly in restricted<sup>civil environments.</sup> Using Freedom House data on political and civil<sup>liberties, we tested whether urbanization</sup> is in fact more closely related to economic incentives<sup>in freer countries. Regression 14</sup> shows the coefficient on an interactive term<sup>that allows RUW to change with
agricultural products and any domestic price-distorting measures. In addition, the price equivalent of any direct interventions on inputs is included.

One advantage of using _RRA_ (instead of _NRA_ ) as the dependent variable is that, especially in developing countries, an important indirect source of agricultural taxation is trade protection for the manufacturing sector as a component of import-substitution policies. Thus, _RRA_ is a more useful indicator in an international comparison of the extent to which a country’s policy regime has an anti- or proagricultural bias. However, as a robustness check and to assess whether changes in agricultural policy or industrial protection are important elements in the media-protection relationship, we ran a series of additional regressions using the _NRA_ as the dependent variable.

# **_Mass media variables_**

To test our hypotheses, we use the penetration of TV sets as an indicator. More specifically, our variable is the natural logarithm of TV sets per 100 inhabitants, based on data from the Arthur S. Banks Cross National Time-Series Data Archive, supplemented by UNESCO Statistics on TV and data from the International Telecommunication Union (2010).<sup>5</sup> 5 Both the Arthur Banks and the International Telecommunication Union data are based on data originally collected by UNESCO. These data are collected annually from 1970 onward. From 1960 to 1970, the data were collected every five years. Thus, for that period, we only use data for 1960, 1965, and 1970 without any interpolation. A potential limitation of these data lies in the fact that some countries have a licensing scheme whereby TV sets (or radios) must be registered. Because households may have more than one TV receiver or may not register, the number of licensed receivers may understate the true number of TVs and 13
and 37 percent of rural workers.

# **6. Conclusion**

This paper explores the implications of the 19<sup>th</sup> ICLS standards on measures of employment in SubSaharan Africa obtained from multi-topic household surveys. It shows that there is a large geographic variation in the share of farmers classified as employed under the revised standards, reflecting differences in agricultural production systems. In parts of Malawi and Nigeria, only one in five farmers report producing for sale at the beginning of the agricultural season, while in parts of Ghana approximately four out of five 21
additional environmental challenges in comparison with what occurs in the more advanced economies.

Starting with capital markets, Rajan and Zingales (1998) and Beck _et al._ (2008) clearly show that firms in financially dependent industries grow much faster in financially developed countries; in contrast, new small firms in DCs are credit and equity rationed in the vast majority of cases because their financial markets are underdeveloped (see Nugent and Nabli, 1992; Banerjee and Newman, 1993; Ayyagari, Demirgüç-Kunt, and Maksimovic, 2008; Lian, Sepehri and Foley, 2011 and Section 4.2.2 below). In fact, capital markets in DCs are characterized by: 1) a lower depth (measured, for instance, by a low ratio of bank deposits to GDP; see Paravisini, 2008, for the case of Argentina; Banerjee and Duflo, 2004, for the case of India); 2) by a lower level of competition between financial intermediaries generating misallocation of funds (see Banerjee, Duflo and Munshi, 2003, studying misallocation of capital in India; Cole, 2009, discussing agricultural credit in India); 3) by higher information asymmetries due to institutional and infrastructural underdevelopment (see Klapper and Love, 2011, for a general discussion, while Canales and Nanda (2008) discuss lending to small businesses in Mexico)<sup>17</sup> .

By the same token, a non-transparent regulatory environment with regard to labor market rules, taxation, red tape procedures, property rights and bankruptcy laws, is particularly harmful to firms’ growth in DCs and may be fatal for young entrepreneurial activities (see Goedhuys and Sleuwaegen 1999; Sleuwaegen and Goedhuys, 2002; Beck, Demirgüç-Kunt and Maksimovic, 2005; Chen and Puttitanun, 2005; Lee _et al_ ., 2011). For instance, in a recent study, Ardagna and Lusardi (2010), dealing with GEM microdata from 37 countries including 8 DCs, showed that stringent entry regulation, soft contract enforcement rules and labor market rigidities play
# **Figure 1. GDP growth and service output growth, 2000-2007**

<!-- Start of picture text --> 0 5 10 15<br>Average service output growth<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations. Note: Y axis is GDP growth controlling for initial income per capita and initial service value added (% of GDP).

# **3.1. Growing importance of services**

In Figure 2 we show the sectoral contribution of different sectors in GDP growth. Sectoral contribution of services to GDP growth has risen for most countries in the world. Also, for some developing countries, services have become more important than industry in driving the GDP growth. Some developing countries have gone for service led growth at an earlier stage of economic development contrary to what is suggested by the traditional theories. Table A1 presents the sectoral shares and growth rates in GDP for the world and different regions. Figure A1 plots the contribution of sectors in GDP growth for the world and the developing economies. Services are a major contributor in global growth and their share in global production has increased considerably. For example, the contribution of service sector to growth in 2000-07 versus 1990-99 increased by two percent for developing countries (see Figure 3).

6
the forcing (running) variable using the margin of victory between the winner and the runner-up candidate.

Figure 3: Timeline Legislative Elections in Mexico, 1993-2012 <!-- Start of picture text --> 1995 2000 2005 2010<br>1994 1997 2000 2003 2006 2009<br><!-- End of picture text --> _Notes:_ The legislative election years included in our analysis are highlighted in red. They correspond to the earliest elections that occurred between the baseline and final road data points.

We track more than 8,000 unique legislative election outcomes at the municipalitylevel between 1994 and 2009. Specifically, we use the IFE data that match each municto the district in which it is located. Given the of con- ipality congressional sensitivity districts to data seem gressional politically-motivated redistricting, municipality-level appropriate for our analysis. This stems from the fact that municipality boundaries are more stable, allowing the analysis of relatively constant geographic unit. Furthermore, while the INEGI regularly publishes GIS data on municipality boundaries at different periods of time, similar time-varying data are not available for congressional districts. Finally, because there are typically several municipalities in one congressional district, having municipalities as the unit of analysis makes it possible to account for locally targeted pork within district.

# **IV. Empirical Analysis**

## **A. Identification Strategy**

Given the non-randomness of public investment, municipalities where the president’s party was successful, and their counterparts won by the opposition, may differ in many unobservable ways that influence the distribution of roads. To address the _selection bias_ inherent to these unobserved differences, we rely on a close election RD design. The 10
each ISCO 2-digit occupation. Telecommutable jobs that do not require internet are obtained by subtracting telecommutable jobs that require internet from all telecommutable jobs. This alternative index does not change the total share of jobs that can be done from home with respect to our baseline results significantly. It increases, marginally, the share of jobs that can be done at home and require internet access but lack connectivity, particularly for lower income countries. The results of using PIAAC to identify internet dependence are presented in Figure 2b. In this case, the share of jobs that can be performed from home without internet declines, from 3.1 to 2.6 percent in high income countries and from 1.7 to 1.0 percent in low income countries.

Our next robustness check uses a different method to allocate internet connectivity along the income distribution. We should recall that the Gallup data contain the share of people with internet 11
type collection of instruments, with different tradeoffs between tariff cuts on higher and lower tariff rates.

We examine the potential outcomes of applying this family of formulas in three industrial country markets—Europe, Japan and the USA—and three developing country markets—Brazil, India and Thailand. As an illustration, we target cuts in average bound tariff rates in each country, considering agricultural and industrial products separately. Preliminary analysis suggests that, in this situation, only a bold cut, such as the 50 percent target used in the Kennedy Round, would make substantial progress in increasing market access in both developing and developed countries.

In the final section of the paper, we develop two simple methodologies for evaluating the consequences of different tariff formulas. The first approach evaluates the welfare consequences for the importer of different tariff reductions. When the welfare implications of different versions of the flexible Swiss formula are considered over the range from the Swiss formula to a proportional cut, we find that the gains are very similar for different degrees of flexibility. This is illustrated with data for the USA and the European Union. The basic qualitative message is the same in both cases. Increasing flexibility appears to have only a small impact on the efficiency gains accruing from a 50 percent tariff cut. From the point of view of economic efficiency in the importing country, this result suggests that the depth of cut in the average tariff may be more important than the extent to which this reduction is brought about by reducing tariff peaks.

19
included both industrial and service sector organizations. The study showed that financial incentives alone improved task performance by 23 percent, whereas financial incentives combined with social recognition and positive feedback from superiors increased task performance by 45 percent.

**Table 1: James Q. Wilson’s classification of job types**

|||**Actions or intern**|**al production process of the job**|
|---|---|---|---|
|||**Observable**|**Not observable**|
||**Relatively**<br>**easily**<br>**measurable**|**_Production job_**: Simple<br>repetitive stable tasks,<br>specialized skills.|**_Craft jobs_**: Application of general sets of<br>skills to unique tasks, but with stable, similar<br>outcomes.|
|**rom the job**||Examples: Manufacturing,<br>sales, simpler municipal<br>services (garbage collection).|Examples: Auditing; revenue collection;<br>teaching; medical practice; Job placement<br>work|
|**Outputs f**|**Not easily**<br>**measurable**|**_Procedural job_**: Specialized<br>skills; stable tasks, but unique<br>outcomes|**_Coping job_**: Application of generic skills to<br>unique tasks, but outcomes cannot be<br>evaluated in absence of alternatives|
|||Examples: Military|Examples: Administration; managerial jobs<br>in large private sector organizations|

A well-known study by (Lazear 2000) uses individual-level worker data from a glass company and estimates a 44% increase in productivity after switching to a piece-rate salary schedule. In a structurally similar setting, the introduction of piece-rate
We proceed by verifying the extent to which the results are sensitive to the de...nition of agricultural products we employ. In our baseline analysis, we rely on the UN statistics division of the SITC classi...cation. For robustness, in Table 4 we consider a broader de...nition that includes as well other agriculture-related products (Table A.3). The results reported in this table show that our ...ndings remain very similar when this alternative de...nition is used.

# [Table 4 about here]

Table 5 reports the estimates obtained when considering this broader de...nition of agricultural products and the restricted sample of countries that report at least four consecutive years of tari¤ and rainfall data. Once again, the results remain very similar.

# [Table 5 about here]

As mentioned above, a potential concern for our empirical analysis is that tari¤ setting might be constrained by limits associated with GATT/WTO membership. We adopt two alternative strategies to verify the extent to which our results are sensitive to tari¤ binding. First, we restrict the sample to include only country-product-year observations in which applied tari¤s are considerably smaller than the corresponding bound tari¤s. Second, we account for censoring by adopting a Tobit random e¤ects model on the full estimation sample.

Table 6 reports the results yielded by the ...rst of these strategies. While the analysis in the previous section spanned the 1988-2006 period, information on bound tari¤s is only available from 1995. Hence we begin by showing in column (1) that our baseline results remain qualitatively similar when we restrict the sample to include this sub-period only. In columns (2)-(4) we report results from analogous regressions, but now imposing progressively more stringent restrictions on the distance between applied and bound tari¤s. Reassuringly, we ...nd that the coe¢ cient of interest remains remarkably stable across all the subsamples considered
10 school-level heterogeneity that may be correlated with educational outcomes and for any aggregate shock that may affect in the same way all the municipalities of the same department. _Pr.Ven.Shock_<sup>_1993_</sup> _mdt_<sup>isourpredictedcumulativemigrationinflowtomunic-</sup> ipality _m_ of department _d_ and year _t_ , based on the 1993 census (before Hugo CHavez was elected as president in Venezuela).<sup>9</sup> Native _smdt_ (Female _smdt_ ) identifies the subgroup of Colombian (Female) students in school _s_ . **Xmd** is a vector of pre-determined municipality-specific characteristics which we interact with the year fixed effects represented by _φt_ . This interaction effectively controls for municipal-specific changes over time, parametrized by the set of control included in **Xmd** . It is worth noting that the municipality characteristics included in this set are not chosen in an _ad hoc_ way. Rather, following Belloni et al. (2014), the controls are selected using machine learning techniques. In this way we are agnostic about which municipality characteristics are more related to educational outcomes in areas that have hosted Venezuelan migrants. Finally, the error term, _εsmdt_ , is estimated allowing for serial correlation within municipalities.

Our measure of the predicted migration shock follows the standard practice in the literature (see Card, 2001 and Altonji and Card, 1991 for the pioneer approaches and Lewis and Peri, 2015 for a review of the literature on applications) and exploits the disproportionate levels of cumulative migrant inflows to areas with previous settlements of similar identity groups. Specifically, our measure is constructed as:

> 9The distributions of Venezuelans across Colombian municipalities according to the 1993 census is reported in Figure 2 **A.** .
” In each case, the 10-digit HTS products as well as any excluded countries were made publicly available in the Presidential Proclamation that announced the safeguard policy.

The one important piece of policy information that was not readily available from electronic sources for the 2002 steel safeguard and compiled into Bown (2012) is the 10-digit HTS categories of the foreign firm-specific _product exclusions_ that were granted by the USTR. Descriptions of the physical characteristics of the products that were excluded from the safeguard were made public on the USTR's website; however, this information was not sufficiently detailed for systematic efforts to match excluded products to the 10-digit HTS coded import data that were used in the estimation. However, information on the 10-digit HTS codes of the excluded products was available in the actual surveys that petitioners filled out to request that their product be excluded. Such petitions are publicly available, though the data had to be transcribed by hand from hard copy surveys that were available in the International Trade

> 24 See http://dataweb.usitc.gov/, last access date of 16 December 2012.

15
farmers in Andhra Pradesh, South India, spanning the period 1850 to 1980. He repeatedly notes the unique role that absentee landlords play in the rural economy. His analysis suggests that concerns about moral hazard cause absentee landlord to prefer contracts involving simple, once-per-

> 6The increase is larger when accounting for within-district migrants, who constitute an estimated 60% of all internal migrants in 2001. The Census does not cover temporary migration, which is also on the increase due to factors such as changes in weather and National Rural Employment Guarantee Act (Rosenzweig and Udry, 2014; Kaur, 2019; Morten, 2019).

> 7See Hnatkovska and Lahiri (2015) for comparable statistics using National Sample Survey data.

8
SSA: Sub-Saharan Africa). Panel B plots the CDBP against the Economist Intelligence Unit (EIU) banking risk indicator. Panel C plots CDB against the real GDP growth. Lebanon is not included in the graph because of a too-low value for the 2021 real GDP growth. Panel D plots CDP against the general government gross debt.

To evaluate how the CDBP could be used in country-monitoring exercises, we plot it against a measure of banking sector risk computed by the Economist Intelligence Unit (EIU). The two indicators appear to be weakly related, suggesting that information conveyed by CDBP could complement the EIU banking sector risk indicator as the CDBP focuses on the weakest tail of the distribution of banks according to their exposure to a credit shock.

9
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Mali has also made significant improvements in the nonresidential market. According to enterprise surveys in 2003 and 2007, the share of firms relying on their own generator fell from 45 percent in 2003 to just over 23 percent in 2007, and the share of power consumption supplied by own generation fell by half to 16 percent over the same period. This was a result of Mali improving not only electricity coverage but also the quality and reliability of the service. In fact, for those Malian firms with access to power, the quality of service is relatively good compared with neighboring countries. Losses due to power outages are less than 2 percent of sales, which is among the lowest on the continent. The duration of power outages is also below that of most of neighboring countries (table 11).

**Table 11. Performance of the electricity sector in West African countries**

||Burkina Faso (2009)|Cape Verde (2006)|Côte d'Ivoire (2009)|Ghana (2007)|**Mali (2007)**|Mauritania (2006)|Niger (2006)|Nigeria (2007)|Senegal (2007)|**Simple average**|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of power outages in a typical month|10.8|12.5|4.5|9.7|**4.4**|3.7|20.7|26.8|11.8|14.4|
|Average duration outages (hours)|3.3|5.3|4.6|12.6|**3.9**|2.9|0.5|8.2|6.2|
2.2 Figure 1 shows world production of raw sugar (including the raw sugar equivalent of directly produced refined sugar), refined sugar and non-centrifugal sugar. Between 1964 and 1993 world production of centrifugal sugar grew at an annual rate of 0.97%. Production crossed 100 mln MT in 1982 and reached a peak in 1992 (117.7 mln MT).

Figure 1: **World production of sugar** <!-- Start of picture text -->
- CENTRIFUGAL<br>+NON  CENTRIFUGAL<br>Mt  21T  REFINED (BEET & CANE)<br>; __XS  YMCAL5NDRThAR<br><!-- End of picture text -->

Source: See Data S4 for details.

World production of refined sugar production has followed a similar pattern to the production of raw sugar, reflecting an average extraction rate of refined sugar from raw sugar of about 92.3 percent. By comparison world production of non-centrifugal sugar is much smaller, only about 12 mln MT in the early 1990s. As noted previously, a significant but unknown proportion of this is Indian khandsari.

In terms of raw sugar equivalents. India was the world's largest sugar producer during 1988-94, accounting for over 10.3 percent of the total production (see Data S4 for details). Its share of world sugar production is about half its share of world sugar cane production, because about 40 percent of its cane is used for khandsari and gur, and because of beet sugar production in other countries. If a quarter of the cane not used to produce mill sugar were used for khandsari production, the Indian share in world sugar production would be about 12 or 13 percent. India was followed by Brazil<sup>4</sup> (6.9 %), Cuba (6.7%) and China<sup>5</sup> (5.9%). Other large producers in the early 1990s were USA, Mexico, France, Germany and