rafmacalaba/data-use-mentions-extended huggingface dataset:
,308|33||913|||
|Zambia, RSZ|502|25|3,286|377|4|1|
|Zimbabwe, NRZ|390|8||195|||
|**Railway concessions**|**350**|**23**|**2,945**|**491**|**5**|**2**|

_Source:_ AICD railways database. _Note:_ Labor productivity = ̳000s traffic units per employee; locomotive productivity = millions of traffic units per locomotive; carriage productivity = ̳000s passenger-km per carriage; wagon productivity = ̳000s net tonne-km per wagon.

There is a high degree of variability in the performance of southern African rail operators. At one end of the spectrum, Spoornet has by far the highest levels of productivity of any railway in Sub-Saharan Africa, in part due to the large coal and mineral volumes that it handles. In a second category come the networks in Angola, Botswana, Mozambique (CDN), Namibia, and Zambia, which perform moderately well on productivity. In a third category come the networks in the Democratic Republic of Congo, Malawi, Mozambique (CCFB), and Zimbabwe, which have very poor performance indicators.

As noted above, rail freight faces huge delays while crossing national borders. For example, along the north-south corridor, rail transit times from Lubumbashi to Durban have been reported to be as high as 38 days at an effective velocity of around 4 km per hour. Travel accounted for only 9 days, and interchange and border crossing for the remainder (an astounding 29 days!). The delays can mainly be attributed to the time taken to unload and interchange freight across different locomotives as the cargo passes from one national network to another, as well as lengthy administrative processes at borders. These delays cost as much as $200 per railcar. For railways to be a competitive mode for the movement of
<!-- Start of picture text --> b. Concentration shares: transfers<br>60<br>=<br>&— 40<br>oO<br>=<br>7)<br>(=<br>2<br>g<br>Is a<br>8 20 om<br>e) ae<br>1S) _-_-—-o ia<br>0<br>, fc a yg « : @g vo ion<br>Poorest 7 3 4 5 6 7 8 9 Richest<br>Decile of Market income<br>13 Direct transfers 4 Indirect subsidies MG Education ME Health -e- Market income<br><!-- End of picture text --> reliajotes: Authors' estimates based on PECS 2016/2017; LFS 2017. [1] Concentration shares show the proportion of each tax/transfer paid by / benefitting each decile. They sum to 100%. [2] Households are ranked over Market income
**Table 1:** Transitions among young (18-21 years old) between work statuses

||No work|Informal work|Formal work|Total|
|---|---|---|---|---|
|No work|78_._93|15_._43|5_._64|100|
|Informal work|26_._37|63_._07|10_._56|100|
|Formal work|15|14_._92|70_._07|100|
|Total|53_._06|29_._09|17_._85|100|

_Notes:_ Author’s calculations using panel data from 2018 and 2019 ENOE surveys. Table presents the average probability of maintaining or changing work status from one period to the next. The rows reflect the initial status, and the columns reflect the final status. Youth are followed for four quarters (3 month periods) between 2018 and 2019 in staggered cohorts.

the probability of transitioning to formal work from non-employment (5.6%) it is small in magnitude and less than the likelihood of transitioning from formal to informal employment (14.9%).

In sum, the data suggest that, similar to many other LMICs, including Argentina and Brazil (ILO, 2015), informal work is not a stepping stone to formal work for youth, and that the informally employed are prone to greater employment instability. These findings are also in line with recent evidence by Donovan et al. (2020) who show that across developing countries transitions between jobs are more common and are less likely to lead to wage gains than in developed economies. Overall, these results suggest that, similar to evidence from other settings, early career choices matter and have long-lasting effects on employment prospects (Oreopoulos et al. (2012)).

# **2.2 Formal vs. informal sector wage growth**

In addition to job stability, choices over different types of employment also
# **1 Introduction**

Understanding the facts on the size of productive units in developing economies is critical for uncovering barriers to development. The business size distribution reflects the incentives and difficulties businesses face to innovate and grow and is thus crucial to identifying distortions hampering aggregate productivity for a given productivity distribution across businesses (Restuccia and Rogerson (2008); Hsieh and Klenow (2009); Hopenhayn (2016, 1992) and a long literature following their footsteps). Thus, an important dimension of the literature addressing the development problem has been the documentation of business size distribution in less developed countries (LDCs) in contrast to advanced economies (AEs). Thanks to these efforts, we know that LDCs have firms that are smaller on average, even within sectors, and that uncovering this relationship requires using sources of data that appropriately cover the bottom tail of the firm size distribution (Bento and Restuccia, 2021, 2017, 2021; Poschke, 2018). Business size is also crucial in the debate on within-country inequality, which has lately focused on how large firms’ (labor and product) market power affects workers’ incomes, vis-a-vis business owners.<sup>1</sup> Although the emphasis has been on AEs, some recent efforts extend the analysis to LDCs (Brooks et al. (2021),Amodio and de Roux (2021),Amodio et al. (2022)), where inequality has been historically very high and remains so, despite a slightly decreasing trend after the year 2000.

The study of the business size distribution has relied heavily on firm-level data. While firm data has been crucial in developing these branches of the literature and has brought a great deal of useful knowledge, it has limitations that are particularly severe for LDCs, because even the most comprehensive firm datasets exclude large segments of the productive sector in these countries. We aim to address this bias. Using official employment/household survey data, we characterize the (employment-weighted) size distribution of the universe of productive
volatility and standard macroeconomic data to cross-check the sentiment and uncertainty measures, and link these to output and inflation movements. The model results corroborate the findings of the textual analysis and find that both supply and demand played an important role in driving growth and inflation outcomes. On uncertainty, while both reflect significant increases in uncertainty during COVID-19, the SBVAR suggests that this uncertainty was significantly larger during COVID-19.

The recent debate over the nature of economic shocks has important implications for of macroeconomic If the of the was optimal design policies. impact pandemic mainly supply-related linked to mitigation measures that will end, then a strong countercyclical not be warranted but rather an mechanism response may insurance-type including through unemployment benefits and loans. If, however, the pandemic caused a large demand shock, then a strong countercyclical monetary and fiscal policy response is needed. Our results show that both demand and supply played an important role in the output collapse caused by COVID-19. To appropriately respond, therefore, a blend of measures are required including traditional fiscal and monetary policy stimulus calibrated to the size of the demand shock. Our results also reveal important heterogeneity across sectors and can be used by policy makers to design targeted relief to those firms most impacted by current conditions while limiting possible side effects such as lowering productivity through misallocation of capital and labor.

20
70.8|77.3|82.0|89.3|
|Agriculture CIS-7|100|88.6|78.1|82.8|92.4|97.8|98.0|
|Agriculture other CIS|100|98.8|76.4|67.3|73.5|74.5|74.5|

Source: http://www.cisstat.com The relatively better performance of agriculture in the CIS-7 has been achieved thanks to small farming rather than through the re-organization of agriculture into large and better productive private industrial complexes. Land reforms have been fairly successful in distributing land to a large number of people (see section 5) and the privatization process that saw the dismantling of large _Kolkhoz_ and _Sovkhoz_ and the distribution of land and assets to members resulted in an effective and relatively egalitarian redistribution process, at least in comparison to urban areas and industrial enterprises. In Armenia in 1997 over 99% of agricultural output was produced by household plots.

Small farming is also not a phenomenon limited to rural areas. The 2001-2002 Georgian Household Budget Survey found that almost 60% of all households had access to land including over 30% of households in urban areas. However, the average land size used for agriculture per household was very small, about 0.8 hectares.

**Second** , the decline in industry is mainly explained by the decline in industrial manufacturing. Manufacturing output collapsed during the recessional period and continued to decline very significantly during the growth period (table 2.2). This is perhaps the most worrying trend from a welfare and employment perspective. Historically and worldwide rapid reductions in poverty and increases in employment have been closely associated to the development of the manufacturing sector. That is because the development of manufacturing during the early development stages is

14
, but in 1992, only a small number of households had septic tanks: 4 percent of households in El Alto and 21 percent in La Paz.<sup>7</sup> Households without septic tanks or sewer connections in their homes use stream beds, latrines, public toilets, and toilets in other private homes.

> 3 Estimates place the current El Alto population at 600,000 residents and the La Paz population at 740,000. These estimates are based on census figures from 1992 and the historical growth rate in the two cities. In 1992, the population of El Alto and La Paz was 405,492 and 713,378 respectively. From 1982 to 1992, El Alto grew at 9.4 percent per year and La Paz at 1.6 percent per year. _Censo de Poblaci6n y Vivienda 1992._

> 4 Current coverage figures are based on estimates of the number of housing units in La Paz and El Alto and of the number of households that use each public tap. Because exact data is not available, different sources produce different estimates. In 1992 (the date of the most recent census), 33 percent of El Alto residents, as compared to 55 percent of La Paz residents, reported having in-house water connections. _Censo de Poblaciton y Vivienda 1992._

> 5 _Informe Final: Diagn6stico RPpido sobre las Condiciones de Saneamiento Basico en las Zonas Urbano Marginales de la Ciudad de El Alto,_ 1998, prepared for the UNDP-World Bank Water and Sanitation Program by Centro de Estudios y Proyectos of Bolivia.

6 Like water coverage figures, sewer coverage figures are estimates based on inexact data. Estimates from different sources, therefore, vary.

_7 Censo de Poblaci6n y Vivienda 1992_ 3
# **3 Impact Evaluation**

## **3.1 The Data**

Our dataset is constructed using three primary databases which together form a rich and unique combination. First, we have export data at the transaction level (aggregated to the firm level) from raw customs files over 2000–2008. Each record includes the firm’s tax ID, the product code, the country of destination, and the export value (in US dollars) and quantity (in tons) for over 500 HS8 products to 90 countries. Exports flows are reported annually. Second, the PIP’s administration in Brussels provided us with a list of the Senegalese firms that got assistance from the program in each year of the sample period.<sup>14</sup> Finally, we obtained data on employment and sales from the CNI, Senegal’s National Statistics Direction.<sup>15</sup> As the CNI also identifies firms by their tax ID, we could merge the three datasets. Among the reporting firms, almost 3% appear only once in the dataset. That is, they export only one product to one destination one year. As these observations are likely to be mis-reports or individuals, we drop them from our sample. We also drop international organizations and embassies, as well as trading and transport companies (the latter group represents about a quarter of all observations). This leaves us with a sample of almost 2’000 observations.

Let _i_ an exporting firm, _k_ be a product, _j_ a destination country, and _t_ a year. As the PIP targets _products_ (FFV), some of a firm’s products may be covered and some others not. In addition, technical assitance provided under the PIP helps make FFV marketable on EU markets, but does not necessarily help on other markets with different (or no) food-safety requirements. In view of this, we take the ( _i, k, j, t_ ) vector as our
_Brazilian spatial labor dynamics Page 18_ B. Variables used in the Labor Equation and their sources:

|**Variable**|**Source**|**Instrument(s)**|**Source**|
|---|---|---|---|
|Wage in 1991|IBGE - Census|Exogenous||
|Proportion 5-15 over15-55 yrs old|IBGE - Census|Exogenous||
|Proportion native in 1991|IBGE - Census|Exogenous||
|Delta market potential|DECRG - IE|Exogenous||
|Teacher qualification in 1991|IBGE - Census|Exogenous||
|Homicides per capita in 1991|DATASUS|Exogenous||
|Proportion farmers in 1991|IBGE - Census|Exogenous||
|Bank Dummy|Central Bank|Exogenous||
|Population in 1991|IBGE - Census|Exogenous||
|Mean Temperature|DECRG - IE|Exogenous||
|Total Rainfall|DECRG - IE|Exogenous||
|Rainfall - 1o Principal Component|DECRG - IE|Exogenous||
|Rainfall - 3o Principal Component|DECRG - IE|Exogenous||
|Employment rate in 1991|IBGE - Census|Illiteracy rate in 1991<br>Fertility rate in 1991|IBGE - Census<br>IBGE - Census|
_Table 2 Transition matrix (3x3) 2014–2016_ Upper bound mobility estimates

||Poor 2016|Vulnerable 2016|Secure 2016|Total|
|---|---|---|---|---|
|Poor 2014|0.024|0.018|0.078|0.119|
|Vulnerable 2014|0.016|0.014|0.072|0.102|
|Secure 2014|0.066|0.065|0.648|0.779|
|Total|0.106|0.097|0.798||

# Lower bound mobility estimates

||Poor 2016|Vulnerable 2016|Secure 2016|Total|
|---|---|---|---|---|
|Poor 2014|0.105|0.030|0.000|0.135|
|Vulnerable 2014|0.001|0.065|0.045|0.111|
|Secure 2014|0.000|0.001|0.752|0.754|
|Total|0.106|0.097|0.798||

Note: Estimated based on the synthetic panel, using a poverty line of MYR 527 and a vulnerability line of MYR 701. N = 56917. Cells give the fraction of the population that is in each transition categories.

We investigate upward and downward mobility by aggregating some of the nine categories. Upward mobility is defined as moving up one category or more, downward mobility as moving down one category or more. In addition, we will also present the share of those secure in both rounds, as this limits the scope for upward mobility. Figure 4 displays the development of the population shares of these groups over time the 2004–2016 period.<sup>27</sup> As we have seen, the secure group has grown steadily, especially since
balance and the current account of the balance of payments). Capital stock data (both public and private) were derived using the perpetual inventory method, using relatively small depreciation rates, as indicated earlier. In solving the model, we use the net output price as the numéraire, and therefore keep its value fixed in all the experiments that are reported below.

To calculate the poverty effects of policy shocks, we first linked the model to a household survey, using the methodology outlined earlier. The data that we use are from the 1999/2000 _Household Income, Consumption, and Expenditure Survey_ (HICES) conducted by the Ethiopian Central Statistical Authority. The survey covers 17,332 households, of which 8,660 are from rural areas and 8,672 from urban areas. Given an initial poverty line (at current prices), we calculated the headcount index for the survey year. For 2003 onward, based on the projections of the model, each observation in the sample is adjusted using the rate of growth of nominal consumption per capita, whereas the poverty line is adjusted using the growth rate of composite prices. Given these projections, a new poverty rate is calculated for each period. We used the same procedure (using actual data on consumer prices and consumption per capita) to update our estimates of the poverty rate for 2001 and 2002.

We also used the partial growth elasticity approach mentioned earlier to relate the “base” poverty rate and the rate of growth of real consumption per capita. Three different values for that elasticity are specified: -1.0 (which corresponds to the case where growth is distribution neutral), -0.5, and -1.8. The elasticity of -0.5 is close to the elasticity of the poverty headcount index with respect to the change in (mean) expenditure estimated by Christiansen, Demery, and Paternostro (2003, Table 4, p. 326) for Ethiopia. The elasticity of -1.8 (which is within the
<!-- Start of picture text --> Size of Fiscal Revenue from Taxes<br>50<br>43.3<br>45<br>37.5 38.0<br>40 35.6<br>33.2 36.7<br>35 30.3 33.0 4.1<br>27.4 28.0 2.5<br>30 24.3 2.8<br>2.7 12.0 11.2<br>25 23.0<br>20 17.9 19.7 3.6 21.3 0.9 21.4 3.4 4.3<br>5.6 0.3 7.2 6.0<br>15 7.1 1.8 6.0 10.3 25.1 6.0<br>10<br>3.8 2.4 17.1<br>5 7.0 12.6 11.4 13.3 6.9 9.0<br>0 -0.4<br>2014 2016 2014 2013 2017 2015 2016 2015<br>-5<br>RUS KYG ARM GEO MDA TJK UKR BLR<br>Indirect Taxes PIT SIC Other direct Taxes Total Revenue Total Taxes<br>% of GDP<br><!-- End of picture text -->

# **Figure 7** : Share of Major Revenue Sources in the GDP

_Source:_ Authors’ calculations using CEQ Master Workbooks (MWBs).

_Note_ : Figures in black bold are governments’ total revenues from taxes comprising PIT, SIC, other direct taxes and indirect taxes. Countries are sorted in the ascending order of the total revenues from taxes. Total revenues in red are general government revenues which also includes social contributions (which Armenia and Georgia don’t have in referenced years) as well as oil revenues (which
# needs drive up issuance globally.

Many of the key determinants of the scale and type of foreign demand for domestic debt are beyond the control of debt managers, such as global fund flows. In other areas, debt managers do not have direct control, but may have some degree of influence; for example, debt managers do not control macroeconomic and fiscal policies, but debt management can and does influence macro and fiscal outcomes.

However, debt managers, through their issuance choices and other remits, can profoundly affect their market’s attractiveness to foreign investors in a number of ways. A recent IMF survey of major asset managers found that “market liquidity” and “range of instruments available” were amongst the top five factors considered in cross-border investment decisions by asset managers and pension funds, respectively.<sup>17</sup> Discussions in the 2008 World Bank/IMF/OECD Bond Market Forum pointed to debt managers being able to play a “significant” role in fostering liquidity, with liquidity based on: “...(i) concentrating issuance in critical tenors; (ii) well-functioning repo markets and ability to short issues; (iii) plain vanilla derivatives markets; (iv) facilitating investor demand and price discovery; and (v) supporting a network of primary dealers”.<sup>18</sup> One area that may merit special attention from EM debt managers is the possibility of benchmark index inclusion (or, where already included, the risk of exclusion). Examining the key criteria for some major indices is revealing (Table 2). Debt managers cannot control meeting criteria like country income level or overall market size, but they certainly can influence the instrument mix and maturity structure of the public debt over time. In this respect, maximizing the odds of index inclusion largely boils down to benchmark-building beyond the 1-year tenor mark, since inclusion often involves minimum specific issue sizes in addition to total market size requirements. A focus on conventional fixed-rate, bullet bonds,
Figure 12: Formal and informal employment distribution by age cohort in manufacturing: Ghana (2013) <!-- Start of picture text --> Entrant 1−5 years<br>6−10 years 10+ years<br>Employment in formal establishments Employment in informal establishments<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>Share of employment (%)<br>80<br>60<br>40<br>20<br>0<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the selected countries; see section 3. _Note:_ The reference is the total employment for each age cohort and formality status. The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

25
empirical, broadening the variety of shocks analyzed. A subset of this literature focuses on violence. Some studies look at criminal violence (Dustmann and Fasani 2016; e.g. Foureaux-Koppensteiner and Manacorda 2016), but I concentrate the review on those emphasizing violence in conflict-affected countries.<sup>5</sup> I also concentrate on the short-term impacts of violence exposure in utero, although other studies (e.g. Akresh et al. 2012; Akresh, Lucchetti, and Thirumurthy 2012; Tranchant, Justino, and Müller 2020) indicate that in utero effects can last through childhood, and that there are negative health consequences of exposure to war violence at other stages in life.

Camacho (2008) analyzes the Colombian case using birth records from the Vital Registration System (VRS) (1998 to 2003) and municipal landmine explosions data. She finds that a landmine explosion occurring during the first trimester of pregnancy in the municipality where the mother resides is associated with a reduction in birthweight of 8.7 gr, while there was no effect for explosions in the second or third trimesters. Using a similar empirical specification, Mansour and Rees (2012) look at the al-Aqsa Intifada in the West Bank using DHS data and district-level fatalities caused by Israeli security forces as the violence measure. The authors find that exposure

> 4 For instance, fetal alcohol syndrome, wherein the newborn's facial features reflect the mother's alcoholism, or stunted or missing limbs associated with the intake of thalidomide, a once commonly prescribed medicine for morning sickness (Almond and Currie 2011).

> 5 This also excludes studies that analyzed a one-off, large-scale event (e.g. the September 11 attacks (Currie and Schwandt 2016)).

4