rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Conclusion**

Women’s labor force participation in Bangladesh, while on a steady uphill trend for several decades, remains low and has been declining in urban areas over the past decade. In addition to constraining economic growth, evidence suggests that female labor force participation could result in greater investments in children’s education and health, poverty reduction, and overall improvements in household well-being, as well as promoting women’s voice and empowerment. Among the factors that have been identified as potential drivers of low and declining FLFP in Bangladesh, access to childcare has received little attention in the literature, especially in urban and low-income contexts.

This paper aimed to address this gap using a unique dataset from the slums and low-income neighborhoods of Dhaka, Bangladesh’s largest city. The analysis of labor market outcomes measured the magnitude of the disadvantage that women face in the world of work due to lack of access to childcare. Unlike most previous studies, it used a direct measure of access to childcare, as well as focusing on the urban context where FLFP has been declining in recent years. The time-use analysis not only validated and enriched the labor market analysis with a different outcome variable (number of hours spent on paid market work), but also revealed additional insights about how mothers with differentiated access to childcare divide their time between paid work, unpaid work, and leisure. Finally, the analysis of secondary childcare provision, a common mechanism mothers in developing countries use to cope with the lack of institutional childcare, took this analysis one step further to reflect a fuller picture of the childcare burden experienced by mothers, even when they are engaged in other activities.

The findings support the existence of a significant motherhood penalty, especially for women who have young children but do not have access to childcare support. This finding is confirmed through labor market analysis and time-use analysis of paid work, unpaid work, and other activities. While mothers of young children emerge as the most disadvantaged group in the labor market analysis, though, the timeuse analysis reveals another interesting finding. It suggests that working mothers with young children who spend a fair amount of time on market work incur the largest time burden, since they engage in as much unpaid work as mothers of young children who do not engage in as much market work. This group is just as likely to perform childcare as a secondary activity as well, providing further support to the finding that they shoulder a significant double burden. All the findings pass several robustness checks.

The findings imply that future policy and programmatic efforts that seek to increase women’s participation in the labor market need to consider childcare as an area of intervention. This is particularly important in urban areas, where women’s labor force participation has been declining in recent years, and where anonymity, weaker social support systems, and concerns about safety may not give parents a 29
13 administrative decentralization may be regarded as an inheritance from the pre-reform period.

Concretely the decentralization of public-sector administration in China shows up, for instance, in great provincial and local government powers in the fields of infrastructure investment, government regulations of trade, and various social arrangements (social insurance as well as human services). The regional decentralization of public-sector administration is therefore an important factor behind the economic and social heterogeneity in China. It is also reflected in the small share of public-sector employees that work for the central government.<sup>9</sup> An obvious advantage of the administrative decentralization is that local initiatives, and local preferences, can play an important part. It is, however, often difficult to clarify who is responsible for what in cases when central political ambitions are not followed up at local levels.

An important prerequisite for the shift to decentralized economic decision-making to households and firms in China is, of course, that markets and hence price formation have largely replaced administrative processes as the basic mechanism for allocating resources and coordinating decentralized decision-making (dimension 4 in Figure 1). Indeed, markets are the only realistic method for coordinating decentralized decision-making and hence exploit decentralized and fragmented knowledge in society (a point emphasized, in particular, by Hayek, 1945). In fact, since the mid-1990s, market forces in China determine the prices of the vast majority of products.<sup>10</sup> There are, however, important exceptions. One is the market for natural resources. Moreover, while in the case of industry market forces are constrained by government interventions to mitigate unemployment, in agriculture they are constrained both by the absence of a market for land ownership and by government interventions in the marketing of grain. Therefore, it is an open question in which sector the shift to market prices has been most pronounced.

It is also difficult to generalize about the relative importance of economic incentives in agriculture and industry (dimensions 5 and 6 in Figure 1). I would, however, argue that incentives play a greater role for firms in agriculture than in industry. While farmers make

> 9 According to the Deputy Finance Minister of China, Jiwei Lou, only about six percent of the employees in public-sector administration work in the central administration (address to conference of China’s Economists Society, Shanghai, July 1, 2006).

> 10 See, for instance, Iskander (1996) and Beijing Normal University (2003).
about depreciation. Other storage-related concerns, due to self-control problems or social taxation, are examined below.

As we argued with transport costs, the small quantities represented by the _qmin_<sup>_∗_make</sup> it unlikely that space constraints are the main driver of bulking losses. Table 6 shows that “bulk” purchases in our data are relatively small. The technologies to store small quantities of goods securely for a short period—plastic bins with sealed lids, used jerry cans with screw tops, used plastic water bottles with screw tops—are widely available and inexpensive. If bulk purchasing required a household to store its purchases in a 20 liter drum, or a 100 kg bag, then space constraints could be a more plausible concern.

The above argument also makes it unlikely that concerns about depreciation during storage prevent households from bulk purchasing. Figure 4 plots the distribution of the average number of days worth of consumption represented by _qmin_<sup>_∗_,whichisalsoanindication</sup> of the average required storage duration for a bulk purchase. Half of the storage periods are a week or less; 75% are less than two weeks; 95% are less than a month. If depreciation is the concern, depreciation rates for stored items would need to be exceedingly high. Yet, there is little reason to believe that they are. Some of the study items—kerosene, cooking oil—are essentially free from depreciation related to moisture or pest exposure, which are the primary concerns. For the other items, we look to the existing evidence on post-consumer storage losses. A 2011 FAO report indicates that post-consumer losses in sub-Saharan Africa for a wide range of commodities—grains, roots, tubers, pulses, fruit, vegetables, meat, and seafood—are the lowest in the world, ranging from 0–2% (Gustavsson et al., 2011). Other work looking at the depreciation rate of crops stored by farmers post-harvest finds depreciation rates of 1-5% over periods of 6 months or longer in Ghana, Malawi, Tanzania, and Uganda (Kaminski and Christiaensen (2014); University of Ghana (2008), as cited in Zorya et al. (2011)).

If there is an exception to this argument it is likely to be for bananas. For a small number of banana-purchasing households, _qmin_<sup>_∗_isover20kgforcookingbananas,andover</sup> 8 kg for sweet bananas. We cannot rule out that these households avoid bulk purchasing because of concerns about depreciation. Yet, this would explain only a tiny fraction of financial losses from not buying in bulk.

23
50 that, hI _tms_ of output per unit of purchased Input, the smai private farms were more efficient than the ekdos. On the other hand, NgUyen and Martinez (1979:831) find that, according to the 1960 and 1970 data, the ejldo sctor obtained a higher crop output value pr unit of capltal expendture than private farms; howver, they compare ejldos with private farm over five hocatares which bi arguably not the most rigorous test of relativo effiency.

111. Efficiency estimates place the ejido In more favourable light If the yardstick Is restricted to purchased capital nputs, If relative efficiency Is measured hI terms of total factor productivity (land and labour as well as capital), the ejido performed less well than the small private farm in the pre-1970 period. in particular, relative efficiency estkiates are highly sonsitive to the value Imputed to unromunerated family labour: If these Inputs are assigned a positive opportunity cost (a reasonable assumptlon In the light of Gregory's (1986) study on the Mexican labour market), then ejidos emerge as much lss officlert users of resources (Nguyen & Martinez,1979:631; Wockstehi,1970:405). Eckstein and assoclates conclude "when total factor productivitles are compared, the small private farms come out ahead in all cases. The relative position of ejidos and large private farms varies with the Inclusion or exclusion of owners' family labour as an **hput** cost" (1978:115).

112. These conclusions are based on the assumption that the census data are reilabie and convey an accurate picture of trends hI the Mexican countryside. Yates (1 981 a;1981b) provides an Interesting discussion of the problems associated with the census data. He Indicates one respect In which the census data on outputs and Inputs are likely to be Inaccurate: "The private farmers have a strong motive (income tax) for exaggerated reporting of expenses, just as they have for under-reporting receipts, whereas ejidatarios, enjoying statutory exemption from
follow-up survey and experience a decline in per capita household expenditure, relative to the owners of businesses that survived.<sup>29</sup> Columns 5 and 6 show treatment effects on business practices and operations among men, but again not among women. However, given that business training led to differential attrition among male businesses, we follow Lee (2008) and construct nonparametric bounds on the same business outcomes.<sup>30</sup> The bounds, presented in Table 4, create intervals that are rather wide, and so for all aggregate categories the impact of business training on male business could be positive and significant or negative and significant, depending on the assumptions about the characteristics of businesses in the control group that attrite. Appendix Table A3 runs a regression with business failure as the dependent variable against baseline characteristics for businesses in the control group. Land wealth and interest in business training are negatively correlated with business failure while credit constraints are positively correlated, all suggesting that business failures may be driven by worse quality entrepreneurs operating at a smaller scale.<sup>31</sup> Consequently, it is likely that business training led to a positive and significant increase in business income among male businesses.<sup>32</sup>

# <u>5.2 Individual and Household Outcomes</u>

Table 5 examines the impact of the two treatments on household outcomes. In Panel A, CO members offered business training (irrespective of the lottery assignment) show a significant increase of roughly 7 percent of a standard deviation in assets and expenditures. According to Appendix Table A5, showing the individual components of the aggregated outcomes, both monthly expenditures and housing quality improved significantly. Among individuals not offered business training, those assigned to be

> 29 The model also predicts that business failure will be higher among lottery winners if the most productive technology H were riskier in the sense that ( ) ( )  Since this is not supported by the data, we conclude that ( ) and ( ) must be similar. 30 The idea behind Lee (2008) bounds is as follows. Since attrition in the control group is 6.1 percent larger than in the treatment group, 115 observations from the treatment group are eliminated to make both groups comparable. The upper bound is computed as the difference between the treatment and control group when observations are removed from the bottom of the distribution. Similarly, the lower bound is computed by removing observations from the top of the distribution.

> 31 We note that business knowledge is negatively correlated with business failure, as the model predicts, but it is not statistically significant. Business knowledge is however positively and significantly correlated with having a business, risk tolerance, stress and being a CO office bearer (results not reported).

> 32 In Appendix Table A3 we do not find that individual characteristics are more predictive of actual business failure among males. The R-squared is 0.05 for both males (column 2) and females (column 3).
Fourth, suppose that the PIP provides estimates of wages and salaries that will recur as a result of the new investment at constant prices, N_SAL<sup>PIP</sup> . Total wage payments at current prices is thus Fifth, suppose that the PIP provides estimates of user charges at constant prices that accrue directly to the budget (possibly through transfers from public enterprises), denoted by USER<sup>PIP</sup> .<sup>5</sup> Total government resources at current prices excluding aid, GOVR, are thus Substituting (9), (12) and (13) in (4) implies that total government spending implied by the PIP is For a given path of domestic and foreign borrowing (DB and ER·FG, respectively) aid requirements associated with the PIP can thus be calculated residually from equations (7), (8), (14), and (15): The foregoing discussion shows that the degree of efficiency of investment has only indirect effects on aid requirements. In particular, the lower is the efficiency parameter α in equation (10), the lower will total maintenance spending be in equation (11). From (12), (15) and (16), aid requirements will be

> 5User charges may be higher for some components of infrastructure (such as telecommunications or, to a lower extent, toll roads) than others (such as health services, for instance). It should also be kept in mind that even a small bill on public utilities may represent a substantial burden for poor households. In establishing projections in that area, it is therefore better to err on the side of caution.

9
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# Income Insecurity and Underemployment in Indonesia's Informal Sector

## Franck Wiebe

National University of Singapore and Consultant, World Bank This paper was written as part of a broader labor market study on Indonesia undertaken by Country Department III, East Asia and Pacific Region. It was presented at a joint Ministry of Manpower-World Bank workshop entitled "Indonesian Workers in the 21 st Century: Workshop for Economic Reforms and Labor Market Restructuring for Indonesia", Jakarta, Indonesia, April 2-4, 1996. The author thanks Nisha Agrawal, Frida Johansen, Andrew Mason, Alejandra Cox Edwards, Jacqueline Baptist, Christopher Bennet, and David Lewis for their valuable comments on an earlier draft, and Yudo Swasono and Endang Sulistyaningsih for their support of the research within the Ministry of Manpower in Indoncsia.
## **ACKNOWLEDGEMENTS**

This report was commissioned by the World Bank and funded by DFID. Additional thanks go to Andrew Steer, Nisha Agrawal, and Rob Swinkels of the World Bank, Nguyen Dinh Cung of CIEM, Bob Warner of CIE, Sarah Bales, and Jed Friedman for their valuable comments on earlier drafts of the report.

# **Abbreviations**

|CIEM|Central Institute for Economic Management|
|---|---|
|DOLISA|Department of Labor, Invalids and Social Affairs|
|DPI|Department of Planning and Investment|
|GDP|Gross Domestic Product|
|GSO|General Statistics Office|
|ILSSA|The Institute for Labor Science and Social Affairs|
|MOLISA|Ministry of Labor, Invalids and Social Affairs|
|MPDF|Mekong Project Development Facility|
|MPI|Ministry of Planning and Investment|
|SME|Small and Medium Sized Enterprises|
|SOE|State Owned Enterprise|
|VAT|Value Added Tax|
**Table 7. Expenditures, Savings, Insurance and Loans**

|||**Midlin**<br>|**e Survey**<br>||||**En**<br>|**dline Surve**<br>|**y**<br>|
|---|---|---|---|---|---|---|---|---|---|
|||Control|Treatm|ent|- Control||Control|Treatmen|t - Control|
|||<br>Group|<br> <br>||<br>||<br>Group|<br> <br>|<br>|
||Obs|Mean|Coeff.||_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Per capita HH expenditures last 30 days (FDJ)**||||||||||
|Total|914|13,462|-649||_0.428_|879|10,106|675|_0.274_|
|Durables|914|274|-153||_0.231_|879|93|139|_0.118_|
|Non-durables|914|13,188|-496||_0.534_|879|10,013|536|_0.377_|
|Food|914|7,502|-339||_0.618_|879|4,666|46|_0.778_|
|**Ln of per capita HH expenditures**||||||||||
|Total|914|9.22|0.09|_**_|_0.021_|879|9.04|0.04|_0.293_|
|Durables|189|5.91|0.26||_0.161_|120|5.67|0.18|_0.562_|
|Non-durables|914|9.21|0.10|_**_|_0.013_|879|9.03|0.04|_0.358_|
|Food|909|8.60|0.12|_***_|_0.005_|879|8.31|0.04|_0.257_|
|**Home Durables**||||||||||
|Index of home durables|n.a|n.a|n.a||n.a|1011|0.05|0.02|_0.897_|
|**Savings & Insurance**||||||||||
|The HH has any type of savings or insurance|963|0.221|0.062|**|_0.021_|903|0.160|-0.007|_0.740_|
|**Loans**||||||||||
|The household buys at the grocery store at credit|684|0.412|0.014||_0.701_|895|0.285|-0.028|_0.256_|
|Amount owed to the grocer (in FDJ)|682|5,574|-32||_0.977_|894|4,688|-1,291|_0.136_|
|A HH member has an outstanding loan|684|0.057|-0.011||_0.528_|895|0.032|-0.001|_0.931_|
|HH reimbursed a mortgage or house- related<br>loan in last 30 days|n.a|n.a|n.a||n.a|767|0.023|0.028|**<br>_0.038_|

Notes _:_ Coefficients from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, surveytime dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent.

46
Table 5

# Firm Indebtedness and Use of Public Programs

||∆Debt / S|ales (2019)|∆Guarante<br>Sales|ed Debt /<br>(2019)|∆Non-Gua<br>Sale|ranteed Debt /<br>s (2019)|
|---|---|---|---|---|---|---|
||(1)<br>With<br>Previous<br>Loans|(2)<br>Without<br>Previous<br>Loans|(3)<br>With<br>Previous<br>Loans|(4)<br>Without<br>Previous<br>Loans|(5)<br>With<br>Previous<br>Loans|(6)<br>Without<br>Previous<br>Loans|
|Use Credit Program|0_._145|0_._130|0_._139|0_._118|0_._006|0_._012|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Use Employment Program|0_._008|0_._002|0_._001|0_._000|0_._007|0_._001|
||(0.002)|(0.001)|(0.000)|(0.000)|(0.002)|(0.001)|
|Use Credit Program _×_ Employment Program|_−_0_._013|_−_0_._016|_−_0_._003|_−_0_._009|_−_0_._010|_−_0_._007|
||(0.003)|(0.003)|(0.001)|(0.002)|(0.003)|(0.002)|
|Increase in Sales Dummy|0_._021|0_._004|_−_0_._001|0_._001|0_._021|0_._003|
||(0.003)|(0.001)|(0.001)|(0.000)|(0.003)|(0.001)|
|Decrease in Sales Dummy|0_._017|0_._002|_−_0_._002|0_._000|0_._019|0_._001|
||(0.003)|(0.001)|(0.001)|(0.000)|(0.003)|(0.001)|
|Dependent Variable Mean|0.054|0.028|0.070|0.020|-0.016|0.008|
|Dependent Variable Std. Dev.|0.172|0.082|0.087|0.055|0.148|0.054|
|Number of Firms<br>|62,950|51,729|62,950|51,729|62,950|51,729|
|R<sup>2</sup>|0.190|0.359|0.627|0.644|0.017|0.019|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|

This table reports linear regressions of the change in firms’ leverage on a set of firm-level characteristics, separately for firms with and without previous loans, for the credit program eligible firms sample. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. The dependent variable is the change in the debt outstanding between December 2020 and December 2019, relative to 2019 sales. Use credit program and employment program are dummy variables for program participation. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. All columns include industry and municipality fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level. For change in debt over 2019 sales and change in guaranteed debt over 2019 sales, observations in the top and bottom 1% of the distribution are winsorized. Change in non-guaranteed debt over sales is constructed as the difference between change in debt outstanding and the change in guaranteed debt, over 2019 sales.

47