rafmacalaba/datause-encoder-data huggingface dataset:
to competition for rackets at the production sites themselves. By contrast, Krauser (2020) suggests that a relative absence of violence around Congolese mining sites compared to roads has to do with the incentives for armed actors interested in extracting rents from mines: to attract the mobile workforce of artisanal miners–who can ‘vote with their feet’ and leave a dangerous mine–armed actors are nudged to project violence elsewhere.

Violence itself can take multiple forms (looting/riots versus battle events) and originate from various actors (civilians versus organized actors). The impacts of road projects can differ across different forms of violence. Scholars have used different measures of violence as dependent variables either in sensitivity tests or in order to shed light on potential channels of causation (Besley and Persson 2011; Bazzi and Blattman 2014; Harari and Ferrara 2018; McGuirk and Burke 2020). In particular, the conflict literature makes the distinction between violent events related to the control of territory (factor conflict) and those related to grabbing resources (output conflict). These two can react differently to road rehabilitation, the same way they have been shown to react differently to local income shocks (e.g., crop price shocks in McGuirk and Burke (2020)). By increasing farm wages, for example, rising grain prices can reduce the supply of labor to armed groups, thereby causing a decline in conflict battles in rural areas. At the same time, high prices or the availability of harvests could provoke conflict over the appropriation of the commodity itself in the form of looting. Controlling for both cell fixed effects and country-year fixed effects, McGuirk and Burke (2020) find that a 1 standard deviation rise in producer prices lowers the probability of conflict by around 15% in food-producing areas, but increases the probably of conflict in no crop producing areas.

This discussion highlights the complex and essentially contested connections between roads, violence, and resource rents, and the necessity to control for the possible effect that minerals may have on the link between roads and violence.

# **3 Data**

## **3.1 Political Violence**

Our outcome variable is political violence in the Democratic Republic of Congo (DRC). Figure 3 gives a first impression of the cross-sectional distribution of political violence as measured by the two most popular datasets, the ACLED and the UCDP.<sup>2</sup> It is clearly visible that many regions are extremely violent, and few regions are completely peaceful. This motivates an approach in which we make all of the DRC our test sample instead of only the most conflict-affected regions in the East of the country: Ituri, Nord-Kivu, and Sud-Kivu.

> 2See Eck (2012) and Raleigh et al. (2023) for a discussion of the relative merits of these two datasets in conflict detection and measurement. Another important source of data, Kivu Security Tracker, which tracked and publicly reported incidents of violence in Eastern DRC, ceased operations and was put offline in 2023 after a critical security vulnerability was discovered in its database.

8
Two percent of sampled households refused to participate in the interview. In the followup survey, 88 percent of the baseline households were re-interviewed. The rate of attrition from the baseline sample was not statistically different between the treatment and comparison groups (12 percent each). Households that could not be found or interviewed were replaced with randomly selected households from the same zones.

# **_Outcome Measures_**

**Maternal health services:** The outcomes include one indicator variable for any prenatal care utilization, one for completing 4+ prenatal care visits, and one for institutional delivery.

**Quality of prenatal care:** We assess the quality of prenatal care delivered by computing the share of clinical content items that should compose a typical first prenatal consultation, as recommended in the Rwandan clinical practice guidelines for prenatal care, to the actual clinical content items delivered during a prenatal care consultation.<sup>18</sup> This measure has been used extensively in the literature to measure quality.<sup>19-25</sup> The items in the measure cover medical history questions, physical examinations, lab tests and follow-up procedures (Box 3). The Cronbach alpha scale of reliability for the 38 item score is 0.78, indicating satisfactory internal consistency. In the impact analysis, we standardize the score by subtracting out the baseline mean and dividing by the baseline standard deviation. The unit of measurement is interpreted as standard deviations in quality.

The prenatal quality score was computed at the individual patient level using two samples. First, enumerators interviewed women who visited the facility as they exited the facility. Second, the same information was collected in the household survey from women who gave birth in the last 18 months and received prenatal care from the facility in whose catchment 6
Annex 4: <u>Ghana - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

||**Share in**|||**25% increase**<br>**Upper**<br>**bound**|**50% increase**<br>**Upper**<br>**bound**|**25% increase**<br>**Lower**<br>**bound**|**50% increase**<br>**Lower**<br>**bound**|
|---|---|---|---|---|---|---|---|
|**Food item**|**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons.& Prod.) **|**Impact**<br>**(Cons.& Prod.) **|
|||||<br>**Ghana National**||||
|Rice|3.1|74.6|**28.5**|29.0|29.5|29.0|29.4|
|Bread|1.9|84.6||28.9|29.2|||
|Flour|0.0|2.8||28.5|28.5|||
|Maize|2.7|66.9||28.8|29.0|28.3|28.2|
|All|7.7|96.3||29.6|30.4|29.2|29.7|
|||||**Ghana Urban**||||
|Rice|3.0|74.7|**10.7**|11.2|11.4|11.0|11.2|
|Bread|2.0|91.4||11.1|11.2|||
|Flour|0.0|2.6||10.7|10.7|||
|Maize|1.6|59.5||11.0|11.3|10.8|11.0|
|All|6.6|96.8||11.5|11.8|11.4|11.7|
|||||**Ghana Rural**||||
|Rice|3.2|74.4|**39.3**|39.8|40.5|39.8|40.4|
|Bread|1.7|79.4||39.7|40.1|||
|Flour|0.1|2.9||39.3|39.3|||
|Maize|4.1|72.6||39.5|39.8|38.9|38.7|
|All|9.0|95.9||40.4|41.6|40.0|40.5|

Source: Authors’ estimation using country household survey data 23
in the form of statistically justified confidence intervals. We encourage users to take these into account when comparing governance across countries and over time.

We also note that comparisons of governance across countries and over time will often involve comparing estimates of governance that are based on different sets of underlying data sources. This reflects the reality that not all data sources that feed into the WGI cover all countries, which in turn allows the WGI to rely on many rich data sources with less than global coverage. Comparing estimates of governance for two countries based on differing underlying data sources poses no conceptual problems: for both countries, the conditional mean in Equation (2) is the best possible estimate of governance given the available data for that country, and these estimates can be compared (of course with due regard to margins of error). The same point is true when comparing governance at two points in time for the same country. However, for some purposes users may also wish to consider how comparisons of governance might look based on a common set of data sources, or the extent to which differences in the set of data sources contribute to differences in scores. To enable this further analysis, the WGI website makes all the source data and the weights assigned to different indicators readily available to WGI users at www.govindicators.org.

Finally, we briefly remind the reader that when comparing estimates of governance over time, it is important to bear in mind that the global averages of the indicators have been normalized to zero in every period. This means that changes over time in the WGI estimates of governance capture countries’ movements relative to a global average that is assumed to be constant. Whether these relative changes correspond to absolute changes of course depends on whether the global average of governance is constant or not. We take this issue up in Section 5.3 where we show that there is little evidence of trends over time in global averages of governance as measured by the WGI data sources, implying that the relative changes observed in the baseline WGI estimates also correspond to absolute changes.

# 5. Methodological Issues

In this section we explore in greater depth three potential issues with the WGI source data and aggregate indicators. We first consider the possibility that measurement error may be correlated across data sources, contrary to our baseline assumption in the WGI that each data source brings independent information on the dimension of governance to which it is assigned. This issue is relevant because it has implications for both the margins of error in the WGI as well as the relative weights assigned to different data sources. We then consider the robustness of the WGI to alternative weighting schemes, focusing 19
_Table 2: LFWP by country, with and without Mincer controls_

|**Country**|**ISO**<br>**Code**|**LWFP**<br>**conditional**<br>**on**<br>**occupation**<br>**and**<br>**industry,**<br>**compare**<br>**to col (3)**<br>**of Table 1**|**LFWP**<br>**conditional**<br>**on**<br>**occupation,**<br>**industry,**<br>**education**<br>**and**<br>**experience,**<br>**compare to**<br>**col (4) of**<br>**Table 1**|**Magnitude of**<br>**selection on**<br>**unobservables**<br>**relative to**<br>**education and**<br>**experience**<br>**required to**<br>**explain LFWP**<br>**conditional on**<br>**occupation**<br>**and industry**|**First Year**|**Last**<br>**Year**|**Number of**<br>**observations**|**GDP per**<br>**Capita (2005**<br>**US Dollars)**|
|---|---|---|---|---|---|---|---|---|
|Niger|NER|1.15<br>(0.18)|0.785<br>(0.18)|2.1|2007|2011|5,794|339|
|Comoros|COM|0.936<br>(0.73)|0.621<br>(0.81)|2.0|2013|2013|809|1,373|
|Cameroon|CMR|0.681<br>(0.05)|0.552<br>(0.05)|4.3|2007|2014|21,296|1,311|
|Moldova|MDA|0.315<br>(0.06)|0.397<br>(0.06)|‐4.9|2006|2012|2,412|1,953|
|Chad|TCD|0.47<br>(0.20)|0.32<br>(0.18)|2.1|2011|2011|2,545|863|
|Bahamas, The|BHS|0.324<br>(0.07)|0.267<br>(0.07)|4.6|2001|2001|1,752|31,911|
|Kyrgyz Republic|KGZ|0.272<br>(0.09)|0.261<br>(0.09)|25.1|2011|2011|3,280|921|
|Madagascar|MDG|0.305<br>(0.06)|0.256<br>(0.06)|5.2|2012|2012|16,088|408|
|Albania|ALB|0.265<br>(0.07)|0.246<br>(0.07)|13.1|2005|2005|1,847|3,063|
|Lebanon|LBN|0.289<br>(0.10)|0.227<br>(0.11)|3.6|2011|2011|354|7,453|
|China|CHN|0.292<br>(0.01)|0.2<br>(0.01)|2.2|2002|2013|29,841|4,026|
|Thailand|THA|0.242<br>(0.01)|0.18<br>(0.01)|2.9|1988|1995|61,061|2,893|
|Tajikistan|TJK|0.279<br>(0.09)|0.179<br>(0.31)|1.8|2009|2009|1,301|719|
|Papua New<br>Guinea|PNG|0.175<br>(0.08)|0.137<br>(0.09)|3.6|2009|2009|1,134|1,811|
|Paraguay|PRY|0.157|0.118|3.1|2007|2012|13,680|4,146|
|||(0.05)|(0.05)||||||
|Mexico|MEX|0.186|0.117|1.7|2008|2012|74,013|9,476|
|||(0.02)|(0.02)||||||
|Vietnam|VNM|0.101|0.09|8.6|1997|2010|103,805|1,297|
|||(0.02)|(0.02)||||||

9
in Table 8. The results of this placebo exercise show that when the linkages between FDI and domestic firms are randomly assigned, there are no statistically meaningful differences in gender labor practices of linked firms relative to other domestic firms. The coefficients are either insignificant or have counter-intuitive signs. This finding gives us some confidence that selection effects are not the driving force behind our previous findings.

Table 8: Placebo exercise

||**vs. total femal**|**es**|**vs. fe**|**male admins**<br>**vs.**|**female no**|**n-admins**|
|---|---|---|---|---|---|---|
|Supplier|**Beta**<br>**S.D**<br>3.057269<br>91.83<br>|306|**Beta**<br>0.109|**S.D**<br>**Beta**<br>-<br>721<br>0.930583<br>5.83|<br>941704|**S.D**<br>95.02880689|
|Customer|-<br>3.455867<br>58.42|432|0.08|723<br>0.96381<br>-5.582|900779|73.03804938|
|Siblings|3.820518<br>58.65|742|0.069|929<br>0.783962<br>4.463|742474|70.83692468|
|Partners|6.416674<br>96.21|086|0.141|-<br>699<br>0.945396<br>7.16|868317|98.87093914|
||Number<br>wor|of fem<br>kers|ale|Number of female<br>administrative workers|Number<br>administ|of female non-<br>rative workers|
||Mean|Std|. dev.|Mean<br>Std. dev.|Mean|Std. dev.|
|FDI suppliers|3.057|91|.833|-0.110<br>0.931|5.839|95.029|
|FDI customers|-3.456|58|.424|0.087<br>0.964|-5.583|73.038|
|FDI siblings|3.821|58|.657|0.070<br>0.784|4.464|70.837|
|FDIpartners|6.417|96|.211|-0.142<br>0.945|7.169|98.871|

Notes: this table reports the mean and standard deviation of the bootstrap coefficients on the new indicators for FDI suppliers, FDI customers, FDI siblings and FDI partners from 100 regressions, where domestic firms are randomly assigned to FDI suppliers/customers/siblings/partners status according to industry proportions.

# **5. Conclusion**

This paper studies whether the presence of FDI firms has positive spillover effects on the gender labor practices of domestic firms in developing countries. Based on a unique linked firmto-firm data set of apparel and textiles sectors in Bangladesh, the paper first shows that FDI firms hire more female workers, particularly administrative and production workers. Domestic firms 18
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# of Countries Year of Beijing Amendment (1999) Ratification<br>30<br>25 One Nonsignatory Party<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>MOE non-Article 5 Countries |_| Article 5 Countries<br><!-- End of picture text -->

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# of Countries Year of Kigali Amendment (2016) Ratification<br>30<br>25 68 Nonsignatory Parties<br>12 non-Article 5 Parties<br>20 56 Article 5 Parties<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>MO non-Article 5 Countries |_| Article 5 Countries<br><!-- End of picture text -->
> activity was carried out by an organization with a fixed leadership. Group activities that

did not involve an organization we call _network_ activities while all others were _organizational._ In addition, the respondent was asked about all groups that any member of the household belonged to, whether the member was "active" and the frequency of participation in those groups in the last three months and the purpose of the group (e.g.

religions, production, social service, etc.).

Table 1: Classification scheme of the four types of social activities

|Elementsofthe<br>questionnaire|Designationsofthe differents|ocialactivities:|Examples|
|---|---|---|---|
|Visitstoand<br>fromfriends,<br>neighbors,<br>relatives|**Sociability**||Visits withfriends,<br>neighbors|
|Inventoryofall<br>group social|**Network**(activitiesingroups <br>leadership)|_without_fixed|Communitywork<br>_(gotongroyong),_|
|activities<br>involving<br>membersofthe<br>household|**Organizational**(activitiesin<br>groups_with_fixedleadership)|**Desa**<br>**Government**|_e.g.desa_Legislative<br>council(LKMD),<br>_desa_women'sgroup<br>(PKK)|
|||**Social**<br>**Organizations**|Religious<br>Organizations,<br>YouthGroups,<br>CreditUnion,etc|

Finally, the household was prompted about whether any member in the household participated in the activities of the _desa government_ organizations. For present purposes the key distinction is between activities in those organizations that were created as an integral component of _desa government_ and all other _social organizations11 ._

> 10 There were eight less _desas_ because one of the districts was in NTT close to East Timor and was not safe for researchers.

> 1" This is based on the same information (the roster of all group activities) but is a different

> scheme than that used in analysis of the LLI I data (Grootaert 1999) that divided groups into nine functional categories by the primary purpose of the group (e.g. production group, religious group, recreation, etc.).

I1
The average fall in employment for these firms ranged from 2 to nearly 6 percentage points. The biggest reduction in employment in conjunction with the falling value-added taxes where experienced by SOEs. However, there is no evidence of increasing physical capital or overall investment increase associated with the value-added tax reduction. Consistent with the reduction in employment, total factor productivity increased for those firms, but profits were not significantly affected.

The IV estimation results, where value-added taxes paid by the enterprise are instrumented with the 2004 policy change, are reported in Tables 7.1 and 7.2. In Table 7.1 we define differences in outcome variables as the growth from 2003 to 2007. The first column reports the first stage results of regressing the dummy variable for VAT tax reduction on the treatment. As expected, the coefficient is positive and statistically significant for most firms, indicating that the share of value-added taxes in sales fell for firms in the treatment group.

The results for employment are similar to our difference-in-difference matching estimation as well as the OLS results in Tables 6.1 and 6.2. Overall, the tax reduction had a negative effect on the number of employees. Second, consider the impact of tax reduction on firm investment, when using IV estimation, we cannot find a significant effect. Similarly, the IV results suggest after the value-added tax was reduced, firms’ profit (standardized by industrial sales) did not change significantly.

# **6.2 Other Specifications and Robustness Tests**

In July 2007, the value-added tax reform was extended to 26 middle cities. We did not exclude 2007 in our results. However, in unreported extensions we tested the 19
<u>(1.418) (0.966) (1.005) (1.181)</u> Same set of controls in Table 5 Yes Yes Yes Yes Panel B and Panel C of Table 6 replicate the econometric model shown in Tables 4 and 5, respectively. In Panel B, the interaction between the four governance variables and _investor_ are all negative and statistically significant. This confirms our earlier findings that use the baseline governance variables. The instrumental variables estimation of the same regression, whose output is in Panel C confirms that the negative coefficients of the interaction terms are robust to alternative econometric modeling that takes into account endogeneity. Overall, we conclude that there is a negative relationship between governance and the use of short-term debt by firms, and this negative association is robust to the way governance variables are defined.

# **4. Conclusion**

In this paper, we use international corporate governance data and show that the association between short-term debt and governance varies across countries. The firms with good governance do not rely on the external monitoring of the creditors and so they do not incur the costs of shortterm debt such as liquidity risk. However, we show that institutional environment plays a key role in determining the association between short-term debt and governance. As there is a trade-off between the benefits of short-term debt in reducing agency problems and its costs associated with liquidity and distress, firms with better governance choose higher amounts of short-term debt when it is more effective. In particular, we find that short-term debt and governance act as substitutes in high investor protection countries because the benefit for costly external monitoring using shortterm debt is lower.

To address potential endogeneity concerns, we use legal origin as an instrument for investor protection and find that our results continue to hold. We take an additional step and create a number of alternative governance quality measures and test whether the baseline results are specific to the initial set of governance variables. Our findings indicate that there is a strong negative relation between governance and use of short-term debt under these alternative variable definitions. Overall, the results in this paper emphasize the importance of institutional environment and in particular the legal rights granted to creditors and investors in determining the relationship between corporate governance and debt maturity.

28
(IFPRI, 2008).<sup>4</sup> Instead, the chief explanations have focused on high domestic demand, expansionary monetary policy, a shift from food aid to cash transfers, and structural factors due to reforms and investments in infrastructure (Ahmed, 2007; Dorosh and Subran, 2007; World Bank, 2007; IMF, 2008a; IMF, 2008b).

At the same time, there has been an absence of rigorous work to identify empirically the relative importance of each factor – external, domestic or structural – contributing to inflation. It is thus of vital importance to improve the understanding of the causes of inflation in Ethiopia to allow adequate policies to be put in place. The purpose of this paper is to fill this gap and thoroughly analyze the determinants of inflation in Ethiopia using monthly data for the current decade, with a focus on food prices. The use of high-frequency data allows us to measure trends in global commodity price inflation and other recent factors, and assess their respective role on domestic price developments during a relatively short period.

Our results show that inflation in Ethiopia is associated with the dominant role of agriculture and food in the economy. It is the external sector that largely determines inflation in the long run (about three to four years). Specifically, domestic food prices adjust to changes in world food prices, measured in Birr. There is also evidence that non-food prices adjust to changes in world producer prices. There are large deviations from long-run equilibrium prices, mainly due to the importance of the domestic market for agricultural products and inflation inertia. Domestic food supply shocks along with inertial factors have a strong effect on inflation in the short run (over a year or two). This finding, however, does not imply that domestic and world food prices are always close to each other. It only suggests that there are forces making sure they do not drift too far apart, which is consistent with observed price fluctuations of domestic prices between import and export parity bands. The evolution of the money stock does not seem to affect prices directly, though money supply growth significantly affects cereal price inflation in the short-run.

Before presenting our results, three caveats need to be mentioned. Even though our empirical results appear robust, they are silent on how world food prices are transmitted to domestic prices – especially surprising in the case of Ethiopia, as it does not trade much on agriculture commodities. The results are also silent on the causes of

> 4 Through a survey in border towns, preliminary evidence from IFPRI (2008) shows insignificant informal trade flows. At the same time, Ulimwengu, Workneh and Paulos (2009) find significant shortterm price effects between the world maize market and Ethiopian regional markets bordering Sudan.

4