rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Intimate Partner Violence (IPV) is a pressing global public health and policy problem, but measuring its true prevalence is challenging because factors including social taboos, emotional pain, fear of retribution, or feelings of shame or embarrassment cause women to hesitate in reporting IPV to friends or family, as well as to physicians or to law enforcement officials (WHO 2012; Garcia-Moreno et al. 2013). Spurred by the lack of systematic data on IPV and recognizing its epidemiological nature, organizations such as the WHO began to run large-scale, multi-country surveys to measure the prevalence of IPV in the 1990s (WHO 1996).<sup>1</sup> These surveys reveal that nearly a third of ever-partnered women have experienced physical or sexual IPV during their lifetime (Devries et al. 2013).

Many public health professionals worry that the true rate of IPV may be higher, and that women may be understating their IPV experience even in surveys. It remains unclear if this is the case. On the one hand, some of the stigmas that drive under-reporting may be mitigated by the confidentiality afforded by a professionally done survey (as articulated in a consent form, for example), and by the fact that the surveyor is unlikely to be known by the survey respondent or her partner, or to have reason to interact with the respondent again. The survey setting also differs critically from that in normal life because the survey _directly asks_ about IPV, rather than leaving the onus of initiating the conversation to the woman herself.<sup>2</sup> On the other hand, some of the same stigmas may still apply; for example, the victim may feel ashamed about her situation, hesitate to confide in another individual, or be scared of being overheard (despite survey precautions to guard against this).

To address some of these concerns, an alternative approach that is widely recommended is the use of confidential self-interviewing (SI). In this approach, women self-administer IPV questions privately, which ensures that their answers are shielded even from the enumerator.<sup>3</sup> In this paper,

> 1For example, the WHO multi-country study on women’s health and domestic violence was initiated in 1997 and the DHS Program started collecting information on IPV in 1990 (the first IPV module was fielded as part of the standard DHS in Colombia).

> 2In fact, the medical literature has identified one of the key measurement approaches for IPV to simply ask the person. The WHO also recommends direct questioning as the “gold standard” method of measuring IPV. See: https://apps.who.int/iris/bitstream/handle/10665/85239/9789241564625 ~~e~~ ng.pdf.

> 3The answers are not fully anonymized, however, since researchers have access to this data later on, but instructions during this part of the survey, and the consent form, clearly indicate that this data will be kept securely, so any risk of data breach is remote. Further, the researchers would have no reason to interact directly with the respondent outside of this research setting.

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# variables.

Tables 3, 4, 5, and 6 illustrate the effects of the program on the primary outcomes of interest. They show that even after treated participants received information about the program, there were no changes in prosocial behaviors or voting intentions in any of our outcomes. Although the coefficients are small, they are generally bigger than two percent. This suggests that the lack of significance is not due to statistical imprecision since our sample size was designed to identify minimum effects of at least two percent.

Table 3. Treatment Effect on Voting Intentions

|Vote intention in 2023<br>Mayoral<br>elections|Vote intention in 2026<br>Presidential<br>elections|
|---|---|
|(1)|(2)|
|I(Treatment)<br>-0.024|-0.025|
|(0.023)|(0.023)|
|R-squared<br>0.018|0.013|
|Observations<br>1,040|1,040|
|Mean values (Control Group)<br>0.839|0.855|

_Notes:_ This table reports an OLS estimate. Dependent variables in columns (i)–(ii) are indicator variables [=1] if the respondent has the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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contributed substantially to the overall decline in the unemployment rate. Among the bottom 20 percent of the population, unemployment fell from 18 percent in 2009 to only 10 percent in 2014. In rural areas, the unemployment rate decreased from 14 percent in 2009 to 8 percent in 2014, which is lower than urban areas (17 percent in 2014). The unemployment rate decreased by 4 percentage points for women but only by 1 percentage point among men between 2009 and 2014.

There is considerable variation in unemployment among the states, with higher unemployment among women in better-off states. Compared to 2009, unemployment increased primarily in Sudan’s Northern, Khartoum, and Central regions and decreased markedly in Kordufan and Darfur regions. Unemployment is above the national average for almost all states with the lowest poverty incidence in 2014: Al-Gezira (23 percent), River Nile (17 percent), Khartoum (17 percent), and Northern (15 percent). The states with the lowest unemployment rates were Blue Nile (5 percent); South Kordufan, Sennar, and Kassala (6 percent each); and North Kordufan (7 percent). Women were more likely to be unemployed in the richest states: Al-Gezira (58 percent), River Nile (45 percent), Northern (40 percent), Khartoum (30 percent), Kassala (19 percent), and Sinnar (17 percent). In those states, adult female unemployment rates were two to five times the unemployment rates of males (Figure 7). The fact that unemployment is less prevalent among women in poorer states, that are also mainly rural, reflects that most of the women in those states are engaged in agriculture. It is relatively easy to find work in the agriculture sector compared to other sectors. Overall, people in the poorer states are more likely to be working compared to those in the richer states, which are mainly concentrated in the urban areas, where there are limited opportunities to engage in agricultural activities.

# **Figure 7. Unemployment rate by state and gender**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Total Male Female<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

Youth unemployment increased between 2009 and 2014, especially in urban areas. Unemployment among the population between 15 and 24 years increased by 2 percentage points from 20 percent in 2009 13
Table A.11: Stock of per capita household saving

|**Dependent variable:**||**Per capita stock of s**|**aving in 2**|**007**|
|---|---|---|---|---|
||**Livestock**<br>(1)|**Market investments**<br>(2)|**Jewelry**<br>(3)|**Durable goods**<br>(4)|
|**A: Below poverty line**|||||
|_Firstborn girl_ * _Expected dowry_|315.99|-558.80|-883.42|178.84|
||[583.19]|[1,157.03]|[864.38]|[407.08]|
|N|546|833|1,176|1,175|
|**B: Above poverty line**|||||
|_Firstborn girl_ * _Expected dowry_|-717.60|-2,267.07|-1,088.76|-265.10|
||[1,411.16]|[1,584.68]|[969.70]|[1,794.44]|
|N|962|1,102|1,463|1,462|

NOTES: This table reports the coefficients corresponding to specification (2) for different types of saving. The sample is restricted to households where all children have the same mother. Each column is a separate regression. The dependent variables in each column are the flow of per capita household saving in livestock, market investments, jewelry, and durable goods. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Panel A uses the sample of below poverty line families, while panel B examines above poverty line households. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.
**Table** 7: **The sharp increase in foreign investment**

||FDI<br>Inflows<br>1992|FDI<br>Inflows<br>1997|Annual growth<br>of FDI inflows<br>1993-97 (%/6)|
|---|---|---|---|
|Total disbursed FDI ($ million)|316|2,074|45.7|
|-Agriculture, forestry and fishery|3.9%|13.7%|87.3|
|-Heavy industry, construction, oil & gas|31.4%|27.7%|42.1|
|-Light and food industry|7.9%|22.8%|80.0|
|-Services, real estate, transport &<br>communication|56.8%|35.8%|32.8|

_Source: IMF_ (1999); annual growth of foreign investment (FDI) inflows is in current dollars Most of this foreign investment is capital-intensive. Although table 7 above indicates that the share of FDI flowing into light industry has increased in recent years, most investmnent entered oil-related production, heavy industry or real estate. Table 8 shows that by the end of 1998, less than 13% of the total _stock_ of FDI was in the labor-intensive light industrial sector, where about 80% of production is exported (according to information from the Ministry of Planing and Investment). Strikingly, even within the non-oil industrial sector, more than half of the investments are in heavy industry. This shows that the amount of export-oriented and labor-intensive foreign investment attracted by Vietnam's high human capital and low labor costs is still modest. It is thus unsurprising that foreign investment, although accounting for 31.8% of industrial production in 1998 and accounting for almost half of industrial growth over the period 1995-98, still employs only 11.5% of all industry workers (see table 5).

Table 8: The small sbare of investment in light industry

||FDI stock by<br>08.1998|
|---|---|
|Total disbursed capital (million $)|10,139|
|Shares in total disbursed capital_(%)_||
|Heavy Industry|24.1|
|Oil & Gas|22.2|
|Light Industry|12.4|
|Services, Transport&Communication|10.3|
|Real Estate|17.8|
|Others|13.1|
|_Source:_IMF, 1999||

This structure of foreign investment at least partly reflects Vietnam's high barriers to heavy industrial imports, which not only protect State enterprises but also attracts foreign investors into import-substituting and capital-intensive activities, often in joint ventures with State companies. It has been estimated that more than 70% of foreign investment occurs in sectors with effective rates of protection above 50% (CIE, 1998). In particular, Vietnam's tight quotas on steel and cement-related imports provide an incentive for multinational companies to establish their production in local plants. Such form of foreign investmnent is generally inefficient. It creates local monopolies and raises the price of products relative to those that would have prevailed under free imports. Thus, although foreign investment has boosted local production it has done little to create employment. Improving the climate for 9
# **Development through Seasonal Worker Programs:**

**The Case of New Zealand's RSE Program**<sup>**#**</sup>

John Gibson, _University of Waikato_ David McKenzie, _World Bank_ Forthcoming in Robert E.B Lucas (edited) _International Handbook on Migration and Economic Development._

> # We thank Charlotte and Richard Bedford for their interviews of RSE employers which we use in this paper, Paul Merwood and Anne Masoe for providing new statistics on RSE returnees and overstaying, and Halahingano Rohorua for village and government officials in the Pacific Islands. We thank the Manjula Luthria, New Zealand Department of Labour, Tonga Department of Labour, Vanuatu Department of Labour, MFAT, and other members of the RSE Interagency Governance Committee for their collaboration in the initial years of this research.
# **4.1 Methodology**

Given that the data are structured as spatiotemporal panel data, a fixed-effect regression methodology could be used, with the expenditure ratios as the dependent variable and the damage indices as independent variables. That being said, the different ratios are necessarily related. We have therefore chosen to use the method for seemingly unrelated regression (SUR) as explained in Blackwell III _et al._ (2005).

Their methodology is based upon Baltagi (2001), Judge _et al._ (1988) and Wooldridge (2002). In short, they use a system of SUR with error components. It is assumed that all coefficients of constant terms are the same across the system and that all independent variables are quantitative and require restriction across the panels in their equations, while fixed-effect dummies vary by panel.

In our case this translates into a set of equations. The basis is: where the left hand side is as the ratio: where _j_ are different economic sectors or classifications, _i_ is the district, _t_ is the year and _C_ 12
involvement of civil society organizations in implementation. The development of strong monitoring systems for ECD programs as well as the timely use of monitoring data to make adjustments along the way will be vital for ensuring better implementation and interventions of higher quality in the future.

The lessons associated with low-take up rates are probably equally important. The three ECD modalities all address supply constraints, but they will not fulfill their potential if demand constraints are not addressed as well. The limited demand for preschool services in the treatment villages may have been related to the low quality or relevance (in terms of location, hours of operation, and/or intensity) of the programs provided. Yet qualitative evidence indicates that other demand-side constraints—such as the lack of time to take children to school and pick them up, families’ limited resources to absorb the costs associated with preschool (such as new clothing or school supplies), and/or the lack of information on the benefits of preschool—are also likely to have limited participation in the programs. To inform the design and implementation of future ECD programs, such demand-side constraints and the choices that families make for their children in the absence of ECD programs must be more thoroughly documented. Options for additional interventions may include, among others, communication campaigns aimed at increasing parents’ awareness of the benefits of preschool, the use of conditional cash transfers for preschools to alleviate potential financial constraints to attendance, and/or creative pickup/drop-off strategies at the community level to reduce the time burden on individual families. The low take-up rates identified through this evaluation also indicate that it might be advantageous, in the future, to perform implementation pilots prior to evaluating impacts, so that operational issues can be addressed at an early stage.

Beyond the lack of positive overall effects, this evaluation found that exposure to formal preschool negatively affected the cognitive development of five-year-old children. This surprising result seems to be related to the fact that children in the treatment group entered primary school closer to the official entry age, whereas children in the control group tended to enter earlier. In practice, and for this particular age group, the preschool intervention substituted for an existing primary school “intervention” that was informally available to children below the official entry age. Even though the preschool program 22
Table 4: Import Uncertainty and Export Dynamics: South-North Trade

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.009<br>|0.011<br>|-0.061***<br>|-0.085***<br>|-0.091**<br>|
||(0.007)|(0.008)|(0.016)|(0.024)|(0.041)|
|Log direct imports (%)|-0.017|-0.006|-0.026|-0.023|0.086|
||(0.014)|(0.018)|(0.037)|(0.058)|(0.104)|
|Log direct exports (% sales)|-0.026***|-0.027*|0.110***|0.121***|0.122*|
||(0.006)|(0.014)|(0.032)|(0.046)|(0.069)|
|Log experience|-0.015|-0.009|0.076*|0.103|0.129|
||(0.015)|(0.018)|(0.040)|(0.066)|(0.117)|
|Log nb. of employees|-0.012|-0.033***|0.101***|0.129***|0.119*|
||(0.012)|(0.010)|(0.021)|(0.032)|(0.062)|
|Log TFP|0.043|-0.072***|0.239***|0.379***|0.281***|
||(0.028)|(0.022)|(0.044)|(0.071)|(0.108)|
|Common border|-0.007|-0.035|0.019|-0.008|-0.104|
||(0.030)|(0.035)|(0.046)|(0.072)|(0.104)|
|Colony|-0.087**|-0.020|0.122|0.188*|0.077|
||(0.039)|(0.040)|(0.076)|(0.112)|(0.279)|
|Log distance|0.108***|0.031**|-0.067***|-0.092***|-0.149***|
||(0.010)|(0.012)|(0.020)|(0.032)|(0.055)|
|Trade agreement|-0.059***|-0.026|0.100***|0.217***|0.200**|
||(0.020)|(0.022)|(0.039)|(0.061)|(0.094)|
|Log tarifs|0.004|0.014*|-0.020|-0.015|-0.015|
||(0.007)|(0.008)|(0.012)|(0.019)|(0.030)|
|Log exporter GDP cap.|0.079|0.025|0.107|0.058|3.327*|
||(0.104)|(0.128)|(0.202)|(0.282)|(1.815)|
|Log importer GDP cap.|0.019|-0.072|0.186*|0.187|0.319|
||(0.047)|(0.055)|(0.111)|(0.166)|(0.244)|
|Log exporter entry cost (% GNI)|0.045|-0.016|-0.020|-0.205|0.281|
||(0.042)|(0.050)|(0.095)|(0.136)|(0.437)|
|Log importer entry cost (% GNI)|0.016|-0.010|0.010|0.005|-0.058|
||(0.016)|(0.020)|(0.037)|(0.059)|(0.093)|
|Constant|-2.378**|0.438|-3.986**|-3.898|-33.135**|
||(0.959)|(1.196)|(1.924)|(2.790)|(16.561)|
|Observations|10,363|8,693|8,513|7,857|4,290|
|R<sup>2</sup>|0.191|0.198|0.104|0.099|0.120|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

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There will be a change in the aggregated level of spending on the goods affected as well as changes in the composition of the sourcing of these goods. Thus, there is a potential source of trade diversion.

It is straightforward to determine the amount of imports from trading partner B that is replaced by imports from trading partner A in response to a preferential trade reform. However, trade diversion by definition refers to a (welfare decreasing) substitution of imports from a more efficient source to a less efficient source because of a preferential reduction in tariffs. Trade diversion thus occurs, for example, if the EU gets duty free access to the US market and only thereby can sell its products at a lower price than other more efficient exporters who are not granted the same preferences. Yet, there are also situations in which diverted trade cannot be classified as trade diversion. Consider the following example: after the EU is granted tariff free market access it takes over some of the market share that was previously held by other trading partners who already enjoyed tariff free market access prior to the EU being granted the tariff reduction. This would not be trade diversion as it would actually reflect a move of consumption towards a more efficient producer that was previously disadvantaged in terms of market access. In order to fix language, we use the term trade correction for this phenomenon.

In order to accurately assess the adverse impact of preferential trade agreements relative to, say, a reduction in MFN tariffs it is important to take into account this distinction. For TRIST, an additional Excel file has been developed that sources information from the simulation tool and calculates estimates of trade diversion volumes that are amended for ‘trade correction’. The tool uses a simple and intuitive methodology. A detailed explanation is available upon request.

As a practical example, we consider a sequential scenario: we assume that Nigeria has liberalized all tariffs on imports from the EU to zero as part of an EPA. The resulting import allocations and tariff rates are taken as the ‘base scenario’ for the following policy experiment, namely an FTA between Nigeria and China. Table 10 shows how different trading partners would be affected in terms of their exports to Nigeria if Nigerian tariffs on all Chinese imports were preferentially driven to zero as part of the FTA. The table distinguishes two categories of diverted trade as outlined above: diverted trade can be either welfare reducing (trade diversion) or it can happen in response to a decrease in preferences previously granted which is not welfare reducing (trade correction).

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# _3.2.2 Institutions_

As mentioned above, Pakistan’s performance on access is clouded in uncertain statistics. However, we do know that there is no specific rural electrification agency or fund with a mandate to improve access rates. Not surprisingly the country has a low score on the energy access regulation indicators for new connections, solar home systems, and for mini-grids (Table 10). There are no incentives or requirements for utilities to expand access, and off-grid solar is not yet regulated. Grid connected roof-top solar has just been provided with a tariff and consumers of all DISCOs can now use net metering.

**Table 10: Regulatory framework for electricity access in Pakistan and comparators, 2015**<sup>**35**</sup>

||India,<br>AP|India,<br>Odisha|India,<br>Raj|Pakistan|<sup>International</sup><br>benchmark|
|---|---|---|---|---|---|
|**Energy Access Regulation**|81%|78%|81%|16%|56%|
|_Regulation of New Connections_|75%|66%|77%|48%|65%|
|_Regulation of solar home systems_|100%|100%|100%|0%|66%|
|_Regulation of mini-grids_|67%|67%|67%|0%|44%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

# **_3.3 Efficiency and financial viability_**

The low efficiency of the electricity sector in Pakistan has been a feature of the country for many years. The creation of the independent regulatory body (NEPRA) and the unbundling of WAPDA led to regular collection and publication of statistics concerning various aspects of the operation of the sector, including separate reports on performance evaluation for the state-owned DISCOs and for KE.

To evaluate the performance of state-owned companies it was decided to concentrate on information for a single DISCO for those indicators where information on the aggregate of all DISCOs was not published in the annual State of Industry Reports. Such information was published in company reports and websites but does not cover the whole period from 1998 (the year of unbundling). There are only minor differences in the institutional set-up for the various DISCOs, so that the performance of one can stand as a reliable indicator for all, and therefore for the publicly owned sector as a whole. As a guide to the relationship between institutional factors and performance of the state-sector, the case of LESCO was examined. This was one of the three candidates for privatization under the incoming government of 2013, suggesting that it was considered a “good” performer at that time. In NEPRA’s 2015-2016 Performance Evaluation Report LESCO was ranked third out of the ten DISCOS.

# _3.3.1 Performance_

> 35 For the detailed access regulation index, see the annex.

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