rafmacalaba/data-use-mentions-extended huggingface dataset:
(1999) who found that educational gender gaps have a direct and negative impact on economic growth.<sup>14</sup> The transmission channels from the education gender gap to growth are expected to vary along the stages of development and across countries. According to Barro and Lee (1996), the growth impact of female education is transmitted through its negative effect on fertility. Under the hypothesis of reduced fertility rate, the quantity-quality substitution in reproductive outcomes attained as a result of increased life expectancy improves female time use and time allocation, and hence their contribution to growth. At the same time, an increase in female human capital endowment may also enhance the growth process through the productivity channel [Hill and King (1995)].

Still, the correlation which has been the basis for inference in this section does not imply causation. While skill improvements through the reduction of educational gender gap may increase female labor force participation rates and growth under loosening social norms (either through productivity channels, fertility channels or both), the level of economic development may also affect gender equality, and specifically, the educational gender gap. Illustrating that dependency structure, longitudinal data and cross-country analysis have suggested a U-shaped relationship between female labor supply and economic development [Goldin (1995)].

The narrowing gender gap in education and increasing female labor force participation rates, especially in the rapidly growing economies of the region, may therefore be attributed to two concurrent factors: growth effects and loosening social norms and cultural beliefs over time.<sup>15</sup> In a dynamic setting of rapidly changing institutions, gender inequality should not expect to be constant over time or across countries. The expected contribution of women to growth in the region should therefore be positive and increase over time. The next sections draw on a growth accounting framework to estimate the relative contribution of women to growth over the last three decades.

# **III Analytical framework**

Standard growth accounting frameworks make it possible to decompose the change in output
of the initial tariff and according to the Schwarz criterion the speci fi cation without it is preferred, we choose to focus on the latter, which follows our theoretical model more closely as well.<sup>49</sup> The food products industry, SIC 311, contains approximately half of all the products in our sample that do not enter the EU duty-free through preferential agreements. Although this category does not include primary agricultural products (it includes processing of food related products) it does share one important characteristic with agriculture: high protection. To the extent that this feature is timeinvariant, then it is immediately addressed by the fact that we estimate the equations in differences. If on the other hand the amount of the reduction depends on the initial tariff, which is on average higher for 311 than other industries, this could potentially affect the results because half of the products in 311 are non-PTA products and they represent a large share of all non-PTA products in the sample. However, this last concern should be addressed by including the initial tariff as a regressor, which, as we have just described, does not affect the results signi fi cantly.

To study whether the stumbling block effect is merely driven by a cross-industry difference in the average tariff cut, we re-estimate the model by dropping the observations in 311 and obtain estimates that are qualitatively similar to those in table 2 in terms of the signs and signi fi cance of the coefficients. The point estimates of the relative growth effect in the basic speci fi cation are higher when we exclude products in 311 but the 95% con fi dence intervals overlap with those of the corresponding speci fi cation in table 2.<sup>50</sup> A fi nal concern that we address is the potential measurement error in the production data for petroleum re fi neries (sic 353) and miscellaneous petroleum and coal products (sic 354). Given that these are sensitive sectors, the production data for them can be inaccurate and is in some cases missing.
2018-2019 GHS survey rounds and within Nigerian states implementing opengrazing bans, which have been attributed as a contributor to increased violence in the conflict. We find that the shift toward informal work and away from agricultural work is larger within states implementing an open-grazing ban and during the 2018-2019 survey round, especially during the post-harvest season.

Finally, we investigate the consequences of this labor response to exposure to herder-related violence. We specifically estimate “knock-on” or lagged effects that materialize during the post-harvest period of the 2018-2019 GHS survey and are associated with exposure to herder-related violence in the previous planting season. We find that households exposed to herder-related violence and who have also been previously exposed to this violence (i) shift their consumption toward food and away from other expenditures, (ii) are more likely to sell raw crops to relatives/friends or via the farmgate, and (iii) are more likely to operate a non-farm enterprise.

Despite our finding that agricultural households exposed to herder-related violence in Nigeria might use the informal sector as a way to cope with the increased risk associated with agricultural production, this ‘safety net’ does not seem to effectively guard against these households experiencing adverse economic consequences. Therefore, herder-related violence can have wide-reaching indirect costs beyond the direct costs associated with the loss of human life and the destruction of property. In the absence of the elimi21
# **3. 3 FDI Flows into the Service Sector in Indonesia**

The relaxation of regulatory restrictions, together with an increasingly attractive domestic market and relatively lower labor costs have contributed to an increase in FDI flows. Having dipped in 2009 with the onset of the global financial crisis, FDI flows have since surged, reaching $18.9bn USD in 2011.FDI has been strong across all sectors, but particularly in the services sector, which accounted for 40.1% of all inward investment into Indonesia in 2011 (Figure 6).<sup>9</sup> To a large extent, this has been driven by FDI into the two service sub-sectors that were both the largest and the fastest growing in 2011: Transport & Communications and Trade, Hotel & Restaurant (of which the bulk is accounted for by Wholesale & Retail; Figure 7).

While FDI flows now broadly reflect the size of Indonesia’s economy, the country accounting for both 1.2% of world FDI flows and 1.2% of world GDP, it lags far behind regional peers. In 2011, the South East Asia region accounted for 3.1% of global GDP, but 5.3% of global stocks of FDI and 7.6% of global FDI flows.

Cross-country evidence suggests that the statutory restrictions measured in the OECD FDI regulatory restrictiveness index are negatively correlated with FDI stocks (Figure 8).

# **3. 4 Liberalization Path in the Indonesian Telecom Sector**

Between 1966 and 1998, Indonesia’s telecommunications sector was tightly controlled, and reform was incremental. From 1980 onwards, government acted as both regulator and monopoly operator. With investment capital in short supply, however, the government began to allow the incumbents to form strategic alliances with foreign firms as of 1989. Although a degree of competition emerged in the mobile phone sector after partial privatizations in 1995, government remained the dominant player until the East Asian crisis.

In 1998, given the commitments made
trade, which is convenient for the gravity model. Another downside however is that this database groups all digital services sectors together, as shown in Annex Table A3. The years covered for the panel analysis is 2005-2016.

The results using the ITPD-E data with respect to the baseline regression following equation (3) are presented in Table 8, which means that it covers a panel data set and includes the two sets of control variables of the data restrictiveness index and digital enabling environment. As such, this table is readily comparable with Table 7, the more so because the sectoral grouping for Digital+ and business services are carefully cleaned and matched between the two data sources. The results are reported in Table 8. The results for GRAVo and C ∗intlo are reported in Annex Table A6. In Table 8, the total number of observations is lower than using TiVA data which is due to missing oo oo observations. The list of countries in this dataset is also different and not balanced compared to the TiVA data. The results reported in columns 1-3 are without any control variables included. A first finding is that none of the variables for the CB component have a significant coefficient outcome. That stands in contrast to some of the significant findings in the bottom panel of Table 4, to which these results need to be compared. When looking at the DR component, the results are largely in line with Table 4, except for the insignificance for the conditional model in column 2, even though the sign remains positive. The difference in results may be due to the fact that even though the sector groupings are matched as closely as possible, some sub-sectoral differences remain or cannot be captured adequately between the two data sources.

Column 4-6 repeat the baseline regressions and adds the two sets of control variables. Note that in case of adding the OECD DSTRI instead of the ECIPE DTRI, the results would omit a large number of developing countries.<sup>26</sup> The result that now stand out is the negative
**Figure 7: Real Spending per Student Has Generally Risen in Low-Income and Middle-Income Countries, but the Gap between Income Groups Has Widened**

_Public education spending per student (constant 2015 PPP $), 1998–2001 to 2014–17_ <!-- Start of picture text --> a. Primary education b. Secondary education<br> 3,500 3,500<br>UMIC<br> 3,000 3,000<br> 2,500 UMIC 2,500<br> 2,000 2,000<br> 1,500 1,500 LMIC<br> 1,000 LMIC 1,000<br> 500 500<br>LIC<br>LIC<br> - -<br>1998–2001 2002–05 2006–09 2010–13 2014–17 1998–2001 2002–05 2006–09 2010–13 2014–17<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ LIC = low-income country, LMIC = lower-middle-income country, and UMIC = upper-middle-income country.

# **_Recent Spending Increases Have Mostly Financed School Expansion in Low-Income Countries_**

Increases in government funding for education can go towards expanding the number of student places, increasing the average amount spent on each student, or a combination of the two. To determine if countries are finding a balance between quantity and quality, it can be useful to explore how policy makers have used additional public education funding and how their country’s level of education development has influenced their choices. The data can also help to identify the transition points where countries shift spending priorities from increasing access to improving quality. These transitions are where spending inefficiencies potentially can increase if policy makers fail to pay due attention to how increases in levels of spending per student are used. To understand how governments have historically used funds over the last two decades, we broke down public
sensitivity of sugar demand to changes in prices (see e.g., Nicita, 2004b). This information is not available from labor surveys, so we focus only on the impact on labor income. Because sugar prices are increasing this means that at least in this sense our calculations overestimate the impact on real income. However a back of the envelope calculation suggest that overestimation is not a serious problem. The weight that the Brazilian statistical office (Instituto Brasileiro de Geografia e Estat ́ıstica – IBGE) puts on sugar in its consumer price index is 0.8. So on average sugar accounts for 0.8 percent of household expenditure and therefore an upper bound for the real income loss associated with a 10 percent increase in sugar prices (i.e. abstracting from any substitution possibility and frictions in price transmission) is around 0.08 percent.

15
premium and seemed to be more beneficial for banks with lower exposure to the oil sector (Table A7 in the Appendix).

# **5. Conclusions**

The spread of COVID-19 represents an unpresented global shock, with the disease itself and mitigation efforts –such as social distancing measures and partial and national lockdowns measures– both having a significant impact on the economy. In the immediate aftermath, the financial sector, particularly banks, were expected to play an important role in absorbing the shock by supplying vital credit to the corporate sector and households. In an effort to facilitate this, central banks and governments around world enacted a wide range of policy measures to provide greater liquidity and support the flow of credit. An important policy question is the potential impact of these countercyclical lending policies on the future stability of the banking systems and to what extent their strengthened capital positions since the global financial crisis will allow them to absorb this shock without undermining their resilience.

In this paper, we use daily stock prices and other balance sheet information for a sample of banks in 53 countries to take a first look at this issue. Our contribution is twofold. We first assess the impact of the pandemic on the banking sector and investigate whether the shock had a differential impact on banks versus corporates, as well as those banks with different characteristics. Second, using a global database of financial sector policy responses and an event study methodology, we investigate the role of different policy initiatives on addressing bank stress as perceived by markets, in the aggregate, as well as across different banks.

Our results suggest that the adverse impact of the COVID-19 shock on banks was much more pronounced and long-lasting than on the corporates as well as other non-bank financial 27
financial resources handled by them. NABARD should play a leading role in improving the knowledge base. A database of SHG federations needs to be created. Assessments and impact studies of the different federation models, and development of performance standards and rating indices need to be carried out. APMAS is in the process of developing a rating index. NABARD should also facilitate of mutual learning among the promoter agencies that have promoted SHG federations and the federations themselves.

Some aspects of SHG federations that need further study include: (a) the effect of federating on transaction costs and delinquency rates; (b) the effect of federating on SHG promotional costs, and outreach of the SHG-Bank Linkage program; (c) cost-benefit analysis of federating; (d) analysis of SHG federations compared to other federated models in microfinance – village banking and credit unions; and (e) impact of SHG federations on community social capital.

- 25 -
such tradeoffs is not commonly needed in policy making at the country level. But when one faces a tradeoff, because a policy spans more than one dimension, those with a stake in the outcomes will almost certainly be in a better position to do determine what weights to apply than the analyst calibrating a measure of poverty. The specific country and policy context should determine what tradeoff is considered appropriate. This will often be a political decision, though hopefully a well informed one.

So we may well be asking too much of a single measure of “poverty” to have it include things like child mortality, schooling, violence or empowerment, on top of material living standards. It is one thing to agree that consumption of market commodities is an incomplete metric of welfare—and that for the purpose of assessing poverty one needs to also account for indicators of non-market goods and services—and quite another to say that a single “poverty” measure should embrace all these things.

This suggests that we may need to focus our efforts and resources on developing the best possible distinct measures of the various dimensions of poverty deemed relevant to a given setting—aiming for a credible set of “multiple indices” rather than a single “multidimensional index.” Granted, that route forgoes the option of identifying a joint distribution, but that is hardly a large loss in practice when that one’s characterization of the joint distribution is so severely data-constrained (notably in finding a single household survey that covers all the dimensions that matter). And, of course, taking the multiple-index route in the context of poverty monitoring does not preclude researching the joint distributions in specific data sets, when feasible.

Some degree of aggregation will almost certainly be needed even within the multipleindex approach. This raises an important but poorly understood issue concerning the space we choose to aggregate multiple dimensions. We must decide whether this is to be done in the space of attainments—the variables we use in assessing individual welfare for the purpose of deciding who is poor and
assets into each individual-level interview.<sup>6</sup> Across all LSMS+ supported surveys, information on individual asset ownership and valuation was entirely self-reported.

The LSMS+ assets modules also examine nuances of ownership that are not well-covered in surveys on wealth. This includes differentiating between respondents’ exclusive versus joint reported ownership of each asset in a given asset class.<sup>7</sup> In the case of joint ownership, respondents were asked to identify who shared the asset within the household (through those individuals’ unique household roster identifiers), as well as the number of men and women outside the household who shared ownership of the asset. For residential and non-residential land, different ownership and rights constructs were covered as well. For residential and non-residential land, for example, individuals were asked about overall reported ownership, economic ownership (being able to control money over the proceeds from hypothetical land sales), as well as documented ownership/whether the respondent’s name was on a title or certificate to the land. Whether landowning respondents had rights to sell, bequeath, rent, use as collateral, and invest were also asked. Additional questions were asked on whether these nuances of ownership and rights were held jointly or exclusively. This level of detail highlights important patterns of ownership and decision-making that can inform policy efforts to expand access to financial services, land, and property rights in general. Our results also show, under the individual-level survey approach, a substantial share of agreement among 6 Time and budget constraints can hamper interviewing all adult household members, leading to the risk of nonresponse. Among the LSMS+ surveys that have been completed so far however; response rates have been relatively high among eligible adults. In Malawi, 82 percent of all eligible adults were successfully interviewed; in Tanzania, Ethiopia and Cambodia, these shares were 80, 96, and 92 percent, respectively (see Hasanbasri et al., 2021c, 2021d for more details).

7 The one exception was for
dummy variables for Argentina and 5 dummy variables for Brazil). ***,**, and * denotes statistical significance at 1, 5, and 10 percent levels, respectively. _Source_ : Authors' calculations based on _Encuesta Permanente de Hogares,_ 2003:Q1 – 2013:Q _; and Pesquisa Mensal de Emprego,_ 2007:M1-2012:M8.
and the richest states in the country ⎯ we have divided them into the “poor North” (Bihar, Uttar Pradesh, Orissa, Madhya Pradesh and Rajasthan), “rich North” (Punjab, Haryana, Gujarat and Maharashtra)<sup>23</sup> and “other North” (Himachal Pradesh, Jammu & Kashmir, and West Bengal) (Table 2). The poor Northern states have some of the poorest outcomes in the country not only in nutrition, but also in terms of education and child survival, reflecting a history of poor governance.

The need for the program varies substantially across states (Table 2). It is especially high in the poor Northern states, where over half of the children aged below 3 were moderately or severely underweight. These are large states, with well over 40 percent of the population of the country, and therefore contribute a high proportion of the total malnourished children in the country. At the other end of the spectrum are states such as Kerala and several of the northeastern states, with the lowest levels of child malnutrition. The northeastern states tend also to have good human development indicators in terms of

> 21 Das Gupta and Bhat 1997. On son preference more broadly, see Miller 1981, Dyson and Moore 1983, and Das Gupta 1987. On gender differentials in tribal populations, see Maharatna 2000.

> 22 Sikkim is not included where data are required for 1992, since these are not available.

> 23 By the time of the 2001 Census, Uttar Pradesh, Madhya Pradesh, and Bihar had some additional states carved out of them: Uttaranchal, Chhattisgarh, and Jharkhand respectively. To facilitate intersurvey comparison, we use the old state definitions.

8
)

||(1)<br>events|(2)<br>events|(3)<br>events|(4)<br>fatalities|(5)<br>fatalities|(6)<br>fatalities|
|---|---|---|---|---|---|---|
|project ongoing|-0.006|||-0.028|||
||(0.017)|||(0.025)|||
|project completed||-0.062**|||-0.080**||
|||(0.027)|||(0.039)||
|road project stock|||-0.026|||-0.048*|
||||(0.018)|||(0.027)|
|Observations|18722|18722|18722|18722|18722|18722|
|R2|.496|.496|.497|.36|.36|.36|

Note: * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001. Standard errors are clustered at the cell level. Cell and year-fixed effects are always included. Events and fatalities are in logs (+1).

Second, we also get a strong negative relationship between road projects and battles using ACLED data. Battles relate to events linked to fights for territory and armed clash. The coefficients remain negative and significant for the number of events but are not significant any more for the number of fatalities. Last, the results for non-state violence and state-based violence using UCDP are insignificant (Table A3).

25
imperfections, allowing US firms to achieve their desired financial structure. Thus, assuming that there are technological reasons why some industries depend more on external finance than others, the index offers an exogenous way to identify the extent of external dependence of an industry anywhere in the world.

To construct the index, we use data on US firms in the Compustat data set over the period 2000-2006. We exclude the years coinciding with the financial crisis where the assumption of unconstrained markets is unlikely to hold. For each firm, we sum the capital expenditure and cash flow from operations over the 7 year time period and take the ratio of this difference to the sum of capital expenditures over this period as a measure of a firm’s dependence on external finance. We then take the industry median for each ISIC 2-digit industry to obtain the industry dependence on external finance.

Finally, we obtain country level macro data such as the inflation rate and the GDP growth rate from the World Development Indicators, and data on regulatory quality from the World Governance Indicators.

# **III. Empirical methodology**

We adopt a two-pronged approach in our empirical strategy. First, we use the ES sample to present evidence on the access to finance and employment growth relation across a large number of developing countries, addressing causality issues using country fixed-effects estimations and a difference-in-difference setup using the introduction of credit bureaus as an exogenous shock to the supply of credit. Next, we use the Orbis sample that covers fewer countries but offers several million firm-level observations and allows us to take an even closer 12