rafmacalaba/datause-encoder-data huggingface dataset:
women’s political representation significantly increases modern contraceptive use and decreases the use of traditional contraception, with stronger effects on rural women, potentially due to improvements in the supply of family planning services that we document above. Rural women’s likelihood of using any contraceptive method also increases significantly. Our estimates imply that increasing the share of seats held by women by one s.d. results in an increase in modern contraceptive use among rural women by 4 percentage points (an 8 percent increase relative to the mean).<sup>19</sup> Consistent with the contraception results, exposure to female leaders increases birth spacing in rural areas (Panel D of Table 3). These estimates are based on the woman– year panel dataset and imply that increasing the share of seats held by women at the district level by one s.d. increases birth spacing among rural women by 0.15 years, corresponding to 2 percent of the mean.<sup>20</sup>

**Intrahousehold misalignment of fertility preferences.** Previous studies show that household decision-making about childbearing and contraceptive use may be characterized by inefficiency and noncooperative behavior (Ashraf et al. 2014; Anukriti 2020; Ashraf et al. 2020). As women’s use of contraceptives may not be perfectly observable to the male partner, moral hazard can arise due to hidden action and asymmetric information. Ashraf et al. (2014) show that an increase in the supply of family planning services can make the influence of moral hazard more salient in couples with misaligned preferences over the number of children, leading to spousal conflict.

Spousal discordance in fertility preferences is prevalent in our sample—44 percent of couples disagree on their ideal number of sons and, in 23 percent of couples, the husband’s ideal number of sons exceeds the wife’s.<sup>21</sup> Moreover, 30 percent of husbands express that they want to have more sons than they currently have. In such a setting, greater use of contraception by the wife may lead to spousal conflict if the husband is opposed to contraceptive use because he desires additional sons. To explore this channel, we examine heterogeneity in the impact of exposure to female leaders on IPV by whether the husband’s ideal number of sons differs from the couple’s actual number

> 19Panel B of Appendix Table A.7 shows that these results are robust to alternative specifications with polynomials of different degrees, different bandwidths and additional controls. Only two estimates are imprecise, with p-values of 0.10 and 0.21.

> 20Consistent with this result, Appendix Table A.9 suggests that exposure to female leaders reduces a woman’s likelihood of giving birth and her number of children; however, these estimates are not significant at conventional levels.

> 21Indian households are characterized by substantial “son preference” (Das Gupta et al. 2003; Jain 2014; Jayachandran 2017), which significantly influences fertility and family planning outcomes in India (Bhalotra and Van Soest 2008; Anukriti 2018).

11
# **5 Profile of the Economic Actors**

**The second step in the analysis involves forming a picture of the economic actors in terms of their resources and income generating activities.** This is important as it not only tells us about the potential embedded in the production factors labor and human capital, but also about the feasible growth trajectories that would include a major part of the work force without jeopardizing productivity growth.

**Togo is a mostly rural country with 37% of the population residing in urban areas, of which over 60% live in Lomé.**<sup>**27**</sup> As will be discussed below, socio‐economic conditions, employment and living standards differ widely between urban and rural areas with Lomé standing out even compared to other urban areas in the country. For instance the headcount ratio of poverty is 24.5% in Lomé but varies between 28 and 79% in the urban areas in the other regions. Poverty is even higher in rural areas, with Savanes having an incidence of 90%.

**However, Togo has still a relatively equal income distribution with a GINI in 2006 of 34% compared to the 44% average for SSA (** see also Figure 18 **).** Given the very low average income level, this means – as we have seen ‐ that a major part of the population is poor. It also means that there is a limited share of the population with enough capital to make major, risky investments even though opportunities may be there.

**Figure 18: Income shares of the highest and lowest 10%, West African Countries (2001‐2007).**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Togo Guinea‐Bissau Liberia Senegal Mali Nigeria Burkina Faso Ghana Sierra Leone Niger Gambia, The Cote d'Ivoire Cape Verde Cape Verde Gambia, The Cote d'Ivoire Nigeria Ghana Niger Liberia Senegal Sierra Leone Mali Benin Guinea‐Bissau Burkina Faso Togo<br><!-- End of picture text --> _Note:_ The first (second) group of bars is for the highest (lowest) 10%. Data points were from different years for different countries but all within the period 2001‐2007. Togo data is from 2006. _Source:_ DDP data, World Bank.

## **5.1 Demographic Dynamics**

**Togo has a high fertility rate.** However, 4.8 births per woman is not higher than expected in a country with the same per capita income level (see Figure 19).

> 27 Most of the analysis in this section is based on the Core Welfare Indicators Questionnaire (QUIBB/CWIQ) 2006. The poverty line applied is FCFA 164842, using the FAO/ WHO equivalence scales (see World Bank (2007a)).

22
poverty gap in line with the richer states of the S-SE. This result, however, should be viewed with caution, since that state has a very small share of total population and the result could be due to sampling bias. The PNAD survey does not cover the rural areas of the Northern states, where poverty is usually concentrated.

More information about the labor structure of the economy can be seen in Table 4 and Table 5. In these tables sectoral wage bills are split into the model's 10 occupational groups. The occupational groups are defined in terms of a unit wage ranking. More skilled workers, then, would be those in the highest income classes, and vice-versa. As can be seen in Table 4, Agriculture is the activity that uses more unskilled labor (40.5 percent of that sector’s labor bill), while Petroleum and Gas Extraction and Petroleum Refinery are the most intensive users of skilled labor (10<sup>th</sup> labor class) using activities, with Financial Institutions coming next. If labor inputs were measured in hours (rather than in values) the concentration of low-skill labor in Agriculture would be even more pronounced.

Agriculture is also the sector that hires most unskilled labor in Brazil, around 41 percent of total workers in income class 1 (Table 5). The Trade sector is the second largest employer of this type of labor. As for the higher income classes, we see that the Financial Institutions and Public Administration sectors hire the largest numbers of well-paid workers.

Table 6 shows the distribution of occupation wages (OCC) classes among the household income classes (POF classes). In this table, the rows show household income classes, while the columns show the wage earnings by occupation. It is evident from this table that the wage earnings of the higher wage occupations (OCC10, for example) are concentrated in the higher income households, and _vice-versa_ . Most of the wages earned by workers in the first wage class (OCC1) accrue to the three poorest households, POF[1]-[3]. All the workers in the highest wage 14
# Demand and Supply Shocks: Evidence From Corporate Earning Calls<sup>∗</sup>

Franz Ulrich Ruch<sup>†</sup> Temel Taskin<sup>‡</sup>

**Keywords:** Sentiment; Stock market; Demand shocks; Supply shocks; Uncertainty; COVID19; Pandemic; Great Recession.

**JEL Codes:** G10; E32; C11; C32.

∗This paper benefited from valuable comments and suggestions made by Ata Can Bertay, Justin Damien Guenette, Alain Kabundi, Sergiy Kasyanenko, William F. Maloney, Franziska Ohnsorge, Shijie Shi, Max Rudibert Steinbach, Eka Vashakmadze, and the seminar participants at the World Bank and at the 10th CompNet Annual Conference at the Bank of France. This paper was written when Temel Taskin was a World Bank staff member. The findings, interpretations and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed to the World Bank, its Executive Directors, or the countries they represent. The views do not represent the official Bank of Canada view.

> †Equitable Growth, Finance and Institutions, Prospects Group, The World Bank. Email: fruch@worldbank.org

> ‡Bank of Canada. Email: ttaskin@bankofcanada.ca
# **References**

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Bossert, Thomas J., Till Bärnighausen, Diana Bowser, Andrew Mitchell, and Gülin Gedik (2007). “Assessing Financing, Education, Management and Policy Context for Strategic Planning of Human Resources for Health,” Geneva: World Health Organization.

Bossert, Thomas J. and J.C. Beauvais (2002). “Decentralization of Health Systems in Ghana, Zambia, Uganda and the Philippines: A Comparative Analysis of Decision Space,” _Health Policy and Planning_ , 17(1):14-31.

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Brinkerhoff, Derick W. and Thomas J. Bossert (2008). “Health Governance: Concepts, Experience, and Programming Options.” Health Systems 20/20. Washington, DC: USAID. February. http://www.healthsystems2020.org.

Chaudhury, Nazmul, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan and F. Halsey Rogers (2006). “Missing in Action: Teacher and Health Worker Absence in Developing Countries.” _Journal of Economic Perspectives_ , 20(1): 91–116.

Chaudhury, Nazmul and Jeffrey S. Hammer (2004). “Ghost Doctors: Absenteeism in Rural Bangladeshi Health Facilities.” _World Bank Economic Review_ 18(3):423-441.

Dehn, Jan, Ritva Reinikka and Jakob Svensson (2003) “Survey Tools for Assessing Performance in Service Delivery,” in François Bourguignon and Luiz A. Pereira da Silva, eds. _The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools_ . New York, NY: Oxford University Press. pp. 191–212.

Ekman, Björn. (2004). “Community-Based Health Insurance in Low-Income Countries: A Systematic Review of Evidence.” _Health Policy and Planning_ . 19(5):249-270.

Emanuel, Ezekiel J. and Linda L. Emanuel (1996). "What Is Accountability in Health Care?" _Annals of Internal Medicine_ 124(2):229-239. January.

______________________________________________________________________________________ -39-
Participatory Development Associates. "Participatory Poverty and Vulnerability Assessment (PPVA): Understanding the Regional Dynamics of Poverty with Particular Focus on Northern Ghana." Accra, 2009.

- Raballand, Gaël, Patricia Macchi, Dino Merotto, and Carly Petracco. "Revising the Roads Investment Strategy in Rural Areas: An Application for Uganda." _World Bank mimeo_ , 2009.

- Ruijs, Arjan, Caspar Schweigman, and Clemens Lutz. "The impact of Transport- and Transaction-Cost Reductions on Food Markets in Developing Countries: Evidence for Tempered Expectations for Burkina Faso." _Agricultural Economics_ , 2004: 219-228.

Tsegai, D. "Migration and Household Income Differentials." _11th EADI General Conference on Insecurity and Development._ Bonn, 2005.

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Table 4: U.S. Monetary Transmission Across EA and KC Groups

||**Max**|**. R* Respo**|**nses**|**Ma**|**x. S Respon**|**ses**|
|---|---|---|---|---|---|---|
|**Group**|**NS**|**BRW**|**SS-FG**|**NS**|**BRW**|**SS-FG**|
||||_Panel A: Fixer _<br>|_Sub-sample_<br>|||
|Fixer|1.34|1.07|0.23|0.23|0.05|0.08|
||[0.04, 2.74]|[0.36, 2.64]|[-0.09, 0.79]|[0.09, 0.35]|[0.00, 0.13]|[0.02, 0.12]|
|Low KC|1.15|1.09|0.12|0.23|0.07|0.09|
||[0.02, 1.92]|[0.54, 2.44]|[-0.12, 0.48]|[0.10, 0.35]|[0.00, 0.14]|[0.05, 0.13]|
|High KC|1.52|1.07|0.51|0.17|0.04|0.04|
||[0.13, 4.04]|[0.05, 3.14]<br>|[-0.06, 1.54]<br>_Panel B: Floater_|[0.04, 0.27]<br> _Sub-sample_|[-0.02, 0.10]|[0.01, 0.09]|
|Floater|1.43|1.00|0.80|0.24|0.07|0.10|
||[0.12, 3.41]|[-0.11, 2.73]|[0.17, 1.81]|[0.09, 0.43]|[0.01, 0.14]|[0.04, 0.15]|
|Low KC|1.28|1.15|0.35|0.22|0.09|0.10|
||[0.22, 2.48]|[0.18, 2.43]|[0.02, 0.79]|[0.11, 0.38]|[0.02, 0.14]|[0.04, 0.15]|
|High KC|1.59<br>[-0.34, 4.80]|0.71<br>[-0.20, 2.97]|1.25<br>[0.25, 2.65]|0.27<br>[0.09, 0.49]|0.05<br>[0.00, 0.14]|0.09<br>[0.04, 0.16]|

Notes: (1) This table reports the median of maximum IRFs over 1-30 months for countries in each sub-group as indicated by the **Group** column; (2) Statistics in the brackets show the lower and upper bound for 68% confidence band; (3) “Floater (Fixer)” indicates countries with average EA above (below) median.

The above analyses suggest that the divergence between AEs and EMDEs is central to our analysis. In Table 5, we look more closely into countries within the EMDE, exploiting the rich regional diversity in our sample. In this way, we provide a more granular view on the evaluation of capital control effectiveness.

First, we point out that there exist some salient outliers whose deviations from the general picture are not explained by our country factors in a systematic way and might be due to unobserved country heterogeneity (institutional background, historical reasons, etc.). As suggested by the relative R* responses, among countries in the 25 lowest degree of KC, the interest rate responses are smaller and less significant for Netherlands, Belgium, France, and Zambia. Furthermore, within the group of 25 countries with the highest degree of KC, we find countries—Brazil, Mexico, Angola, the Russian Federation, and Kazakhstan in particular—that exhibit relatively strong R* responses. On the other hand, the S responses of these countries are still in line with our previous results, even though they behave counter-intuitively in the case of R* responses.

Second, we confirm in Table 5 that our analyses still apply when regional economic features are considered. For example, among the seven largest EMDEs (EM7)–i.e., Brazil, China, India, Malaysia, Mexico, Türkiye, and Russia; the seven countries considered to be substantial in economic scales compared to other EMDEs–for those that use capital controls more pervasively like China, India, 20
# **Constrained**

Equation 9 comes from a derivation of the first order condition from exports, given by: 16 Thus the solution to the export maximization problem is as such:

- If there are no binding future constraints for all future periods up to (1 + _S_ ), i.e.Σ = 0, then the marginal value of increasing export sales is strictly positive. Exports in this case will be the maximum amount possible which is 100 percent; _ψ_ = 1.

- If there are binding future constraints in the period up to (1 + _S_ ), the extent that financial constraints are binding during that time will determine the amount of exports. The choice of _ψ_ affects the size of Σ, the shadow values _ν_ and _ν_<sup>_′_</sup> as well as _E_ � _∂∂VX_<sup>_′′_</sup> �. The choice of labor, capital and export percentage will be determined where all three equations are satisfied. Exports in this case can take on values between 0 and 100 percent due liquidity constraints.

Let _V_<sup>_∗E_</sup> ( _Ai_ ) denote the value of the value function of optimal domestic profits as a function of firm specific technology _Ai_ and let _X_<sup>_E∗_</sup> ( _Ai_ ) denote the minimum cash requirement for unconstrained profits to occur. Additionally, let _V E_ ( _Ai, Xi_ ) denote the value of the value function where profits are at constrained optimal (where _Xi < X_<sup>_E∗_</sup> ( _Ai_ )). It is necessarily so that constrained optimal profits are lower than unconstrained profits: _V_<sup>_∗E_</sup> ( _Ai_ ) _> V E_ ( _Ai, Xi_ ) The cash requirement, _X_<sup>_E∗_</sup> ( _Ai_ ), to satisfy Σ = 0 can be calculated by iterating backwards the budget constraint from the period when export revenue is received. At that period, cash on hand must be greater than expected cost. ie _Xt_ + _s >_ (1+1 _φ_ )<sup>_Et_[</sup><sup>_Costt_+</sup><sup>_s_] for constraints to not bind.Likewise for the period before that, and</sup> so on and so forth. The minimum cash required for constraints to not bind for each period thus can be given as:<sup>17</sup>

> 16This utilizes a backward iteration of _∂∂VX_<sup>=</sup><sup>_E_</sup> <u>�</u> _∂∂VX_<sup>_′′_</sup> � + _ν_ (1 + _φ_ ) that allows us to summarize the change in the value function at time (1 + _S_ ) in terms of the history of shadow values leading up to it: _E_ � _∂∂VX_<sup>11++</sup><sup>_SS_</sup> � = _E_ � _∂∂VX_<sup>_′′_</sup> � _−_ Σ.

> 17The cash threshold is defined for where the firm also expects to export in the future. An alternative scenario is that, due to

11
Table 1: Descriptive Statistics

||**Observations**|**Mean**|**S.D.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Panel A: Election Outcomes**|(1)|(2)|(3)|(4)|(5)|
|Total no. of candidates|9,200|10.36|7.26|1|301|
|Efective number of parties|9,197|2.92|0.97|1.05|12.50|
|No. of female candidates|9,200|0.49|0.94|0|49|
|No. of male candidates|9,200|9.87|6.83|1|252|
|No. of independent candidates|9,200|5.30|6.22|0|298|
|No. of parties|9,200|5.03|2.61|0|24|
|Constituencies with _≥_one female candidate|9,200|0.34|-|0|1|
|Female winner|9,200|0.05|-|0|1|
|Aligned winner|9,200|0.57|-|0|1|
|Winner from incumbent party|9,200|0.30|-|0|1|
|**Panel B: Candidate Characteristics**|(1)|(2)|(3)|(4)|(5)|
|Completed primary schooling|33,895|0.93|-|0|1|
|Completed secondary schooling|33,895|0.78|-|0|1|
|Accused in criminal case|35,033|0.16|-|0|1|
|Accused in serious criminal case|35,033|0.08|-|0|1|
|Total declared assets (INR in millions)|35,033|20.38|-|0|10,205|

_Notes:_ Panel A shows descriptive statistics for variables from the SHRUG dataset on district _×_ year-level state election outcomes during 1974 to 2018. Panel B shows descriptive statistics from affidavit data provided by the Association of Democratic Reforms on candidate characteristics for state elections during 2004 to 2018.

# **5 Empirical Strategy**

To test the model predictions and study the DPEP’s impacts on political outcomes, we adopt a fuzzy regression discontinuity design (hereafter RDD; Hahn et al. (2001)) and estimate a non-parametric local linear regression using a selected bandwidth of data around the cut-off. For optimal bandwidth selection, we use the Calonico et al. (2019) procedure. As mentioned earlier, districts that had a female literacy rate below the national average (39.3 percent) based on the 1991 Census of India were targeted by the DPEP. However, not all eligible districts were treated, yielding a fuzzy RDD based on the 1991 district female literacy rate as the running variable. Figure 1 clearly shows a sharp increase in treatment probability for districts with female literacy rates just below the eligibility threshold. However, it also shows that many eligible districts remained untreated, and some ineligible districts received treatment. Nevertheless, the discontinuous change in treatment probability at the eligibility cut-off provides a suitable setting for our chosen methodology and allows us to causally es14
# **3 Appendix F: Follow-Up Results**

Table (F1) Impacts of the Program on Primary Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Housing Quality Index|0.09|(0.19)|[0.47]|-0.01|(0.99)|1,321|
|Log total consumption (USD PPP)|-0.05|(0.10)|[0.47]|4.54|(0.78)|1,316|
|Depression (CESD, std)|0.26*|(0.15)|[0.13]|-0.01|(1.00)|1,285|
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.01|(1.00)|859|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Housing quality index is defined in table 2. Total Consumption is defined in table 3. Depression is defined in table 7 (higher here reflects more depression). Child SDQ score is defined in table 8 (higher here reflects more socioemotional difficulties).

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 1 in the preanalysis plan.

31
thus suggesting that the positive effect of FDI on the demand for skilled workers that was previously obtained was the result of some endogeneity bias. In Brazil, the endogeneity of FDI is not supported by the corresponding test, so the OLS estimates are preferred to the IV estimates.

# 6. Conclusion

One of the most important bene fi ts that developing countries can potentially reap from their increasing levels of integration in the world economy, besides the static gains associated with increased international specialization, is the access to technologies developed in industrialized countries. Indeed, the intrinsic difficulties associated with transferring technology through market transactions make international trade and investment crucial channels for the international diffusion of technology. Since most of the international income gaps are related to technology and productivity gaps, increases in international economic integration are expected to go a long way towards reducing the enormous inequalities existing between rich and poor countries.

But what about the effects on the levels of inequality found within developing countries? The evidence from research conducted for the U.S. and other industrialized countries suggests that skilled-biased technological change has been the main factor driving increases in inequality between skilled and unskilled workers. Thus, one could expect the same effects in developing countries, already characterized by high levels of inequality, as they increasingly adopt technologies developed in the industrialized world. However, as fi rms in developing countries expand their international economic activities, one could also expect a greater exploitation of their countries’ comparative advantage in the production of goods that make intensive use of unskilled labor. This should lead to a greater demand for that type of labor, and could thus counteract the effects that a greater adoption of skill-biased technologies could exert on local levels of wage inequality.

26