rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research**

<!-- Start of picture text --> Women in Development<br>WPS 913<br><!-- End of picture text --> This papcr **-a** product of thc Womcn in Development Division, Population and Human Resourccs Department -was prepared as a background paper lor the report on Gender and Poverty in India. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Lynn Bennett, room F5-009, cxtension 82772 (May 1992, 94 pages).

Das presents the Women's Development Program **(WDP)** - launched in six districts in Rajasthan, India in 1984, and now extending to nine - as a case study in awareness-building and group formation among rural women.

A departure from the traditional pattern of' viewing women as the objects of welfare, WDP has been a distinct success. Rural women have realized that their deprivation is not unchangeable, that alternatives exist, that they **are** competent enough to choose between alternatives, and that they are not alone.

for women's groups and nongovemment organizations. WDP stressed the need to build awareness and confidence among women as essential to integrating them into the development proccss.

The institutional perspective or a _place_ or center where women can be offered continuing education anid Lraining gave way to the concept _ol'_ an _intformal network_ of women's groups. Education was del'ined as the acquisition of knowledge, whicih may not necessarily mean literacy. It could even mean the acquisition of skills for perneraling or controlling resources.

One important feature of the project document was that it was provisional and tentative, essentially a guide to structure and ifhianicial patterns. Other programs focus on strengthening delivery systems; WDP's sole aim was to fbonn groups that would consolidate Lhemselves for - their own development these groups, once formed, would initiate any action they needed and decided upon.

The second important element of the project document was the degree of freedom visualized WDP has demonstrated that illiteratc women can be cffective group organizers. The question this project opens up is this: organize women for what'? Too much fbcus on training in communication and group inieraction could become an end in itself. Matlilat Samakhya, a program to organize wrinen for education, used the process of WDP but changed its aims and emphasis.

What emerges f'r-om **die** WDP experience is _the process_ it unleashed: the organization, interaction, and participation of womcn.

ThePolicy Res-,arch Working l'aperS.ricsdisseml **i** n iates ih findlings ofr work under wiy in thc Bank. Anohjcctiv of thc scrics is to get these findings out quickly, even if prcsentations arc Icss than fully polisl'ed The findings. interpretations, and conclusions in **thcsc** papers do not necessarily rclrcsent ofFicial Bank policN.

1'roduced by thc Policy Rescarch lDissemination Ccnter
and leads to a closer fit to the observed size distribution, especially in the thickness of the right tail. On the other hand, the size distribution derived in the Klette and Kortum (2004) model is logarithmic. This distribution is skewed but the tail is much shorter than the one observed in the data. Through introducing random sized products, Lentz and Mortensen (2008) improve the size distribution that emerges in Klette and Kortum (2004), but still they cannot capture the thickness of the right tail. My model succeeds this without postulating any exogenous variation in the size of the products.

The second improvement caused by this extension is on the relation between the variance of growth rate and size. This relation has drawn much less attention than the relation between the growth rate and size, both in theoretical and empirical research. Some recent work by Stanley et al (1996), Bottazzi (2001), and Sutton (2002, 2007) illustrate that variance of growth rates declines at a rather slow rate as size of an establishment increases. The mixing of producers with different efficiency levels at any size allows my model to explain the flatness of this relation. On the other hand, the models of Klette and Kortum (2004) and Klepper and Thompson (2007) yield too steep slopes.

The third improvement of the model is on the independent relation between size, age, and the hazard rate of exit. Evidence shows that as size and age increase, the hazard rate of exit decreases<sup>2</sup> . In my model, exit is determined by the number of products owned by the producer. Mixing of establishments with different efficiency-types allows age and size to be correlated with the number of products through different channels. This yields a negative relation between the hazard rate of exit and size conditional on age and a negative relation between the hazard rate of exit and age conditional on size. Klette and Kortum (2004) and Luttmer (2007) can only explain the relation between the hazard rate of exit and size<sup>3</sup> and Klepper and Thompson (2007) explain both relations through introducing random sized products.

The final improvement is on the pre-exit behavior of establishments within a birth cohort. Evidence on Chilean manufacturers shows that there exists size dispersion among entrants and on average, establishments with larger startup sizes live longer than the smaller ones. I explain these observations through type-heterogeneity. All entrants start with a single product. But,

> 2This relation has been demonstrated by several studies including Dunne, Roberts, Samuelson (1988) and Evans (1987a, 1987b).

> 3Without conditioning on size, both models can generate a negative relation between the hazard rate of exit and age.

2
Appendix Table 4: Harvests and productivity for rubber plots at baseline

||Total harvest<br>($)|Rubber<br>harvest ($)|Cocoa harvest<br>($)|<br>Other crops<br>harvest ($)|Total yield<br>($/ha)|Rubber yield<br>($/ha)|Cocoa yield<br>($/ha)|Other crops<br>yield ($/ha)|
|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|-431.03***|-221.69***|-57.61*|-39.49|-83.05***|-60.21***|-10.51|-6.18|
||(105.27)|(82.59)|(29.26)|(25.80)|(23.65)|(18.92)|(7.18)|(7.75)|
|Couples' Training|400.13***|269.14***|-22.10|65.75***|58.62**|46.68**|-8.16|7.89|
||(114.12)|(94.08)|(30.20)|(25.06)|(27.66)|(21.83)|(7.40)|(7.00)|
|Seedling Subsidy + Couples'<br>Training (beta)|-30.90|47.45|-79.72***|26.26|-24.43|-13.53|-18.66***|1.70|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.79|0.65|0.01|0.33|0.43|0.64|0.01|0.83|
|Observations|925|925|925|925|925|925|925|925|
|Mean for control group|839.07|446.86|173.74|91.64|200.97|118.27|39.83|20.94|
|Mean for individual training|407.01|234.13|111.20|49.61|133.60|76.19|27.39|17.51|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%. Household-level regressions.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size. Contemporaneous control variables in all regressions: Rubber-cocoa intercrop, Any other intercrop.

4
Ilzetzki, E, E. G. Mendoza, and C. A. Vegh. 2013. “How Big (Small?) are Fiscal Multipliers?” Journal of Monetary Economics 60 (2): 239-254.

Kabundi, A., and F. Ohnsorge. 2020. “Implications of Cheap Oil for Emerging Markets.” World Bank Policy Research Working Paper 9403, World Bank, Washington, DC.

Kaminsky, G. L., and C. M. Reinhart. 1999. “The Twin Crises: The Causes of Banking and Balance-of-Payments Problems.” American Economic Review 89 (3): 473-500.

Koh, W. C., M. A. Kose, P. S. Nagle, F. L. Ohnsorge, and N. Sugawara. 2020. “Debt and Financial Crises.” World Bank Policy Research Working Paper 9116, World Bank, Washington, DC.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” World Bank Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2020. Global Waves of Debt: Causes and Consequences. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, C. Reinhart, and K. Rogoff. 2021. “The Aftermath of Debt Surges.” World Bank Policy Research Working Paper 9771, World Bank, Washington, DC.

Kose, M. A., and F. Ohnsorge, eds. 2019. A Decade after the Global Recession: Lessons and Challenges for Emerging and Developing Economies. Washington, DC: World Bank.

Krugman, P. 1979. “A Model of Balance-of-Payments Crises.” Journal of Money, Credit, and Banking 11 (3): 311-325.

Krugman, P. 1999. “Balance Sheets, the Transfer Problem, and Financial Crises.” International Tax and Public Finance 6 (4): 459-472.

Krugman, P. 2014. “Currency Regimes, Capital Flows, and Crises.” IMF Economic Review 62 (4): 470-493.

Krugman, P. 2019. “Perspectives on Debt and Deficits.” Business Economics 54 (3): 157-159. Laeven, L., and F. Valencia. 2020. “Systemic Banking Crises Database II.” IMF Economic Review 68 (2): 307-361.

Ley, E. 2009. “Fiscal Policy for Growth.” PREM Note 131, World Bank, Washington, DC.

Lian, W., A. F. Presbitero, and U. Wiriadinata. 2020. “Public Debt and r - g at Risk.” IMF Working Paper 20/137, International Monetary Fund, Washington, DC.

Lo, S., and K. Rogoff. 2015. “Secular Stagnation, Debt Overhang and Other Rationales for Sluggish Growth, Six Years On.” BIS Working Paper 482, Bank for International Settlements, Basel.

Mauro, P., and J. Zhou. 2021. “r – g < 0: Can We Sleep More Soundly?” IMF Economic Review 69 (1): 197-229.

Mendoza, E. G., and M. E. Terrones. 2012. “An Anatomy of Credit Booms and Their Demise.” NBER Working Paper 18379, National Bureau of Economic Research, Cambridge.

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_<u>Ambient Environment/ Weather:</u>_ As mentioned above, the baseline survey was conducted during February-March (hot, rainy season) 2009, round 2 in April (transition between hot and cooler season) 2010 and round 3 in July-August (cooler, dry season) 2010. Ambient environment, as expected showed variability to some extent. Climate was mostly warm during the baseline and round 2. On average, 50% of the days during the baseline survey and 60% of the days during other two rounds were free from rain. Humidity was considerably lower in winter (round 3).

# **5. Monitoring Household Air Pollution: Methods and Descriptive Statistics**

Household kitchen concentrations of PM2.5 and CO were measured over periods lasting 24 hours in each study household. The air samplers and real-time monitors were placed on a wall in the kitchen area, 1.0 meter from the stove and 1.5 meters above the floor.

# _Monitoring of PM2.5_

PM2.5 was measured in every study household using the UCB Particle Monitor, which uses a light-scattering detector.<sup>25</sup> For consistency, in each household the UCB monitor used in baseline sampling was used for subsequent rounds. Additional PM2.5 measurements were taken with a TSI DustTrak 8520 Aerosol Analyzer (TSI Inc., USA) in 25% of houses to validate the results from the UCB Particle Monitor. The DustTrak also uses a light-scattering detector. These two instruments recorded real-time kitchen concentrations throughout the sampling period.

Gravimetric PM2.5 samples were also collected in the households where the DustTraks were employed, in order to calibrate the light-scattering measurements. The gravimetric sampling used aluminum cyclones equipped with 37 mm diameter Teflon filters. Casella Apex (Casella Measurement, UK) constant flow pumps were operated at a flow rate of 1.5 liters/minute, achieving a median particulate matter cut point of 2.5μm. The pumps were calibrated using a DryCal DC-Lite primary flow meter (Bios International, USA) to within ±5% of the target flow rate. Analysis of the gravimetric samples was conducted in a temperature- and humidity-controlled lab at the University of California Berkeley using a Mettler-Toledo balance. The balance was calibrated annually by a certified Mettler-Toledo representative.

> 25Litton et al., 2004; Edwards et al., 2006; Chowdhury et al., 2007

10
the characteristics of non-issuing firms with firms that issue different types of instruments. In particular, we compare: (1) firm size, measured by assets and turnover in 2011 U.S. dollars and the number of employees; (2) firm growth, measured by the annual growth rate of assets, turnover, and employees; (3) the liability structure of the firm, measured by the firm’s leverage and the ratio of longterm debt to total firm liabilities; (4) firm profitability, measured by the ratio of retained earnings to assets, the return on assets (ROA), and the return on equity (ROE). When comparing those characteristics across issuers and non-issuers, we use the median firm for the sample period, after taking the average over time for each firm. For the rest of the paper, we use the matched SDC-Orbis data set on capital raisings and balance sheet information for the period 2003-2011.

Issuing and non-issuing firms differ along several dimensions. Issuers are much larger than firms that do not issue equity, bonds, or syndicated loans (Table 3). Moreover, debt issuers, and especially bond issuers, tend to be much larger than equity issuers. During our study period, while the median non-issuing firm in the Arab region had around $81 million in assets, the median equity issuer had assets of around $240 million, the median bond issuer had assets of around $10.4 billion, and the median loan issuer had assets of around $5 billion. Similar qualitative results are found when looking at firm turnover and the number of employees; issuers are typically larger in size than non-issuers, debt issuers are larger than equity issuers, and bond issuers tend to be the largest.

Issuing firms also tend to grow faster than non-issuing firms. While the median non-issuer grew at a rate of 7.5 percent per year (looking at assets) between 2003 and 2011, the median equity, bond, and syndicated loan issuer grew at a rate of 13.9 percent, 19.5 percent, and 18.2 percent, respectively. Differences between issuers and non-issuers are also sizable when looking at turnover growth rates. Turnover grew at a rate close to 17 percent per year for the median issuer, compared to 12.9 percent for the median non-issuer. For the growth in employees, although issuers of equity, bonds, and syndicated loans seem to have quantitatively higher rates than non-issuers, only the 13
||**Table 6 (Continue**|**d)**|||
|---|---|---|---|---|
||**Discrepancy**<br>**Husband: joint**<br>**wife: she d**|**scenario 1:**<br>**ownership,**<br>**oes not own**|**Discrepancy**<br>**Wife: joint**<br>**husband: he d**|**scenario 2:**<br>**ownership,**<br>**oes not own**|
||**(1)**<br>**Reported**|**(2)**<br>**Economic**|**(3)**<br>**Reported**|**(4)**<br>**Economic**|
|Wife: listed as main decision-maker on<br>|-0.049**|-0.112***|0.002|0.057*|
|cropping activities for any plot<sup>(3)</sup>(Y=1 N=0)|[-2.43]|[-3.79]|[0.06]|[1.73]|
|**Assets and infrastructure**|||||
||-0.037|-0.068*|-0.035|-0.018|
|Woman: owns mobile phone (Y=1 N=0)|[-1.40]|[-1.85]|[-1.04]|[-0.49]|
||0.03|0.026|-0.004|-0.026|
|Man: owns mobile phone (Y=1 N=0)|[1.29]|[0.85]|[-0.17]|[-0.72]|
|HH h ltiit Y=1 N=0|-0.080**|-0.048|0.026|-0.026|
|as eecrcy ( )|[-2.29]|[-0.90]|[0.70]|[-0.45]|
|HH h id Y=1 N=0|0.082|0.063|0.093*|0.022|
|as ppe water ( )|[1.06]|[0.86]|[1.71]|[0.28]|
||0.002|-0.001|0.001|0|
|HH distance to nearest road (km)|[0.87]|[-0.39]|[0.90]|[0.01]|
|HH faced a shock affecting income/assets|0.031|-0.023|-0.015|-0.088**|
|(Y=1 N=0)|[1.29]|[-0.65]|[-0.51]|[-2.38]|
|**Language/region**|||||
||-0.025|0.002|-0.056*|-0.034|
|Language of HH head: Chewa|[-0.70]|[0.05]|[-1.95]|[-0.89]|
||-0.039|-0.01|-0.036|-0.068|
|HH religion: Muslim|[-0.94]|[-0.20]|[-1.15]|[-1.20]|
|Rl|0.023|0.077*|0.042|0.023|
|ura area|[0.64]|[1.68]|[1.11]|[0.32]|
|Ri Nh|-0.044|-0.114|0.045|-0.03|
|egon: ort|[-0.69]|[-1.53]|[0.67]|[-0.44]|
||0.043|0.065|-0.005|-0.102**|
|Region: Central|[1.30]|[1.38]|[-0.19]|[-2.09]|
||0.02|-0.023|0.224*|0.461***|
|Constant|[0.22]|[-0.19]|[1.80]|[3.78]|
|**Observations (number of parcels)**|1,354|1,303|1,373|1,344|
|**R2**|0.073|0.114|0.039|0.072|

# <u>Notes:</u>

(1) The results are from linear probability models, weighted by the response weight. ***=p<0.01, **=p<0.05,

*=p<0.10. T-statistics are presented in brackets, and account for clustering at the enumeration area level.

(2) Zeroes for each reported ownership variable reflected agreement for reported ownership, and likewise for economic ownership.

(3) These include decisions concerning crops to be planted, input use and the timing of cropping activities.

Figure 5 plots the share of couples across all decision-making scenarios — by whether women are the main decision-maker in cropping activities — to better understand how greater decisionmaking for women in agriculture is associated with different combinations of spouses’ reporting over ownership and rights. Figure 5 shows that where women are listed as the main decisionmaker, couples are much more likely to agree that she is the exclusive reported owner (and, albeit to a lesser extent, economic owner), as well as with exclusive rights to sell and bequeath. Appendix Table A6, which presents regressions for agreement outcomes across reported and economic ownership, also shows similar findings. Similar to the discrepancy results in Table 6, Figure 5 27
- Hsu, Feng-Chi, Baugh, Kimberly E, Ghosh, Tilottama, Zhizhin, Mikhail and Elvidge, Christopher D (2015). DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. _Remote Sensing_ , **7,** number 2, 1855–1876.

- Ingram, David R (1971). The concept of accessibility: a search for an operational form. _Regional Studies_ , **5,** number 2, 101–107.

- Jaeger, David A and Paserman, M Daniele (2008). The cycle of violence? an empirical analysis of fatalities in the Palestinian-Israeli conflict. _American Economic Review_ , **98,** number 4, 1591–1604.

- Jedwab, Remi, Kerby, Edward and Moradi, Alexander (2017). History, path dependence and development: Evidence from colonial railways, settlers and cities in Kenya. _The Economic Journal_ , **127,** number 603, 1467–1494.

- Jedwab, Remi and Moradi, Alexander (2016). The permanent effects of transportation revolutions in poor countries: evidence from Africa. _Review of Economics and Statistics_ , **98,** number 2, 268–284.

- Jedwab, Remi and Storeygard, Adam (2017). The average and heterogeneous effects of transportation investments: Evidence from Sub-Saharan Africa 1960-2010. _mimeo_ .

- Koenig, Jean-Gerard (1980). Indicators of urban accessibility: theory and application. _Transportation_ , **9,** number 2, 145–172.

- Kwan, Mei-Po (1998). Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. _Geographical Analysis_ , **30,** number 3, 191–216.

- Min, Brian (2015). _Power and the vote: Elections and electricity in the developing world_ . Cambridge University Press.

- Niksic, Orhan, Eddin, Nur Nasser and Cal`ı, Massimiliano (2014). _Area C and the Future of the Palestinian Economy_ . World Bank Publications.

- Pinkovskiy, Maxim L (2013). Economic discontinuities at borders: Evidence from satellite data on lights at night. _Unpublished manuscript, Massachusetts Institute of Technology_ .

- Redding, Stephen J and Sturm, Daniel M (2008). The costs of remoteness: Evidence from German division and reunification. _American Economic Review_ , **98,** number 5, 1766–97.

41
of a multi-pillar structure is a better alignment of different pillars with the basic objectives of pension systems, i.e. the first pillar for redistribution, the second pillar for saving, and both pillars for insurance. The practical argument is that there is considerable uncertainty regarding the robustness of different pillars over the long run. We know that unfunded public pillars cannot alone provide the answer but we do not know yet whether fully funded private pillars can do so. This suggests that a policy of diversifying across providers would make good practical sense, at least until we accumulate enough evidence that the funded private pillar can be stable, robust and efficient in the long run. The following issues would need to be covered in establishing a multi-pillar structure<sup>2</sup> :

*** Consider structure of public pillar.** This relates to whether the public pillar should take the form of state guarantees for a minimum pension as in Chile, or a two-part structure as in Switzerland, or a flat benefit for all workers as in the Netherlands and more recently Argentina, or a means-tested universal benefit for all residents as in South Africa. Each has its advantages and disadvantages, which would require careful consideration. The role of state guarantees, not only for minimum pensions but also for the solvency of pension fund managers and insurance companies, would also need to be addressed in the context of the objectives of general social policy.

- **Create mandatory or voluntary second pillar.** This will require deciding on the voluntary or mandatory nature of the second pillar and defining its coverage, minimum level of contribution, tax treatment, and extent of choice by workers.

2 A fuller discussion of the issues raised by multi-pillar structures is contained in World Bank (1994). See also Vittas (1993a) for a discussion of the benefits of combining the strong points of different pillars.

17
Table S.8: Flexible length specification: Conflict

||ACD|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|ACD Confict|0.31<sup>_∗∗∗_</sup><br>(0.06)|||
|ACD Post-Confict|0.056<br>(0.058)|||
|WGI Instability||0.107<sup>_∗∗_</sup><br>(0.045)||
|WGI Instability>Median|||0.155<sup>_∗∗∗_</sup><br>(0.059)|
|Log Ruggedness|0.066<sup>_∗∗_</sup>|0.099<sup>_∗∗∗_</sup>|0.099<sup>_∗∗∗_</sup>|
||(0.028)|(0.033)|(0.032)|
|Log of Rainfall|-.110<sup>_∗_</sup>|-.107|-.110|
||(0.065)|(0.07)|(0.07)|
|Log dist to coast|-.017|-.041|-.045|
||(0.039)|(0.044)|(0.043)|
|Population Density|0.116<sup>_∗∗∗_</sup>|0.093<sup>_∗∗∗_</sup>|0.087<sup>_∗∗∗_</sup>|
||(0.016)|(0.016)|(0.016)|
|Log of Surface Area|0.052<sup>_∗∗∗_</sup>|0.066<sup>_∗∗∗_</sup>|0.077<sup>_∗∗∗_</sup>|
||(0.02)|(0.022)|(0.019)|
|20km<length<50km|-.099<sup>_∗_</sup>|-.102<sup>_∗_</sup>|-.109<sup>_∗_</sup>|
||(0.059)|(0.058)|(0.058)|
|50km<length<100km|-.147<sup>_∗∗_</sup>|-.137<sup>_∗∗_</sup>|-.149<sup>_∗∗_</sup>|
||(0.064)|(0.067)|(0.066)|
|Length>100km|-.227<sup>_∗∗∗_</sup>|-.199<sup>_∗∗∗_</sup>|-.206<sup>_∗∗∗_</sup>|
||(0.07)|(0.075)|(0.074)|
|Estimate|-.008|-.014|-.016|
||(0.056)|(0.059)|(0.059)|
|Contract|-.068|-.055|-.068|
||(0.076)|(0.076)|(0.076)|
|Log of GDP pc (1985)|-.029|-.032|-.049|
||(0.044)|(0.045)|(0.043)|
|Foreign frm or JV|0.262<sup>_∗∗_</sup>|0.299<sup>_∗∗_</sup>|0.288<sup>_∗∗_</sup>|
||(0.114)|(0.117)|(0.119)|
|World Bank|0.012|0.034|0.012|
||(0.095)|(0.094)|(0.091)|
|Bilateral Donor|0.192|0.199|0.182|
||(0.133)|(0.136)|(0.132)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.899|0.898|0.898|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

11