rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Determinants of School Enrollment: female youngest cohort, with and without test scores

||Public<br>general|**With t**<br>Public<br>vocational|**est scores**<br>Private<br>general|Private<br>vocational|Public<br>general|**Without**<br>Public<br>vocational|**test scores**<br> <br>Private<br>general|Private<br>vocational|
|---|---|---|---|---|---|---|---|---|
|Junior secondary test scores|||||||||
|Middle tercile|4.7<br>(5.3)|8.9**<br>(4.3)|-2.5<br>(5.0)|-11.2***<br>(4.3)|||||
|Top tercile|19.6***|12.9***|-9.7**|-22.8***|||||
||(5.9)|(4.9)|(4.6)|(4.2)|||||
|Personal characteristics|||||||||
|Repeated grade in junior high|65.5***|-16.1***|-26.4***|-23.0***|65.3***|-16.2***|-26.4***|-22.8***|
||(2.4)|(1.7)|(2.4)|(4.4)|(2.8)|(2.0)|(2.4)|(4.9)|
|Lived in small town at age 12|-0.9|2.0|-2.5|1.4|-0.1|2.6|-3.0|0.5|
||(4.3)|(3.3)|(3.6)|(3.9)|(4.2)|(3.5)|(3.6)|(4.1)|
|Lived in big city at age 12|-2.8|3.8|11.5*|-12.5***|-1.7|4.9|10.2*|-13.4***|
||(5.6)|(5.5)|(6.1)|(4.5)|(6.0)|(5.7)|(6.0)|(4.6)|
|Height|0.5|-0.3|0.3|-0.5|0.4|-0.3|0.3|-0.4|
||(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.3)|(0.4)|
|Parental education|||||||||
|Father graduated elementary|-8.1|-3.5|-5.2|16.8|-11.6|-2.6|-5.0|19.1|
||(10.4)|(12.1)|(9.7)|(12.7)|(10.8)|(10.2)|(10.6)|(13.5)|
|Father graduated junior high|-4.4|-14.2*|1.4|17.2|-8.2|-12.7**|1.5|19.4|
||(10.3)|(7.7)|(12.0)|(13.8)|(11.4)|(6.4)|(13.1)|(14.5)|
|Father graduated senior high|<br>0.2|-12.7|0.6|11.9|1.3|-9.9|-1.1|9.7|
||(11.8)|(9.1)|(12.9)|(13.2)|(13.2)|(8.7)|(13.2)|(12.1)|
|Father graduated university|8.4|-17.0**|4.1|4.5|8.8|-14.9**|2.0|4.1|
||(13.1)|(7.1)|(14.8)|(10.7)|(14.3)|(6.8)|(15.2)|(10.0)|
|Father attended vocational school|1.6|2.5|-8.6|4.5|-0.6|1.6|-8.1|7.1|
||(8.1)|(7.2)|(6.4)|(6.6)|(8.0)|(7.1)|(6.7)|(7.0)|
|Mother graduated elementary|1.4|8.5|7.6|-17.5***|2.5|8.0|7.9|-18.5***|
||(8.2)|(8.7)|(7.2)|(6.2)|(8.3)|(8.5)|(7.5)|(6.4)|
|Mother graduated junior high|8.6|1.5|6.7|-16.8**|11.5|2.1|5.9|-19.6***|
||(9.0)|(6.7)|(8.5)|(7.1)|(9.7)|(6.9)|(8.5)|(7.1)|
|Mother graduated senior high|<br>2.2|9.2|4.6|-15.9|4.0|10.1|4.3|-18.4*|
||(10.9)|(11.9)|(11.0)|(9.7)|(11.6)|(13.0)|(11.4)|(10.2)|
|Mother graduated university|13.2|-1.3|-2.2|-9.6|13.4|-1.5|-2.6|-9.2|
||(14.2)|(6.9)|(11.0)|(12.7)|(13.6)|(6.7)|(11.1)|(13.1)|
|Mother attended vocational|<br>-0.9|-3.8|5.8|-1.1|-0.1|-3.7|5.2|-1.5|
||(8.1)|(6.7)|(10.1)|(9.2)|(9.1)|(7.3)|(10.5)|(10.0)|
|Base case probability|40.2|10.7|30.3|18.8|44.4|9.6|28.1|17.9|
|Observations|||771||||766||
|R-Squared||0|.218|||0|.186||

Notes: *** 1% significance, ** 5% significance, * 10% significance; figures are marginal effects in percentage points; estimation includes province of junior secondary graduation fixed effects and all variables listed in Appendix 1; standard errors in parentheses, <u>they are robust to heteroskedasticity and clustered at subdistrict level.</u> 14
We ask several questions under each domain and sub-domain. Our pre-analysis plan then specifies aggregating these measures to form standardized indices. This reduces concerns about multiple hypothesis testing by focusing on one aggregate outcome in each family of questions. Appendix 3 provides the exact questions used in forming each question, and Appendix 9 provides treatment impacts on each specific question used in these aggregate measures.

# **5.4 Do Higher Investment Readiness Scores Predict Better Firm Investment Readiness and Investment Outcomes?**

The investment readiness program resulted in higher investment readiness scores from judges. To investigate whether these judges’ scores are informative about future outcomes for the firm, we use the control group sample to run the regression: �������� �������������� ���������� ���′�� ��� (2) We carry out this estimation first with no additional controls, and then with controls _X_ for country (dummies for Serbia and for Croatia), whether or not the firm had received funding from an outside investor at baseline, and the business sector (dummies for business and productivity, and lifestyle and entertainment sectors). We estimate this separately by survey, to examine results at different time horizons.

Table 6 presents the results. Column 1 shows that 10% of control firms had died by the first followup, and 25% by the second follow-up, two years post-intervention. These high death rates are higher than the average rates in developing countries, and likely reflect the firms being young and in relatively developed countries (McKenzie and Paffhausen, 2018). We then see no significant association between higher investment readiness scores and subsequent survival. Columns 2 through 6 then examine the associations between higher investment readiness scores and our different measures of subsequent investment readiness actions and investment steps and outcomes. We see that the judges’ scores of investment readiness are statistically significant predictors of the subsequent willingness and interest of the owner in taking on equity investment, whether the firm is meeting specific needs of investors before investment can take place, whether the firm has taken steps towards external financing, and whether they have received external financing. This is true both in the short-run (six-month) survey, and in the two-year survey. The magnitudes range from 0.14 to 0.33, suggesting that a one-unit change in the judge scores (which had a mean of 2.9 and 24
<mark>Imhoff, M. L., Bounoua, L., Ricketts, T., Loucks, C., Harriss, R., & Lawrence, W. T. (2004). Global patterns in human consumption of net primary production.</mark> _<mark>Nature</mark>_ <mark>,</mark> _<mark>429</mark>_ <mark>(6994), 870.</mark> Lobell, D. B., Hicke, J. A., Asner, G. P., Field, C. B., Tucker, C. J., & Los, S. O. (2002). Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98. _Global Change Biology_ , _8_ (8), 722‐735.

<mark>Lu, X., & Zhuang, Q. (2010). Evaluating climate impacts on carbon balance of the terrestrial ecosystems in the Midwest of the United States with a process‐based ecosystem model.</mark> _<mark>M</mark> itigation and adaptation strategies for global change_ , _15_ (5), 467‐487 <mark>Machado, R., & Serralheiro, R. (2017). Soil salinity: effect on vegetable crop growth. Management practices to prevent and mitigate soil salinization. Horticulturae, 3(2), 30.</mark> <mark>Mateo‐Sagasta, J., & Burke, J. (2011). Agriculture and water quality interactions: a global overview. SOLAW Background Thematic Report‐TR08, 46.</mark> <mark>Miller, R. L., Bradford, W. L., & Peters, N. E. (1988).</mark> _<mark>Specific conductance: theoretical considerations and application to analytical quality control</mark>_ <mark>. US Government Printing Office.</mark> Mukherjee A, Bhanja SNand Wada Y 2018 Groundwater depletion causing reduction of baseflow triggering Ganges river summer drying Sci. Rep. 8 12049 <mark>Munns, R., & Gilliham, M. (2015). Salinity tolerance of crops–what is the cost?.</mark> _<mark>New phytologist</mark>_ <mark>,</mark> _<mark>208</mark>_ <mark>(3), 668‐673.</mark> Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., ... & Hagemann, S. (2015). Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative. Geoscientific Model Development, 8, 2315‐2328.

<mark>Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. (2004). A continuous satellite‐derived measure of global terrestrial primary production.</mark> _<mark>B</mark> ioscience_ , _54_ (6), 547‐ 560.

Sanower, Hossain (2019). Present Scenario of Global Salt Affected Soils, its Management and Importance of Salinity Research. International Research Journal of Biological Sciences, 1: 1‐3.

<mark>Strobl E, Strobl RO (2011) The distributional impact of large dams: Evidence from cropland productivity in Africa.</mark> _<mark>Journal of Development Economics</mark>_ <mark>96(2): 432‐450.</mark> <mark>Tanji, K. K., and N. C. Kielen. 2002.</mark> _Agricultural Drainage Water Management in Arid and Semi‐arid Areas._ Rome: Food and Agriculture Organization of the United Nations.

<mark>Thompson, W.R. 2004. “Complexity, Diminishing Marginal Returns, and Serial Mesopotamian Fragmentation.”</mark> _<mark>Journal of World‐Systems Research</mark>_ <mark>10 (3): 613–52.</mark> <mark>Tum, M., & Günther, K. P. (2011). Validating modelled NPP using statistical yield data.</mark> _<mark>b</mark> iomass and bioenergy_ , _35_ (11), 4665‐4674.

11
links, as depicted in the upper panel Figure 7. Notably, firms with at least one international link tend to maintain more numerous connections, with an increasing average number of links throughout our sample period, as shown in the lower panel of Figure 7.<sup>22</sup> 23 These dynamics suggest complexity in firms’ strategic approach: while these firms solidify and concentrate their core supply relationships, they simultaneously seek to diversify their market presence, aiming to mitigate risks and capitalize on emerging global opportunities. This dual strategy of concentrated supply and diversified demand mirrors broader trends in global trade and business strategy, emphasizing the importance of flexibility and adaptation in the rapidly evolving agribusiness sector.

> 22When analyzing a firm’s global footprint, we distinguish between three types of links: domestic links, regional links, and cross-regional links. We then categorize firms into those that form only domestic but no international links, those that are involved in within-region linkages but not in cross-regional activities, and those that have at least one cross-regional link. Regions correspond to the seven World Bank geographic regions as detailed in the figure notes of Figure 7.

> 23A non-parametric Kruskal-Wallis test, a multi-sample application of the Wilcoxon rank sum test, suggests a statistically significant different link distribution between only domestic, only regional, and crossregionally active firms at the _<_ 1% level. All three pairwise comparisons tested with the Wilcoxon test confirm this observation. Furthermore, the 2014 links distribution is statistically and significantly different from the 2022 link distribution for all three firm types, according to separately conducted Wilcoxon tests.

22
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(iii) Direction of Exports and Imports. Tables 5-8 show the airection of exports and imports of the countries for years 1980, 1985, 1990 and 1991.7 Beyond some special relationships to be noted below, the center of gravity of intra-regional trade is Japan. In 1990, virtually all countries except Hoatg Kon' and Singapore exported more than tO% of their gzoods to Japan.<sup>8</sup> The proportion of cxports of the countries going to ASEAN-4 was quite sinall. With the exception of Singapore which has a special relationship with Malaysia, these exports ranged from 2.4% for Indoiiesia to 7.7% for Japan. Exports going to China, potentially the second-largest market **im** the region after Japan, were also small.

There are two noteworthy subregional trade relatiouiships within East Asia. First, China channels a substantial part of its exports to the United States and EC through Hong Kong. In 199C, China sold 43% of its exports to Hong Kong of which approximately 32:% were reexported. China's imnports from Hong Kong are also large and amounted to 26.5% of its total imports in 1990. Taiwaa exported 13% of its goods to Hong Kong. The high intensity of trade among China, Hong Kong and Taiwan is reflective of the special, albeit market driven, trade and investment relationships in the sub-region. Second, in 1990, Malaysia sold 23% of its exports to and bought _15%_ of is- imports from Singapore. Indonesia sold 7% of its exports to 71n Tables 5-8, we .eport the export and import shares for years 1980, 1985, and 1990 as derived from the U.N. Comtrade data and for years 1990 and 1991 as derived from the IMF Direction of Trade (DOT) data . A comparison of shares obtained from the two sources for the year 1990 shows that the two sets of numbers are approximately consistent with each other. The main exceptions may seem to be China in the case of exports and Hong Kong in the case of imports. But this is simply because the DOT data have been corrected for re-exports of Chinese goods by Hong Kong.

8Though the latest year for which Tables 5-8 provide informatior. is 1991, in the text I rely on the 1990 U.N. Comtrade data. This is because the other latest trade data discussed in the paper are for that year and from the U.N. Comtrade data bases.

9
<!-- Start of picture text --> 12 months<br>~~o<br>=}<br>“oOoeg:<br>ag<br>i]<br>-20 -10 0 10 20<br>Change in household size since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 6 months<br>~~o<br>=}<br>“oOoeg:<br>ag<br>i]<br>-10 5 0 5 10<br>Change in number of children (0-17) in the HH since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 6 months<br>~~o<br>=}<br>“oOoeg:<br>ag<br>i]<br>-10 5 0 5 10<br>Change in number of adults (18 and over) in the HH since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 6 months<br>~~o<br>=}<br>8<br>=oey:<br>ag<br>i]<br>2<br>-20 -10 0 10 20<br>Change in household size since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 12 months<br>~~o<br>=}<br>8<br>=oey:<br>ag<br>i]<br>2<br>-10 5 0 5 10<br>Change in number of children (0-17) in the HH since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 12 months<br>~~o<br>=}<br>8<br>=oey:<br>as<br>i]<br>2<br>-10 5 0 5 10<br>Change in number of adults (18 and over) in the HH since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 18 months<br>o~~<br>=}<br>8<br>eoSt:<br>as<br>i]<br>2<br>-20 -10 0 10 20<br>Change in household size since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 18 months<br>o~~<br>=}<br>8<br>eost:<br>ag<br>i]<br>2<br>-10 -5 0 5 10<br>Change in number of children (0-17) in the HH since baseline<br><!-- End of picture text --> <!-- Start of picture text --> 18 months<br>o~~<br>=}<br>8<br>eost:<br>ag<br>i]<br>2<br>-10 5 0 5 10<br>Change in number of adults (18 and over) in the HH since baseline<br><!-- End of picture text -->
# **The Impact of Paid Maternity Leave on Women’s Employment: Evidence Using Firm-Level Survey Data from Developing Countries**

By: Mohammad Amin<sup>a</sup> and Asif M. Islam<sup>b</sup> Keywords: Maternity leave, Women employment, Gender, Labor market JEL Codes: J13, J16, J18, J22

> a Corresponding author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank, Washington, DC. Email: mamin@worldbank.org

b Senior Economist, Office of the Chief Economist, Middle East and North Africa Region, World Bank, Washington DC. Email: aislam@worldbank.org. Phone: 202-458- 5246.

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.

<mark>We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the data available. We also thank Jorge Luis Rodriguez Meza and Norman Loayza for providing useful comments. All remaining errors are our own.</mark>
Expectations concerning place-based climate risks influences economic geographic outcomes. For example, in the United States the risk of floods negatively affects firm entry, employment and output, and is associated with a reduction in aggregate output, of which only 20% is attributed to direct damages while the remaining 80% stems from expectation effects (Jia et al., 2022). In a recent study, Cevik and Miryugin (2022) document the impact of climate change vulnerability on corporate performance using a large panel dataset of more than 3.3 million non-financial firms from 24 developing countries over the period 1997–2019. Their results suggest that firms operating in countries with greater exposure to climate risks have difficulty in accessing debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability. These effects are significantly greater for smaller firms, especially in high-risk sectors and countries, and those with weaker capacity to adapt to and mitigate the consequences of climate change. Their results are in line with previous findings suggesting the positive relationship between climate risks and cost of debt, which can result in a decline in firms’ leverage (Ginglinger and Moreau, 2019).

Such exposure to climate risks are detrimental to the firm’s financial position. For instance, while actual climate changes negatively affects firms’ stock returns, earnings and equity valuations (Venturini, 2022; Bansal et al., 2016; Addoum et al., 2021; Hugon and Law, 2019; Pankratz et al., 2019), the risks of such events may lead to a reassessment of the value of a large range of firms’ assets (plants, property, and equipment) and to increased operating costs, such as relocation costs and insurance costs, resulting in lower profits and reduced repayment capacity. Greater physical climate risk led to lower leverage in the post Paris Agreement period, owing to both demand and supply side effects. On the demand side, firm’s optimal leverage reduced due to the larger expected distress costs, while on the supply side higher operating costs induced bankers and bondholders to increase the spreads when lending to riskier firms (Ginglinger and Moreau, 2019). It is therefore not surprising that firms exposed to climate change risk bear higher costs in financial markets when trying to access credit. In the United States one-standard-deviation increase in the risk of sea-level rise is associated with a loan spread that is 4.2 basis point higher (Jiang et al., 2019). The effects are larger on corporate bonds, reaching up to 7 basis points, especially for firms and industries vulnerable to extreme weather conditions (Allman, 2021).<sup>4</sup> When confronted with known unknowns, investors have an incentive to pursue an option

> 4Nevertheless, such climate-related financial risks are still mispriced and not fully reflected in asset valuations (Caselli and Figueira, 2020), although recently financial markets are increasingly recognizing such risks (Alsaifi et al., 2020) and firms are becoming more careful in embedding uncertainties in their overall risk management.

13
<!-- Start of picture text --> T = US +eDAus DMA+ YEN YEN<br>F Fr +et *Ft +et<br><!-- End of picture text --> where _eC"_ represents the exchange rate of German marks to dollars, and em represents the exchange rate of Japanese yens to dollars. Expressed in flows, representing the change of values from one year to the next, the equation above becomes: <!-- Start of picture text --> MOT us DMt DMt YN YEN DMt DMt rEN YEN<br>AF; AF, + et AFD + et AFtY +AetDF + Ae,Y F;,<br><!-- End of picture text --> The equation shows that changes in the stock of debt are due to new borrowing: <!-- Start of picture text --> us DMt DM YNw Yn<br>AFu + eD AF, + e, AF.<br>AetM F;,D + J)M:--:DAt /veAV,F;,r t-1<br><!-- End of picture text --> and cross-currency effects (CCE): the equation may, of course, be generalized for more than three currencies.

In the event of a devaluation, there are two effects that should be considered when converting from foreign to local currency. First. is the revaluation effect, which occurs when stocks d-nominated in a foreign currency are converted to local currency. Clearly, a devaluation will increase the domestic value of the stock, even when its value in foreign currency remains the same. Second, is the timing effect which results from the fact that foreign currency denominated stock are converted to local currency values using end-of-period exchange rates, while the period average exchange rates are used to convert foreign currency flows to domestic equivalents.

Consider, for example, Foreign exchange reserves. These include reserves maintained at the IMF. The informnation source for this item is the Central Bank balance sheet, and is usually given in local currency as an end-of-period stock. To compute the flows, stocks first must be converted to US dollars by dividing by end-of-period exchange rates. One way to estimate the cross currency effect is to assume that reserves follow the same composition as foreign debt. With this assumption, we may correct the stock of reserves in US dollars. After this correction, we compute the implicit flow in dollars, and then multiply by the average exchange rate to obtain the flow in local currency. In most cases, however, only a percentage of the stocks is denominated in foreign currency, the rest being in local currency. In thL case, we have to separate the local from the foreign components.

Starting from the balance sheet, the following summarize the steps needed to compute monetary sector capital account flows for some period t:

- a) Obtain value of stocks at times t and t-1, (Stocks, Stock.,). These values are usually given as end-of-period, local currency values. In general, items such as deposits and credit will have local 3nd foreign currency components. Thus:

10
Table 4: Technology adoption is associated with access to knowledge and information

|**VARIABLES**|**GBF Ext**|**GBF Int**|**SBF Ext**|**SBF Int**|
|---|---|---|---|---|
|Lack of capabilities|-0.035**|-0.004|-0.064**|-0.043*|
||(0.016)|(0.011)|(0.027)|(0.022)|
|Government regulations|0.006|-0.004|-0.005|-0.037*|
||(0.019)|(0.012)|(0.029)|(0.021)|
|Lack of fnance|0.023|-0.009|0.068**|0.045**|
||(0.016)|(0.010)|(0.027)|(0.020)|
|Lack of demand and uncertainty|0.008|0.017*|0.002|-0.010|
||(0.015)|(0.010)|(0.023)|(0.018)|
|Poor Infrastructure|0.027|0.008|0.064|0.018|
||(0.025)|(0.014)|(0.040)|(0.029)|
|Other|0.078***|0.023*|0.030|0.052*|
||(0.018)|(0.013)|(0.031)|(0.027)|
|Ln (Employment 2018)|0.055***|0.057***|0.045***|0.042***|
||(0.008)|(0.006)|(0.014)|(0.011)|
|Informality|-0.300***|-0.253***|-0.309***|-0.170***|
||(0.020)|(0.016)|(0.034)|(0.026)|
|Constant|0.620***<br>(0.050)|0.265***<br>(0.034)|0.867***<br>(0.072)|0.373***<br>(0.061)|
|Observations|1,778|1,786|1,074|1,071|
|R-squared|0.460|0.442|0.325|0.262|
|Sector FE|YES|YES|YES|YES|
|Region FE|YES|YES|YES|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 For policymakers, the information on the factual elements that determine lack of adoption is critical to define policy priorities; including areas that deserve further experimentation with impact evaluations that would provide more rigorous evidence of interventions. Yet, perceived obstacles do not necessarily imply that are the most relevant issues faced by the firms. Firms do not know what they do not know. Thus, we now investigate these obstacles based on factual information associated with them. We group them into three main groups: i) Financial constraints; ii) Information and knowledge; and iii) Access to markets and competition, which is associated with uncertainty of demand and consumers’ preference.

# **6.2 Financial constraints**

Previous studies suggest that an inefficient financial system may reduce the firm-level technology adoption within a country even if a technology is more profitable. For example, by studying a model of establishment dynamics with a producer-level data, Midrigan and Xu (2014) found that financial friction distort firm entry and technology adoption decisions, which results in lower level of aggregate productivity. Cole et al. (2016) provides a dynamic 23