rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III. Empirical analysis: the estimation set-up**

To empirically estimate the effects of increased patent protection on bilateral trade flows we use a conventional gravity model. Gravity models have been applied successfully to explain different types of international flows, such as migration, commuting, recreational traffic, and trade. Typically, they specify that a flow from country i to countryj can be explained-by supply conditions in country i, by demand conditions in countryj, and by forces.either assisting or resisting the flow's movement.<sup>1</sup> l Our depend variables are bilateral trade flows for 89x88 countries which were extracted from the United Nations Comtrade database. The data refer to 1989 total non-fuel and high technology trade. The rationale for using high technology trade flows besides total non-fuel trade is based on the a priori expectation that the effects of IPRs protection are stronger for knowledge-intensive trade. For a definition of our high technology aggregate, see Table 1.

Following earlier specifications of gravity models, our explanatory variables are GDP and population of both countries i andj, geographical distance between the two countries, a dummy variable which is one if the two countries share a common border and zero otherwise, and a dummy variable which is one if the two countries share the same language and zero otherwise.<sup>12</sup> The coefficients on GDP are expected to be positive and around unity (Anderson 1979); the coefficients on population are expected to be small and negative, representing economies of scales (Linneman 1966). Positive geographic and cultural distance are expected to have a negative influence on bilateral trade flows; that is the coefficient on geographical distance is expected to be negative. the coefficients on common border and language are expected to be positive. The data appendix gives more information on the countries included and the sources of all variables.

" Gravity models were developed based on intuitive reasoning rather than economic modeling. Due to their empirical success, there have been numerous attempts to shed some light
|**Accounting method**<br>exporting-country perspective|bilateral perspective<br>sectoral-bilateral perspective<br>importing-country perspective<br>sectoral-importer perspective<br>sectoral-exporter perspective<br>exporting-country<br>perspective,<br>source or sink approach|exporting-country<br>perspective,<br>sink approach<br>exporting-country<br>perspective,<br>source approach|
|---|---|---|
|**Trade fow to select**<br>total aggregate exports|bilateral aggregate exports<br>bilateral sectoral exports<br>total aggregate imports<br>total sectoral imports<br>total sectoral exports<br>sectoral/bilateral/sectoral-<br>bilateral exports|bilateral exports<br>any export fow|
|**estion**<br>d in the total exports of a country|ly exposed to:<br>bilateral trade relation (e.g. generic trade frictions<br>untries)<br> a specifc sectoral-bilateral trade relation (e.g. a<br>mposed by a trade partner in a given sector)<br> the imports of a country (e.g. trade restrictions<br>tners)<br>the imports of a country in a given sector (e.g. a<br>l tarif vis-`a-vis all partners)<br> the sectoral exports of a country (e.g.<br>negative<br>on the exports of a given country and sector)<br>eakdown in disaggregated export fows, consistent<br>egate measures|eakdown of bilateral trade balances<br>orts vs GVC-exports|
|**u**<br>e|l<br>a<br>o<br>n <br>i<br>n <br>ar<br> <br>a<br>n <br
<u>Table 1: Employment pre and post COVID and labor market participation by gender</u>**

|Total|Total||Male|Male|% change|Female|Female|% change|F share in|F share in|
|---|---|---|---|---|---|---|---|---|---|---|
|empl.|empl.|%|empl.|empl.|in male|empl.|empl.|in female|total|total empl.|
|BC|AC|change|BC|AC|empl. AC|BC|AC|empl.AC|empl. BC|AC|
|212796|202443|-4.87%|162455|154300|-5.02%|38727|37326|-3.62%|18.20%|18.44%|

Source: Authors’ calculations using the EIFBS. Total/Male/Female empl. BC refers to Total/Male/Female sample manufacturing employment before COVID using sampling weights. Without weights Total empl. BC=594978 and AC=574692 (so nearly 20 thousand jobs are lost in absolute terms post-COVID). Similarly, AC refers to the respective values after COVID. Female share in total employment BC (AC) = column 7 (8)/column 1.

We would expect these facts to be mirrored in the ensuing ‘Gender Gap’. Figure 1 shows that the ‘Gender Gap’, calculated as the difference between male and female employment, has declined post COVID in terms of the median, the mean and in terms of the percentage change in employment by gender (Figure 1).

<!-- Start of picture text --> Figure 1: Gender Gap (M-F) and percentage change in employment pre- and post-COVID-19<br>a
). This is partly because of Africa’s commodity boom, which fueled the emergence of consumption cities, characterized by higher shares of imports and employment in non‐tradable services (Gollin, Jedwab, and Vollrath, 2016). Given their prevalence, this raises the question of whether informal rural household enterprises can serve as pathways out of poverty.

_Households in rural Africa diversify into non‐farm activities mainly for survival_ . While the dominant form of off‐farm income, there is little systematic evidence on the prevalence, patterns and performance of Africa’s rural non‐farm enterprises. Nagler and Naudé (2017) are the first to exploit the enterprise modules of the LSMS‐ISA survey to characterize systematically the rural household enterprise landscape in SSA. They find that non‐farm self‐employment activities in the African household are indeed mostly oriented around survival. The evidence lies in the nature of these activities: most are small, unproductive, informal household enterprises, operated from home, without any non‐household employees and often operating only for a portion of the year. They are concentrated in easy‐to‐enter activities, such as sales and trade, rather than in activities that require higher starting costs, such as transport services, or educational investment, such as professional services.
sample countries are practicing full capital cost recovery (figure 11a); Among the countries that should be able to cover total costs under existing tariff regimes (contingent on their ability to collect bills) are Burkina Faso, Côte d’Ivoire, Kenya, Namibia, and Senegal.

<!-- Start of picture text --> Figure 11. Cost-recovery capabilities of effective tariff regimes<br>a. Weighted effective tariffs and total costs b. Weighted effective tariffs and OPEX<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Historical costs, US cents/kWh Historical opex costs, US cents/kWh<br>(cents/kWh) (cents/kWh)<br>Weighted Effective Tariffs Weighted Effective Tariffs<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ OPEX = operational expenditure.

Lack of full cost recovery can be partly explained by the large weight of residential customers in utilities’ revenue structure, whose charges – for political and social reasons – often fail to reflect costs. Excluding South Africa, roughly 50 percent of the total power billing in Africa is associated with households, for just over 40 percent of the total power supplied (figure 13). This is a very high share by global standards. In South Africa, by contrast, the total share of residential billing stands much lower at 17 percent versus only 8 percent of total power supplied. Only in 30 percent of the sample countries do residential tariffs allow for 100 percent cost recovery (figure 12a). While residential tariffs do better at covering operating costs (figure 12b), this is not enough to guarantee financial sustainability in the medium to long term.

It is important to underscore that setting tariffs at
# **Appendix B. O*NET GEP Steps for classifying green occupations**

<!-- Start of picture text --> 1<br>Locate and review existing literature. About 60 reports<br>Identify and compile job titles. Accumulating a list of all job titles that were<br>2<br>mentioned in the reports<br>Review and sort collected job titles. Eliminate too molecular or too molar job titles<br>3<br>(2%). Kept 467 job titles<br>4<br>Cluster job titles to identify occupations. Similar job titles were clustered<br>5 Identify occupational sectors. A 12 schema was used to group job titles<br>6<br>Determine overlap with ONET. Determine if the occupation was existent in ONET SOC or<br>was a new occupation, and generate the 3 grouping: Green increased demand, green<br>7<br>Identify new and emerging. Group job titles into occupations<br>8<br>Research potential N&E occupations<br>9<br>Build consolidated evidence for final N&E determination<br>10<br>Compile and report N&E skills<br><!-- End of picture text --> _Source_ : Authors based on Dierdorff et al. (2009), Dierdorff et al. (2011) and O*NET (2010).

# **Appendix C. List of consulted material for the creation of the green dictionary**

## **Output approach**

BLS (2010). Green Goods and Services Survey Questionnaire. O.M.B. No. 1220−0181 BLS (2010). Industries where green goods and services are classified. Excel file with list of green industries at NAICS level. https://www.bls.gov/ggs/ BLS (n.d.) Survey Methods and Reliability Statement for Occupational
Appendix 3 shows that smaller enterprises - measured by their turnover - tend to be owned by poorer households and poor female-only households. The question of whether low revenue enterprises face a disproportionate tax burden therefore is important from an equity and gender perspective.

**Table 1: Percentage of households with a non-farm enterprise (NFE), by household type**

||**Period**<br>**(2018/2**|**1**<br>**019)**|**Period 2**<br>**(2021/2022)**|
|---|---|---|---|
|% of households with NFEs|22.9||26.8|
|_Number of households_|_6,770_||_4,959_|
||**Period**|**1**|**Period 2**|
||**Male or dual**<br>**adult household**|**Female only**<br>**household**|**Male or dual adult**<br>**household**<br>**Female only**<br>**household**|
|% of households with NFEs|22.5|25.5|27.2<br>21.2|
|_Number of households_|_5,518_|_1,252_|_4,337_<br>_622_|

10 This paper primarily classifies households by whether there is a male adult, as the assignment of headship is often arbitrary across countries (see World Bank 2018 for a discussion). The presence of a male adult also results in women having better access to land and agricultural labor (see Komatsu et al. 2022).

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_Third_ , the extremely rich _Gojek_ data allows us to provide much more fine-grained, and, therefore, in-depth, evidence on the complementarity versus substitution hypothesis. Notably, **1)** we show the effect(s) of the opening of the MRT on a _daily_ , as opposed to a monthly, basis, allowing us to show how they evolve throughout each phase of Jakarta MRT’s staggered opening; **2)** we document how the MRT opening affects both rush- and non-rush hour ride-hailing activity, as well as regular weekday, and weekend and public holiday ride-hailing activity; **3)** we show that results differ for station locations at which there was no previous alternative BRT stop; and **4)** the data on daily tap-ins and tap-outs into each of the newly opened MRT stations allow us to show that daily _Gojek_ ride volume and the actual use of the MRT system are positively associated, thereby alleviating concerns that the increased ride-hailing volume might be associated with, for example, the opening of new urban amenities around the MRT stations rather than the use of the MRT system itself.

Last, but certainly not least, our study complements several more qualitative studies that provide surveybased evidence on either ride-hailing customers’ (Irawan _et al._ , 2020), or public transport users’ (Saffan and Rizki, 2018; <mark>Sunitiyoso</mark> _<mark>et al</mark>_ <mark>, 2022) commuting behavior to better understand the role of ride-hailing services in public transport trips in Jakarta. Also, we</mark> contribute to a literature that is specifically focused on evaluating the impacts of various transportation policies for Jakarta. Gaduh, Gračner, and Rothenberg (2022) evaluate the impacts of Jakarta’s BRT system, the _TransJakarta_ , on vehicle ownership, travel times and commuting flows. Kreindler _et al._ (2023) add to this by showing how the recent expansions of the BRT
**Figure 17. Simulated change in earnings as a result of the minimum wage increase assuming perfectly elastic labor demand**

<!-- Start of picture text -->
-23.5<br>Total Gender Age Education<br>Male Female 15-19 20-24 25-29 30-34 35-54 55-64 Primary or less Secondary Post-secondary<br><!-- End of picture text -->

_Source: Own estimates using the 2018 Kosovo LFS._ <mark>Sensitivity checks: Wage imputations and misreporting</mark>

**<mark>The main challenge in evaluating the potential effect of the minimum wage policy using the LFS data is that the wages are reported in brackets</mark>** <mark>. Therefore, wage imputation is required, and in the main analysis we have applied a simple imputation technique assuming that wages are uniformly distributed on the reported interval. Alternatively, wages can be imputed conditional on observable characteristics from the 2017 HBS in which exact wages are reported.</mark><sup>32</sup> <mark>The difficulties with this approach are:</mark>

<mark>1. There is underreporting of labor income in HBS (compared to LFS). HBS wage distribution is shifted to the left.</mark> <mark>2. There is uncommon support</mark><sup>33</sup> <mark>in the distributions (difference in observable characteristics between LFS and HBS).</mark>

**<mark>We apply the following algorithm, based on Nopo (2008):</mark>**

<mark>Step 1: Select one individual (without replacement) from the LFS.</mark><sup>34</sup> <mark>Step 2: Select all individuals who have the same characteristics as the individual selected in step 1 from HBS.</mark><sup>35</sup> <mark>
**Tables**

**Table 1:** Turnover, Public Goods, and Nepotism: Correlations (OLS)

|||**Public**|**Goods Ind**|**ex**|**Village**|**government**|**Village h**|**ead survey**|
|---|---|---|---|---|---|---|---|---|
||_Podes_|2021|_Podes_202|1-2014growth|% Ne|w appts|Village h|ead relative|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incumbent runs|0.006|0.102|0.046|0.643|-0.035|0.031|0.031|-0.024|
||(0.064)|(0.077)|(0.377)|(0.511)|(0.029)|(0.034)|(0.040)|(0.047)|
|Incumbent wins||-0.181<sup>_∗∗_</sup>||-1.121<sup>_∗∗_</sup>||-0.123<sup>_∗∗∗_</sup>||0.102<sup>_∗∗_</sup>|
|||(0.080)||(0.492)||(0.033)||(0.046)|
|_P-value_, total effect||0.29||0.22||0.0055||0.091|
|Region FEs
where _rc_ is the average health status as proxied by the afore-mentioned survival rate.<sup>11</sup> Implementing the development accounting exercise requires picking a value for _γ_ . One cannot rely directly on microeconomic evidence in this case, since the survival rate is an aggregate statistics and by definition does not vary within countries. Weil (2007) bypasses this problem by exploiting the fact that, over time, the survival rate is strongly correlated with average height, and that height does vary within countries. Specifically, he postulates a linear cross-country relationship between the survival rate _rc_ and average height _vc_ , _vc_ “ _αr_ ` _γrrc_ , as well as a within-country log-linear relationship between wages and individual height, Since _γr_ and _γv_ can be estimated directly, one can identify _γ_ in (7) as _γ_ “ _γrγv_ . I use the estimates in Weil (2007), which imply _γ_ “ 0 _._ 65. Moreover, I use _β_ “ 0 _._ 10 as before.<sup>12</sup> Table 3 shows the development accounting results. Compared to Table 1, human capital varies more between the United States and poor countries. The measure of _Success_ increases to 11.9%, suggesting that accounting for health differences moderately increases the inferred contribution of human capital.

Another potentially important dimension of human capital is labor market experience. The process of learning does not stop at the end of one’s schooling career, and it is reasonable to think that rich and poor countries might offer differential opportunities for human capital accumulation even at a later age.

Traditionally, the development accounting literature dismissed this dimension of human capital on the ground that the _quantity_ of potential experience is not positively correlated with GDP per worker. While life expectancy is higher in rich countries, longer schooling careers imply that individuals in those countries enter later in the labor market. Klenow and Rodr ́ıguez-Clare (1997b) and Caselli (2005) document that on balance this leads, if anything, to a higher
contrary based on weak labor markets and the tightening of border controls in the industrial countries after the terrorist attacks of September 11, 2001.

2 The relatively stable nature of remittances suggests that countries with access to significant remittance inflows may be less prone to damaging fluctuations, whether in output, consumption, or investment. In extreme cases, remittances might reduce the probability of financial crises. Such considerations are strengthened by the fact that remittances, unlike capital inflows, are unrequited transfers, which do not create future debt servicing or other obligations (IMF 2005).

3Based on the 2001/2003 survey, total remittances constituted nearly 3 percent of income of the average household.

> 4 The Middle East region has been the primary destination, having received almost 90 percent of total Sri Lankan contract workers.

26
one model of reform and the way in which reforms are implemented will vary depending on what works, it is possible to identify the basic necessities that need to be tackled to facilitate further development of the market in every developing country, regardless of its individual circumstances. These basic necessities apply to and will support all forms of primary market finance.

# _1.  Create a supportive environment._

- Put fiscal practices on solid ground.  Unstable macroeconomic conditions make it hard to predict behavior.

- Ensure that there are basic protections of property rights, and that data registries exist.  These are the necessary foundations for disclosure and enforcement and to promote market confidence.

> 73 P. Henry and P. Lorentzen, 2003, p. 181

> 74 Demirguc-Kunt and Beck, February 2004. _See, also, “_ “Other things have to go with it [government to government assistance[, including  .  .  .  improving the climate for the private sector, and we don't just mean the climate for foreign direct investment, but even more importantly to improve the climate for the indigenous private sector, which has proven over and over again to be the engine for growth in successful developing economies.  Interview with Paul Wolfowitz before the G-8 Summit in Gleneagles, Scotland, July 2005.

> 75 Ladekarl and Zervos 2004

30
method, as coefficients obtained from OLS are biased since they do not take endogeneity into account. IV or control function estimation addresses this concern provided they make use of valid instrumental variables (Blundell, Daerden and Sianesi 2005). Standard estimates also typically neglect heterogeneity in returns across different levels of education. Yet, investment in education may depend on whether returns are convex or concave, which is a subject of debate, including in Tanzania. While many of these concerns had long been neglected in analysis for developing countries, and especially for African countries (see Schultz 2004), they are now increasingly taken into account.<sup>11</sup>

> 8 The alternative method replaces years of schooling by dummy variables for different levels of education, and obtains the returns to education by dividing the estimated coefficient by the corresponding years of schooling for each level of education relative to the level of education below.

> 9 Firm survey often target a specific sector, for instance the manufacturing sector, or the private sector.

> 10 Glewwe  (1996) also suggests that substantial variability in school quality could bias returns to education and therefore should be included as right hand side variables. .

> 11 Note that recent work indicates that the Mincerian model and its underlying assumptions do not necessarily hold in all contexts, even when addressing these concerns.  Heckman, Lochner and Todd (2003) find empirical support for the model’s predictions for the US for 1940s and 1950s, but not thereafter. Relaxing key assumptions seem to lead to different inference, and the authors conclude that Mincerian returns, in their context, do not necessarily provide good guidance to policy analysis.  Caution is therefore needed, both in the interpretation of the results and

5
<!-- Start of picture text --> Minerals nec, 2001 Minerals nec, 2011<br>COL<br>BRA<br>CHN TUR PERZAF<br>RUS CHN<br>ZAF BRA<br>IND TURRUS<br>PER CHL IND COL<br>THA CHL<br>THA<br>4 6 8 10 12 4 6 8 10 12<br>Log GDP per capita (current USD) Log GDP per capita (USD)<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>Total LVAX share (%) Total LVAX share (%)<br>10 10<br>0 0<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX and World Development Indicators.

The job intensity of South Africa’s manufacturing exports continues to be low vis-a-vis comparator countries, while the job intensity of mining and energy is higher than all other countries. The following two figures show a comparison of the job intensity of exports across sectors and comparator countries (in terms of the total number of jobs per $1 million of gross exports in Figure 16 and the direct number of jobs per $1 million of gross exports in Figure 17). Unlike the labor value added intensity of exports (which was lower than the BRIC countries but higher than other comparators), the job intensity of exports tends to be lower than all comparators, with the exception of mining and energy. Again, with the exception of mining and energy, the direct job intensity of exports tends to be lower than comparators across sectors.

**Figure 16: Total job content per $1 million of gross exports across macro sectors 2011**

<!-- Start of picture text --> Agriculture<br>Construction<br>Electricity, gas and water<br>Financial
, F. (1979): "Decomposable Income Inequality Measures", _Econometrica_ , **47** , pp.90120.

- Bourguignon, F., F.H.G. Ferreira and N. Lustig (2005): _The Microeconomics of Income Distribution Dynamics in East Asia and Latin America_ (Washington DC: The World Bank and Oxford University Press).

Cardoso, E. (1992): “Inflation and Poverty”, NBER Working Paper No. 4006 (Cambridge, MA).

Cardoso, E., R. Paes de Barros and A. Urani (1995): "Inflation and Unemployment as Determinants of Inequality in Brazil: the 1980s", Chapter 5 in Dornbusch, R. and Edwards, S. (eds): _Reform, Recovery and Growth: Latin America and the Middle-East_ ; (Chicago: University of Chicago Press for the NBER).

- Corseuil, C.H.L. and M.N. Foguel (2002): “Uma Sugestão de Deflatores para Rendas Obtidas a partir de Algumas Pesquisas Domiciliares do IBGE”, IPEA Texto para Discussão #897, Rio de Janeiro.

- Cowell, F.A. (1980): "On the Structure of Additive Inequality Measures", _Review of Economic Studies_ , **47** , pp.521-31.