rafmacalaba/data-use-mentions-extended huggingface dataset:
> percentile values of the capital regulatory variable across countries for each of the World Bank regulation and supervision survey waves. The figure fails to provide evidence of international capital regulatory convergence over time.<sup>15</sup>

> 15 <mark>Alternatively, the use of foreign subsidiaries as a means to bring about lower capital regulatory standards may have declined over time as home-country regulations regarding banks’ international operations have tightened according to a confidential IMF survey covering the period 2006-2014 (see IMF, 2015). In the empirical work, a time trend in such regulations is controlled for by the inclusion of time fixed effects.</mark>

13
which is summarized into the INNO term. This Innovation Score is then computed as ∑INNO�� ��N� where _N_ is the total number of questions. The innovation score is averaged by sector and year. In this graph, the fitted values line is plotted on the basis of excluding the sectors Coke & Petroleum and Other manuf. & Repair as they appear to be extreme outliers. (Note that the two sectors are also excluded from the regressions.) An upwards sloping correlation is visible in the sense that more ICT‐services intensive sectors have a higher value on our innovation score. The regressions will show whether the index of data policy restrictiveness in Malaysia, as shown in Figure 8, has any role to play.

Results are reported in Table 5. In there, the regression coefficients for R&D expenditures in column 1 gives significant results, which is somehow counterintuitive. This unexpected result could be seen in light of a reaction by firms to perform more R&D as a consequence of the restricted access to foreign markets for their innovation activities that otherwise is essential for digital innovation. In column 3, the coefficient result gives a negative and significant outcome. It indicates that firms in data‐intense sectors (proxied by their share of ICT‐services inputs) faced with higher levels of data policy restrictions is associated with a lower use of firms’ intangible assets as part of their production. Both variables of purchased and produced intangible assets in columns 2 and 3 respectively provide negative coefficient signs but are statistically unimportant.

# **4.2.2 Vietnam**

In the case of Vietnam, we have a different set of variables although the first variable to use overlaps with the Malaysian data: both report whether the firm performs the size of R&D activities. The second innovation variable measures more precisely whether the firm’s R&D activities are targeted 13
## **2.3.3 Central Bank**

Monetary policy is conducted by the central bank, which manages the short-term nominal interest rate _Rt_ = (1 + _rt_<sup>_b_),inresponsetofluctuationsindomesticoutputgapandinconsumer</sup> price inflation gap using a Taylor-type rule. This managing rule allows the central bank to smooth nominal interest rates through open market operations.<sup>4</sup> where _Yt_ denotes the country’s growth domestic product (GDP) and _φt_ are _i.i.d._ normal innovations with a standard deviation of _σr_ .

# **2.4 Rest of the World**

Following the 2014 World Economic Outlook (WEO) released by the IMF, Uganda is considered as a small open economy. Consequently, a plausible assumption is to assume that domestic developments do not affect the rest of the world economy. However, the foreign economy’s dynamics (oil prices) have an impact on the domestic economy. For simplicity, we assume that the foreign interest rate, foreign output and the world inflation rate are exogenous and follow AR(1) processes.

# **2.5 Market Clearing and Competitive Equilibrium**

In a competitive equilibrium, the markets for goods, labor and capital all clear. The goods markets clears when the demand from the agents can be meet by the production of the final good. To do this, we define real aggregate GDP as the sum of value added in the three sectors, measured by their steady state prices.

> 4The use of the previous period interest rate allows us to match the smooth profile of the observed interest rate in the data.

19
, then next in line for implementation, were designated as treatment and ward MMC 27 as control.

This design raises several concerns. First, as the wards chosen for impact evaluation are located at the fringe of Maseru in areas with lower settlement densities, the extent to which they are representative of the intervention area as a whole is doubtful. Second, controls are more distant than treatment areas from the CBD throughout, limiting comparability. Finally, as treatment and control are contiguous, information spillovers may occur. The administrative data discussed below allow us to check if such concerns are justified.

# **3. Data and analytical approach**

Administrative data from before and after regulatory reforms suggests that the 2010 reforms triggered discrete and sustained changes in (i) the quality of service defined as time needed to register a transaction; (ii) the share of plots registered in the name of females either individually or jointly; and (iii) the number

> 6 This applies to land held under civil or customary law. Prior to passage of the Act, women were considered as perpetual minors who could contract or own property only via assistance from a guardian, i.e., their father, husband or their husband’s heir.

> 7 In addition to the 2010 Land and LAA Acts, laws and regulations were passed to regulate systematic land tenure regularization, planning, surveying, registry operation and dispute resolution.

7
Policy Research Working Paper 9591

# **Abstract**

The U-shape theory argues that at early stages of development, countries experience a reduction in the female labor force participation, eventually followed by a recovery. In Morocco, female labor force participation is now lower than it was two decades ago due to several factors that are discussed in the paper. There is also a persistent 50-percentage-points gender gap in labor force participation rates, despite improvements typically related to development and female inclusion—such as a higher gross domestic product per capita, lower fertility rates, and better access to education. At the same time, urban job creation has not been able to offset rural job destruction nor the increase in the working age population for both genders. Using data from the Moroccan Labor Force Survey, the World Values Survey, and the Arab Barometer, probit models and a multinomial logit are estimated to explore the challenges affecting female insertion into the labor market. The findings show that higher educational attainment increases the probability of female participation, but this relationship has decreased over time, not being enough to offset other obstacles caused by other individual and household characteristics. Being married and the presence of other inactive women are found to decrease female participation. The educational level of the head of household (typically men) increases female inactivity, suggesting that potentially gender roles may drive women out of the labor market and slow the recovery in women’s participation.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
country and good. Given that the impact of tariffs on imports depends exclusively on the import demand elasticities and that these have been estimated earlier for each HS 6 digit tariff line in 117 countries (see Kee, Nicita and Olarreaga, 2004), we can substitute these estimates into (1) instead of trying to estimate them again. This does not only allow us to avoid imposing any structure on the estimation of those parameters (to avoid running out of degrees of freedom), but also solves for the endogeneity of tariffs (and import demand elasticities) to tariffs as discussed in Tre fl er (1993) or Lee and Swagel (1997). This is achieved by sending the tariff variable (and the now “known” parameters) to the left-hand-side. This constrained speci fi cation may introduce heteroscedasticity in the error term κn,c given that import demand elasticities have been estimated with error and this error in the measurement of the elasticities can be associated with measurement error in NTBs. A White correction could be undertaken to solve for this.<sup>6</sup> After substituting the estimated elasticities and sending the tariff term to the left-hand-side, Equation (1) becomes: According to equation (2), the impact of NTBs (core and domestic support) varies across countries and tariff lines (i.e., βs have subscripts n and c). Given that the international data on NTBs does not have adequate time variation, our sample is essentially a cross section of HS 6 digit tariff lines and products. Thus some structure would have to be imposed on the β parameters to allow them to vary across tariff lines and countries without running out of degrees of freedom. We will therefore allow them to have a product speci fi c impact and the country speci fi c impact will depend on the variables that capture country characteristics; more precisely on endowments as in Leamer’s (1990) comparative advantage approach:

> 6 Alternatively, one could try to correct parametrically using the information
**|
|Desired professional occupation|0.5|-0.015|0.115*|
|Expected professional occupation|0.023|0.002|0.035**|

_Note_ : This is based on intent--to-treat estimators from Table 2 of Macours and Vakis (2016). The share of leaders measures the share of female leaders with the productive investment package over all female leaders in a beneficiary's registration assembly. The data for school attendance and school expenditures are individual-level data for all children of age 7-18 years in a household. The data for food expenditures are household-level data. Excluded from the data are households with female leaders. Highest and lowest 0.5 percent of outliers in expenditures are trimmed. Robust standard errors in parentheses, corrected for clustering at the community level. _*** p_ <0.01, _** p_ <0.05, _* p_ <0.1 37
5**|
|Intensive Positive|42.9|20.2|30.8|35.9|32.1|48|27.2|39.4|
|Intensive Negative|-14|-29.8|-25.8|-20|-27.2|-19.6|-26.1|-21.4|
|**Net Intensive**|**28.8**|**-9.6**|**5**|**16**|**4.9**|**28.4**|**1.1**|**17.9**|
|**Total**|**17.6**|**12.9**|**15.4**|**12.3**|**11**|**33.9**|**4.8**|**24.4**|

Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

38
possibly debt or financial crises (Boskin, 2020). All of these could cause private firms to reduce investment. This channel is referred to as the “crowding out” effect of public debt. The impact of high public investment on private investment therefore remains an open empirical question.

This paper examines the impact of public debt on private firms’ investment using the World Bank Enterprise Surveys, which provide a large sample of firms in many developing countries. Our contributions are threefold. First, we find that higher public debt significantly and robustly reduces private investment on the extensive and intensive margins. In other words, firms are less likely to invest, and they invest less when public debt rises. Firm panel estimations reveal that a given firm is 0.1 percent less likely to buy land and 3
in reducing conflict (Tir and Stinnett, 2012; Brochmann, 2012). Our investigation also complements studies that consider the effectiveness of treaties given their design (Dinar et al. 2015; Mitchell and Zawahri 2015; Tir and Stinnett 2011, 2012).

While most basins have no recorded instances of either cooperation or conflict in the BAR database, among those that do, the mere presence of a treaty seems to promote additional cooperation (or reduce conflict) beyond the signing of the treaty itself. Furthermore, the existence of several agreements signed between riparians in a given dyad seems to increase cooperation even 18
Annex I provides a list of all the variables. Net investment, I, is the dependent variable in

> 7 To be included in AMADEUS, a company must comply with one of the following size criteria: i., a turnover greater than EurolO million; ii., a number of employees greater than 150; iii., total assets greater than Euro 10 million. Note that the AMADEUS data set presents the data in USD million. For more information on the data and the adjustments of data for fluctuations in USD/Leva exchange rate, see Annex 1 and Annex 2, respectively.

11
takes into consideration the specific constraints around using phones for agricultural and rural surveys, including the nature of the questions, abbreviated interview duration, and the need for post-survey adjustments to account for uneven mobile phone coverage, as well as the advantages of phone surveys, namely the flexibility to schedule interviews appropriately around the agricultural season and the reduced costs and simplified logistics compared to in-person interviews.

The paper is organized as follows. In Section 2, we present the conceptual framework (based on the TSE paradigm) and adapt it to evaluate the role phone surveys can play in agricultural and rural survey systems in the coming years. We also introduce the phone survey programs and experiences on which we draw throughout this review. In Section 3 we discuss issues of coverage and nonresponse bias that emerge when designing and implementing phone surveys on agriculture, focusing on sampling and representativeness (3.1), respondent selection (3.2), and post-survey adjustments (3.3). Section 4 discusses issues of measurement including questionnaire design and survey timing (4.1), the role of the respondent (4.2), and the role of the interviewer (4.3). Section 5 discusses cost considerations. We conclude in Section 6.

> 2 The literature in high-income countries distinguishes at times between fixed and mobile phone surveys. In lowincome settings, and particularly in rural areas, mobile phones are by far the most prevalent. In what follows, we therefore just use the term phone survey, having in mind that in most instances the means of contact will be a mobile phone.

3
1 and 4 potential fertility years for women of ages 16 to 45. In total, I observe 3,148 years of potential fertility with births in 920 of them (29.2 percent). Table 4 shows the annual birth probabilities of married women by 5-year age groups. The annual birth probabilities decrease from 0.405 for ages 16 to 20 to 0.06 for ages 41 to 45.<sup>17</sup>

# 3.2 Malawi Second Integrated Household Survey

The MDICP data do not include detailed measures of household consumption. For this reason, I use the Malawi Second Integrated Household Survey (IHS-2) to impute a better measure of consumption of households in the MDICP sample. The dataset, gathered by the Malawi National Statistics Oce in 2004-2005, is part of the World Bank's Living Standards Measurement Study program. It includes comprehensive data on consumption and expenditures of households, as well as on local prices. Specically, the dataset includes a measure of annual consumption expenditure aggregates in real value. The survey was elded in 26 out of Malawi's 27 districts, including all three MDICP districts. After restricting the sample to households in the districts covered by MDICP and excluding households without a woman as a head of the household or as a wife of the head, the estimation sample includes 612 households. 220 of these households are from Balaka, 188 from Mchinji, and 204 from Rumphi.<sup>18</sup>

# 3.3 Mortality Statistics

I supplement the use of the MDICP data with statistics about survival after infection and child mortality from other studies that use data from samples with repeated HIV testing and frequent follow-ups. These data provide more precise information on times of infection and death than MDICP does. Hallett et al. (2008) estimate probabilities of survival after infection by tting a Weibull distribution to survival data presented by Todd et al. (2007) 17The annual birth probability is dened as the proportion of alive and married women of
at least one country per region with two countries each from Sub-Saharan Africa and South Asia— the two regions where the prevalence of extreme poverty is the highest in the world. Our choice of countries was also constrained by our interest in having data on important covariates of poverty, including completed years of schooling, asset ownership, literacy, access to electricity, agriculture as source of income, piped drinking water and improved sanitation. Table 2 shows the dispersion in these selected covariates of poverty with Colombia, Indonesia, and Nigeria have relatively high levels of education (at least an average of 7 years of schooling for the head of households) while India and Pakistan have relatively low levels of education (an average of about 5.5 years of schooling).<sup>11</sup> We have no data on years of schooling for Mali and Tajikistan. The downside of limited data for some covariates in some countries is offset by the fact that we analyze pooled data from eight different countries that are carefully selected.

In addition to select nonmonetary covariates of poverty, we also use principal components analysis (PCA) to compute an asset index in each country to proxy wealth. The asset information that is available for each country varies, but our interest in examining the poverty covariates is to assess whether within each country the covariates are more strongly correlated with poverty as measured by the per-capita or square-root allocations. PCA extracts a latent, underlying variable which in our case, we interpret as a proxy for wealth, from a set of related indicators. (For examples and details of this, see Pritchett and Filmer, 2001; Vyas and Kumaranayake, 2006; Filmer and Scott, 2012; Harttgen and Vollmer, 2013.) The idea is to exploit variation in a range of related variables and estimate the principal components or factors that are orthogonal to each other. In this case, we have data on the ownership of different household assets for each country, such as having

> and 2017 for Colombia. By convention,
l POLICY RESEARCH WORKING PAPER 2347

# Summary findings

Bloch and Rao examine how domestic violence may be used as a bargaining instrument, to extract larger dowries from a spouse's family. The phrase "dowry violence" refers not to the dowry paid at the time of the wedding, but to additional payments demanded by the groom's family after the marriage. The additional dowry is often paid to stop the husband from systematically beating the wife.

Bloch and Rao base their case study of three villages in southern India on qualitative and survey data. Based on the ethnographic evidence, they develop a noncooperative bargaining and signaling model of dowries and domestic violence. They test the predictions from those models on survey data.

women who come from richer families (from whom resources can more easily be extracted). Larger dowries - as well as greater satisfaction with the marriage (in the - form of more male children) reduce the probability of violence.

In India marriage is almost never a matter of choice for women, but is driven almost entirely by social norms and parental preferences. Providing opportunities for women outside of marriage and the marriage market would significantly improve their well-being by allowing them to leave an abusive husband, or find a way of "bribing" him to stop the abuse, or present a credible threat, which has the same effect.

They find that women whose families pay smaller dowries suffer increased risk of marital violence. So do

- - This paper a product of Poverty and Human Resources, Development Research Group is part of a larger effort in the group to examine crime and violence in developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Vijayendra Rao may be contacted at vrao@worldbank.org. May
-12-

CU is the index of capacity utilization, CRY is credit to the private sector (scaled by GNP), RL is the effective real cost of borrowing and SII is the composition of public fixed investment. Substituting (1), (2) and (4) into (3) yields: Equation (5) was estimated on annual data for Turkey over the period 1970-86. In line with the literature, lagged output was used as a proxy for expected output. The stock of credit to the private sector as a share of GNP was used to capture the overall quantity of financing available to the private sector. Public investment was split into two components: infrastructure and noninfrastructure. The infrastructure component includes irrigation, power. transport and communications and health and education. Health and education were included because their public investment component involves the building of schools and hospitals. Provision of textbooks, medicines etc. is included in current expenditures.

A capacity utilization index is available only for the industrial sector based on a quarterly survey carried out by the State Institute of Statistics (see Table 4). Since no economy-wide index exists, this index was used to measure cyclical swings in the economy in relation to existing capacity.
actual beneficiary status from the cash transfer program. Robust (White-Huber) standard-errors are clustered. To maximize power, this estimation is undertaken by including observations from the booster

> between the two teams, the list of children was extracted by the coordination team from the household data uploaded on the server (after verification). The pre-filled child questionnaires when then downloaded onto the child survey supervisors’ laptops, before being transferred to enumerators’ tablets. Child survey teams were responsible for verifying children’s age for all children identified by the household survey team and collecting data for all children in the appropriate age range. Thorough quality control procedures were put in place, including through automated checks on the tablets, as well as systematic verification of data collected by enumerators and supervisors. Additional verifications, including automated back-checks, were undertaken by the coordination and quality control teams once data were uploaded to the server.

13
the parameters in question.<sup>9</sup> The data sources that we use do not provide any information on the separate components of fixed capital, and so, we choose a single set of parameters.<sup>10</sup> Thus the capital stock in any given year is the sum of the relative efficiency for that year of all past investments.

Data on the value of gross fixed capital formation in agriculture (in local prices) were obtained from national accounts data. If the data series on investment is not sufficiently long,

> 9 The _s_ curvature parameters ( _β_ ) and lifetime parameters ( _L_ ) in Figure 1 are taken from Ball et al. (1993). For buildings, _β_ =0.75 and _L_ =38 years; for agricultural machinery, _β_ =0.50 and _L_ =9 years.

> 10 Judging the available evidence, we used the following parameters: curvature of 0.70, mean service life of 20 years, and standard deviation of 8 years.

5