rafmacalaba/data-use-mentions-extended huggingface dataset:
,000 bpd refinery.<br>**Shortages**: When shortages occurred due to<br>heavy rain in Oct 2011, the Directorate of<br>Hydrocarbons and Mines of the Ministry of<br>Economy was in constant communication<br>with all oil companies to ensure 15 days of<br>stockholding.|
|Ethiopia|<br>**Pricing policy**: Ministry of Trade examines fuel prices every month<br>and adjusts them, although not regularly. Government stopped the<br>policy of subsidizing petroleum fuels in 2008 and set domestic<br>prices higher than import costs beginning in Oct 2008 to repay the<br>debt accumulated in the Oil Stabilization Fund. The price of<br>kerosene, which is untaxed and also cross-subsidized to some extent,<br>is lower than other fuel prices. Prices are frozen for months at a<br>time: kerosene prices did not change between Mar and Oct 2011,<br>and between Oct 2011 and Jul 2012 (the last month for which price<br>data are available on the Web site of Central Statistical Agency);<br>gasoline and diesel prices did not change between Jan and July<br>2012. The content of ethanol blended in gasoline was increased from<br>5% to 10% in mid-Mar 2012 to lower gasoline prices.<br>**Information**: The historical data from monthly consumer price<br>surveys, including petroleum products, used to be posted on the<br>Web site of Central Statistical Agency by location. The last month<br>for which the price information was available is Jul 2012.|<br> <br>**Shortages**: Prolonged LPG shortages in<br>2011 prompted households to turn to<br>electricity for cooking. A shortage of<br>ethanol caused a gasoline shortage in early<br>2012.<br>
# The Limits of Commitment: Who Benefits from Illiquid Savings Products?<sup>_∗_</sup>

Niklas Buehren Markus Goldstein Leora Klapper Tricia Koroknay-Palicz Simone Schaner<sup>_†_</sup>

> _∗_ We thank Emmanuel Apiors, Virginia Ceretti, and Ervin Dervisevic for excellent research assistance, Innovations for Poverty Action Ghana for expert field implementation, and Robert Osei for helpful comments and advice. This project would not have been possible without the support of the North Volta Rural Bank and its staff. We are particularly indebted to Patrick Ata and S. S. Mohenu, who provided invaluable advice and leadership throughout the project. This paper is an output of the Africa Gender Innovation Lab. Financial support for this study was provided by the World Bank’s Umbrella Facility for Gender Equality (UFGE), the World Bank’s Development Research Group Finance and Private Sector Development Team’s Knowledge for Change Program (KCP II), the World Bank’s Africa Region’s Vice President’s Office, the World Bank’s Ghana Country Management Unit, and the Bill and Melinda Gates Foundation as part of their work on financial inclusion. The study protocol was approved by IRBs at the University of Ghana, Legon (CPN 009/13-14) and Dartmouth College (CPHS # 24177) and registered in the AEA RCT registry (AEARCTR0001630) and the International Initiative for Impact Evaluation’s Registry for International Development Evaluations (study ID 5409d4c83af40). 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.

> _†_ Buehren, Goldstein, Klapper, and Koroknay-Palicz: The World Bank. Schaner: The University of Southern California, NBER, and BREAD. Buehren: nbuehren@worldbank.org, Goldstein: mgoldstein@worldbank
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level. RE - random effects estimation procedure, POOLED IV 2SLS - pooled two stage least squares procedure with instrumental variables on panel data; robust standard errors utilized. The detailed description of all the models applied in our analyses is given in Annex 4._ _Source: OECD, Eurostat, World Bank, Heritage Foundation, IZA, own calculations._

# _<u>Model 2 – Share of non-contributing labor force</u>_

Estimation of Model 2 using the share of labor force not contributing to the pension system as dependent variable for samples S2 and S3 (there is not enough data to estimate the model on 21
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-
the GHS-FUA spatial package (Moreno-Monroy et al., 2020) and it is measured as the population living in urban centers in a metropolitan area divided by the land area of the urban centers in that metropolitan area.

# _Moderator variables for firm and sector characteristics_

We assess the moderating effect of tradability by looking at sector, exporting behavior, and foreign ownership. The sector classification provided in the enterprise surveys is based on the International Standard Industrial Classification of All Economic Activities (ISIC). In line with the taxonomy of De Gregorio et al. (1994), all firms active in manufacturing (ISIC 15-37) are part of the tradable sector, while firms active in services, including construction (ISIC 45), wholesale trade (ISIC 50-51), retail trade (ISIC 52), hotels and restaurants (ISIC 55), transportation services (ISIC 60-64), business services (ISIC 70-74) and personal services (ISIC 80-98) are part of the nontradable sector. Utilities, mining, and financial services are not included in the Enterprise Surveys. Our _manufacturing_ dummy is therefore 1 if a firm operates in the manufacturing sector and 0 otherwise.

We then explore the moderating effect of exporting and foreign ownership. The _exporter_ dummy is set at 1, if a firm exports at least 10% of its sales, and 0 otherwise. The _foreign-ownership_ dummy also takes the value 1 if at least 10% of the establishment is foreign owned, and 0 otherwise. While all exporting firms belong to the tradable sector, in our sample about one fifth of all manufacturing establishments, which represent the tradables sector in our analysis, are exporters and only around 10% are foreign owned. By contrast, only 6.6% of services firms, which in our classification belong to the non-tradables sector, are exporters. Therefore, our non-tradable sector includes mostly firms serving the domestic market, while our analysis of the moderating effect of exporting and foreign ownership considers to the extent possible the influence of services firms operating in the tradable sector
kets deepen, returns fall. This effect is largest at the 90th percentile, indicating that in particular financial openness and development remove “home runs” from the economy. This pattern is similar in Table 8, Panel B, for fund investments.<sup>21</sup> The quantitative magnitude of these results is substantial: using the specification in Column (6), a one standard deviation higher financial openness is associated with a reduction in the PME by 0.60 at the 90th percentile, or, for an 8-year investment, a (1 _._ 60<sup>(</sup><sup><u>1</u></sup> 8<sup>)</sup> _−_ 1) x 100 = 6 _._ 05 percentage point lower return than the S&P 500 _each year_ . As a benchmark, in the data between 20002001, as Poland prepared to enter the European Union, its value on the Chinn-Ito openness index increased by approximately one standard deviation. A one standard deviation increase in banking sector development reduces the PME by 0.73 at the 90th percentile, or, for an 8-year investment, a (1 _._ 73<sup>(</sup><sup><u>1</u></sup> 8<sup>)</sup> _−_ 1) x 100 = 7 _._ 09 percentage point lower return than the S&P 500 _each year._ A one standard deviation increase in private sector credit to GDP is 39 percentage points, approximately equal to the growth experienced by Brazil from 1990 to 2020, or double the growth experienced by Kenya during the same 30-year period.

Our interpretation of these results is that capital controls and limited banking system depth have prevented capital from flowing to viable projects in less financially open and developed economies. Were this not the case, we would not observe a change in returns as economies
of SMEs. Though access to finance is widely understood to represent a major obstacle to the development of SMEs in FCS countries, to our knowledge the topic remains relatively unexplored in the literature. Yet understanding the key drivers of SME financial inclusion in FCS countries can help policy makers design and implement more targeted interventions.<sup>1</sup> To that end, this paper looks at the impact of macroeconomic and financial sector variables, the legal and institutional constraints, and the role of the business environment. In particular, using the responses from formal SMEs in the World Bank Enterprise Surveys (WBES), this paper attempts an answer to the following question: to what extent do economic fundamentals and institutional characteristics affect SME financial inclusion in FCS countries _relative_ to non-FCS countries?

Economic fundamentals matter for SME financial inclusion. Higher incomes and better physical infrastructure increase savings and the pool of funds in the economy and improve access to finance (Dabla-Norris et al 2015), while macroeconomic and financial stability can positively affect credit and other financial services to SMEs (Rojas-Suarez 2016). Banking market structure

> 1 Increasing support for private sector development is, among others, a critical pillar of the World Bank Group’s strategy for FCS countries (WBG 2020).

2
The constructed datasets _only_ contain countries that experienced crises in the late 1980s and 1990s. The average number of observations per country is around 20 out of 28 industries. Tables V and VI provide an overview of the averages of variables used for the regressions with the relative changes in PCM and EST, respectively.

In the robustness checks, we also use GDP per capita, inflation, and domestic credit to GDP as country-level controls. All are drawn from the World Development Indicators of the World Bank. We collected three other corruption indices. We use the Corruption Perceptions Index from Transparency International (henceforth TI). Since this index was first constructed in 1995 and has limited coverage, we use an average of the index for the period 1996-1997. We also collected a second corruption index by Beck et al. (2000, henceforth when referring to the indicator: Beck), which is an average for the period 1982-1995. Finally, we use the corruption index of Kaufman and Kraay (1999, henceforth KKZ).

# **IV. Main Results**

This section deals with the main results of the paper and some initial robustness checks. All standard errors for OLS regressions reported in the paper are robust to heteroskedasticity using the Huber-White estimator of variance. In the main results, we focus on access to finance for young firms as being the specific channel through which DPCMREL and DESTREL are affected. For DPCMREL, the results are reported in Table VII, Columns (1)-(4), where external dependence is interacted with several institutional indices. They reflect the basic result for Hypothesis 1 of the paper.

18
# **1. Introduction**

With the launch of the Human Capital Project (HCP), the World Bank has called on countries to accelerate progress in the human capital of their populations (World Bank, 2018). There are many reasons why human capital outcomes might fall short of expectations. For example, countries may underinvest in the skills, health, and resilience of their citizens because the returns on these investments can take a long time to materialize. In comparison, investments in physical capital – bridges and roads – could materialize more quickly. Moreover, differently from investment in human capital, they often generate financial returns (for example, tolls). These arguments led development practitioners to conjecture that countries borrow less for human development interventions in the face of increased borrowing costs. For instance, when countries graduate from IDA (International Development Association) status to IBRD (International Bank for Reconstruction and Development)<sup>3</sup> status in their engagement with the World Bank, they also face an increase in borrowing costs. Countries with per capita income below $1,215 have access to IDA funds at concessional terms, while richer countries have access to IBRD funds (which typically charge LIBOR plus a fixed spread).

The discrete change in financing terms when countries graduate from IDA to IBRD could discourage countries from borrowing for “soft sectors” like Education, Health and Social Protection (together grouped as the Human Development group in the World Bank) in favor of sectors where investments have more immediate and tangible returns. However, other mechanisms might be at play. Take total Bank borrowing for example. On one hand, as interest rates go up when countries graduate IDA, the demand for Bank loans should decline. In addition, IDA graduation also operates as a signal of creditworthiness for other lenders (bilateral lenders as well as the market), thereby permitting crowding in other lenders, which could further lower borrowing from the Bank. On the other hand, IDA graduation alleviates borrowing limits imposed
rate is less than a sixth that of men (10% vs 66%). The same is true in Egypt, where employment rates for skilled women are 60% of men’s whereas for the unskilled it is around a fifth. This implies that if gender employment gaps were closed, more of the women who would start work would be unskilled, with potentially significant effects on the wage distribution and the size of potential gains.

The final reason is that the past 75 years have seen an increase in the skill premium in the US and elsewhere, which standard Cobb-Douglas production functions (as in earlier sections) are unable to explain. If skills are particularly important in production for operating complex capital equipment, then changes in the skill distribution of the economy through changes in female employment and education can have large knock-on effect on the stock of capital per worker, which could amplify output changes.

In this section we estimate the effects of changes in skills shares with higher female employment using the production function in Krusell et al. (2000) and first posited by Stokey (1996), where physical capital and skill are complements. This production function helps us also to examine the potential distributional impacts on changes in wages from changes in female employment rates.

> 19Source: World Bank World Development Indicators for 2020, the latest year available.

> 20The exceptions are Qatar and United Arab Emirates, where employment rates are similar across men and women.

12
poverty line would have to be slightly higher than $3 per person per day using 2011 PPP values. Similarly, if we want to obtain the same poverty rate with the new moderate poverty line as the one obtained with the currently-used moderate poverty line of $4 per person per day (i.e., 24.3 percent), the new moderate poverty line would have to be approximately $5 per person per day.<sup>27</sup> However, according to Jolliffe and Prydz (2015), this approach presents two fundamental limitations. First, by fixing the poverty rate, we would be giving more weight to the LAC poverty headcount based on 2005 PPPs than the one based on 2011 PPPs. However, since the poverty rates would be different across countries, we would end up preferring the 2011 PPPs for country-specific comparison. Second, by keeping the poverty line fixed, we would not maintain the same standard of living in 2011 as in 2005.

One relevant question is whether these new regional poverty rates differ considerably with respect to the current LAC poverty rates based on 2005 PPPs. The difference between the poverty rate with the previous poverty line based on 2005 PPPs and the revised numbers in the first four rows of Table 3 is the result of two factors: (i) changes in the value of the regional poverty lines and (ii) changes in the per capita income distribution due to temporal and spatial adjustments, which are explained by changes in the CPI and PPP values, respectively. If the nominal value of the new moderate poverty line were the same as the previous extreme poverty line (i.e., $2.5 per person per day), LAC’s extreme poverty rate in 2013 would have been approximately 7.8 percent at 2011 PPP values instead of 11.5 percent at 2005 PPP values. Similarly, if the nominal value of the new moderate poverty line were the same a currently-used $4 per person per day, LAC’s
regressor (instrumented by their one-year lagged value), and find a significant although small impact on trade flows from donor to recipient.

From a development perspective, only a few of the recent studies focus on the more relevant question of whether aid raises the export capacity of recipient countries. Cali and te Velde (2011) regress trading costs and the value of exports on lagged AfT disbursements and control variables, using data from the OECD's Creditor Reporting System that separately identifies aid to trade facilitation and infrastructure from aid to productive capacity.<sup>9</sup> Using a large panel of developing countries, the authors address the possibility of endogeneity and measurement errors in AfT flows by instrumenting those with the Freedom House’s index of civil liberties. The message that emerges across their various specifications is that aid to trade facilitation and infrastructure seems to have a significant effect in reducing trade costs and in increasing export values, while

> 8 Underperformance in trade is captured by multiple indicators. Countries that underperform in trade can be those in the lower two quintiles of performance measured along five dimensions: (a) those experiencing relatively slow growth of exports of goods and services, (b) those losing global market share, (c) those suffering deterioration in competitiveness in existing markets, (d) those exporting slow-growing products or to slow-growing markets, and/or (e) those over-reliant on only a few exports. Also, countries that underperform in trade are those that under-trade with bilateral partners, controlling for market size and distance, those with low levels scores on the World Bank logistics performance index for transport or for customs, and on an indicator of peak tariffs.

> 9 Trading costs are measured by the trading across borders indicators of the Doing Business database.

9
The debate around the weight of intrinsic motivation within overall incentives has been crystallized in the debate led by Le Grand about whether public service workers are ―knaves or knights‖ (Le Grand 2003). Le Grand argues that post-war public administration theory in the UK (and in Europe more generally) saw public servants as public-spirited altruists, a misleading interpretation of reality which was recognized in, although not adequately redressed by, the 1980s introduction of various New Public Management reforms.

On the opposing side of the argument, (Pink 2009) has developed the critique of monetary and other extrinsic incentives into a broader theory, hypothesizing that they are both counterproductive, as they frequently undermine intrinsic incentives, and unnecessary, as intrinsic incentives can be harnessed and used to maximize individual productivity. His theory suggests that tasks can be constructed to: (i) maximize an individual‘s sense of _autonomy_ (drawing inter alia on the ―self determination theory‖ expounded in the overview paper by (Ryan and Deci 2000) and the cross-country attitude survey undertaken by (Chirkov, Ryan, et al. 2003)); (ii) _mastery_ (continuous incremental learning and improvements rather than distant targets citing inter alia (Sauermann and Cohen 2008) who use data on over 11,000 industrial scientists and engineers to show that intrinsic motives, particularly the desire for intellectual challenge, appear to benefit innovation more than extrinsic motives such as pay); and (iii) _purpose_ (drawing inter alia on (Niemiec, Ryan, et al. 2009) who show from a follow up study of 246 students who graduated from two US colleges that those who met intrinsic aspirations for personal growth, close relationships, community involvement, and physical health had better scores for psychological satisfaction and health than those who pursued attainment of the extrinsic aspirations for money, fame, and image.

Another psychological argument, known as the ―Yerkes-Dodson law‖, highlights the phenomenon of ―choking under pressure‖ (Ariely, Gneezy, et al. 2009). If individual