rafmacalaba/datause-encoder-data huggingface dataset:
This paper aims to investigate off-farm employment decisions of men and women in SSA. We contribute to the literature in three main ways. First, by using the nationally-representative longitudinal household survey data generated as part of the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative, we provide recent empirical evidence on off-farm employment participation rates that is representative for five countries in SSA, namely Ethiopia, Malawi, Nigeria, Tanzania and Uganda. The unit record data are at individual-level, and consist of multiple panel rounds, whereby individuals were tracked over time. Second, we focus both on off-farm wage and self-employment and provide much needed evidence on labor markets. Third, we analyze the drivers of entry into employment and continued employment. This allows us to unravel the dynamics behind off-farm employment, and to understand why people enter or exit off-farm employment, taking gender differences into account.

The rest of the paper is structured as follows. First, we review the literature on off-farm employment in SSA, followed by an explanation of the data collection and construction of variables. Next, we report off-farm employment participation rates and job characteristics, followed by an analysis of entry into and exit from off-farm employment. We end by summarizing the main findings and outlining research and policy implications.

# **2. Background**

Off-farm employment comprises both being wage employed outside the farm-household (i.e. offfarm wage employment) and running a non-farm enterprise (NFE) (i.e. off-farm self-employment). We specifically consider off-farm activities and do not call it the non-farm economy, since offfarm wage employment can still take place in the agricultural sector. For example, a growing share of workers in SSA are employed on large-scale plantations (Van den Broeck and Maertens, 2016). The main difference between off- and on-farm activities is that workers are required to leave their household farm. Particularly for women, this is not always straightforward due to social norms and limited transportation possibilities (Fox, 2016; Slavchevska et al., 2016), hence our focus on off-farm activities. We take both wage and self-employment into account, because their fundamental differences in terms of start-up investments, job security and flexibility will help to better explain the drivers of entry into off-farm employment and continued off-farm employment.

3
determined by the country's initial position in the global distribution. Hence, the global distributional effect of higher-than-average growth rates in poor countries (those in the lower part of the global distribution) will have an inequality-reducing effect; the global distributional effect of changes in within-country dispersion will also depend on the country's initial position in the global distribution (for a detailed discussion along these lines see Bussolo, et al. 2007).

# **4 The global income distribution in 2030**

We use the methods developed in the previous section to “roll” the global economy to 2030. The macroeconomic assumptions underlying this exercise as well as the prospects for global growth have been discussed in World Bank (2006; Chapter 2) and Bussolo, et al. (2007). In this study we concentrate on the global distributional effects behind the expected changes in per capita incomes and its distribution within countries.

Figure 2: Global Lorenz Curves <!-- Start of picture text --> 0 20 40 60 80 100<br>Cumulative % of Population<br>Distribution 2000 Distribution 2030<br>100<br>80<br>60<br>40<br>Cumulative Income Share (%) 20<br>0<br><!-- End of picture text --> In Figure **2** we plot the Lorenz curves for the observed global income distribution in 2000 10
- 83 -

Based on an average daily car traffic volume of 45,000 during the morning peak period and 24,000 during the afternoon peak and the number of operating days of 283 in a year, the cost per transaction comes to S16.5 cents for the fiscal year 1989 [US9.4 cents in 1990 figures] _wwhout_ car parks.6<sup>3</sup> By employing the index of cost per transaction alone, the Singapore Area Licensing Scheme is among the least costly of alternative methods of charging for road use.

89. In a review of the impact of the ALS after nine years of operation, a team of World Bank researchers observed that had the ALS package of traffic management measures not been implemented, there would have been bumper-to-bumpet **r** affic conditions universally in the Restricted Zone by 1982 (Behbehani, Pendakur and Armstrong-Wright, 1984, p. 50). Further, the deferred or canceled investments for roads amoun **t** o savings to society of the order of _S$1.5_ billion.

90. The paucity of benefit-cost studies on the Singapore ALS means that it is difficult to come up with strong conclusions as to its economic **viability.** Holland and Watson bnefly reported an economic evaluation in their 1978 article. A lower bound estimate of an economic rate of return of 15% is obtained for the first year. This figure seems to be on the low side for such a major undertaking. Nevertheless, it includes only time savings and not savings in operating costs and fuel which are normally calculated in transport projects. (urther, it does not employ the standard assumption of value of tme being a funcdon of the wage rate, but assumes that everyone has the same value of time.) Since at least three fourths (or nine tenths) of the capital cost of the scheme, which went into the construction of fringe car parks, could have been saved, the economic rate of return would have become 60% (or 150%) in the first year. The estimxate here does not take into account: a) the costs to commuters of rescheduling or the losw in welfare for 'hlose tolled off the desired mode, and b) the gain in welfare to the ones who valued time highly and were tolled at the (lower) average rate upon enterng the Restricted Zone.

**T0 Me** difference in the number of days a **year aises becaus the** post-June 1989 **schm operated an a** five and a half day a week bis. bat is, _(5_ + 45000169000) x **_52_** - 11 holidays = 283 days.
Shirotori, M., B. Tumurchudur, and O. Cadot (2010) “Revealed Factor Intensity Indices at the Product Level”, Policy Issues in International Trade and Commodities Study Series No. 44, UNCTAD UNCTAD (2010), “Non-Tariff Measures: Evidence from Selected Developing Countries and Future Research Agenda”, Developing Countries in International Trade Studies, United Nations Conference on Trade and Development, New York and Geneva, ISSN: 1817-1214 UNCTAD (2012), “Classification of Non-Tariff Measures”, Geneva, February Walkenhorst, P. and T. Yasui (2009), “Quantitative Assessment of the Benefits of Trade Facilitation”, in Overcoming Border Bottlenecks: The Costs and Benefits of Trade Facilitation, OECD, Paris WTO (2012), “World Trade Report, 2012, Trade and Public Policies: A Closer Look at Non-Tariff Measures in the 21st Century”, Geneva 34
||a > Xsg<br>**MFls Usi**<br> <br>NGOs and MF<br>|**Table 5. K**<br>-:**Category A**<br>**ng Other Peop**<br>Non-Profit NG<br>Is with Limited<br>|**ey Risk Manage**<br> <br>**les' money:**<br>Os,<br>Deposit Taking,<br>____________<br>|**ment Factors**<sup>**a**</sup><br>**MFIs U**<br>Savin<br>______________<br>R f|<sup>**nd Indicators**(</sup><br>**Category B**<br>**sing Member**<br>Credit Unions<br>gs & Credit Coo<br>_____________<br>Std|<sup>Continued)</sup><br> <br>**s' Money:**<br>;<br>peratives<br>Suested|**MFIs U**<br>Specia<br>License<br>~~N<br>Rane of|**Category**<br>**sing the Publ**<br>lized/Limited E<br>d Mutual-Ow<br>on-Bank<br>Financial<br>Suested|C<br>**ic's Money:**<br>quity B3anks;<br>nership Banks<br>Institutions<br>Suggested|
|---|---|---|---|---|---|---|---|---|---|
|Risk Management Factors<br>and Indicators<br>|Range of<br>Observed<br>Values|Suggested<br>Thresholds<br>for Internal<br>Governance|Suggested<br>Guidelines for<br>External Self-<br> <br>Regulatory|ange o<br>Observed<br>Values|uggese<br>Thresholds<br>for Intemal<br>Governance|gg<br>Guidelines for<br>External<br>Regulation|g<br>Observed<br>Values|gg<br>Thresholds<br>for Internal<br>Governance|Guidelines for<br>External<br> <br>Regulation_I_<br>|
|**IV. Income StatementBo**<br>**Structure**|||||||||Suevsn<br>|
|Effective yield on loan portfolio|30-45%|_Ž_market|none required|19-25%|_Ž_market|none required|28-45%|_Ž_market|none required|
|Net interest margin as % of<br>average assets|10-25%|_Ž_18%|none required|10-15%|_Ž_15%<br>|none required<br>|12-20%<br>|_Ž_12%<br>|none required<br>|
|Unadjusted return on ave.<br>assets|3-5 %|_Ž_3%|none required|2-4%|_Ž_3%<br>|none required<br>|1-7%<br>|_Ž_2%<br>|none required<br>|
|Unadjusted return on ave. equity|<br>9-18%|12-16%|none required|6-11%|_Ž_12%|none required|4-32%|_Ž_12%<br>|none required|
|Operational self-sufficiency- %|110-140%|> 115%|Ž115%|118-147%|_Ž_115%|none required|107-148%|Ž115%|none required|
|Financial self-sufficiency- %|95-125%|>100%|_Ž_100%|103-127%|_Ž_115%|none required|103-137%|_Ž_110%|none required|
|Administrative expense as %<br>of average assets|15-20%|< 15%|none required|7-15%|< 12%|none required|4-15%|<10%|none required|
|**V. Credit Risk**|||*||t|||||
|Delinquency as % of loans<br>> 90 days overdue (P.A.R.)|2-6%|<5%<br>|5%|7-10%<br>|<5%<br>|S 5%<br>|1-6.5%<br>|< 5%<br>|5-10%<br>|
|Loan loss reserve as % of<br>total loan portfolio|0.5-2 %|Ž2%|2-5 %|1 - 3 %|> 3%<br>|_Ž_3%<br>|0.75-2.5%<br>|_Ž_5%<br> <br>|Ž5%<br>|
|Loan loss reserve as % of<br>portfolio at risk|not available|100%|100%|100%|100%|100%|not available|<br>100%|100%|
|Portfolio concentration:||||||||<br>||
|20 largest borrowers as<br>% of loan portfolio|not available|minimize|none required|not available|minimize|None required|not available<br>|<br>minimize<br>|_•_25%<br>|
|Loans to DOSRI as % of<br>institutional capital|not available|< 5%|< 5%<br>|not available<br>|< 5%<br>.<br>|c 5%<br>_<br>|not available<br>|s equity of<br>borrower<br> <br>|• 5%<br>|
|Sectoral and geographical<br>concentration|not available|minimize|none required|not available|minimize|None required|not available|<br>minimize|< 10%|

23
**<u>Table 5: Elasticities from Stochastic Frontier Translog Production Function</u>**

||||**Model**|||
|---|---|---|---|---|---|
||**No traits**|**Alltraits**|**Pre-selection **|**LARS**|**Stepwise**|
|Area (log)|0.460***|0.460***|0.460***|0.461***|0.462***|
||0.000|0.000|0.000|0.000|0.000|
|Value of Pesticide (log)|0.023**|0.025**|0.025**|0.025**|0.025**|
||(0.028)|(0.020)|(0.020)|(0.019)|(0.018)|
|Value of P & K (log)|0.163***|0.164***|0.163***|0.162***|0.161***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Value of N (log)|0.178***|0.180***|0.179***|0.179***|0.179***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Cost of Threshing/ha (log)|0.075|0.068|0.067|0.069|0.070|
||(0.305)|(0.362)|(0.367)|(0.353)|(0.347)|
|Cost of Ploughing/ha (log)|0.231***|0.225***|0.226***|0.225***|0.227***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Labor Days (log)|-0.014|-0.011|-0.011|-0.010|-0.011|
||(0.240)|(0.370)|(0.366)|(0.396)|(0.341)|
|Parcel Fallow Last 5 years|-0.185***|-0.177***|-0.177***|-0.180***|-0.179***|
||(0.002)|(0.004)|(0.004)|(0.003)|(0.003)|
|Soil Quality Fair|-0.082***|-0.079***|-0.079***|-0.079***|-0.078***|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|Soil Quality Poor|-0.410***|-0.402***|-0.402***|-0.400***|-0.399***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|4.510***|4.634***|4.635***|4.633***|4.612***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|

21
is equal to the discounted value of foregone unskilled wage income. Income is also assumed to be evenly shared within each family (that is, between employed members and trainees) so that, at each point in time, consumption expenditure is the same for each member of a family.

The optimization problem of a family with ability  and size 0 is where  0 is the constant subjective discount rate, and <sup></sup> <sup>is the static utility function</sup> of each household member, which is defined as where <sup>isconsumptionbyeachindividualmemberofthefamily.</sup> With <sup>denotingthefamily’sstockofassets,itsflowbudgetconstraintis</sup> where  is the market interest rate,  the economy’s output of final goods, and  ∈ (0 1) the tax rate on income.

The solution to the family’s dynamic optimization problem yields the standard intertemporal equation, together with the transversality condition lim→∞(<sup>)−</sup> <sup>exp(−)=0.Infamiliar</sup> fashion, equation (4) states that per capita consumption expenditure grows over time if and only if the market interest rate exceeds the subjective discount rate.<sup>7</sup> Consider now labor supply decisions.<sup>8</sup> As noted earlier, training and employment decisions are made to maximize each family’s discounted wage income, which is equivalent to maximizing each member’s discounted wage income. There are two types of costs associated with training. The first is time devoted to training, which depends on whether the individual member earns the unskilled wage, <sup>,orbecomesaskilled</sup> worker and then earns the wage <sup></sup> <sup>.Thesecondisthetrainingcost,.</sup> Thus, it is optimal for an individual with ability  ∈ (<sup></sup> <sup></sup> ) to train and become

> 7When the market interest rate is relatively high for instance, family members want to save more now and spend more later, resulting in positive growth in per capita consumption expenditure over time. Note that the left-hand side of (4) does not depend on ability, and thus neither on wages earned in production.

> 8The discussion here dwells in part on Dinopoulos and Segerstrom (1999) and Agénor and Canuto (2012). See also Hori (2011) and Davis (2013).

7
> <sup>43</sup>

# **Annex 2:  Leader tenure and the duration of democracy**

It is clear that elected leaders who succesfully pursue unobservable, undemocratic efforts to drive out competitors would be less accountable and, therefore, more likely to engage in corruption and less inclined to provide broad-based public goods. Substantial evidence also indicates that leader tenure truncates the continuous periods of competitive elections. If elected leaders are successful in suppressing competition, they eventually win more than 75 percent of the vote and drop out of the “competitive elections” category that defines the elections variable. Alternatively, leaders who suppress electoral competition encourage non-electoral competition, increasing the likelihood that unelected leaders will replace them. Again, the country is dropped from the “competitive elections” category.

In the _DPI_ , there are 36 episodes in which countries leave the most competitive electoral category. In all 36 cases, the average tenure of incumbents prior to the change in electoral competitiveness is higher than average: 7.3 years, compared to 4.3 years for all country-years during periods of continuous competitive elections. In addition, the majority party controlled 66 percent of the seats in the legislature on average, compared to 58 percent, on average, among all countries in the “most competitive” category.

In fourteen of the 36 cases, the government remained in power, continued to hold somewhat less competitive elections (in which multiple parties competed, but the government party received more than 75 percent of the vote) and simply accumulated a more dominant political position. In these cases, the government majority in the legislature rose to 77 percent, on average. In 13 of the 22 cases in which the incumbent lost power, on the other hand, the new government took control extra-institutionally and not even sham elections were held. (Following the DPI coding of _LIEC_ and _EIEC_ , the group of 19 countries fell from scores of 7 on _LIEC_ or _EIEC_ to 6, while the 13 countries that
Policy Research Working Paper 10333

# **Abstract**

How do civil service management practices differ within and across governments? How do core attitudes of public servants—such as their motivation or satisfaction—differ within and across governments? Understanding how public administrations around the world function and differ is crucial for strengthening their effectiveness. Most comparative measures of bureaucracy rely on surveys of experts, households, or firms, rather than directly questioning bureaucrats. Direct surveys of public officials enable governments to benchmark themselves and scholars to study comparative public administration and the state differently, based on micro-data from actors who experience government first-hand. This paper introduces the Global Survey of Public Servants, a global initiative to collect and harmonize large-scale, comparable survey data on public servants. The Global Survey of Public Servants can help scholars compare public administrations around the world and understand the internal dynamics of governments, with the published Global Survey of Public Servants data freely available online.

This paper is a product of the Development Impact Evaluation Group, Development Economics and the Governance Global Practice. 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/prwp. The authors may be contacted at drogger@worldbank.org and zhasnain@worldbank.org.

_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
inadequate and de. iorating infrastnictural conditions.<sup>4</sup> The social benefits resulting from the provision of subsidized goods and services to the public in the form of staple goods, energy, transportation and others became an increasing financial burden to the governments, when many of these SOEs incurred substantial financial losses, draining resources from the budget as well as the domestic financiai markets. In 1982, the Mexican government had to spend 4 percent of its GNP in transfers and subsidies to its SOEs. During 1989 to 1991, SOE losses are estimated to have reached 9 percent of GDP in Argentina and, on avei - over 5 percent in Sub-Saharan African countries.<sup>5</sup> SOEs also generally failed to enhance export growth and to provide the country with additional foreign exchange. )n ,ie contrary, with relatively easy access to foreign capital during the lending boom of the 1970s, SOEs absorbed a substantial amount of loans taken on in the international financial markets, rendering them a substantial contributor to the ballooning of sovereign debt during the 1970s. With cieditors e.fectively treating them as part of the goverment based **or.** public guarantees issued by the authorities, SOEs coL;d obtain loans independent of their economic pe.formance. Hence, during the period of 1970 to 1982, the real stock of debt of SOQ.s grew at an average annual rate of 10.8 percent, comnared to an average 7.4 percent growth of Non-SOE debt, reaching $164 billion at con-cant 1991 dollars, or 23.6 percent of the total stock of debt. Servicing the old debt proved to be a heavy burden for the governnents: SOE debt alone accour:ted for almost 30 percent of total debt service. Creditors managed to avoid extensive defaults by continuously rescheduling the outstanding debt. Within this process almost $100 billion liad to be rescheduled, accounting for 16.5 percent of total reschedulings for the developing world. With SOEs being unable to service their debt and requiring reschedulings, public guarantees became reality in the sense that the government effectively had to take over the rescheduled portion of the SOE debt, further increasing the stoclz of public debt and exacerbating the debt burden for the governments of developing countries.<sup>6</sup> Confronted with the generally disappointing performance of the SOEs and the dire financial straits, many developing countries found themselves in during the 1980s, privatization became an important economic policy tool n many developing countries. The transfer of ownership is generally hoped to improve the efficiency with wh,ich these strategically important goods and services are provided, forcing the firms to ifolow market signals by eliminating the existing soft budget constraints. Equally important, maiiy governments hope to reduce budgetary pressures resulting from subsidies and tra,isfer payments, while at the same time generating revenues as well as foreign exchange through the sale of these assets.

> 4 For an example in the case of Latin America see World Bank (1991).

> 5 Kikeri, Nellis, and Shirley (1992), P. 15.

> 6 Based on the World Bank's _Debt Reporting System_ (DRS).

6
# Why Liquidity Matters to the Export Decision of the Firm

Rosanna Chan<sup>_∗_</sup>

## **1 Introduction**

The growth of exports is a key priority for many developing countries as a means to access foreign earnings and to stimulate economic growth.<sup>1</sup> Yet, among developing countries, access to external finance is a major problem. The World Bank Investment Climate Surveys, covering more than 26,000 firms across 53 developing countries, find that the cost and access to finance is considered by firms to be the top five problems they face ( **?** ). An important question to ask is how do financial constraints affect the export decision of the firm. This question is particularly relevant in light of the 2008 financial crisis, where the freeze on credit coincided with a drop in world exports of more than 30 percent<sup>2</sup> that cannot be entirely explained by the drop in aggregate demand.

> _∗_ Economist, South Asia Finance & Private Sector Development, World Bank. Email rchan@worldbank.org I am grateful to Albert Park, Dan Silverman, Jim Levinsohn, Jan Svejnar, Chris House and Andrei Levchenko for their advice, Mary Hallward-Driemeier at DECRG, World Bank for data and institutional background, Aneeqa Aqeel, Oli Coibon, Jane Dokko, Chris Kurz, Sara Lalumia, Rahul Mukherjee, Joo Youn Park, Bill Powers, Heidi Schierholz, Mine Senses, Lina Walker for their insightful comments and encouragement. I would also like thank seminar participants at the Universities of Michigan, University of Melbourne, Australia National University, University of Wisconsin and the Minnesota International Economic Development Conference for their valuable input. All errors are my own. The views expressed here are those of the author and do not necessarily reflect official views of the World Bank. JEL: D01, D2, D21, D22, D24, D92, F02, F10, F14, F15, F36, O12, G20 Keywords: credit constraints, financial constraints, working capital, firm behavior, international trade, trade costs, transaction costs, entry costs to trade, time, trade barrier. All errors are my own.

> 1See **??**

> 2Quarterly trade data from WTO, compiled from data sourced from IMF, International Financial Statistics; Eurostat, Comext Database; National statistics; Global Trade Atlas.