rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8540

# **Abstract**

Off-farm income constitutes a significant share of the household livelihood portfolios across Sub-Saharan Africa. Yet, the determinants and dynamics of individuals’ participation in off-farm employment activities have not received adequate attention due to the weaknesses in individual-level data collection and the lack of longitudinal studies. This paper uses national panel household survey data from Ethiopia, Malawi, Nigeria, Tanzania, and Uganda; provides empirical evidence on individual-level off-farm (wage and self) employment participation rates; analyzes the extent and drivers of entry into off-farm employment and continued employment; and conducts the analysis by gender and rural/urban location. A significant share of the rural and urban working-age individual population is found to participate in off-farm employment, ranging at the national level from 34 percent in Ethiopia to 58 percent in Malawi.

Men participate in wage and self-employment to a significantly greater extent compared with women across time as well as within and across countries, apart from women’s participation in non-farm enterprises being more common in Nigeria and Tanzania. The population weighted cross-country gender difference in off-farm employment stands at 9 percentage points, but this has declined over time in most countries. A substantial share of the population, amounting to about 39 million individuals across the five countries, is estimated to have entered and exited employment between 2010 and 2016, pointing to the dynamic nature of off-farm employment. Drivers of entry into off-farm employment and continued employment are country- and gender-specific, with demographic factors, occurrence of shocks, and job characteristics emerging as the most important determinants.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at goedele.vandenbroeck@kuleuven.be.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
expectancy depend significantly on biological factors. (c) Variation in life expectancy rates across countries is very small compared to international differences in health levels as measured by morbidity, anthropometric indices, nutritional intake, and so on. (d) Life expectancy levels will at best capture investments in health aimed at increasing the "quantity of life" and not "quality of life".

For these reasons, health regressions must be interpreted differently and more narrowly than schooling regressions.

Y Per capita income is used to represent household income. For cross-section estimations for the years 1965 and 1987, these data were from World Bank (1990). For the pooled cross-section time-series estimations, per capita income data were from Summers and Heston (1988), for the years 1965 and 1985. This was done because pooled estimations require per capita figures in constant prices, and Summers and Heston report deflated per capita income (using three types of deflators) that are generally considered superior to price data in World Bank (1990).

t The time allotted to agriculture, industry, and services (t1, t2, and t3 respectively) are proxied by their shares in total employment. The data for employment shares are from United Nations (1990). i 1sWe also experimented with sectoral shares in national income, using data for GDP shares from World Bank (1990). Technically, since t is interpreted as the probability of being employed in sector i, the share of each sector in total _employment_ is the appropriate measure of t . One rationale for using shares in total _Income_ (GDP) is that this measure' reflects both the probability and _returns from employment_ in each sector. However, GDP share data are less reliable. Only the results of regressions using employment 24
half of individuals who sign up for a similar contribution-based commitment product end up paying a fee to drop out early. We go beyond this to show that negative effects need not be limited to program dropout – in our case, agents turn to debt to offset savings commitments, which is costly and runs the risk of propagating debt cycles. We also demonstrate that negative side effects are concentrated among people who exhibit behavior most consistent with self-control problems at baseline. This suggests that when individuals are naïve about the extent of their self-control problem, commitment may do more harm than good.

Another contribution of our paper is to show that illiquid savings products _can_ improve individuals’ financial lives for reasons unrelated to a direct commitment effect. Here, our work relates to Brune et al. (2016), who find that a commitment savings product increased savings, agricultural investment, agricultural output, and consumption of Malawian farmers even though amounts actually committed were quite low. We build on this by showing that benefits accrue to individuals least likely to be liquidity constrained, and by showing that the commitment product crowds in other savings both during and after the commitment period.

The remainder of the paper proceeds as follows: We first describe our sample and the experiment in Section 2. We present our results in Section 3, which includes an analysis of overall impacts, heterogeneity by baseline overdraft status, and discussion. Section 4 concludes.

# **2 Experimental Context and Design**

## **2.1 Partner Bank and Study Sample**

The study sample is comprised of 320 salaried workers receiving electronic salary payments into bank accounts at the North Volta Rural Bank (NVRB).<sup>3</sup> NVRB is a small bank that mostly serves individuals living in rural areas, where the majority of economically active individuals are self-employed in agriculture (Ghana Statistical Service, 2013).

By virtue of being salaried workers, our sample is better off than the average citizen in the bank’s catchment area. Column 1 of Appendix Table A1 illustrates baseline characteristics in the control group: three-quarters of study participants were men, two-thirds had a postsecondary education, and 90 percent were government employees, with teacher the most common occupation.<sup>4</sup> In comparison, across Ghana and in the Volta Region, approximately

> and Kumar, 2007; Gelber, 2011; Chetty et al., 2014; Beshears et al., 2017).

> 3Appendix A provides additional detail on sampling and inclusion/exclusion criteria.

> 4The remaining 10 percent of workers are NVRB employees. Our results are unchanged if we drop these individuals from the analysis.

4
43 that changing apparel prices have general-equilibrium effects on the male-female wage differential and on women’s wages throughout the economy (not just in apparel).

**Table 14: Short and Long Run Using Hourly Wages**

|Short and LongRun|Cambodia||Sri Lanka||
|---|---|---|---|---|
|VARIABLES|(1)<br>loghourlywage|(2)<br>loghourlywage|(1)<br>loghourlywage|(2)<br>loghourlywage|
|female|-0.193***<br>(0.020)|-0.295***<br>(0.031)|-0.546***<br>(0.022)|-0.538***<br>(0.022)|
|age|0.042***|0.043***|0.146***|0.147***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|age2|-0.001***|-0.001***|-0.002***|-0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|education|0.026***|0.026***|0.045***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.334***|-0.055|0.005|0.021*|
||(0.027)|(0.065)|(0.010)|(0.012)|
|1999-2004 dummy||-0.049**<br>(0.024)||0.070***<br>(0.007)|
|2005 and above dummy|0.279***|0.241***|-0.002|0.028***|
||(0.015)|(0.024)|(0.007)|(0.008)|
|T&G*(1999-2004 dummy)||0.492***||-0.042**|
|||(0.078)||(0.019)|
|T&G*(2005 and above dummy)|-0.237***|0.217***|-0.024|-0.042*|
||(0.033)|(0.076)|(0.022)|(0.023)|
|female*(1999-2004 dummy)||0.144***||-0.025**|
|||(0.034)||(0.013)|
|female*(2005 and above dummy)|-0.020|0.085**|-0.010|-0.019|
||(0.023)|(0.033)|(0.013)|(0.014)|
|Hazard ratio|0.073|0.068|0.682***|0.685***|
||(0.053)|(0.053)|(0.043)|(0.043)|
|Constant|5.823***|5.792***|0.199|0.171|
||(0.126)|(0.130)|(0.144)|(0.144)|
|Observations|31,729|31,729|146,635|146,635|
|Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||

# **5. Conclusions**

Our results contribute in several ways to the debate surrounding the value of apparel exports and employment for women in developing countries. First, we demonstrate that apparel workers earn significant wage premiums relative to their other options in the economy.

Especially in Cambodia, these premiums increased significantly when Cambodia started exporting apparel. Second, we show that women’s wages relative to men (the gender wage gap)
# **_Table of Contents_**

|**I. INTRODUCTION ...............................................................................................3**|
|---|
|**II. THE STATE OF FINANCIAL LIBERALIZATION IN CHINA ............................4**|
|_Liberalization of trade in financial services_................................................................... 4|
|_Financial liberalization and the domestic financial system_........................................... 5|
|**III. INCLUDING FINANCIAL SERVICES IN PTAS – KEY CONSIDERATIONS..7**|
|_Approaches to liberalizing trade in financial services_................................................... 7|
|_Including financial services in PTAs – international experience_................................... 8|
|_Implications for China_.................................................................................................... 9|
|**IV. THE ARCHITECTURE OF FINANCIAL SERVICES IN PTAS......................11**|
|_GATS versus NAFTA template_...................................................................................... 11|
|_The architecture of financial services in PTAs – international experience_.................. 12|
|_Implications for China_.................................................................................................. 14|
|**V. PREPARING FOR FINANCIAL SERVICES NEGOTIATIONS ......................15**|
|_Negotiating financial services in PTAs – international experience_.............................. 15|
|_Implications for China_.................................................................................................. 16|
|**APPENDIX I: LESSONS FROM LATIN AMERICA AND THE CARIBBEAN ....18**|
|_PTA developments in LCR_............................................................................................ 18|
|_Coverage of financial services in LCR PTAs_................................................................ 18|
|_Country case studies: negotiating team, strategy and consultation process_................ 21|
|_Financial services liberalization commitments compared to the GATS_....................... 23|
|_Financial services liberalization commitments compared to the status quo_................ 23|
|_Preferential nature of de novo liberalization commitments_......................................... 24|
|_Comparing ‘architectural’ models for liberalization purposes_.................................... 24|
|**REFERENCES ...................................................................................................25**|

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**Table 4:** Hidden debt and default spells

||Hair|cut|Duration|of spell|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Hidden debt|1.23***|1.03**|2.04**|1.94**|
||(0.45)|(0.41)|(0.83)|(0.83)|
|Real GDP growth||-0.49<br>||-0.47<br>|
|||(0.37)||(0.74)|
|Debt to GDP ratio||0.25***||0.22**|
|||(0.05)||(0.10)|
|Real GDP p.c.||-0.76||-0.48|
|||(0.59)||(1.19)|
|Institutional strength||-0.55*||-0.99*|
|||(0.29)||(0.59)|
|Share of bond debt||-0.00||-0.24|
|||(0.18)||(0.36)|
|Share of debt to WB||0.41*||0.20|
|||(0.24)||(0.49)|
|Constant|33.78***|18.24***|36.83***|26.13*|
||(2.24)|(6.76)|(4.07)|(13.59)|
|Observations|148|140|148|140|
|R-squared|0.048|0.314|0.040|0.138|

_Notes:_ This table shows the results of OLS regressions of two outcome measures of sovereign defaults on our measure of hidden debt in a cross-section of external sovereign default episodes in 1970-2020. The dependent variable in columns (1) and (2) is the net present value loss suffered by creditors as a percentage (“haircut”). The dependent variable in columns (3) and (4) is the duration of the default spell in months, measured as the time between the default event and the restructuring. Data is from Cruces and Trebesch (2013), Asonuma and Trebesch (2016) and Asonuma et al. (2023). The hidden debt variable measures the share of unreported debt at the onset of the default episode as percentage of GDP and is constructed as defined by equation (1) in Section 3.2. Standard errors are reported in parentheses. See Appendix Section A.4 and text for details on and sources of the control variables included.

Table 4 shows the results from this exercise. Columns (1) and (2) show that higher unreported debts are indeed associated with higher creditor losses in statistically and economically meaningful ways. A one percentage point increase in the amount of unreported debt (as a percentage of GDP) is associated with an increase in the haircut of 1.23 percentage points. This correlation remains strongly significant and similar in size when we control for other common predictors of creditor losses and restructuring outcomes (Graf von Luckner et al., 2023; Bai and Zhang, 2012). Columns (3) and (4) repeat the exercise but focus on the duration of the default episode. We find a strong positive correlation between hidden debt and the length of the default spell in the cross-section of external sovereign debt crises since 1970. A one percentage point increase in the amount of unreported debt is associated with an increase in the default spell by approximately two months. Again, this result holds when controlling for other common determinants of restructuring outcomes.

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as a whole. While manufacturing employment generally grew 17% from 1994 to 2005, employment in women-owned establishments in the unorganized sector grew 138%, roughly doubling the share of total activity accounted for by these establishments. As the second breakout for employment data highlights, this ownership growth is substantially stronger than even the employment growth for women in the unorganized sector, which expanded 26%. Our study analyzes in part the extent to which political reservations for women can account for this strong performance on the ownership margin.

Among the major states, those with the highest share of new proprietary businesses in the unorganized sector owned by women in 1994 are Karnataka, Tamil Nadu, Andhra Pradesh, and Kerala. Those with the lowest share of female entrepreneurs are Uttar Pradesh, Haryana, Maharashtra, Madhya Pradesh, and Rajasthan. Similar patterns hold across states when comparing overall business ownership rates by gender. All but one state (Sikkim) saw an increase in the share of new businesses owned by women during the period we study.

Women’s entrepreneurship and business ownership rates relative to men are highest among traditional and low-technology industries such as tobacco, paper, textiles, and wood products. These same industries comprise the industries in which most women-owned businesses are found in absolute terms. During our sample period, more than 90% of new female-owned businesses were found in 6 of 22 broad two-digit industries: textiles, tobacco, wood products, food products, furniture, and chemical products. At the two-digit level, nine industries saw an increase in the share of new businesses created by women between 1994 and 2005, with the largest increases for female entrepreneurship being among these traditional industries.

Our final data source is comprised of six rounds of the NSSO’s household-level Employment-Unemployment survey conducted from 1983 to 2009. The sampling frame for these surveys captures a representative sample of the Indian population, and aggregate figures are calculated based on the sampling weights provided with the data. The surveys lack the ownership fields required to study entrepreneurship, but they do contain information that allows us to model women’s employment in unorganized manufacturing. In the typical survey, 3%-4% of women are employed in manufacturing, with over 60% of women listing domestic activities. We specifically use the designation of the respondent’s employer as an Own Account Enterprise (OAE) to proxy for the unorganized sector. These surveys aid our empirical work in three ways: 13
# **3 MACROECONOMIC VOLATILITY: TRENDS**

During the past 20 years output growth volatility has decreased in almost every region in the world, including East Asia (Figure 1). World volatility has fallen from an average of 3.8 percent in the 1970s, to 2.7 percent nowadays.<sup>7</sup> The decrease has been even more marked for non-OECD East Asia, where output growth volatility has decreased from 7.85 percent in the 1970s, to 2.35 percent in 2006. The reduction in macroeconomic volatility began at a very strong pace in the 1970s, slowed down in the 1980s and 1990s (a spike can be even observed during the financial crisis in the late nineties), but gained momentum again at the beginning of the new century driving current volatility below pre-1997 crisis levels (Figure 1). However, despite the large decline in recent years, average output growth volatility in nonOECD East Asia remains significantly higher than in OECD countries, but slightly lower than the world average for non-OECD countries.

One of the striking features of reduced volatility in East Asia is that the process happened in countries at different levels of economic development. As shown in Figure 5, the decrease in output volatility has been observed in high, middle, and low-income countries. The most striking reduction in output growth volatility can be observed among high-income nonOECD East Asian countries, where volatility is now less than a third of what it was in the 1960s and close to OECD levels (below 2 percent). The performance of middle-income countries is also remarkable, with the most marked reduction happening in the post-financial crisis years. In contrast, in the last twenty years low-income East Asian countries did not make significant progress in reducing output growth volatility, which remains high (4.23 percent).

Output growth volatility is computed using a 5 or 10 years rolling window, hence it is too early to assert the impact of the current financial crisis. Nonetheless, to the extent that economic growth already slowed significantly in most East Asia economies, the crisis is bound to raise volatility – making it even more important to mitigate the impact of external factors on volatility by improving domestic structural features (see below, next section for details).

> 7 As reported by the IMF in the 2007 World Economic Outlook. Volatility is measured using a weighted average of a 10-year rolling window of countries’ GDP standard deviation. Weights consist of average GDP per capita in 2000 US$.

8
implies a sustained increase in current spending. In a country such as Mauritania, which is highly exposed to external shocks, the long-term commitments imposed by investment may – for instance – end up limiting the fiscal space available for countercyclical stabilization or may force painful budgetary cuts in the event of a revenue downturn, etc. As is often said, but it is worth repeating, there is no such a thing as a ‘one-size-fits-all’ recipe.

**In the past Mauritania’s public policies have not always contributed to enhancing competitiveness and facilitating diversification.** Government spending has focused on consumption (especially via food and fuel subsidies) rather than investment<sup>45</sup> (especially infrastructure development and education expenditures). Recent policy shifts suggest this may be starting to change, as public expenditures have been reoriented toward phasingout subsides in favor of conditional cash transfers and increased public investment in critical areas such as energy and transportation infrastructure. This new fiscal policy stance may significantly contribute to improving the ANS rate and thereby protecting or increasing the stock of national wealth. Regular updates of the analysis presented here would help the government and its development partners to remain abreast of changes in the longterm trajectory of national savings.

# **RESOURCE RENTS MAXIMIZATION AND EFFICIENCY IN PUBLIC SPENDING: SHORT- AND LONGTERM PERSPECTIVES**

**As described above, the central challenge of managing resource rents is striking the right balance between investment and consumption.** A key element in this tradeoff is the marginal returns offered by different types of public investment. Mauritania suffers from a substantial dearth of essential infrastructure. For instance, presently just 30 percent of the national road network is paved, only 20 percent of the country's arable land is cultivated, and just 11 percent of irrigable land is irrigated, due to a lack of infrastructure..<sup>46</sup> This strongly suggests that increased investment in physical infrastructure will generate strong economic returns. However, if on one hand shortfalls of produced and human capital are certainly constraining development, on the other hand - and in the short to medium term especially – the absorptive capacity of Mauritania is likely to be low and an overemphasis on these forms of capital may result in unproductive investments. So, the time path for the use of revenues needs to be carefully thought out; one possibility could be to keep at least some part of the revenues in a sovereign wealth fund in the short term.

> 45 As an indicator of this, during the period 2003-2007 the average current expenditure equaled 24.1 percent of GDP, while the average for capital expenditure was equivalent to 8.3 percent of GDP. In the same period, the cumulated expenditure on wage bill, good and services, and subsidies was around three quarters of total expenditures and net lending.

> 46 World Bank World Development Indicators, WDI, available online; and FAO website: <u>http://www.fao.org/isfp/country-information/mauritania/en/</u>

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