rafmacalaba/datause-encoder-data huggingface dataset:
Altruistic parents have a reason to care about their children‘s preferences for work because if the grown-up children suffer from economic hardship, the parents will want to help them. The stronger the work norms of the children, the less will the parents have to help them. Altruistic parents have, therefore, an economic reason to instill strong work norms in their children. However, if the welfare state takes over the obligation to help children who happen to suffer economic hardships, the parents have less incentive to instill work norms in their children. Lindbeck and Nyberg develop a theory that formalizes these intuitions. Interestingly, data from the World Values Survey in 1998 are consistent with their account. The seven countries that rank lowest in terms of the share of respondents who consider a willingness to work hard important to learn at home are advanced European welfare states. Denmark, where only 2 percent of the respondents hold this view, ranks last out of 42 countries, followed by Sweden, Finland, Norway, the Netherlands, Austria, and West Germany.<sup>11</sup>

# **4. Conclusion**

Over two decades ago, when economists were laying the groundwork for an economics in which information was endogenous, Stiglitz (1985) argued that the standard theory ―is not robust to slight alterations in the informational assumptions. It is but a special—and not very plausible— ̳example‘ among the possible set of informational assumptions which can be employed to characterise an economy‖ (p. 21). Here we have suggested that the assumption of exogenous preferences—unaffected by the outcomes in society—is but a special and not very plausible example among the possible set of assumptions about preferences that can be employed in explaining economic outcomes.

A considerable body of research suggests that institutions may have effects that go far beyond the incentives that they provide – they may create framing and priming effects that influence the accessibility of memories and the perception, interpretation and, hence, the meaning of facts. It is possible that exposure to certain institutions and environments influences whether in a figure such as Figure 2, some societies have the ability to see that the two lines are

> 11 Another interesting paper on the importance of endogenous preferences is Doepke and Zilibotti (2008). They develop a theory of preference formation that is a key component in explaining the reversal of fortunes between the landowners and the emerging bourgeoisie in the Industrial Revolution. In their approach, economic incentives induce parents to instill particular preferences in their children ( _e.g_ . a strong work ethic and a low discount rate in the case of the middle classes); these preferences in turn have important effects on economic development and social stratification.

18
Warr, P. 2011. “Thailand’s Development Strategy and Growth Performance.” _United Nations University World Institute for Development Economics Research,_ Working Paper No. 2011/02.

Wodon, Q. 2012. _Improving the Targeting of Social Programs in Ghana._ Washington, DC: World Bank.

Wolter, D. 2009. “Ghana: Agriculture is Becoming a Business”, _OECD Journal: General Papers,_ Vol. 2009/2 _._ World Bank. 1980. _World Development Report 1980._ New York: Oxford University Press.

World Bank. 1990. _World Development Report 1990: Poverty._ New York: Oxford University Press.

World Bank. 1990. “Indonesia: Strategy for a Sustained Reduction in Poverty.” World Bank Country Study, Report 10009.

World Bank. 1993. _The East Asian Miracle: Economic Growth and Public Policy._ New York: Oxford University Press.

World Bank. 2001. _World Development Report 2000/2001: Attacking Poverty._ New York: Oxford University Press.

World Bank. 2003. _World Development Report 2004: Making Services Work for Poor People._ Washington, DC: World Bank and Oxford University Press.

World Bank. 2004. “Poverty in Mexico: An Assessment of Conditions, Trends and Government Strategy.” Report No. 28612‐ME.

World Bank. 2005. “Mexico Income Generation and Social Protection for the Poor.” Report No. 32884‐MX.

World Bank. 2006. _World Development Report 2007: Development and the Next Generation._ Washington, DC: World Bank.

World Bank. 2006. “Decentralized Service Delivery for the Poor.” Report No. 35692 World Bank. 2006. “Making the New Indonesia Work for the Poor.” Report No. 37349‐ID.

World Bank. 2007. _World Development Report 2008: Agriculture for Development._ Washington, DC: World Bank.

World Bank. 2007. “Mexico 2006–2012 Creating the Foundations for Equitable Growth.” Report No. 39993‐MX.

World Bank. 2009. _World Development Report 2009: Reshaping Economic Geography._ Washington, DC: World Bank.

World Bank. 2010. _World Development Report 2010: Development and Climate Change._ Washington, DC: World Bank.

World Bank. 2011. _World Development Report 2012: Gender Equality and Development._ Washington, DC: World Bank.

World Bank. 2011. “Republic of Ghana: Tackling Poverty in Northern Ghana.” Report No. 53991‐ GH.

World Bank. 2012. “Agribusiness Indicators: Ghana _.”_ Report Number 68163‐GH. Washington, DC: World Bank.

World Bank. 2013. _World Development Report 2014: Risk and Opportunity_ . Washington, DC: World Bank 52
of 1 if the state requires a super majority approval in Congress, and zero otherwise. The third dummy indicates whether the rainy-day fund has a cap or not.

The following specification was estimated to explain the observed differences in the states' rainy-day fund balances.

_Rainy,, = A/Rainy5 ,,., + l 2Withdrawi, +,f 3Cap5,, +f34Deposi(, +f lincomen, 5 +, 6 GrowtA, +_ eV, + _e,,_ (2) where _Rainy_ is the rainy-day fund balance in state s at the end of year t; _Withdraw_<sup>isa</sup> dummy variable indicating if the rainy-day fund has an stringent withdrawal rule; _Cap is_ a dummy variable indicating if the rainy-day fund has a cap; _Deposit_ is a dummy variable indicating if the rainy-day fund has an stringent deposit rule, _Income_ is gross state product (GSP) per capita in state s at time t, _Growth_ is the growth rate of gross state product, and co are state fixed effects.

Results. Table 4 shows the regression results for (2) by using data from the<sup>1985</sup> to 2000 period. We found that, relative to the mean, states with stringent deposit rules have 124 percent more money on their rainy-day fund accounts than states without those strict rules. Also, we found that states with stringent withdrawal rules, on average, 137 percent more dollars on their rainy-day fund accounts than states without those tough withdrawal rules. Contrary to expectation, the coefficient for the rainy-day fund's cap is not statistically significant, although it has the expected sign. Further, high-income states have higher rainy-day fund balances than low-income states. Finally, states with high economic growth rate have higher rainy-day fund balances than those states with lower rates. These results are consistent with those from Sobel and Holcombe (1996).

All of the results depicted in Table 4 are statistically significant. Also, using Hausman's specification test, we find that the regression results from a random effects specification are biased. However, its coefficients are statistically significant and have the same sign as the fixed effects results.

9
Statistical packages—notably Stata—are equipped to handle such multi-stage sampling and weighting.<sup>32</sup> Since the probability of selection, p L ) , is set before enumeration, implementers will most likely want to apply weight adjustments based on unit refusals and the jj (sseess misclassification of units during fieldwork.<sup>33</sup> Implementers can adjust w based on the total number of encountered informal businesses in a BA. The ISBS specifically applies three such 2(jj) adjustments, based on different assumptions, all of which remove confirmed formal businesses: i) counting all unit refusals as ineligible (strict assumption); ii) counting unit refusals with visible signage or permits as ineligible (median assumption); and iii) counting all unit refusals as eligible (weak assumption).

# **6. Discussion and Conclusions**

While this paper lays out a proposed methodology for surveying informal businesses— along with several practical guidelines and informative results—several challenges remain. The methodology requires intensive enumeration, which must go hand-in-hand with monitoring. Some of these challenges can be minimized by technology, through data and fieldwork quality control, including path monitoring. There also remain risks to under-counting of informal businesses that are not readily visible, including those in households or that intentionally remain less visible, perhaps to avoid detection by authorities. This is borne out by the range of the share of businesses operating in the household. Examples from iterations of the ISBS include 59 percent of informal Somali businesses operate within the owners’ household compared to just 14 percent in Lao PDR. Missing these types of businesses during enumeration is a potential form of design-based bias.

> 32 In Stata, this can be done by correctly specifying the svy: prefix via svyset. For example, the following code snippet will declare survey data, with two-stage sampling, with stratification in the first stage (given by strata).  The first stage weight (wt_1) is at the level of an identified cluster (id_cluster), while the second stage is identified at the level of id_j - - with a second-stage weight. For a discussion, see: <u>https://www.stata.com/support/faqs/statistics/stratified multiple</u> - <u>stage designs/</u>

> svyset id_cluster [pw=wt_1], strata(strata) || id_j [pw=wt_2]

> 33 In the case of ISBS, p L ) is a function of a ’s position in an enumerated roster. Enumerators may encounter either refusals or list an ineligible business (e.g., one that is registered), resulting in the need for a weight jj adjustment. (sseess jj

24
# **4 Robustness**

This section details the alternative specifications used to test the robustness of our analysis. We describe how the quantification of the impact of shocks on consumption per capita depends on the choice of specification. Then, we come back to some of the assumptions behind the calculation of vulnerability and test the robustness of the results to changes in these assumptions.

We first assess the robustness of the results when only drought or only excess rainfall is considered at a time.<sup>19</sup> The results show that drought effect remains strong and significant when flooding is not accounted for. The impacts of excess rainfall become slightly weaker if drought is controlled for (Table A.2 and Table 6). We also check if exclusion of indicator for households’ access to MASAF makes a difference. The limited coverage of MASAF means, however, exclusion of the indicator did not make much difference.

## **Table 6: Vulnerability to Poverty when randomly drawing weather and idiosyncratic shocks as well as residuals**

||2004/05 (IHS 2)|2010/11 (IHS 3)|2015/16 (IHS 4)|
|---|---|---|---|
|**Static Moderate-Poverty**||||
|Headcount poverty|52.9|51.0|53.7|
||(0.5)|(0.5)|(0.5)|
|Poverty gap|18.1|19.1|18.3|
||(0.2)|(0.2)|(0.2)|
|Severity of poverty|8.2|9.4|8.3|
||(0.1)|(0.1)|(0.1)|
|**Dynamic Moderate-Poverty**||||
|Expected headcount of poverty|49.3|46.1|49.2|
||(0.3)|(0.3)|(0.3)|
|Pr(Moderate poverty)_>_0.5|51.5|47.4|52.3|
||(0.5)|(0.5)|(0.5)|
|Pr(Moderate poverty)_>_0.29|69.8|65.4|69.8|
||(0.4)|(0.4)|(0.4)|
|Expected poverty gap|32.5|30.0|32.6|
||(0.2)|(0.2)|(0.2)|
|Expected severity of poverty|27.2|25.0|27.3|
||(0.2)|(0.2)|(0.2)|

19 It should, however, be noted that in some years both drought and excess rainfall can occur. For example, in the 2015/16 growing season, even if there was excess rainfall at the onset of growing seasons, extended dry spells were recorded later in the season.

25
terciles. These findings suggest that working capital is a more determinant factor in traders' activities than either human or social capital.

Finally, gender appears to have a significant impact on sales activity, such that male traders, who represent only 20% of the sample in Benin, have median sales less than 4 times that of female traders and nearly 3 times higher in Malawi, where female traders represent 35% of the sample.

**Table 21. Value of Annual Sales** (US$) **by Firm Assets in Benin and MalawiA**

|||Benin|||||Malawi||
|---|---|---|---|---|---|---|---|---|
|**Working Capital**|Mean|S.d.|Median|N|**Working Capital**|<br>Mean|S.d.|Median N|
|<$167|6730.17|33769.20|1256.20|156|<br><$78|4587.98|10152.16|2189.39 193|
|$167-$667|11584.85|22628.95|3315.41|226|$78 - $244|9324.91|13745.82|<br>5762.43 201|
|>=$667|49678.56|192965.70|16227.29|201|>= $244|111471.30|292941.00|25222.22 204|
|**Human Capital**|||||**Human Capital**||||
|**<-1**employee|20553.89|148507.28|2743.84|324|0- 1 employee|13227.18|43077.18|4584.099 427|
|1 -2 employee|s 16250.84|26356.02|4942.71|138|<br>> I emnploye|e 120549.4|321935.10|20373.28 163|
|> 2 ernployee|s 36126.53|<br>72551.90|12296.5|2 137|||||
|**Social Capital**|||||**Social Capital**||||
|**<**10|7648.122|12362.21|2402.91|165|<br><15|23437.30|85306.21|<br>3830.24 185|
|10-33|26843.05|174167.70|4370.39|234|15-36|56147.91|243645.00|<br>6556.48 213|
|>= 33|30734.48|63358.51|9800.92|205|>= 36|46022.60|157136.00|8773.51 200|
|**Gender**|||||**Gender**||||
|Male|38804.14|73557.73|14402.37|120|Male|57823.06|214243.50|<br>8781.95 387|
|Female|18983.95|122557.00|3724.44|486|Femnale|<br>14798.12|68196.87|<br>3177.16 211|
|**Total**<br>|22908.74<br>|114750.80<br>|5366.64<br>|606<br>|<br>Total<br>|42642.02<br>|178148.7<br>|0 6579.04 598|
|**a**US$ equivalent.A|t**the**time o|fsurvey, exc|hange rate|s use|dareI US$=CFA 6|00**and**I US $|= MK45.||

# **_5.2 Transport: Arbitrage over Space_**

**Transport Practices and Rates.** One fifth of surveyed traders do not undertake any transport, by which they mean that they buy and sell from the same market. The others transport products across markets, nearly always with an extemal transporter.

Most transport (86%) takes place in trucks. Half of these trucks are small pick-ups. Some 13% of all transport takes place with non-motorized means of transport such as handcarts and oxcarts. Respondent 39
Figure 4: Scatter plot of our financial services input intensity against Rajan and Zingales (1998) external financial dependence measure for the U.S.

<!-- Start of picture text --> Electrical & Optical<br>Transport Equipment<br>Coke & Refined Petroleum<br>Rubber and Plastics<br>Chemicals<br>Basic Metals Machinery, Nec<br>Wood<br>Pulp & Paper Manufacturing, Nec<br>Mineral<br>Food, Beverages & T<br>Textiles<br>Leather & Fo<br>0 .02 .04 .06 .08<br>Financial Services Input Intensity<br>.2<br>0<br>-.2<br>-.4<br>External Financial Dependence<br>-.6<br><!-- End of picture text --> Notes: Our financial services input intensity measure is based on the WIOD data in 1995, while Rajan and Zingales (1998) external financial dependence measure is for year 1980.

32
Policy Research Working Paper 10674

# **Abstract**

Affirmative action is a promising solution to the crucial challenge of bridging the gap in women’s access to higher education in low- and middle-income countries (LMICs). This paper uses public universities’ matriculation data from 2013–2018 and difference-in-differences estimators to examine the causal impact of a gender quota on women’s educational opportunities in Afghanistan. The quota increased the proportion of women in the treated concentration group by nine percentage points and the share of women from low socio-economic status by three percentage points. The expansion was associated with a 0.04-unit decline in the average score ratio of female-tomale applicants, driven by a reduction in the score threshold needed for women’s admission. The effects were condensed in competitive concentrations, where the overall share of women and women with low SES increased by 17 and four percentage points, respectively. The findings suggest that affirmative action is a viable option for addressing the gender gap in fragile settings..

This paper is a product of the Development Impact 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/prwp. The author may be contacted at rnajam@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

_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
27 Tangermann, S. “OECD Agricultural Policies and the Interests of Developing Countries.” _American Journal of Agricultural Economics_ 87(2005): 1128-44.

Thiele, R. (2004), ‘The Bias Against Agriculture in Sub-Saharan Africa: Has it Survived 20 Years of Structural Adjustment Programs?’ _Quarterly Journal of International Agriculture_ 42(1): 5-20.

Townsend, R.F. “Agricultural Incentives in Sub-Saharan Africa’, World Bank Technical Paper No. 444, Washington DC, August 1999.

Tyers, R. and K. Anderson. _Disarray in World Food Markets: A Quantitative_ _Assessment_ , Cambridge and New York: Cambridge University Press, 1992.

Valdes, A. “Surveillance of Agricultural Price and Trade Policy in Latin America During Major Policy Reforms.” World Bank Discussion Paper No. 349, Washington DC, November 1996.

Valdes, A. (ed.) “Agricultural Support Policies in Transition Economies.” World Bank Technical Paper No. 470, Washington DC, May 2000.

World Bank. _World Development Report 1982_ , Washington DC: World Bank, 1982. World Bank. _World Development Report 1986_ , Washington DC: World Bank, 1986. World Bank. _Global Economic Prospects 2001_ , Washington DC: World Bank, 2001.
42 3. INDONESIA: DIESEL AND KEROSENE PRICE INCREASES **1982-85**

# **Introduction**

The Republic of Indonesia has an equatorial climate and covers a total area of 1,905 thousand square kilometers, including half as archepelagic seas separating the islands. It has a total population of 178.2 million and GNP per capita of US$ 570, makdng it one of the most populous low-income countries in the world.

# The Economy

Since the establishment of the "New Order" Govemnment in 1967, Indonesia has developed from a low-income country with per capita income of US$ 50 in 1967 to achieve annual growth rates of per capita income during the last two decades averaging over 4.5 percent per year. Common objectives of economic policies during 1967-84 were to achieve stability, growth and equity. These objectives were outlined in a series of five-year plans (Repelitas), which were open to annual review. In these plans one observes a transition from food production and infratructure in Repelita I to improvement in living standards and equitable distribution in Repelita II, which ended in March 1979. The achievements during the first two plans were impressive. The second plan period averaged a record 7.7 percent growth per year, with sectoral growth rates averaging 4.6 percent for agriculture and 13 percent for industry. The achievements during this period were a result of active goverment policies, as outlined in Repelita I and '1, and the commodity boom for oil and other primary commodities.

The 1980s began with a favorabl **e** xtema shock initiated by the second intemational oil price hike, which led to a substantial increase in revenues from the oil sector. Indonesia's revenues from oil export peaked at nearly US$ 16.5 billion in 1981-82. However, the subsequent faill in intemational oil prices led to a continuous fall in oil revenues to 50 percent by 1985-86, or US$ 9 billion. The prices of other primary exports also fell during this period.<sup>2</sup> ' These adverse development **s** et in motion a number of adjustrnent measures, one of them being an upward revision of domestic energy prices. Substantial domestic energy price increases for petroleumn products (except gasoline) and electricity were implemented during 1982-85.

_n_ Data for 1990.

9 In 1985. the World Bank index of 33 primazy commodities was 20 percent below its level in 1979-81 and reached its lowest level in the 27 years since the index has been compiled.
With the auction data, we estimate how much pension funds request at different maturities and what price they offer for each quantity requested. We also compare the behavior of pension funds and large insurance companies. Table 2, Panel A shows the results, indicating when the differences between requests at different maturities (within institutions) are statistically significant. Estimated quantities are reported as a share of the total auction amount. Panel B reports the ratio of the share requested by insurance companies and pension funds at each maturity, in the same auction. When an investor is not bidding for an issuance we impute a zero for the quantity requested, but the estimations for prices only include information for those investors that present a bid. For this reason, the ratios of shares reported in Panel B do not include the cases when pension funds do not bid for a security, biasing the results against finding larger shares bid by insurance companies.

Table 2, Panel A shows that pension funds request larger shares of the issuance than insurance companies at most maturities, except for 30 year instruments. The shares requested by insurance companies increase monotonically with time to maturity. When comparing prices offered by both institutional investors, pension funds offer significantly higher prices for five and ten year bonds, while no differences are observed between prices offered for 20 and 30 year bonds. Moreover, a larger amount bid for by pension funds is expected since pension funds are significantly larger investors than insurance companies. Therefore, the smaller request of 30 year bonds reflects a lower preference by pension funds for those long-term instruments, especially considering that similar prices are offered for them across institutional investors. Furthermore, although pension funds typically ask for larger shares of issuances, the ratio between the quantities demanded by 20