rafmacalaba/datause-encoder-data huggingface dataset:
market altogether or suffer from job discountinuity. Countries around the world have implemented maternity leave policies to provide a better balance between work and family life. However, the efficacy of these policies in improving women’s employment remains to be properly established, especially for the case of developing countries. Theoretically, maternity leave can either increase or decrease women’s employment. On the one hand, higher maternity leave increases the cost of hiring female workers and therefore discourages female employment. On the other hand, higher maternity leave encourages women to enter the labor market, leading to greater female employment. The net effect of these competing effects determines the overall relationship between maternity leave and female employment. The present paper takes one step in estimating this relationship using firm-level data on 111 developing countries. The results show that a large positive impact of greater maternity leave duration on the share of workers in the private formal sector firms that are female. Several important heterogeneities were uncovered in this relationship that allow us to better understand who benefits more from maternity leave.

The findings are important for policy makers in a variety of ways. First, we find that maternity leave increases women’s employment in the formal sector in the developing and emerging countries. However, this is so only when maternity leave is fully funded by the government and not otherwise. Thus, an effective maternity leave policy has budgetary implications that policy makers need to grapple with. Second, maternity leave policies seem to benefit more women with relatively low wages and skills. This is important for the design and targeting of maternity leave. For instance, policy makers may wish to condition the duration and the extent of government funding of maternity leave on the education or income level of the beneficiaries. Such conditioning is more egalitarian and also more effective in improving the overall employment of women in the formal sector. Third, by providing greater job continuity, 39
# **TYABL 0** COIDms

|Abstract . . . . . . . . . . . . . . . .iv|
|---|
|Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>1|
|I.<br>Finance and Development . . . . . . . . . . . . . . . . . . . . . .<br>1|
|II. Financial Innovations . . . . . . . . . . . . . . . . . . . . . . .<br>3|
|Nature and Characteristics<br>.......<br>.. .. .. .. . .<br>.<br>3|
|Preconditions. . . . . . . . . . . . . . . . . . . . . . . . .<br>6|
|III. Role of Public Policy . . . . . . . . . . . . . . . . . . . . . . .<br>7|
|Cost Risk Increasing Intervention. . . . . . . . . . . . . . .<br>7|
|Cost-Risk Reducing Intervention. . . . . . . . . . . . . . . .<br>8|
|IV. The Syndicate Experience. . . . . . . . . . . . . . .<br>. . . . . . .<br>8|
|Characteristics<br>of the Syndicate . . . . . . . . . . . . . . .<br>9|
|Transaction Costs and Functiknal Cost Structure. . . . . . . .<br>9|
|Factors Accounting for Low Transaction Costs . . . . . . . . . 10|
|Central Bank Response to Syndicate Innovations . . . . . . . . 14|
|V.<br>Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15|
|Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17|
|Appendix Tables. . . . . . . . . . . . . . . . . . . . . .<br>. . . . . . . 24|

ii
_How Fast Hai_ **_Chinea_** _Indawsy Growu? 35_

# TABLE 12: ENERGY DATA FOR STATE SECTOR INDEPENDENT **UNiTS,** 40 BRANCES, 1980 **AND** 1985

# A. Raw Data from Indusuial Cesus

||Energy Consunption|Tone of *t|d. coal per|
|---|---|---|---|
||10,000 tons std. coal|10,000 yua|n of_GVIO_|
|Branch|<br>1980<br>1985|<br>1980|<br>1985|
|1|2283<br>2543|18.24|16.22|
|2|1084<br>1147|8.27|7.15|
|3|46<br>62|5.61|5.68|
|4|106<br>116|**5.3**|4.18|
|5|69<br>76|6.61|6.11|
|6|124<br>132|7.65|8.39|
|7|0.0001<br>0.0001|3.2|3.87|
|a|156<br>183|4.06|4.98|
|9|44<br>65|4.04|3.91|
|10|740<br>1120|2.21|2.31|
|11|311<br>447|5.64|4.53|
|12|36<br>68|0.41|0.43|
|13|1<br>5|0.68|0.2|
|14|952<br>1234|1.72|1.63|
|iS|10<br>13|0.44|0.42|
|16|42<br>48|1.87|1.78|
|17|91<br>132|_4.S_|5.46|
|i8|9<br>10|1.89|1.5|
|19|594<br>746|9.21|8.15|
|20|27<br>36|0.79|0.67|
|21|11<br>13|_0.86_|0.58|
|22|6<br>9|1.27|0.87|
|23|7431<br>9127|38.89|34.44|
|24|1560<br>1613|9.43|7.76|
|25|300<br>330|23.08|20.37|
|26|5829<br>6211|18.08|13.78|
|27|237<br>322|3.97|2.68|
|28|321<br>481|9.71|5.4|
|29|160<br>195|2.19|1.84|
|30|39<br>59|1.58|1.37|
|31|2631<br>3594|21.44|19.34|
|32|5687<br>6397|19.01|16.36|
|33|620<br>754|5.2|4.64|
|34|135<br>_IS4_|2.69|2.19|
|_3S_|1170<br>1343|3.17|2.1|
|3|370<br>43S|_A.51_|1.66|
|37|194<br>224|1.8S|1.23|
|38|80<br>99|1.32|0.5|
|39|38<br>43|1.28|0.89|
|40|4<br>7|1.17|1.31|
|**sum**|33S48.00<br>39593.00|8.91|**6.99**|

Source: Indistia ceamns materias, 3: 346-355 <!-- Start of picture text --> (¢e)<br><!-- End of picture text -->
the size of funding available for the village. Selected projects are prioritized according to the number of votes received, so the sequence of implementation reflects relative popularity.

_Consultation Meeting:_ The village council convenes and moderates a meeting, open to all villagers - men and women - to discuss and select projects for funding. There is no specific requirement on the level of participation for the meeting to be valid. Informal points-of-procedure (such as a show-of-hands) may be employed during the meetings, but no formal vote takes place. Based on the outcome of the discussion but at its own ultimate discretion, the village council selects and prioritizes projects for funding. This procedure mimics the customary practice in rural Afghanistan of calling a _jirga_ or _shura_ to decide on important community matters.

Under both selection procedures, the list of proposed projects is prepared using an identical agendasetting procedure, whereby the village council compiles the list after consultation with villagers. The method of project selection was known when the lists of proposed projects were prepared. As strategic choice of proposed projects could have been affected by knowledge of the selection procedure, we examine the effect of the procedure not only on the choice of projects _per se_ , but also on the selection and prioritization of projects conditional on the set of proposed projects.

# III.2. **Sample and Randomization**

The randomization of selection and prioritization procedures occurred in 250 villages assigned to receive NSP and which formed the treatment group for the randomized impact evaluation of NSP (Beath, Christia, and Enikolopov 2011, 2012). The 250 villages are evenly split across ten districts in northern, northeastern, eastern, central, and western Afghanistan.<sup>5</sup> Despite the necessary exclusion of southern areas from the sample due to security concerns, the 10 districts are broadly representative of Afghanistan‘s ethno-linguistic diversity, with five predominantly Tajik districts, four predominantly Pashtun districts, one predominantly Hazara district, and two districts with significant populations of Uzbek and Turkmen minorities. The basic characteristics of households in the sample appear generally comparable with the population of rural Afghanistan, with households in the study sample being slightly poorer, having worse access to medical services, and experiencing slightly better access to electricity (Beath et. al., 2010).

> 5 In each of the ten districts, 50 villages were selected for inclusion in the study, with 25 of the 50 assigned NSP according to a matched-pair cluster randomization. These villages received NSP following the administration of a baseline survey in September 2007, with the remaining 250 control villages assigned to receive NSP in spring 2012.

7
<!-- Start of picture text --> 3 81 - ¶76 0.29-<br>E<br>b E= E * 0<X ;0 0<br>3 2 6 11.tanm d16mle mm 121 ocl olm I 1$ 5cm(kog scale) 8 lm 1 48 2.25 5t dm000 m local MI,.1kt1. h0cm I (log scl)allna 8b in 8 loa n 1 . hOInoM 2 (logscle 82 lb,<br>Age Household size Adult males<br>0 19 102 ¶ -j 063<br>° °° f2 Idaltn 8 (mm loal mdLt 1I tlm (log scale) lb ld3ionce 0 00 2 .26 f local mrout in5h I6 loom (log ecab) i lboanCce b -0.02 .12 Isk hor local 8 maak M1.0 In I 2 po (log saee) 8$e lblb<br>Children Education of adult males Education of adult females<br><!-- End of picture text --> Figure 13. Household characteristics and distance<sup>fromlocalmarket</sup>
C14d_contractunit3

**[Specify unit in which length of contract or agreement is given]**

1. days 2. weeks 3. months 4. years 888. Don’t know 999. Refused

**Go to** à **C15a**

C14e_temp3 What was the main reason that you do not have a permanent contract or permanent verbal agreement in this previous MAIN job? **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT ALL THAT APPLY.]**

1. This employer did not offer me a job with a permanent contract

2. I did not want to have a job with a permanent contract 3. I was unable to do continuous work 4. I was not able to find any job that offered a permanent contract or verbal agreement 5. Other (specify) 888. Don’t know 999. Refused C15a_sick3 How many days of sick leave did your employer provide PER YEAR in this previous MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C15b_casual3 How many days of PAID earned or casual leave did your employer provide PER YEAR in this previous MAIN job?

**[If “As needed” then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C15c_holiday3 How many days of holiday did your employer provide EACH WEEK in this previous MAIN job?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16a_maternityactual3 Did you take maternity leave from this previous MAIN job?

1. Yes

2. No à **Go to C17** 888. Don’t know à **Go to C17** 999. Refused à **Go to C17**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16b_maternityactuallength3 How many weeks of maternity leave (paid or unpaid) did you take?

**[If don’t know then put “Don’t Know”]** **<u>[If refused then put “Refused”]</u>**

4
chemoprophylaxis is available elsewhere. 8 The effects of the bed-nets on the incidence of clinical malaria and the prevalence of malaria infection will be reported separately.

# The interventions in the **PHC** villages

In July 1989, all bed-nets in the PHC villages were first washed and then dipped in a Permethrin solution designed to produce a dose of about 500mg per m<sup>2</sup> of net. Altogether, 5380 bed-nets were treated in this way, 88% of all nets in use in the villages. In all, 92% of all children in the PHC villages slept in treated bed-nets during the intervention trial. Before the intervention began, it should be noted, 96% of children in the P}IC villages had been sleeping in untreated bed-nets. Even in the non-PHC villages, 77% of children had been sleeping in untreated bed-nets. Then, all children in the PHC villages in the target age group, 6 months to five years, were randomized into two groups for receipt of either 1/4 strength Maloprim (25mg dapsone + 3.13 mg pyrimethamine) or a placebo of similar colour and taste. Chemoprophylaxis was continued for 20 weeks, July to November 1989, the main period of malaria transmission. Detailed compliance records were kept and random urine tests were conducted for the presence of dapsone. Compliance in the treatment and placebo groups was the same (95%) and more than 75% of the children in the treatment and control groups took their tablets on 90% of the required occasions.

# Mortality surveillance

Once the study area had been identified, a full census of the 73 villages was carried out, listing all usual residents of 6 p Alonso, SW Lindsay, JKM Armstrong, M Conteh, AG Hill, PH David, G Fegan, A de Francisco, AJ Hall FC Shenton, K Cham, BM Greenwood. Reduction of mortality in Gambian children by insecticide-treated bed-nets. Lancet 337: 1499-1502, 22 June 1991.

7 Alonso et al, forthcoming.

5
convenience we assume that this probability is 1, but any constant probability will yield the same result. In equation (8'), case 1 is equivalent to assuming that the income elasticities of country poverty weights and project success probabilities are equal in magnitude but opposite in sign. This amounts to assuming that institutional and administrative difficulties in poorer countries are counterbalanced by higher weights assigned to abatement, so that only damage matters in the allocation of resources.

The second approach (case 2) uses more information, by incorporating our OEDbased estimate of success probability as the measure of pi in equation _(5')._ For this case, we assume that the Bank assigns equal weight to damage abatement in all partner countries (i.e. all income weights co, are the same).

Recently, the World Bank's Environment Department has completed a detailed accounting of the Bank's environmental projects by sector. We have used this information to estimate current Bank investments by country for air pollution control, improved water and sanitation, and natural resource management. Using our estimated optimal shares, we have distributed total Bank investments in the three problem categories across all developing countries.

Since Figures 1, 2 and 3 represent quintile ranges for the environmental problem measures across countries, they also display the relative size of optimal investment shares for case I (optimal shares equal to shares of environmental problems). Figures 5, 6 and 7 provide the same information by problem for case 2. Figure 8 displays the results when we sum across the three environment sectors to obtain estimates of total optimal environmental investment in case 2. Full results for both cases are reported in Appendix A.
available for the years 2003-2006. Given that our sample runs between 1995 and 2003, it is not feasible in our case to take the same approach as Helpman et al. (2007).

We therefore propose an alternative. In developing countries, and particularly in Africa, firms’ ability to cover the fixed costs of complying with foreign standards is influenced by the level of financial development in the exporting country. If credit is expensive and/or hard to come by, then it will be more difficult for firms to pay the fixed costs of exporting. We therefore expect a measure of exporter financial development to be directly correlated with export propensity.<sup>17</sup> Even though such a measure could conceivably be correlated with export-conditional trade flows as well—since firms might need credit to support ongoing costs in addition to the fixed costs of startup—we expect that the connection will be much weaker, thereby justifying inclusion of financial development in the selection equation but not in the outcome equation.<sup>18</sup> (For recent theoretical work in this vein, see Chaney, 2005, and Manova, 2006.) Results from this approach are presented in Columns 4-5 of Table 13. As expected, financial development—as measured by domestic credit to the private sector as a percentage of GDP—is positively and significantly (5%) associated with export propensity.<sup>19</sup> We interpret this as indicating that credit constraints can exert a significant impact on African textiles and clothing exporters. All other estimated coefficients have the same signs and very similar magnitudes to

> 17 We leave it to future research to examine the possible role of foreign direct investment in loosening the credit constraint postulated here.

> 18 We check this assumption empirically by conducting an additional regression (not reported) that includes financial development in both the selection and outcome equations. We find, as expected, that the coefficient is positive and 5% significant in the former, but positive and insignificant at the 10% level in the latter. In all other respects, results are very similar to those reported in Columns 4-5 of Table 13. (Cf. Manova, 2006, who finds evidence that financial development is significant in both the selection and outcome equations.)

> 19 Note that domestic credit as a percentage of GDP enters the equation in levels, not logarithms. Hence, the estimated coefficient can be interpreted as an elasticity.

- 21 -
was randomized. The coefficient on the treatment dummy then reflects the outcome of a paired-means _t_ -test. Because outcomes may be more similar within forests than between forests, standard errors are clustered at the forest level. These regression results are presented in the odd-numbered columns of Table 2. The other version includes, in addition to the treatment dummy and block fixed effects, a series of individual characteristics (gender, age and the size of land holdings) as controls. The results using this regression model are presented in the even-numbered columns of Table 2.

Table 2: The impact of threshold group payments on intermediate and ultimate measures of cooperation, compared to linear group payments.

|||**In**|**termediat**<br>**of coop**|**e measure**<br>**eration**|**s**||**Ultimate **<br>**coop**|**measures of**<br>**eration**|
|---|---|---|---|---|---|---|---|---|
||Frequen<br>deliber|cy group<br>ations|Trust in<br>group m|fellow<br>embers|Assessed<br>equal|lack of<br>efort|Share of trees<br>alive|Share of trees<br>well alive|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|treatment|0.705<sup>+</sup>|0.950<sup>+</sup>|0.140|0.163<sup>_∗_</sup>|-0.054<sup>+</sup>|-0.109<sup>_∗∗_</sup>|-0.077<sup>_∗_</sup>|-0.095<sup>_∗∗_</sup>|
||(0.393)|(0.580)|(0.095)|(0.088)|(0.032)|(0.046)|(0.041)|(0.040)|
|Constant|5.290<sup>_∗∗∗_</sup>|6.239<sup>_∗∗∗_</sup>|3.995<sup>_∗∗∗_</sup>|4.294<sup>_∗∗∗_</sup>|0.179<sup>_∗∗∗_</sup>|0.152<sup>_∗∗_</sup>|0.453<sup>_∗∗∗_</sup>|0.426<sup>_∗∗∗_</sup>|
||(0.191)|(0.834)|(0.046)|(0.276)|(0.016)|(0.052)|(0.062)|(0.059)|
|Observations|272|230|251|212|290|242|62|62|
|F-Test|3.22|2.81|2.19|4.84|2.90|187.20|3.41|5.81|
|Controls|N|Y|N|Y|N|Y|N|N|
|Block fxed efects|Y|Y|Y|Y|Y|Y|Y|Y|

Robust standard errors clustered at the forest level in columns (1)-(6), and at the block level for columns (7) and (8). The participants’ characteristics controlled for in columns (2), (4) and (6) include gender, age, and land area. + _p <_ 0 _._ 125, _∗ p <_ 0 _._ 10, _∗∗ p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01.

We obtain the following results. First, as shown in columns (1) and (2) of Table 2, we find some evidence that the threshold incentives resulted in increased maintenance planning activity. Participants in the threshold group payment treatment report having met significantly more frequently with their fellow group members (on average 6.0 times, versus 5.3 times in the linear group payments treatment), and this difference only just fails to be significant at the 10% level in regression (1); _p_ = 0.106. Outcomes do not appreciatively change when controlling for individual characteristics; see column (2).

While we find (weak) evidence of the threshold group payments inducing more frequent group deliberations, we find stronger effects for the other two intermediate measures of cooperation. When only using block fixed effects average trust in one’s fellow group members is not significantly higher in the threshold group payment treatment than in the linear group payment treatment (see column (3); _p_ = 0.173), but the difference does become significant when controlling for individual characteristics as well (see column (4); 17
(2012). Yet, one major difference is that these studies examined the same data set that we investigate but over shorter periods ending in 2005. Gorodnichenko _et al._ (2010) find that inequality decreased during the 2000–2005 economic recovery, probably due to falling volatility of transitory income shocks rather than characteristics such as education, location, household composition, and age. Lukiyanova and Oshchepkov (2012), however, observe that income growth in Russia was strongly pro-poor for the same recovery period 2000-2005, but the overall reduction in cross-sectional inequality was modest. Another recent study by Novokmet, Piketty, and Zucman (2018) combines different data sources to investigate the evolution of inequality of income in Russia over a longer period than ours, but for the same time period that we analyze (i.e., 1994-2015), they found the Gini slightly increasing from 0.54 to 0.55.<sup>7</sup> We find rising income levels and decreasing inequality for the country over the past two decades. We also find that decreasing inequality was mostly caused by stronger income growth for the poor (i.e., pro-poor growth), rather than their relative upward movement along the income distribution (i.e., upward mobility). In particular, for the period 1994-2015 as a whole, the poorest tercile experienced a growth rate that is more than ten times that of the richest tercile. There was also faster income growth in the second medium-term period 2004-15 than in the first medium-

> 7 There are a couple key differences between Novokmet _et al._ ’s (2018) study and ours. First, Novokmet _et al._ (2018) focus on the top 1 percent in the income distribution, while we focus on those not at the top but are the majority of the population. Second, while we analyze total household income, Novokmet _et al._ derive their measure of welfare based on pre-tax national income, which includes tax information that Novokmet _et al._ acknowledge is not perfect in the Russian setting. Novokmet _et al._ also exclude private and public transfers that are important income components in Russia. Indeed, private transfers are estimated to make up as much as 9 percent of household incomes during the period 1994-2000 (Kuhn and Stillman, 2004); the corresponding figure for social benefits (such as child benefits) from the government is around 5 percent during the period 2011-2015 (Rosstat, 2018). Novokmet _et al._ (2018) also assume that the RLSM can only capture the bottom 90 percent of the income distribution, which is different from Kozyreva _et al._ ’s (2015) observation that the RLMS can capture 96 percent of the population. We return to more discussion in the robustness check section. More generally, another difference between the cited studies and ours is that these studies do not offer a detailed analysis for the different periods as we offer in this paper. We also employ a different analytical framework. For studies on welfare dynamics for Russia in the 1990s, see, for example, Commander, Tolstopiatenko, and Yemtsov (1999), Lokshin and Popkin (1999), Jovanovic (2001), and Lokshin and Ravallion (2004).

6