rafmacalaba/datause-encoder-data huggingface dataset:
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16 the 'rewards part" of the gender wage gap.' In what follows, the nature of our results necessitates the separation of the effects from the difference in the constant terms and the differences b-tween rewards but the previous qualifications should be borne in mind.

In what follows, the nature of our results Bearing these remarks in mind we ran separate earnings function for women and men. In addition, we corrected for selectivity, when appropriate the earnings functions of women. This is a well established procedure in order to take into account the possibility that women workers may not be representative of all women in the economy." Table 5 presents the percentage of the pay gap which can be attributed, on the one hand, to differences in the labor market endowments held by women and men and, on the other hand, to differences in the labor market rewards associated with these characteristics.36 Table 5 Decomposition of the Male Pay Advantage in the Region (percent)

|||Evaluated at|
|---|---|---|
|Selectivity<br>Correction|Pay advantage<br>due to|Female means<br>Male means|
|No|Endowments<br>Rewards|3.2<br>2.9<br>96.8<br>97.1|
|Yes|Endowments|20.4<br>17.4|
||Rewards|79.6<br>82.6|
||Total'|100.0<br>100.0|

The overall male pay advantage is 30 log-percentage points.

A number of clarifications need be made in order to correctly interpret the summary decomposition results. First, the pay gap is shown as log-percentage points of the male pay 'See among others, Shapiro and Stelcner (1986); Behrman and Wolfe (1986); Birdsall and Fox (1985); Knight and Sabot (1982) and the collection of papers in Birdsall and Sabot (forthcoming).

35See Heckman (1979) and the extensive discussion of the subject in Psacharopoulos and Tzannatos (1991, Chapter 5).

MThe information presented in the Table 5 is derived from Appendix Table Al. There we report results wlhich are standardized on female means (women paid as men: columns 2-3 and 6-7) and also on male means (men paid as women: columns 4-5 and 8-9). We also report separately the results obtained from coefficients which were uncorrected (columns 2 to 5) or had been corrected for selectivity bias (columns 6 to 9).
Even if production in the South is not subject to robotization, real wages and welfare are impacted by Northern automation. Increased robotization and reduced wages make Northern producers more competitive. This leads to an increase in Northern exports in the robotized industry, and to a corresponding reduction in final goods exports from the South in this sector. At the same time, the South expands its exports of intermediates in this sector. Both total real exports and exports measured as a share of GDP increase in the South (see top panels of Figure 8). As shown in Figures 7 and 8, real wages and welfare tend to increase in the South, reflecting also lower consumer prices. These effects are reinforced when robot prices decline further and the other countries in the North also find it optimal to robotize their own production. Welfare gains in the South are likely to be even more pronounced if robotization also becomes feasible there.

# **6.4 The role of trade frictions**

As emphasized above, trade in intermediate and final goods is the key channel whereby Northern robotization impacts real wages and welfare in the South. A natural question is, then, how trade costs modulate the impact of robot price reductions on Southern workers and consumers. To answer this question, we perform counterfactual simulations on the effects of robot price reductions under two different trade cost scenarios. First, we consider a frictionless trade scenario, in which trade frictions are assumed to fall down to zero (from the level that was initially observed in the 2005 data). We then compute the new equilibrium associated with frictionless trade, and examine the effects of reductions in robot prices from this point. Similarly, we consider a high trade costs scenario, in which the level of trade frictions are assumed to be twice of the level that was initially calibrated.

Figure 9 displays the effects of Northern robotization on North-South trade under the frictionless trade regime. The figure reveals that the effects of Northern robotization on exports to the South in this sector remain qualitatively similar, but the rate of change is now considerably higher. Robotization still makes final-goods producers in the North more competitive, and the associated impact on their exports is now considerably higher. At the same time, the automation-induced expansion of production continues to have a positive effect on exports of intermediates from the South to the North. The magnitude of this effect is now considerably stronger, generating a sizable impact on total exports from the South to the North in the robotized sector.

Figure 10 depicts the impacts of robot price reductions on robot use, labor allocation, wages and real GDP under frictionless trade. Interestingly, we observe that robot price reductions lead 24
# **Distribution Advantages and Challenges**

## **Distribution Advantages**

Bancassurance carries important distributional advantages over traditional insurance agency networks. However, in an age in which insurance is frequently sold via the Internet or via the telephone, the earlier distributional efficiencies that helped Bancassurance develop in some countries are not so clear.

One critical advantage held by the Bancassurance model is that the insurance is often a _minor add-in cost_ when viewed as part of a much larger economic transaction. So the decision to take it (or not) is a relatively simple one. If the customer is taking out a loan that will run for perhaps 25 years, the insurance that helps make the transaction work may be only a small fraction of the interest payments.

Often the _branding_ of the bank’s use of the product concerned is a source of comfort—indeed, for the bank this is a specific example of a moment when the bank’s brand is brought directly to bear by lending credibility to the insurance product.

Third, a key advantage is seen from a _customer sales opportunity_ perspective. The customer is sitting right there thinking about (for example) the new home for which the bank is offering a mortgage loan. The same customer has insurance needs that can be resolved there and then. This is a much easier proposition than for some insurers, where the sales process can require a pre-sale excursion simply to establish the insured’s renewal date so that the timing of a competitive renewal offer can be judged accurately. Some insurers employ telephone sales clerks whose job is to telephone potential customers and ask for their Property and Motor renewal dates; for the bank that is in the middle of selling a loan for a new purchase, this issue scarcely arises.

Extending this is the _transactional data advantage_ that a bank enjoys with the benefit of its prior knowledge of already knowing its customer. In some jurisdictions the use of such information is strictly controlled.

A fifth advantage held by the Bancassurer is the ability to _control the renewal process._ This can easily act to the significant disadvantage of the consumer (a point discussed below under Regulatory Issues for Banks). From the bank’s point of view, however, it may be dynamically important that the borrower maintains proper insurance in relation to the loan—whether that be the Property insurance where the loan is secured by a title to property, or Life insurance in regard to the securing of the income stream intended to repay the mortgage, or otherwise. So the bank has a key position in being able to oversee and ensure the safe renewal of the underlying insurance.

19
# **Deals versus Rules: Policy Implementation Uncertainty and Why Firms Hate It**

Mary Hallward-Driemeier Gita Khun-Jush Lant Pritchett<sup>*</sup>

**Key words:** Uncertainty, Corruption, Enterprise Surveys, Doing Business, Policy Implementation **JEL codes:** H32, O17, D81, J23, K2

> * We would like to thank the NBER for support via the Africa Project.    We would like to thank Bill Battaile, Asli Demirguc-Kunt, Ahmed Galal, Ricardo Hausmann, Marialisa Motta, Stephen O’Connell, Rita Ramalho, James Robinson, Dani Rodrik, Luis Serven, Andrei Shleifer, Sylvia Solf and David Weil for conversations that were, perhaps unbeknownst to them, helpful and to Dan Willey for motivation on this topic **.** Mamadou Bady Balde provided the materials on Rwanda.  The views expressed here do not necessarily represent the views of the World Bank or its Executive Directors.  Corresponding author:  Mary Hallward-Driemeier, mhallward@worldbank.org.
# **3 Results**

## **3.1 Determinants of the gender gap in 2008**

Using the 2008 data, we first run a simple Ordinary Least Squares (OLS) regression of logged values of agricultural productivity in XOF/ha on female household headship. In Table 3, we find a 21 percent agricultural productivity gap to the detriment of female household heads (column 1), which is slightly attenuated to 17 percent once introducing region fixed effects (column 4).<sup>3</sup> Although we find a negative and significant gap of 23 percent for export crops, which are defined as cashews, cacao, coffee, cotton, rubber and palm oil, this gap disappears when adjusting for cross-regional differences through the introduction of fixed effects (column 5). The gender gap in food crop productivity remains unchanged at 24 percent (column 6).

**Table 3. OLS regressions of agricultural productivity without control variables (2008)**

|||**Log [Sel**<br>|**f-reported Pr**<br>|**oductivity (**<br>|**XOF/ha)]**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All crops|Export crops|Food crops|All crops|Export crops|Food crops|
|Female-headed households|-0.234***|-0.264***|-0.279***|-0.182***|-0.079|-0.270***|
||(0.054)|(0.094)|(0.056)|(0.053)|(0.088)|(0.055)|
|Constant|11.356***|11.221***|11.399***|11.584***|11.606***|11.566***|
||(0.020)|(0.027)|(0.022)|(0.102)|(0.151)|(0.108)|
|Region fixed effects|No|No|No|Yes|Yes|Yes|
|Adjusted R-squared|0.004|0.002|0.006|0.074|0.094|0.088|
|**% gap**|**21%**|**23%**|**24%**|**17%**|**8%**|**24%**|
|Observations|5428|3588|4601|5428|3588|4601|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively. % gap values are obtained with the exp(b)-1 transformation (Halvorsen and Palmquist (1980))_ In Table 4, we introduce the descriptive variables shown in Table 1 into our regression as controls, keeping region fixed effects. Here, we see that indeed selection into export crops is driving some of the gap, which decreases from 31 percent to 27 percent when introducing a dummy capturing whether the household farms export crops (column 2).

Conditional on controlling for female headship, we see that farming a smaller land area, having an older household head, having a household head who does not suffer from a disability, using more female household or hired labor, or especially male household labor, managing a higher number of fields, producing more crops, and applying inputs such as chemical fertilizer and pesticide are all positively related to productivity. However, conditional on using these other inputs, use of purchased seed is negatively related to productivity. Household child labor use, the dependency ratio, education and marital status are not significantly related to productivity when simultaneously controlling for all the other variables mentioned above.

> `3` <mark>We follow Halvorsen and Palmquist (1980) in calculating the percentage effect of a dummy variable on a logged dependent variable as exp(b)-1.</mark>

8
Figure 3: Payments to Key Stakeholders across Industries <!-- Start of picture text --> Real estate<br>Energy<br>Textiles, apparel, & luxury goods<br>Financials<br>Communication services<br>Materials<br>Auto & components<br>Consumer staples<br>Information technology<br>Other consumer discretionary<br>Industrials<br>Hotels, resorts, & cruise lines<br>Health care<br>Multiline & specialty retail<br>Other transport<br>Airlines<br>Restaurants<br>0 20 40 60 80 100<br>Labor expenses Accounts payable Interest payments<br><!-- End of picture text -->

**Note** : This figure shows the share of operating expenses owed to workers, suppliers, and creditors across U.S. listed firms. The figure shows 2000-2016 averages by industry.

**Source:** Authors’ calculations based on Compustat data.

and seek investors that might be better suited for their financing needs. Firms also have long-term relations with customers that have become loyal to their products and services. These relationships or matches, and the knowledge embedded in them, can be thought of as an important intangible asset or organizational capital of firms. Survey evidence for 500 large firms in ten emerging countries suggests that firms value these crucial relationships (Beck et al., 2020). The surveyed firms were negatively affected by the pandemic shock and their immediate response was to reduce investment while protecting their long-term relationships with stakeholders.

Pushing viable firms into bankruptcy during the COVID-19 pandemic would mean that the different relationships would need to be reconstructed in the recovery following the crisis. This churning process of destroying and then recreating relationships and contracts is far from efficient. For instance, maintaining employment in firms during the temporary negative shock could enable firms to keep specific human capital within the firm and avoid the costly processes of separation and then of re-hiring and training when the crisis is over. For workers, it could preserve experience and specific human capital and prevent the long-term career costs of layoffs (Davis and Von Wachter, 2011). These benefits may be particularly large in emerging and developing economies, where formal firms (and jobs) account for a relatively small, laboriously-built fraction of the economy, but concentrate the bulk of society’s organizational capital (Alfaro et al., 2020). Nonetheless, 9
38 provided goods and services, which are generally not imputed into the survey indicators, and because of possible under-reporting by respondents. Given that the _levels_ of the two series are not expected to match exactly, analysts are usually concerned by deviant trends, which may indicate a problem with the survey instrument. On the other hand, it may plausibly be argued that National Accounts data have errors of their own, and that many of the 'correction' procedures applied to household data rely on reasonably strong assumptions, such as equiproportional underreporting by source.

In deciding whether to adjust the PNAD data with reference to the Brazilian National Accounts over this period, we examined the evolution of the ratios of GDP per capita to mean household incomes from the PNAD (for the entire country, and without regional price deflation, for comparability). As Table A5 below shows, these were remarkably stable. In particular, the ratios for the starting and end points of the period covered, which are of particular importance for our analysis, are almost identical. In this light, and since even the disparity with respect to 1981 and 1985 are reasonably small, we judged that the costs of making rough adjustments to the PNAD household incomes on the basis of the National Accounts outweighed the benefits.

|Table A5: Ratios of|GDP per capita to PNAD m|ean household incomes, 197|6-1996|
|---|---|---|---|
|Year|GDP per capita (A)|Mean PNAD income<br>(B)|(A) / (B)|
|1976|336.6|190.2|1.770|
|1981|370.2|187.3|1.976|
|1985|378.3|188.6|2.005|
|1996|412.1|233.0|1.769|

Tables A6 - A9 summarize the main items in the questionnaire concerning these variables, and the changes from 1976 to 1996.
evenly across their countries. The WDR 2008 cites poor access to markets, services and high transport costs as prohibiting the full integration of lagging and leading regions of countries. As noted in WDR 2008, connectivity within a country will assist in ending the “geographical poverty trap,” which occurs when generations of people are trapped in the same lagging region/area/village without any means of escape.

Conversely, there are several instances where developing countries lost out when trying to revive lagging areas. Unfortunately, the policies did not succeed in reviving the lagging areas. Between 1970 and 1980 the Mexican government offered large reductions in import duties if firms located outside of the three major cities, this was unsuccessful in relocating firms. The Indian Industrial Policy Resolution of 1956 attempted to direct investment into lagging areas through strict licensing; applications for investment in leading areas were rejected and funneled to lagging ones. In addition, major government projects were also focused in lagging areas. In the 1970s, Thailand offered a tax holiday for companies in lagging areas; nevertheless, this policy did not entice organizations to move to these areas. In response to these unsuccessful examples, the WDR 2009 suggests allowing the market to first choose the location, and then the government to assist in speeding up the pace of development through investment in infrastructure.

Uganda has experienced positive economic growth over the past two decades, since 1988 annual GDP growth has ranged from 3 to 12 percent with an average of six percent growth over the time period (World Development Indicators). Despite this overall growth, poverty remains prevalent in rural areas of Uganda (Deininger and Okidi 2003; Fan et al. 2004, World Bank 2007 and 2009.3). Several articles address this issue in the contexts of roads and access to markets. The prevailing notion is that as household distance from roads increases (on roads which eventually lead to markets), the income/consumption expenditure of household decreases.

Utilizing district level data from 1992, 1995, and 1999, it is established that government expenditures on roads have a significant impact on poverty reduction in rural Uganda (Fan et al. 2004). Further research has shown that market availability increases household participation in export crops which leads to higher income among these households (Balat et al. 2008). By utilizing instrumental variable regressions the authors concluded that farmers with fewer markets for agricultural export crops are poorer than 6
_Access 2009_ introduced statistics on the use of financial services in 139 countries and mapped a broad range of policies and initiatives supporting financial inclusion, focusing on data collection and measurement challenges. Building on this, _Financial Access 2010_ updated statistics on the use of financial services and analyzed changes that took place in 2009 – a turbulent year for the financial sector in most countries around the world. As more countries provided more and higher quality data after the 2009 report, the focus shifted to the analysis of financial inclusion policies around the world (see World Bank, 2008; CGAP and the World Bank, 2009 and 2010).

This paper uses the _Financial Access_ database to count the number of unbanked individuals in the world, and analyze the change of access to formal financial services around the world. Next, it reviews the role of the main financial regulator in relation to the implementation of financial inclusion reforms and mandates to give a broad global picture of the state of financial inclusion policies.

Our findings indicate that 56 percent of adults worldwide are unbanked, although the numbers differ across high-income and developing countries (17 percent and 64 percent, respectively). Overall, we predict a slight improvement in access to financial services in 2009: approximately 50 million more adults have accounts, although the adult population increased by approximately 79 million. The results of the econometric analysis confirm the earlier findings in the literature that higher deposit and/or loan correlation is associated with higher economic and financial development as measured by GDP per capita, the amount of electricity use, the availability of explicit deposit insurance and better credit environment, etc. Although access to deposit services has improved in 2009, the global financial crisis took its toll: volume of deposits and loans shrank. The world as a whole added 65 deposit accounts per 1,000 adults but the number of outstanding loans remained more or less the same.<sup>2</sup> At the same time, global retail networks, consisting of financial institution branches, ATMs, and POS terminals expanded. Per 100,000 adults, there are 167 new POS terminals, five ATMs, and one bank branch.

The analysis of data on financial inclusion mandates under the purview of financial regulators indicates that financial inclusion is high on policymakers’ agendas and reforms are widespread. However, implementation capacity is often limited. Countries that have financial inclusion strategy documents also have more topics under the purview of financial regulators with more resources. Low-income countries have more topics under the purview of financial regulators, but fewer resources.

The rest of this paper is organized as follows. Section 2 describes the methodology and results of counting the number of unbanked adults across the world. The state of and changes in access to deposit and credit services, as well as in the extent of retail networks, are analyzed in Section 3.

> 2 These are calculated for a subset of countries with comparable data in 2008 and 2009.

3