rafmacalaba/data-use-mentions-extended huggingface dataset:
14 than data on total roads because there is less ambiguity about what constitutes a paved road than what constitutes an unpaved road versus a track or trail.<sup>2 9</sup> The size of the national road network is associated with the size of the economy, geographical area, population, income per capita, and population density. Income per capita is a major detertninant of road length at the national level. Both paved and total road length increase at a constant rate with per capita income, as can be seen in figure 2.30 Estimates at the national level (using the techniques employed for vehicle ownership) find no saturation level for road density with respect to per capita income.

**Fgure 2. Per Capita Income and Per Capita Road Length, 50 Countries and 35 Cities**

<!-- Start of picture text --> 100<br>. Nationaltotalroad<br>Australia<br>A Urbanroad<br>-a Nationaltotalroad eq. NZ SA<br>....... Nationalpaved road eq. Brazil<br>9 ~~~Urban road eq. Brzi erF w<br>10 o _*_____* Switzerland<br>.5 Bolivia + + Z r o tBohvia<br>0~~~~~~~~~~~~~~-.<br>Thailand* Korea*Osaka<br>t Malawi 1 .- + SingaporeBeijing .<<br>China Egy Bg T ASeoul<br>Jakarta A * Santiago<br>A<br>B g A Bangkok<br>A A ~~~~A<br>** Surabaya Guangzhou HK<br>O. i *.Suabv<br>0.1 .: .. : lll l l ll -- : :<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><
>**

||Regression on country-<br>survey-averages|Regression on firms with<br>country-survey dummies|
|---|---|---|
|Fixed capital|0.574**|0.387**|
|Labor|0.445**|0.663**|
|Country survey dummy variables included|No|Yes|
|Selected estimates of relative TFP<br>Benchmark: Serbia 2002=0|||
|Croatia 2002|[0.612]|0.798**|
|Hungary 2002|[0.817]|0.922**|
|Poland 2002|[0.543]|0.717**|
|Poland 2003|[0.938]|1.087**|
|Slovenia 2002|[1.146]|1.347**|
|Number of observations|55 country-survey-averages<br>based on 14,687 firms|14,687 firms|

** significant at the 1 percent ; heteroskedastic-robust standard errors; [ ] indicates estimate based on a residual

The figure and the table also show that Serbia is well below its East European peers in productivity. Croatia is a natural comparator country. The country-survey-averages TFP regression implies that the productivity of Serbian manufacturing firms in the BEEPS 2002 survey is 61 percent<sup>7</sup> below that of Croatian firms; the gap based on the PICS 2003 5
|---|---|---|---|
|**Income Groups**|||||
|LIC|1.1|1.1|0.9|0.8|
|LMIC|0.9|0.9|0.7|0.6|
|UMIC|0.5|0.6|0.6|0.5|
|HIC|0.1|0.1|0.1|0.1|
|**Regions**|||||
|AFR|1.0|0.9|0.7|0.6|
|ECA|0.2|0.2|0.2|0.2|
|LCR|0.2|0.2|0.1|0.1|
|SAR|0.6|0.7|0.5|0.3|
|EAP|1.9|2.1|1.9|1.7|
|MNA|0.4|0.6|0.5|0.5|

_Source:_ World Bank calculations using OECD Creditor Reporting System and World Bank online databases.

_Note:_ Overseas development assistance is measured as gross disbursements, and figures only include countries that received some assistance. Income groups are defined by country income group classification in 2017. LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, SAR = South Asia, EAP = East Asia and the Pacific, and MNA = Middle East and North Africa.

While overall levels of official development assistance (ODA) have been declining over the last 15 years, such aid provides an important source of education funding for low- and middle-income countries (see table above). Given average
1998, the same author found that Japanese firms have a tendency to favor investment in countries where Japan has agreements to claim foreign tax credits for income taxes that they would have paid to foreign governments in the absence of tax holidays. 1<sup>2</sup> From a policy perspective, these two findings seem to indicate that tax incentives are more effective when they apply to firms from countries whose governments do not tax their foreign activities.

Some recent evidence has shown that the home country's taxation system is likely to influence the way their multinational companies do business abroad. Hines and Hubbard (1990) and Grubert (1998) found that it is attractive for U.S. firms to use debt to finance foreign investment in high tax countries (compared to the U.S.) and equity in low tax countries. The argument is that the debt generates interest deductions for the subsidiary and so reduces its taxable income in the host country (note that the parent firm has to pay additional taxes, but at a lower rate, in the U.S.). Harris (1993) uses firm-level data to illustrate that the Tax Reform Act of 1986 in the United States pushed U.S. firms with higher equipment/structure ratios to invest abroad more heavily because their tax regime encouraged such an action. A series of other recent studies have found similar results for preferred stock issuances (Collins and Shakelford (1992)) or domestic versus foreign borrowing (Atshuler and Mintz (1995). An interesting finding is that the 1986 Tax Reform has also influenced the form of business organization that the multinational will select in the foreign country. For example, since 1986, American investors have had fewer tax incentives to participate in joint ventures, particularly in low tax foreign countries, and the number for this type of foreign investmnents fell sharply as reported by Desai and Hines (1999).

Finally, the importance of the home-country tax system can also be illustrated by the efforts of tax authorities to
# **3 Data**

The main dataset used in this report is the Jordan Labor Market Panel Survey (JLMPS). This is a longitudinal household survey that was conducted in 2010 and 2016. It contains a host of rich variables on labor market outcomes, gender attitudes and use of digital technologies.<sup>2</sup> Given the large inflow of Syrian refugees experienced by Jordan during this period, the sample includes Jordanian nationals only, aged 15 or more in 2010 and up to 64 in 2016. We restrict the sample to non-forced displaced people who are non-permanently disabled. The sample includes 2,843 women who were present in the 2010 and 2016 waves of the survey, and 2,758 men.

In Table 1 we present descriptive statistics on labor market and technology access variables, and individual and household level characteristics that we use as controls in our econometric analysis. Female labor force participation is low in Jordan. Less than one-fifth of women in our sample were part of the labor force in 2010, and while the percentage increased by 2016, it barely surpassed a quarter of the sample. For the sample of men, the labor force participation rate was high both in 2010 and 2016, averaging 75 percent and 79 percent respectively.

Mobile phone ownership is almost universal and the percentage of women and men living in households that own a laptop increased over time from 10 percent to 28 percent. Access to internet at home more than doubled between 2010 and 2016, increasing from 16 percent to 35 percent.

Figure 1 shows the female labor force participation rate disaggregated by subdistricts. In 2010, 11 of the 84 subdistricts with available data had no women in the labor force. In 2016, the number fell to only 3 subdistricts. On the other end of the scale, in 2010 the female labor force participation rate was below 50 percent in all subdistricts, while in 2016 12 subdistricts had a rate above 50 percent.

> 2Please see Krafft & Assaad (2018) for more information on
[#21] [#22] Publicly available data on sovereign guarantees that support SOE debt is sparse (see section 5/b). It was therefore difficult to ascertain how commonplace such arrangements were.

# c. RBLs by type of lender

From our review of 30 RBLs, we identified 13 different creditors that extended RBLs to African countries during the period studied. These can be grouped based on their economic functions or based on their classification as either an official bilateral creditor versus private/commercial creditor.

Based on functional grouping, the primary sources of RBLs are state policy banks/development banks. The two policy banks of China, China Development Bank (CDB) and China Export-Import Bank (Eximbank) are the top RBL lenders by volume. These two policy banks were initially set up by the State Council of China to carry out economic policies. 13 Within our data set, Chinese policy banks are the source of 76% ($36bn) of the RBLs to Africa, with Eximbank and CDB contributing $17bn and $18bn each.

> 10 The original 2008 loan agreement guaranteed both the infrastructure loan and a loan for the mine. <u>http://congomines.org/system/attachments/assets/000/000/276/original/B5bis-Sicomines-Convention-Incl-Anx-2008-ConsortiumEntreprises-Chinoises-RDC.pdf?1430928308</u>

> 11 A 2009 amendment narrows the guarantee to the infrastructure loan only. http://www.congomines.org/system/attachments/assets/000/000/502/original/ Sicomines-2009-Avenant3ConsortiumEntreprisesChinoises-RDC.pdf?1430928992 12 IMF, Country Report No. 19/244 on Congo: Staff Report (2019),

~ <u>www.imf.org/ /media/Files/Publications/CR/2019/1COGEA2019001.pdf</u> 13 CDB website: http://www.cdb.com.cn/English/gykh_512/khjj/ Eximbank website: <u>http://english.eximbank.gov.cn/Profile
strengthening is a major theme in the Paris Declaration agenda for reforming aid practices. The Paris Declaration (PD) created a set of numerical indicators on improved “alignment” of aid activities with country systems and inter-donor coordination, to be monitored through surveys of donors and recipient countries, with defined targets for the year 2010.

Although the PD principles and associated indicators reflect a broad consensus within the donor community (most notably in the OECD Development Assistance Committee), the empirical basis for this new aid effectiveness agenda is thin (Easterly, 2007). Advocacy for reform of donor practices is based on theory, intuition and scattered anecdotal evidence.<sup>1</sup> This paper does not attempt to add to the thin evidence base underpinning the Paris Declaration principles and indicators. Rather, it is premised on the fact that they have been endorsed by the DAC bilateral donors and multilateral agencies providing the bulk of ODA, and by most recipient countries. Donor behavior is assessed relative to donors‟ own assertions about what constitutes more effective aid practices.<sup>2</sup> Knack and Eubank (2009) provide a theoretical framework for understanding the incentive problems among donors that can produce suboptimal levels of harmonization and alignment in their aid activities. This framework and supporting empirical evidence are relevant so long as most of the donor community believes that use of country systems is currently below the optimal level. The caveat is that we cannot offer confident conclusions about which donors

> 1 See World Bank (2003: ch. 11) for representative anecdotes.  Azam et al. (1999) construct a formal model with learning-by-doing externalities in tax administration, in which aid dependence with low institutional capacity can be an equilibrium outcome.  What little systematic empirical evidence exists in support of the Paris Declaration agenda bears mostly on its harmonization provisions, not the alignment provisions that cover (inter alia) use of country systems.  Anderson (2011), Djankov et al. (2009), Knack and Rahman, 2007) and Knack
(annualpercentage change)|4.4|4.3|7.7|6.8|5.0|4.0|
|Inflation(period average,inpercent p.a.)|7.3|6.1|5.7|5.5|5.4|5.3|
|Exchangerate (Pula/US$)|7.5|8.0|8.3|8.7|9.1|9.4|

Sources: See text.

# **_Fiscal Policy Strategy_**

The Baseline Scenario takes the policy strategy described in the 2010/11 Budget and NDP10. First, in line with the 2010/11 Budget, the Baseline Scenario assumes the retention of the public sector wage freeze in 2010/11. For the remainder of the Plan, the government is assumed to allow no further net increase in the established posts (NDP10 policy commitment), in order to limit the growth of government expenditure (NDP10, para. 6.50). For simplicity, it is assumed that existing vacancies will remain unfilled during the Plan. Other recurrent expenditures, i.e., recurrent expenditure other than the wage bill and interest payments, are assumed to grow at low rates, as indicated in NDP10 (Table 6.6, p. 81).<sup>20</sup> Development expenditure is front-loaded for critical investment projects, but is assumed to decline rapidly at the rate also indicated in NDP10 (Table 6.7, p. 81). This assumption suggests that both other recurrent and development expenditures will decline rapidly as a percentage of GDP.<sup>21</sup> See Table 3.

**Table 3.  NDP10 Expenditure Projections**

(annual real percentage change)

||2010/11|2011/12|2012/13|2013/14|2014/15|2015/16|
|---|---
# **VII Discussion**

Peru’s temporary spatial closures policy attempts to reduce juvenile catch by temporarily banning fishing in the places with the highest relative abundance of juvenile anchoveta. While there is a (noisy) decrease in juvenile catch inside closed areas during closure periods, there are large increases in juvenile catch inside closed areas between the announcement and the beginning of closures, just outside closed areas during closures periods, and inside closed areas one and two days after closures end. On net, this policy worsens the target outcome, increasing total juvenile catch by 48%.

The failure of this policy to achieve its objective is not due to a failure of targeting. Even though the regulator does not use length distribution data to determine closures, they still succeed in closing areas where juveniles are abundant. In the 9 to 12 hours before the beginning of a closure, 47% of individuals caught inside the soon-to-be closed area are juveniles, higher than in any other treatment bin and much higher than the 9% juvenile caught outside the treatment window (Figure C2). Furthermore, within the support of the data, larger and longer closures offer no improvement (Appendix D.1).<sup>34</sup> I illustrate the short- and long-run costs of the temporary spatial closures policy with two simulations. First, I reproduce the method of Salvatteci and Mendo (2005), who estimate the cost of juvenile catch by comparing status quo landings to a counterfactual where juveniles make up a smaller fraction of individuals landed. I similarly project forward the length distribution of individuals caught during my study period until counterfactual juvenile catch is 32% lower than status quo juvenile catch (equivalently, until status quo juvenile catch is 48% higher than counterfactual juvenile catch). Status quo tons landed are 2.1% lower than in my counterfactual projection because anchoveta growth exceeds natural mortality within the support of the data. FOB export revenues were $1.79 billion in 2017 (PRODUCE, 2018 _a_ ).
Figure 19: Global capital flows to emerging and developing markets (EMDEs), US$bn <!-- Start of picture text --> 1,500 80<br>60<br> 1,000<br>40<br> 500<br>20<br> - 0<br>-20<br> (500)<br>-40<br> (1,000) -60<br> (1,500) -80<br>Net capital flows: EMDEs Net capital flows: Turkey<br>Source: Haver Analytics<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**_Initially led by FDI, the CAD has increasingly been financed by shorter-term financing flows_**

Figure 20: Cumulative Current Account Balance and Net Foreign Investment Position (percent of GDP) <!-- Start of picture text --> 0%<br>-10%<br>-20%<br>-30%<br>-40%<br>-50%<br>-60%<br>Cumulative CAB (% GDP) Net foreign assets(% of GDP)<br>-70%<br>-80%<br>Source: Authors’ elaboration using data from the Central Bank of Turkey .<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> 15
Bank staff calculations based on Eurostat _and Amadeus_ _Note_ : Aggregate figures from Amadeus for each country are computed defining labor productivity as total valueadded divided by total number of employees.

22 Firms that stop reporting their financial statements are represented as "not available/missing" for four years following the last available filing.

23 Appendix II shows the kernel density estimations of annualized growth of labor productivity (2003–2008) for each sample for two regional cuts: EU15 and EU12. Both estimations use the Epanechnikov kernel function with a bandwidth of 0.5. Appendix III presents the corresponding firm-level summary statistics. For both samples, the distribution for EU12 firms is higher than for EU15, suggesting that EU12 firms realized greater productivity growth. A Kolmogorov-Smirnov test for equality of distribution functions rejects the null hypothesis at the 1 percent.

7
form of flour) due to close historical ties and a shared 1,600 kilometer border; estimates of Pakistan’s share of the Afghan wheat and flour import market range from 59% (Chabot and Dorosh, 2007) to 79% (Maletta, 2004).

Seasonality plays an important role in food security in Afghanistan. Temperatures vary dramatically across seasons, with hot summers and frigid winters; and the climate in the highlands varies with elevation. In many cases, severe winter conditions affect transportation, and in high mountainous areas roads are often blocked throughout the winter due to heavy snow accumulation. In these communities in particular, households are forced to rely only on food supply stored before the winter.

> 14 In a country like Afghanistan though, where the drug economy is large, the official National Income Accounting data are likely to significantly understate GDP. UNODC (2008) estimates that in 2007 the farm gate value of opium cultivation was US$1 billion, but this dropped to US$730 million in 2008. The potential export value in 2007 of opium, morphine and heroin (at border prices in neighboring countries) was $4 billion (or, in per capita terms, about $160).

> 15 UNDP (2009)

> 16 Based on data from the 2004 National Nutrition Survey.

> 17 Chabot and Dorosh (2007)

> 18 Afghan wheat production has been estimated to be over five times as variable as Pakistan’s wheat production. (Persaud, 2010)

6
an econometric model estimated at the household level. Urban and rural sectors are modeled separately, because they differ in fundamental ways.<sup>8</sup> The model draws on four rounds of past Indian National Sample Survey data, namely the 61st, 66th, 68th, and 72nd rounds, which were fielded in 2004-05, 2009-10, 2011-12, and 2014-15 respectively.<sup>9</sup> The 2014-15 survey, unlike the three previous rounds used in this exercise, did not collect information on aggregate household consumption and therefore cannot generate direct estimates of poverty. The 2014-15 survey, however, contains information on several household characteristics that are also present in past rounds and are reasonably well-correlated with per capita consumption.<sup>10</sup> These common characteristics include household age, size, caste, religion, a few labor market variables, and expenditure on three categories of services that are asked in the same way as previous rounds.

The analysis estimates a regression model that predicts household per capita consumption using these common household characteristics, plus contemporaneous rainfall shocks and a linear time trend. Most coefficients are allowed to vary over time in a linear way, slightly relaxing the usual strong assumption that the relationship between the explanatory variables and log welfare remains constant over time.<sup>11</sup> The model is estimated using data from the first three rounds of the survey. The coefficients from this regression model were then applied to measured values of the same common characteristics in the 2014-15 survey, as well as 2014-15 rainfall levels, to repeatedly simulate levels of welfare and poverty. The results of these simulations were aggregated to generate estimates of poverty headcount rates and their standard errors.

This exercise is similar in spirit to the adjustments for non-comparability in the consumption aggregate that led to “the great Indian poverty debate”.<sup>12</sup> Since then, this type of nowcasting exercise has also been carried out in several other countries.<sup>13</
issue even further, many countries, such as Chile, Peru and Turkey, are individually identified in some of the survey years but aggregated into the regional categories in others, making the analysis close to impossible. To address these complications, we construct the smallest set of countries and regional aggregates that are consistent over time, ending up with 40 geographic units, including the United States. Among these, 25 are separate countries and 15 are regional aggregates of the 150 other countries (or regions) that are listed in birthplace category. The list of our 40 groups and the countries they include are presented in Table 1. This type of aggregation of the world into 40 countries or groups potentially lowers our estimates and the extent of transit migration in the data. For example, if a migrant who was born in Peru goes to Argentina before moving to the United States, we will not be able to identify him as a transit migrant since Peru and Argentina are part of the same category (Other South America) in our data and estimation. For compatibility, we group the countries in the global bilateral migration database and DIOC/DIOC-E along the same lines. Finally, in addition to their country of birth and last residence, we split all migrants as tertiary or non-tertiary educated.

Figures 1 and 2 as well as Tables 2 and 3 provide information on the extent of transit migration to the United States. Table 2 shows what percentage of migrants born in a given country was residing in a different country before migrating to the US. For example, 7% of Canadian born migrants came to the US from a different country. The same ratio is only 1% for Mexican migrants, but over 20% for migrants born in many European countries like the United Kingdom, Italy and the Russian Federation. The same information is presented visually in Figure 1 to highlight regional differences and similarities. We see that transit migration is higher among migrants born in Africa and the Middle East and lower in Latin America, as migrants from the
that size; thus, columns add to 100

> 8 If there were no mixed firms, all informal (formal) workers would be employed by informal (formal) firms. Mixed firms complicate this picture. Unfortunately, we cannot use ENOE (Encuesta Nacional de Ocupación y Empleo) data to correct for this problem, and table 4 is the best approximation to differences in the schooling composition of the workforce between formal and informal firms.

12
quality of digital goods imports. Since higher income countries tend to export higher-quality products, the moderate increase in the share of digital goods imports sourced from relatively lower-income African countries would be expected to lead to a decrease in the quality of imports. We approximate these expected impacts by assuming that the quality of goods supplied from each country remains constant, but the share sourced from each country changes due to AfCFTA. We draw on the quality estimates by country from the previous section, and simulate the change in the shares using the simulations on the effects of AfFTA on digital goods imports presented above.

In Figure A5, we observe that the simulated change in average quality of digital goods imports is 0.4 percent, being higher in countries that observe a more significant increase in digital goods imports due to AfCFTA. It should be noted, however, that the quality of imported goods tends to be higher than that of goods produced domestically, and therefore the overall quality of digital inputs that is available to firms in the region need not fall.

12
of the WGI are those of a standard normal random variable, with zero mean, unit standard deviation, and ranging approximately from ‐2.5 to 2.5 (Kaufmann et al. 2010: 9). Some extreme scores are outside this range. In our sample, five scores are slightly below zero on our rescaled Regulatory Quality and Rule of Law indicators (see appendix 1). All five of these are for Somalia and fall between 2006 and 2009, a period of civil war.

7