rafmacalaba/datause-encoder-data huggingface dataset:
(spousal) unemployment is associated with a higher incidence of violence, although this is not statistically significant in every specification.

# **5 Discussion and Conclusion**

The literature has considered alternative explanations for why men perpetrate violence against women partners. Among these are that men gain self-esteem or an enhanced sense of control from committing violence (Farmer and <mark>Tiefenthaler, 1997)</mark> ; that they have strategic motivations, such as to use violence to extract resources from the woman or her family (Bloch and Rao, 2002); and that violence occurs almost inadvertently in response to emotional cues (Card and Dahl, 2011). Other studies have highlighted psychological drivers of abuse such as a history of childhood trauma, personality and relationship dysfunctionality (Heise and Kotsadam, 2015), or early life exposure to domestic violence (Pollak 2004). Finally, there are studies showing that regional variation in IPV today reflects cultural factors rooted in historical customs (Alesina et al. 2016; Tur Prats 2015).

This paper used comparable and representative repeated cross-sections of survey data from up to 31 countries containing women’s reports of violence committed against them in the preceding year. It identified a systematic contemporaneous association of unemployment shocks with interpersonal violence. The study finds that changes in male and female unemployment rates have opposing association with IPV; these associations are large, at between 2.75 – 2.86% of the mean IPV prevalence rates in the sample. However, they are smaller than the estimates obtained by studies on the impact of alcohol on IPV (Luca et al., 2017). Interpersonal violence is higher when men are more likely to be unemployed and, holding constant male unemployment rates, it is higher if women face improved employment probabilities.

These findings contrast sharply with recent results for the US and the UK (Aizer 2010, Anderberg et al. 2015) where IPV has been seen to be decreasing in the gender wage gap and in increased employment rates of women respectively. These are countries in which women’s labour 18
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country in a fund increases when its weight on the benchmark increases. Holding other things constant, a manager that is more risk averse or has a smaller tracking error will allocate its weights more closely to the benchmark weights. For managers that want to have a tracking error equal to zero, and will be equal to one. For managers that do not follow the benchmark, and will equal zero. For managers that partially follow the benchmark, and will be between zero and one.

The results using all equity funds (Table 4 and 5, Panel A) show that, for a given group of funds, the estimated coefficients for benchmark weights ( and in Equation (4)) vary little across specifications. Using weights (Table 4) or log weights (Table 5) makes very little difference in the point estimate, even though log weight regressions use 900,000 fewer observations (out of 2.5 million). For instance, for the group of all funds and no fixed effects the coefficient obtained in the weight regressions is 0.823, and in the log-weight regressions is 0.821, both statistically significant. The fit of the weight regressions tends to be better than for the log weight ones because of the persistence of zero allocations. The interpretation of the coefficient is different of course; is a slope and an elasticity.

Controlling for the two sets of fixed effects reduces the estimated coefficients for the total sample to 0.77 and 0.75 depending on whether we use weights or log weights, respectively. This suggests that part of the relation estimated without fixed effects is driven by the average levels of country and benchmark weights. For instance, larger countries get higher weights across benchmarks and are also more prominent across funds. Controlling for this through the inclusion of fund-country fixed effects shifts the identification to variation in country weights relative to that average and is close to using only time-series variation (we also use fund-time fixed effects to control for shocks over time at the fund level). Nonetheless, both coefficients are statistically significant and not very different from those without including fixed effects. In general, the overall fit of the regressions is good, both including and excluding fixed effects, ranging from about 0.6 to 0.9 for the total sample.

Where we find meaningful variation in the estimates is when comparing funds with different degrees of activism. The coefficients of benchmark weights are much larger for more passive funds. For instance, for the regressions in weights including fixed effects, the coefficient for explicit index funds is 0.92 while the coefficient for truly active funds is 0.5 (Table 4, Panel A). In fact, the estimated coefficients decline monotonically with the degree of activism. Nonetheless, the results indicate that benchmark weights are 15
stemming from the _separation of ownership from control and the importance of internal finance_ since internal finance facilitates managerial discretion; and (ii) information-thneolefic approacXx which emphasizes asymmetries in information between insiders (managers) and outsiders (suppliers of capital) and the consequent credit rationing faced by firms.

Starting with Baumol et al. (1970), there has been a large literature on the related issue of rates of returns to alternative sources of finance for the firm. The emphasis in these studies has been in looking at the changes in rates of return on alternative sources of finance for a given firm over time; not really in terms of different types of firms. One exception has been the lifecycle approach due to Grabowski and Mueller (1975), where the focus in fact shifts to types of firms from the sources of finance; based on life-cycle and technology considerations, firms are classified as being either mature or dynamic.

One interesting finding from these rates of return studies has been the observed hierarchy in returns, with the returns rising from internal finance to new debt and new equity. Thereafter, one strand of the literature has gone on to compare the firm's rate of return to the cost of capital for alternative sources of finance and establish the fact that in a substantial segment of the U.S. corporate sector, investments have taken place at rates of return below the cost of capital and that this reflects the prevalence of considerable managerial discretion regarding capital expenditures. 12 An alternative interpretation of this finding is to recognize that hierarchy in returns is precisely what one expects from the assumption of the firm facing a financing hierarchy, wherein the cost of finance rises from internal finance to new debt to new equity. After all, the

**12** See Mueller and Reardon (1993) for instance.

11
be distributed as bonuses to clinical staff, with 30% allocated towards reinvestment in the facility. District health teams approve action plans developed by the facilities for these reinvestments.

Rewards for the quantity indicators are on a fee-for-service basis. Table 1 presents the specific incentivized quantity indicators and their corresponding fees. Services targeting child health include the number of fully vaccinated children under the age of 13 months, and growth monitoring and nutrition services for children under 5. Services targeting women include timeliness and quantity of ANC visits, postnatal care consultations, and contraceptive use. Hypertension services target increasing the number of diagnoses, and the number of hypertensive patients provided treatment. The selected incentives deliberately emphasize preventative rather than curative services to avoid excessive or unwarranted service provision. Following the midterm review of the project, the indicators and corresponding fees were revised starting in 2017.

The quality bonus is a share of the quantity payment calculated according to the quality score and the matrix presented in Table A1 in the appendix. Facilities that scored less than 55% on the quality checklist did not receive any quality bonus while facilities with high scores can double the quantity payments. The quality score is determined by a quality checklist containing both structural quality and clinical indicators measured through review of registries and medical records. The quality checklist for the RHCs contains 93 separate indicators in ten categories.<sup>3</sup> Health house checklists are a subset, containing 60 indicators in 8 categories. The quality checklist includes indicators directly related to the incentivized services and quality indicators related to non-incentivized services such as diarrhea management and curative care for acute respiratory infections.

# 3. Methods and Data

## Difference-in-differences

We employ a difference-in-differences approach to identify the effects on the PBF program as the districts implementing the program were purposively selected. In March 2012, eight rural districts were chosen to be included in the World Bank supported HSIP project, four in each of the Sughd and Khatlon regions. The objective was to select districts that were near regional averages in terms

> 3 The quality checklist categories are: administration, facility infrastructure, hygiene and sanitation, laboratory services, medication management, supplies for emergencies, health management information system, child health, maternal health and noncommunicable diseases.

6
The telecom offered a preselected set of subscribers the chance to switch to a postpaid plan with lower rates, and recorded who among these subscribers paid their bills on time. Because the telecom wanted to learn about the risks of transitioning different types of users in this initial exploration, it was permissive and unsophisticated in selecting customers to transition. It selected customers who used their phones sufficiently (who were more likely to benefit from postpaid billing) from across the distribution of credit bureau records.<sup>6</sup> These selected subscribers received a call inviting them to transition to a postpaid plan; those who opted in were switched from prepaid to the cheapest postpaid plan (providing approximately $30 in revolving credit each month). The data cover 7,068 subscribers who were offered postpaid plans and opted in, which is the relevant sample for assessing performance for the telecom. The telecom was aware that paying a phone bill was new for these subscribers, so if a subscriber did not pay their postpaid bill, they were notified by SMS and other channels that their bill was soon to become overdue. If consumers were more than 15 days overdue, their service was cancelled and they were reported to the credit bureau.<sup>7</sup> In our sample, 11% of consumers defaulted.<sup>8</sup> While this form of credit has different features from a traditional bank loan, so do many emerging forms of digital credit; for example, short term loan ladders are common: (Carlson, 2017). For each subscriber, the telecom pulled mobile phone transaction records (Call Detail Records, or CDR). In this setting, many subscribers also had formal financial histories maintained at the credit bureau; the telecom also pulled these records. Bureau records include a snapshot of the number of entities reporting, number of negative reports, balances in different accounts (including consumer revolving, consumer nonrevolving, mortgage, corporate, and tax debt), and balances in different states of payment (normal, past due, written off). It also includes the monthly history of debt payment over the past 2 years (no record, all normal, some nonpayment, significant defaults), and includes a summary score that

> 6 The authors were not able to obtain the telecom’s selection rule, or information on those who were selected out. The data is consistent with the telecom making unsophisticated selection in order to assess the risk of different types of applicant. In the resulting sample, 15% are missing credit bureau records, 26% have perfect bureau summary scores, 41% have near-worst bureau summary scores, and the remainder have summary scores in between. (See Figure S2.)

> 7 For many consumers this would be the first record in their credit history. After this point, the consumer could use a prepaid account. Because the telecom could pause service, the credit could be thought of as one with the subscriber’s phone number held as collateral. However, that collateral is limited, as subscribers could open a new prepaid account with a new phone number.

> 8 This rate is higher than typical microcredit loans in this context.

8
**Table 2:** Comparison of jobs

|||Total|Informal<br>(1)|Formal<br>Temp. (2)|Formal<br>Perm. (3)|Diference|Diference|Diference|
|---|---|---|---|---|---|---|---|---|
|Variable|N|Mean/SE|Mean/SE|Mean/SE|Mean/SE|(1)-(2)|(2)-(3)|(1)-(3)|
|Weekly hours|925|41.325<br>[0.541]|34.831<br>[0.899]|44.280<br>[1.271]|47.650<br>[0.484]|-9.449***|-3.369**|-12.880***|
|Monthly income|895|6469<br>[124.2]|5083<br>[178.2]|6850<br>[359.2]|7897.8<br>[158.7]|-1767***|-1048***|-2780***|
|Wage increase|898|0.438|0.277|0.327|0.630|-0.050|-0.303***|-0.353***|
|||[0.020]|[0.028]|[0.052]|[0.028]||||
|Work: Firm|925|0.556|0.193|0.710|0.926|-0.517***|-0.216***|-0.747***|
|||[0.016]|[0.019]|[0.044]|[0.013]||||
|Work: Governm|925|0.024|0.011|0.121|0.010|-0.111***|0.111***|0.012|
|||[0.005]|[0.005]|[0.032]|[0.005]||||
|Work: Self-Empl|925|0.125<br>[0.011]|0.243<br>[0.020]|0.047<br>[0.020]|0.010<br>[0.005]|0.197***|0.036*|0.235***|
|Work: Family|925|0.276<br>[0.015]|0.526<br>[0.023]|0.103<br>[0.029]|0.041<br>[0.010]|0.424***|0.062**|0.483***|
|Learning on job|822|0.264<br>[0.015]|0.085<br>[0.014]|0.265<br>[0.045]|0.453<br>[0.026]|-0.180***|-0.188***|-0.373***|
|Training in job|822|0.251<br>[0.015]|0.065<br>[0.013]|0.255<br>[0.044]|0.445<br>[0.026]|-0.190***|-0.190***|-0.385***|
|Change position|925|0.146|0.094|0.103|0.217|-0.009|-0.115***|-0.124***|
|||[0.012]|[0.014]|[0.029]|[0.021]||||
|Job satisfaction|925|8.185|8.088|8.056|8.348|0.032|-0.292|-0.297**|
|||[0.057]|[0.087]|[0.166]|[0.078]||||
|Plan stay in job|907|0.732<br>[0.015]|0.651<br>[0.023]|0.724<br>[0.044]|0.822<br>[0.019]|-0.073|-0.098**|-0.182***|
|Number benefts|925|6.121<br>[0.172]|1.914<br>[0.126]|7.374<br>[0.426]|10.547<br>[0.170]|-5.459***|-3.173***|-8.802***|
|F-test (F-stat)||||||18_._504|8_._132|278_._972|
|F-test (p-value)||||||0_._000|0_._000|0_._000|

_Notes:_ Data collected from endline survey for study participants in employment. The last column reports differences in the means across the groups. *, **, *** indicate t-test significance at the 10, 5, and 1 percent critical level. Standard errors are robust. Household vulnerability score is based on the national Mexican AMAI measure which considers the number of bathrooms, bedrooms, cars, and employed persons in the household, as well as internet access and head of household education level.

13
# **3. Empirical Strategy**

# **3.1. (Individual-Level) Ownership of and Rights to Agricultural Land**

This section describes the empirical framework for estimating relative survey treatment effects that the concurrent implementation of IHS4 and IHPS can isolate. The core specification is estimated for the total sample, and separately, for the sub-populations of men and women as: 1τ + γ (1) iih 1iih iih yy = ∝ + ββ + εε where _i_ and _h_ represent individual and household, respectively; _y_ is the binary dependent variable on whether the individual has ownership/rights (detailed in Table 2) over any agricultural parcel in the household; _α_ and _ɛ_ represent constant and error terms, respectively. 1 is a binary variable identifying the adults in the IHS4 sample, with the individuals in the T5/IHPS constituting the comparison category. _C_ is a vector of individual and household attributes presented in Appendix ττ Table A2 to capture any remaining unobserved heterogeneity that may also jointly determine both the dependent variable and household assignment to the IHPS versus the IHS4.

Given the dichotomous nature of the dependent variables, equation (1) is estimated as a linear probability model with weights adjusting for non-response.<sup>23</sup> The T5/IHPS sample is used as the comparison category in equation (1) as it represents the gold-standard in the approach to data collection on asset ownership and rights. Standard errors are clustered at the EA-level, and the regressions are weighted using the response weight variable, as described in Section 2.2.

Since the focus of the analysis is on ownership/rights over agricultural land and in part on the SDG indicator 5.a.1, the reference population is adult individuals living in agricultural households, who have operated land for agricultural purposes and/or raised/tended livestock in the past 12 months, regardless of the final destination of the production. As noted above, the IHS4 sample includes all the adult household members, who are tagged as having ownership or a particular right if they are reported as an owner or a right holder for at least 1 agricultural parcel by the most knowledgeable household member(s) who would have been interviewed in that household. Conversely, the IHPS sample includes only the adult household members who were subject to individual interviews and would have been tagged as an owner or a right holder if they reported themselves as such for at least 1 agricultural parcel.<sup>24</sup>

> 23 Our estimates were similar to the marginal effects derived from Probit regressions, which are available upon request. 24 We gauged the sensitivity of our findings by (i) expanding the analysis sample to include adults from nonagricultural households, and (ii) restricting the analysis sample only to adults who were personally engaged in agriculture (as opposed to simply living in an agricultural household). The resulting differences in our estimates were negligible, and we wanted to be in line with the international standards.

17
# -9 -

countries involved in international wars - e.g., Iran and Iraq - have nkot provided data to the _Nol_ Table, and thus are not in the sample.

## D. Results

The, RelationshjD BDetween Milex andg EconomicGrow. The 'cross-section" regressions for the full sample of 71 countries show the predicted non-linear relationship between military spending and the growth rate, with a statistically significant positive coefficient for MES and a statisdcally significant negative coefficient for MES2. These results which constitute the basic regressions for the study are presented in Table 2. The results are similar for both the seven- and six-year growth periods and the two forms of the growth rate - the growth rate of total GNP (GRl), and the growth rate of GNP excluding military expenditures. For the differences regressions that test the 'dme-series' relationship between a change in MMIS and the change in the growth rate, the coefficients show the predicted non-linear relationship in three of the four cases (see Table 3). The mnmerical values of the coefficients for MES and MES2 predict hat increaes- in milex are associated with faster growth up to 4 - 9 percent of GNP (depending on which regression one uses) and with slower growth thereafter. In view of the lack of robustness of any precise coefficient, no particular importance is attached to the implied point of m --imal growth.

Other RWe&ressor. The results for neighborig countries' military expenditures (NMES) are quite unexpected. The coefficients for NNMES are positive and statistically significant for all the full sample regressions, the ASMENASE region, and in most of the regressions where regions and outliers are dropped. As discussed further below, this appears to be due to more efficient policies induced by countries facing external threats. In the regressions for the SSA and Latin Ameica regions, NMES is not statistically significant. The results for the other regressors are discussed in the endnote.U

> Sensitivity ADalvss. The results for the full sample support the hypothesis of a non-linear relationship between milex and growth. However, the question arises whether these results are sensitive
rather than just the U.S.

Global (food) commodity price inflation equaled 43% during the 12 months ending on March, 2008. While this rate is high, it is not unprecedented. Similar increases in (food) commodity prices were observed between 1971 and 1974 and between 1994 and 1996 [52]. While biofuels were unique to the recent crisis, other (important) factors were common to one or both crises. Furthermore, each of the three periods of peak prices has been marked by a below-normal ratio of stocks to use. An IMF report assessing the impact of rise in food and fuel price on macroeconomic indicators such as balance of payments, overall inflation, and poverty also concludes that biofuels are one among several factors, which coincided to cause the food commodity price inflation [38]. This report also contends that restrictive trade policies were the major reason for the run-up in the price of rice.

**Table 1. Quantitative estimates of impact of biofuel on food commodity prices**

|**Source**|**Estimate**|**Commodity**|**Time period**|
|---|---|---|---|
|Mitchell[47]|75%|global food index|Jan 2002 to Feb 2008|
|IFPRI[59]|39%|corn|2000to 2007|
||21-22%|rice and wheat|2000 to 2007|
|OECD-FAO [51]|42%|coarsegrains|2008to 2017|
||34%|vegetable oils|2008 to 2017|
||24%|wheat|2008 to 2017|
|Collins[15]|25-60%|corn|2006 to 2008|
||19-26%|U.S. retail food|2006 to 2008|
|Glauber[25]|23-31%|commodities|Apr 2007 to Apr 2008|
||10%|global food index|Apr 2007 to Apr 2008|
||4-5%|U.S. retail food|Jan to April 2008|
|CEA[42]|35%|corn|Mar 2007 to Mar 2008|
||3%|global food index|Mar 2007 to Mar 2008|
|Rajagopal et al. [54]|15-28%|global cornprice|2007 to 2008|
||10-20%|global soy price|2007 to 2008|
|Hoyos and<br>Medvedev[16]|6%|global food index|2005 to 2007|

Abbott, Hurt, and Tyner, through a review of several reports on the food crisis, conclude that there are several key drivers of food commodity price increases: the depreciation of the dollar, global changes in production such as weather shocks, changes in patterns of food consumption, and the role of biofuels in commodity price increases [2]. They do not, however, present quantitative estimates of percentage contribution to the total price rise that is attributable to a specific factor such as biofuel consumption. The FAO in its State of Food and Agriculture 2008 Report also states that growing demand for biofuels is only among several factors driving 7
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