rafmacalaba/data-use-mentions-extended huggingface dataset:
, our paper contributes to the debate about the optimal degree of government decentralization (e.g., Oates (1985), Besley and Coate (2003), Treisman (2007), Tricaud (2021)). Our results present a new channel through which descentralization affects economic outcomes. In particular, our results suggest that a higher share of government expenditure accounted for by subnational governments will be associated to a higher segmentation of economic activity both within and across countries within the EU.

The rest of the paper is organized as follows. In section 3, we explain how we built our dataset and present some summary statistics. In section 4, we present some novel stylized facts that motivate using a canonical model of trade with heterogenous agents for our analysis. In section 5, we present our model. In section 6, we explain our strategy to estimate the governments’ home bias. In section 7, we analyse potential concerns and limitations of our empirical findings and explore different mechanisms behind them. In section 8, we explain how we calibrate the rest of the parameters of the model. In section 9, we use the calibrated model to quantify the role of the governments’ home bias in the integration of procurement markets. Finally, in section 10, we conclude.

# **3 Micro Data on Procurement Flows**

We use publicly available data from Opentender, a procurement platform funded by a European Union Horizon 2020 project devoted to increasing the transparency of the procurement sector in Europe. The Opentender dataset consists of merging data from different national sources and 8
1979, 12, pp. 42-56.

- Griliches, Zvi and Mairesse, Jacques. ―Production Functions: The Search for Identification.‖ National Bureau of Economic research (Cambridge, MA) Working Paper No. 5067, March 1995.

- Haddad, Mona and Harrison, Ann E. 1993. ―Are There Positive Spillovers from Direct Foreign Investment? Evidence from Panel Data for Morocco.‖ _Journal of Development Economics_ , 42 (1), pp. 51-74.

Harrison, Ann, and Andres Rodriguez-Clare. 2010 ―Trade, Foreign Investment, and Industrial Policy for Developing Countries,‖ in _Handbook of Development Economics_ , Volume 5, edited by Dani Rodrik and Mark Rosenzweig, North Holland, pp. 4039-4214.

- Hu, Albert and Gary Jefferson. "FDI Impact and Spillover: Evidence from China's Electronic and Textile Industries", _World Economy,_ 2002, 25 (8), pp. 1063-76.

- Javorcik, J. B. 2004. "Does Foreign Direct Investment Increase the

- Productivity of Domestic Firms? In Search of Spillovers through Backward Linkages". _American Economic Review_ , 94 (3), pp. 605-627.

- Lin, Ping, Liu, Zhoumin, Zhang Yifan, (2009) ―Do Chinese Domestic Firms Benefit from FDI Inflow? Evidence of Horizontal and Vertical Spillovers<sup>‖,</sup> _China Economic Review._ 20, pp. 677-691.

- Konings, Jozef. ―Effects of Foreign Direct Investment on Domestic Firms.‖ _Economics of Transition_ , November 2001, 9 (3), pp. 619-33.

- Kokko, Ari. ―Technology, Market Characteristics and Spillovers.‖ _Journal of Development Economics_ , 1999, 43, pp. 279-93.

- Levinsohn J., and Petrin, A. ―Estimating Production Functions Using Inputs to Control for Unobseravbles.‖ _Review of Economic Studies_ , 2003 (70), pp. 317-41.

- Lin, Justin (2009). Economic Development and Transition: Thought, Strategy,
the West Bank and Gaza, while contributing to inequality reduction. However, their coverage is still limited in deciles 2 and 3. Improving social protection coverage among deciles 2 and 3 to compensate for taxes they pay could reduce inequality and poverty in the West Bank and Gaza. Using targeted programs such as the flagship CTP (targeted through the PMT) could represent the most effective way to redistribute resources in the country.

# **10.2 Next steps**

**Future extensions of the current study should aim to reassess the incidence of public health benefits based on actual access to health services.** This could be done using secondary household surveys such as the Demographic and Health Survey or the next PECS, for which data are being collected in 2023.

**Future study should also aim to include the simulation of the flagship and targeted CTP.** In the PECS 2016/2017, social protection transfers are aggregated as government cash assistance versus government near-cash assistance, making it impossible to disaggregate the impacts of individual social programs. The current analysis infers that the CTP is the reported cash assistance program in the PECS. The PECS 2023 will have more disaggregated data of social protection programs to refine this analysis. On the other hand, having access to the PMT targeting formula of the CTP would allow to perform other policy-reform simulations, such as expansions of coverage of the CTP beyond that reported in the PECS. This could be useful to analyze the impact of social protection compensation measures.

**Finally, the fiscal incidence model we developed for the West Bank and Gaza could be used to inform likely distributional effects for new PA policies and reforms.** Examples of these reforms include adjusting the electricity tariff structure to increase progressivity and decrease costs to the poor, reducing the fuel subsidy to increase fiscal space, updating PIT tax brackets, and expanding the number of municipalities applying property tax. Furthermore, the PECS 2023 survey has been updated to address some of the limitations highlighted in this analysis. For example
.9|‐0.7|2.5|‐15.7|‐1.9|15.4|
|||Type|of househo|ld(HH)||||||
|HH without children|‐2.9|‐1.0|1.0|‐1.3|‐1.2|‐0.1|‐8.2|‐9.4|16.4|
|Retiree HH|‐2.0|‐0.4|1.0|0.3|3.1|‐4.2|‐5.2|‐4.3|10.8|
|HH with children|‐0.2|‐5.1|1.8|0.7|0.1|5.0|‐13.5|‐0.6|2.7|
|1 child|0.0|‐5.6|1.1|‐1.8|‐0.1|3.5|‐16.1|0.4|0.8|
|2 children|‐4.6|‐3.2|2.6|0.3|‐0.2|7.9|‐9.5|‐1.5|4.3|
|3 and more children|‐2.7|‐7.1|4.4|2.4|3.6|7.5|‐17.5|‐6.3|15.2|
|singleparent|‐1.9|‐2.4|2.8|‐0.3|‐3.3|6.2|‐4.1|‐18.4|24.9|

Source: Authors’ estimates based on HSS data.

36
# 4.2  How can respondent selection affect the measurement of inequality?

Given the emphasis on self-reported asset ownership in the LSMS+, a natural question might arise as to how different individual-level data on wealth might be if collected instead by one representative per household (i.e., who would report as a proxy for others). Malawi provides a unique setting to make this comparison, where the LSMS+ supported Malawi IHPS 2016 was conducted concurrently with another national survey, the Malawi Fourth Integrated Household Survey (IHS4) 2016/17.<sup>17</sup> Both surveys were implemented by the Malawi National Statistical Office, relying on identical household and agricultural questionnaires and field teams that covered both IHPS and IHS4 EAs.<sup>18</sup> Unlike the IHPS, the IHS4 did not include an individual questionnaire that included LSMS+ assets modules, and a “most knowledgeable” member was to report on other members’ land ownership and valuation (see Kilic, Moylan and Koolwal, 2021, who compare statistics on men’s and women’s agricultural land ownership across the two surveys).<sup>19</sup> Both surveys used identical household and agriculture questionnaires and followed the same protocol for administering interview questions. The two national survey samples were also similarly distributed across interview months and survey strata (see Kilic, van den Broeck, Koolwal and Moylan, 2020, for a detailed discussion).

17 The data, questionnaires and basic information document for the IHS4 2016/17 can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2936 . As with the IHPS 2016, the IHS4 2016/17 was also implemented with</u> technical and financial assistance from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), using the Surveys Solutions Computer-Assisted Personal Interviewing (CAPI) platform.

> 18 On average, field teams spent approximately 3.4 days in
employment, especially indicators of own-use production work).<sup>34</sup> A continued narrow focus on employment and labor force participation would risk underestimating the importance of agriculture for rural livelihoods and undercount women’s work in agriculture.

The new standards also raise difficult issues for the macro measurement of labor productivity. Aggregate labor productivity is typically estimated as the ratio of GDP to total employment, while sectoral labor productivity is estimated as the ratio of a sector’s GDP to the sector’s employment. Under the 19<sup>th</sup> ICLS standards, agriculture for own consumption counts towards production in national accounts (and is hence included in GDP), but such a farmer is no longer considered employed. As a result, estimates of aggregate and sectoral labor productivity computed as ratios of GDP to employment could be severely biased, with an overestimation of labor productivity overall and in agriculture (where the employment estimate, i.e. the denominator, excludes some workers engaged in the production of output in the numerator) and an underestimation of labor productivity in non-agricultural sectors (where the employment estimate includes some workers who are disproportionately engaged in the production of agricultural output). This inconsistency between different parts of the statistical system is a serious dilemma, which (at least in the short term) could only be redressed by re-constructing and disseminating a parallel labor statistics series, which is aligned with the national accounts production boundary. Such a series could also be used to track changes in employment and labor force participation over time, at least in an interim period.

The analysis in this paper highlights the need for national statistical offices to tread carefully in the implementation of the revised standards. First, special attention has to be paid to the timing of data collection. As this paper shows, farmers’ labor allocation across crop and livestock products, and hence their self-reported intended use of output, can vary significantly over the course of the agricultural production cycle, and this poses challenges for cross-country and
If a municipality’s poverty estimate was below the median, it is designated as poor; if the poverty estimate was above the median, it is designated as rich. The results in columns (2) and (3) of Table 1 suggest that incumbents were more likely to win close elections primarily in poor municipalities. Specifically, when the sample is restricted to the poorer half of municipalities, a constant of 0.027 is obtained and there is a discontinuous increase of 0.025 in the pdf at the zero threshold, indicating that incumbents were about twice as likely to win tightly contested elections than to lose them. In the richer half of municipalities, the discontinuous decrease of 0.0061 is small relative to the constant of 0.044 and is not statistically significant at conventional levels. Below, the paper shows that the pattern of violence observed after the election is also concentrated in poor municipalities.

# **3.2 Narrow Incumbent Victories and Civil**

The data on conflict violence come from incident reports collected by the Armed Forces of the Philippines (AFP); they include information on insurgents, government forces and civilian casualties. These incident-level data have been used to analyze the impact of aid and the impact of economic conditions on conflict intensity (Crost et al., 2014; Berman et al., 2011b) – and are similar to SIGACTS the U.S. incident-level military data, which have been used to study the insurgency activities in Afghanistan and Iraq (Berman et al., 2011a; Iyengar et al., 2011; Beath et al., 2011). The data also contain information on the date and the location of the incident and identify the insurgent group involved. During the period under study (November of 2006 through November 2007), 2,745 conflict incidents leading to 1,045 casualties were reported by units belonging to the Armed Forces of the Philippines deployed throughout the country. The focus of this paper is on the number of casualties and the number
) The standard errors, clustered at the country level, are in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. (4) Growth, Spatial Gini, and Sen Index are measured using lights data as described in the data section. (5) Population density is measured as population per square kilometer. (6) Prices are reported as both average and real US dollars. (7) Fixed effects include time-fixed effects, district-fixed effects, and country-yearfixed effects. (8) Climate is measured using the standard deviation of rainfall distribution at the district level. (9) Analysis based on World Bank GEM data has fewer observations because the GEM prices data covers fewer mineral products than in the USGS data.

Source: Authors’ estimations

|Table 6d:Robustn<br>|ess checks <br> <br>|–thelocalec<br>_USGS Avera_<br>|onomicim<br> _ge prices_<br>|pacts ofa<br>|ctivemini<br>_Wor_<br>|ng, othervari<br>_ld Bank(GE_<br>|ables<br>_M) realpr _<br>|_ices_<br>|
|---|---|---|---|---|---|---|---|---|
|_Dependent variables_|_Growth_|_Log(Pop)_|_MLD_|_Theil_|_Growth_|_Log(Pop)_|_MLD_|_Theil_|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|Post x Active mines|0
.002_|_0.002_|
|Geographical areas dummies|YES|YES|YES|YES|
|_Joint significance of geographical areas (p-value)_|_0.030_|_0.012_|_0.005_|_0.001_|
|Observations|36|36|36|36|

P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. In all OLS regressions, we control for three geographical areas dummy variables: coastal, center and mountain areas; Tirana is the excluded geographical area.

The estimates show a highly significant correlation between district-level cooperativeness in the public goods game, and voter turnout in the parliamentary elections. The correlation is robust to the inclusion of the district-level controls generated by the 2008 LSMS survey. The estimated coefficient indicates that a one standard deviation increase in the proportion of cooperating parents in a district is associated, _ceteris paribus_ , with a 2 percentage point increase in participation in the elections.

Table 9 also indicates that turnout is higher in districts where more citizens belong to social organizations, and in the northern region, which as previously discussed is traditionally allied to the Democratic party that is in power since 2005. Consistent with the literature, the estimates also suggest that participation in parliamentary elections is lower in districts characterized by religious heterogeneity.

25
To strengthen women’s lack of control over income and general economic agency, starting at an early age can yield dividends later in life. Studies have shown that teaching girls about their rights in school makes them more intolerant of domestic violence (Friedman et al., 2016), and that adolescent girls’ clubs can not only increase their later employment, but also reduce the likelihood of adolescent pregnancy and early marriage (Bandiera et al., 2020). Among adult couples, men can be taught to take up more of household responsibilities and inform them about gender equality (Doyle et al, 2018), which could free up time for women to do other economic activities. Finally, expanding access to childcare services can help women to work in economic activities (Ajayi et al. 2022, Bjorvatn et al. 2022, Donald and Vaillant 2022).

The remainder of the paper proceeds as follows. Section 2 presents background data and summary statistics on the Burkinabè context and covers our estimation strategy. Our results on the magnitude of the gender gaps and their drivers are covered in Section 3. Section 4 provides additional data and policy recommendations, while Section 5 concludes.

# **2. Setting and Empirical Strategy**

## **2.1 Context and Data**

Burkina Faso is a largely rural country, with about 70 percent of the population living in rural areas (World Bank 2020). Labor force participation (across both paid and unpaid work) is high, increasing from 70 to 80 percent between 2014 and 2019.<sup>3</sup> However, this does not translate to low poverty levels. In 2021, Burkina Faso had a GDP per capita of less than $800 in constant 2015 dollars, and the expected years of schooling, according to the Human Capital Index data, for both boys and girls in Burkina Faso is seven years. Moreover, the country is undergoing political turmoil and a series of military coups<sup>4</sup>
interventions and decisions related to social insurance, particularly when not enough time has passed to observe actual decisions. Liebman and Luttmer (2015), for example, similarly examine how an information intervention affects future intentions over when to draw social security benefits in the US.<sup>13</sup> Concerns related to attrition are also relevant when considering use of an information experiment jointly with the RUMiC survey. Due to the young and mobile nature of the migrant population, attrition across RUMiC waves is a possible concern. From 2015 to 2016, the attrition rate was 28.3%, which is similar to the 28% reported in (Chetty and Saez, 2013). Nevertheless, as the information intervention was implemented in the 2015 sample while the outcome of interest (insurance participation) was recorded in the 2016 wave, one might worry that attrition could affect randomization. To check this concern, a balance test is conducted in the data subsection and robustness checks employ sample selection models and reweighting approaches to examine potential attrition bias.

# **3.3 Estimation Strategy and the Model**

Assuming implementation protocols were followed and that a balance test is passed, the random assignment of the information intervention allows us to identify whether poor understanding of the urban health insurance and the pension programs and enrollment procedures reduces the likelihood that respondents participate in them. We also model the decision to participate in social insurance as a function of a city fixed-effect, exogenously provided information and initial period participation, firm and individual characteristics. The binary choice, _Y_ 1 _ij_ , of whether to participate in health insurance and pension programs, is modeled as: where the subscripts 0 and 1 indicate variables measured in the pre-treatment (2015) and post-treatment (2016) survey rounds, respectively. _Y_ 0 _ij_ is a lagged (pre-treatment) participation indicator; _Tij_ indicates that individual _i_ in city _j_ was assigned to receive the information treatment between the 2015 and 2016 survey rounds; **X** 0 _ij_ is a set of pre-treatment
to be addressed; in particular, we discuss a variance correction that is required because we use imputed data. We present results in Section 5 and conclude in Section 6.

# **2. Data: macro, micro and small area welfare estimates**

The effect of income inequality on economic growth is the subject of a large literature. Aghion et al. (1999) and Thorbecke and Charumilind (2002) review this literature and show that theory does not provide firm predictions of the sign of the effect.<sup>2</sup> Empirical studies in the 1990s have been “.. impressively unambiguous ..” (Aghion et al., 1999, p1617) in concluding that the growth effect of inequality is negative, but more recently some authors have obtained contrasting results (e.g. Forbes, 2000; Banerjee and Duflo, 2003). The most common denominator in these studies is the nature of the data used: the empirical inequality-growth literature is largely based on cross-country data.

Cross-country inequality-growth studies, while providing the bulk of existing empirical evidence, have been criticized for a number of reasons. First and foremost, the quality and internal consistency of datasets, in particular inequality series, have been questioned. Deininger and Squire (1996) challenge the quality of inequality data in growth regressions and offer an improved ‘high quality’ dataset. However, the consistency of inequality data in this set has been challenged by Atkinson and

> 2 Positive inequality-growth effects can be attributed to a positive effect on savings, to the existence of investment indivisibilities or to positive incentive effects of inequality. A negative inequality-growth effect can be explained by political tension, instability and demands for redistribution due to inequality, by reduced investment opportunities for the poor, worsened borrowers’ incentives and by higher macro-economic volatility. A ‘unified’ model that aims to reconcile these conflicting effects is presented in Galor (2000): this paper predicts that the effect of inequality on growth is non -linear, with a positive effect
: schools that went from a lower strata to the greater stratum of the distribution are considered to have being treated, since they showed improvement in the indicator in the period; and schools that went from the highest stratum to a lower strata of the distribution are considered to have undergone a _negative treatment_ . Due to its large number, schools that remained in the highest stratum of the distribution are considered as control group.

Based on the above strategy we constructed a set of variables that characterize different degrees of exposure to treatment, here understood as the increase (or decrease, for _negative_ treatment) in the _percentage of classes taught by teachers with higher education_ and _by teachers with higher education in the field_ . We subdivide the exposition to treatment into three degrees (and four degrees, for the _higher education in the field_ indicator) according to the stratum in which the school was in year 2009: treatment group 1 ( _Treated_ 1) is composed of schools whose indicator was in the stratum immediately below the highest, treatment group 2 ( _Treated_ 2) is composed of schools whose indicator was in the next stratum, and so on; so that the greater the order of treatment, the greater its intensity is. Similarly, the degree of _negative_ treatment can also be characterized in degrees, according to the stratum of the school in 2015.

We see in Table 8 that for both years and disciplines the average scores are higher among students from untreated schools, and are smaller for more intensively treated schools. This is consistent with the hypothesis of positive correlated between teachers’ education and students learning. It would be also expected a higher increase in outcome among treated schools, especially among the most intensively treated ones. Data on Portuguese scores corroborate with this hypothesis, but not for math, as students from treated and untreated schools perceived similar reduction in the average scores.

<u>Table 8 – Mean score on ENEM by condition of exposure to treatment</u>

||||Math<br>||||
Table 3: Main Reason for Choosing Place of Purchase

|**Reason**|**Outco**<br>**All Stores**|**me: Share of purcha**<br>**Informal Stores**|**ses (in %)**<br>**Formal Stores**|
|---|---|---|---|
|Access|41.5|42.1|31.3|
|Price|28.6|29.4|17.7|
|Quality|13.4|11.8|40.6|
|Store Attributes|6.9|6.9|5.0|
|Other|9.6|9.8|5.5|

The table reports for each potential reason households report for using a particular place of purchase, the share of purchases associated with this reason. Each number is an average across the six countries in our core sample in which the household survey asks this questions. These countries are Benin, Burundi, Comoros, Congo Rep., Morocco and RD Congo. In all surveys seven reasons are listed which we classify into five categories as follows: access is defined as ”The retailer is closer or more convenient” and ”The good or service cannot be found elsewhere”, price as ”The good or services are cheaper”, quality as ”The goods or services are of better quality”, store attributes as ”The retailer offers credit” and ”The retailer is welcoming or is a friend” and other as ”Others reasons”. Note that Morocco has a few additional small categories, which pertain to attributes of retailer. The table lists the frequency for all purchases of goods and excludes services, which are less comparable along these dimensions, although their inclusion does not impact the results.

48