rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III.3 Attention to Political Economy Drivers: Evolving Practices and Potential Pay-offs**

Given the considerable importance that political economy factors play in enabling or hindering progress of public sector reforms, an important question is how such factors are or can be taken into account in designing and implementing operations. This section addresses current and emerging practices and this regard, and reviews tentative evidence on whether increased or more systematic attention pays off in terms of better support and better results achieved.

## **III.3.1 Understanding and acknowledging political economy dynamics and risks**

What efforts are teams currently making to assess and address political economy risks and variables to calibrate and manage World Bank support? And is there any (tentative) evidence that more attention to and more analysis of political economy factors can pay off in terms of operational performance? To investigate these questions, this section draws on three sources: Implementation Completion Reports of closed operations and semi-structured interviews with 13 team members and task team leaders of eight ongoing public sector operations;<sup>36</sup> Furthermore, it revisits 17 public sector operations initiated in fiscal year (FY) 2008 that had been included in the 2009 Quality Assurance Group (QAG) review of attention to governance and anticorruption (GAC). The section compares the level of attention to governance and political economy at the project approval stage to project performance as of 2011.

World Bank teams feel that they generally understand political economy factors and anticipate related operational challenges. That said, for a majority of the teams interviewed, efforts to understand political economy variables were limited to internal team discussion about the operation, but were not necessarily made explicit. Unsurprisingly, operations vary as to the frankness and the accuracy of the ex-ante assessment of political economy-related risks, and assessments ex-post of whether these were fair. Furthermore, the way in which risks are assessed (and suggested to be mitigated) is influenced by bureaucratic incentives. Most importantly, once planning for an operation has been started,
,164|7,164|
|R-squared|0.876|0.891|0.872|0.872|0.872|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

22
# **5.1 A Measure of Aggregate Productivity Loss**

In this section we will use the results from the micro level regressions to construct a measure of aggregate productivity loss. The micro findings have the advantage that identification is stronger than in cross-country studies. Their disadvantage is that outside validity is not ensured. We therefore first take the micro findings at face value and analyze the implications for the macro economy in the long run. We then show that the cross-country data is consistent with a productivity loss of a size implied by the micro studies.

Akresh et al. (2012) provide estimates of the height loss in cm due to exposure to the Nigerian civil war for different age groups between 0 and 16. Females exposed to the conflict between age 0 and 3 lost on average of 0.75 cm in height as compared to non exposed females. This loss increased to 1.09 cm for the age group 4-6, 2.16 cm for the age group 7-12 and finally 4.53 cm for the age group 13-16. Identification relies on a difference-in-differences model in which authors interact an indicator for war-exposed ethnicity (the Igbos) with a continuous cohort-based measure of months of exposure to the war. Based on these relatively well identified micro level estimates, we propose an estimate of a long run macro-level productivity loss due to conflict.

Suppose workers enter the labor force at age 17 with differences in productivity levels due to previous exposure to conflict. Once in the labor market, productivity dynamics are the same for all cohorts, and workers exit the labor force at age 60. We assume that if there is a conflict at time _t_ in country _i_ , all children of age _j_ suffer a loss that depends on the intensity of the conflict.

We follow three alternative approaches to measure conflict intensity and map it into a persistent productivity loss. First, we proxy conflict intensity by the per capita number of battle related deaths. To mitigate the effect of very intense conflicts on our loss measure, we prefer
640 migrants as originating from Uruguay. Then 1,390 (0.53*2,640) of these migrants are women and 1,250 (0.47*2,640) are men.

# **{Table A4 here}**

These calculations based on concurrent shares can be calculated only if data disaggregated by gender exist for all other countries in the destination subregion. If not, the world is divided into destination subregions, and gender splits are calculated based on regional shares over time. Continuing from the previous example, assume the data for Denmark, Finland, and Norway are unavailable in 1990, so that the gender split for Uruguayan migrants in Sweden cannot be calculated based on Scandinavian data for 1990. In this case, the data for Scandinavia across all other decades are used to calculate the average ratios of female migrants to total migrants over time. This can be written formally as: 31
management). However, this exercise demonstrates the potential of cost‐effectiveness analysis in complementing our analysis to inform policy decisions.

Across all 14 included programs, the average total cost per participant is US$75 (2011, PPP), with the cheapest at US$0.40 (from providing earnings information for school finishers in Madagascar (Nguyen 2008b)) and the most expensive at US$749 (from the minimum‐size conditional cash transfer program in Malawi (Baird, McIntosh, and Özler 2011)) (Table 6).

In Sub‐Saharan Africa, the most cost‐effective program provided households in Madagascar with information on returns to education (Nguyen 2008a). As Table 6 shows, this program increased students’ learning performance by 0.2 SD at a cost of US$0.4 per student. As described in section 4.1, 1 one SD gain in learning is associated with 6.5 years of equivalent schooling (method 2a). Thus, the effect of the Madagascar program translates into 1.5 additional years of schooling for each participant at a total cost of US$0.4 from implementer and beneficiary. In other words, an investment of US$100 can yield 430 additional years of equivalent schooling (if that were possible). Furthermore, considering the long‐term wage income effects described in section 4.2.2, an 0.2 SD improvement in learning by this program can yield an NPV of US$694 from lifetime increased wage income (discounted at 3%), which provides a benefit‐ cost ratio of 1,957, when taking into account the NPV of the program cost (US$0.4). The median cost‐ effective program in Sub‐Saharan Africa took place in Kenya and provided teachers with incentives based on students’ test scores and improved learning by 0.14 SD at a cost of US$4.2 per beneficiary (Duflo, Dupas, and Kremer 2011)
measures being cumulative generally reveal more children outside of normal ranges than does the latter, which typically only shows high values during periods of acute food shortage. The WHO recommends stunting as a reliable measure of overall social deprivation (WHO, 1986), whereas the MDG nutritional indicator is defined as weight for age. In the remainder of the paper we concentrate on both stunting and being underweight.

Table 2 illustrates the nutrition problems for Tanzania. According to the 1999 DHS, 43 percent of children under five are stunted, 6 percent are wasted and 29 percent are underweight. Though we do not suggest that the Kagera data are representative of Tanzania, a comparison between Tables 1 and 2 shows that the nutritional pattern in Kagera is not very different from that found elsewhere in the country.

Figure 1 indicates that the probability of malnutrition declines with income. At low levels of income, more than 50% of children are malnourished. While this declines with 4
WEO|
|Employment|Employees and self-employed in thousands, extended with employment<br>growth rate from the WEO|Penn World Table, WEO|

The methodology is subject to several caveats, the most important being the endogeneity of the regressors. First, the Hausman test is performed to verify whether the random-effect specification may be appropriate for cross-country regressions. The Pesaran (2004) test is used to control for the presence of cross-sectional dependence in panels with many cross-sectional units and few time-series observations. The robustness of the estimates is also verified by employing the Arellano-Bond General Method of Moments (GMM) estimator, although that is also subject to caveats. For example, it may underestimate the impact of several common determinants of the steady-state level of income, such as human capital, as discussed in Hauk and Wacziarg (2004). The adoption of the GMM estimator requires losing at least two periods of data, which may alter the results when the starting sample is small. Finally, it may also be subject to the problem of weak instruments: the first-stage relationship between differenced independent variables and lagged dependent ones may be weak. The problems are addressed by using the Blundell and Bond System GMM (see Section 4).

To better control for potential endogeneity, the ideal would be to cross-check the estimates based on macroeconomic time series with those obtained from using sector- and firm-level data (see also the discussion in Egert and Gal 2016).

> 6 The baseline model does not control for the lagged dependent variable, but the robustness of the results is tested under this specification (see Section 4).

> 7 As discussed in Lusinyan (2018), the random effect specification may allow to capture both between and withincountry information, the latter being relevant especially for factor utilization.

9
# **5. Impact on Interactions with Partners and Household Efficiency**

# **5.1 Defining Partners**

At the time of migration the principal applicants are typically adults in their late twenties or early thirties, who move with their spouse and children. When applying some would still have lived with their parents or siblings, or otherwise would typically have recently set up their own dwellings in the same small villages as their extended family members. A decade later, by the time of our follow‐up survey at about age 40, most would have formed their own households in Tonga if they had not been selected to migrate. We therefore defined for each principal applicant in the long survey a partner household in Tonga containing either a parent or elder sibling (with priority on parents over siblings, and females over males). A strict rule was used for matching so that the choice of partner household is orthogonal to winning the lottery. In about 15 percent of cases the partner was living in the same dwelling in Tonga as the ballot loser.

Each partner was then interviewed separately from the principal applicant. Table 4 provides some descriptive statistics on partners by treatment group. The partner is the parent of the principal applicant in 44 percent of the cases, and their sibling in the remainder. This is reasonably balanced by treatment status, although the ballot losers are slightly more likely to have a parent rather than sibling interviewed as the partner, but not significantly so. The partner age averages 66 for parents of the principal applicant and 52 for their siblings, for an average age of 54. This is slightly higher for the ballot loser sample reflecting the higher incidence of parents. We see no significant difference by treatment status on other partner characteristics such as gender, education, or earnings. The latter result is consistent with Gibson et al. (2015) who find that the massive gains in income to the migrants are not accompanied by changes in the income of these partner households. We control for partner type
Bold et al. 2017). For the second objective, we test if some of the key constraints to input use identified in previous studies are present in the Ugandan context using a relatively long panel data set. This allows us to account for long‐run household trends, while estimating marginal impacts that reflect a decade of changes. For this part of the analysis, we study the use of pesticide in addition to that of inorganic fertilizer and improved seeds.

Our results suggest that increased input use has the potential to drive productivity growth and render positive financial returns among smallholder farmers, despite the substantial output price volatility that characterizes Uganda. Yields of the four main crops considered<sup>3</sup> —maize, beans, matooke and coffee‐‐ increase in response to the application of inorganic fertilizer (nitrogen), particularly beans and maize. The gain is high across the range of output prices experienced from the year 2000 onwards. The economic return to fertilizer is always positive (across the entire price range) for beans, maize and matooke and positive for the top 75 percent of prices for coffee. Median returns are 180 percent, 74 percent, 64 percent and 39 percent respectively. Commonly available improved seed varieties for maize and beans increase gains by 32 percent on average. However, the cost of using maize hybrid seeds often exceeds the extra revenue gained from using them, reducing its financial return.

In addition, we find that the low quality of inputs prevalent in agricultural input markets, lower the financial returns, often to less than zero, which likely discourages input adoption. Thirty percent of herbicide samples contain less than 75 percent of the active ingredient (Ashour et al. 2016); hybrid maize seeds are equivalent to a mix of 50 percent hybrid and 50 percent traditional; and the average nitrogen 2 Official poverty numbers estimated by the Uganda Bureau of Statistics (UBOS), using the Uganda National Household Survey (UNHS) 2005/06‐2012/13.

3 Under reasonable weather
</sup> which is a sizable number, but limited relative to the 2011 population of 89.39 million. In Table 4, we also report a list of the major disasters that have occurred in Ethiopia since 1960, and verify that this drought does not qualify as a disaster in the Emergency Events Database (EM-DAT) definition.<sup>14</sup>

# **3 Evidence from Antebellum USA**

In this section we present an analysis using data and policies from American history, in the period between late 1700 and mid 1800, known as Antebellum era. We exploit the state-level introduction of bank taxes, which gradually took place over this period. In Section 3.1, we present a slightly different version of the theoretical model in Section 2, showing that lending/profit taxes can generate an increase in safe assets equivalent to the mandatory policy. Section 3.2 gives some historical

> 12Refer to the BBC article “Horn of Africa sees ‘worst drought in 60 years’ ”, 28 June 2011, available at http: //www.bbc.co.uk/news/world-africa-13944550.

> 13Refer to the Huffington Post article “Ethiopia: Hunger During Worst Drought In 60 Years”, 17 August 2011, available at http://www.huffingtonpost.com/2011/08/17/ethiopia-hunger-drought_n_928989.html.

> 14The EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) and defines a disaster as an event satisfying at least one of these characteristics: “ _•_ Ten (10) or more people reported killed. _•_ Hundred (100) or more people reported affected. _•_ Declaration of a state of emergency. _•_ Call for international assistance.”

34
urban communities remain limited. Indeed, much of what the development community ‘knows’ about the coping strategies of the poor (e.g., Besley 1995) comes from large household surveys carried out on _rural_ populations.<sup>iv</sup> Cities tend to be more complex than rural settings. Slum dwellers have access to a much broader range of potential contacts, transcending caste and identity and encompassing both traditional leaders and tiers of formal government. These contacts may provide alternative channels, with informal provision of services substituting for or complementing formal government, or they may interact in more intricate ways, with informal leaders facilitating access to formal leadership. Policy based upon studies of the poor in rural settings neglect the differences in organization and enforcement among urban communities and the range of extracommunity networks that the urban context affords the poor. Without considering the urban context, such studies will be incomplete and misleading.

It is into the space created by these three characteristics that the current study steps. We seek to further understand how collective action problems are addressed within the slums, and the strategies that slum dwellers call upon to tap the resources of the state and markets within India’s capital. Delhi is hardly ‘representative’ of cities in the developing world or even in India. Yet as a focus of multiple tiers of government and the opportunities that these present for the poor, Delhi provides an intriguing venue for studying democratic responsiveness in poor urban communities. Though the particular mechanisms may differ due to Delhi’s institutional distinctiveness, we believe much can be learned about the underlying economic and social strategies that are readily generalizeable.

7
# **3.1 Data**

The surveys, which form the basis for this paper, have been developed as part of a project by the International Trade Department of the World Bank which aims to assist low-income countries (LICs), particularly from Sub-Saharan Africa (SSA), to take better advantage of spillovers from FDI within the context of global value chains. Specifically, the project aims to identify the critical factors for the realization of FDI-related spillovers – including dynamic interactions between FDI and local suppliers.

Acknowledging that the extent and nature of potential FDI-generated spillovers differ importantly by sector and FDI motive, the project focuses not exclusively on manufacturing but includes, besides light manufacturing (apparel) two natural resources-based sectors which are particularly relevant for SSA LICs: mining and agribusiness. Given the share of FDI that goes into natural resources-intensive sectors, particularly in developing countries, understanding better the unique dynamics of FDI linkages and spillovers in sectors like agribusiness and mining represents an important opportunity. In addition, the study includes benchmark countries for these two sectors – Chile (for mining) and Vietnam (for agribusiness) – to be compared with the SSA countries.

Between March and October 2012, three different types of firms have been surveyed by various consultants, namely (i) national suppliers, i.e. firms with a national ownership of at least 75 percent that supply to multinationals in the country, (ii) foreign investors, i.e. firms that have a foreign ownership share of at least 25 percent, and (iii) national producers, i.e. domestic firms that are final goods producers and have a national ownership of at least 75 percent. In cases where reported data seemed unlikely, either consultants or the firms themselves were contacted again to make sure we obtained the correct numbers.

The focus of this section is on foreign investors, but we also compare their characteristics with domestic producers. The foreign investors’ surveys cover 87 firms in Chile (5), Ghana (16), Kenya (
**TABLE 2 Small states’ fiscal reaction functions: Results**

||Ba|seline|Exten|ded model|
|---|---|---|---|---|
||FE|Panel FE-IV|FE|Panel FE-IV|
|Lagged primary balance ratio|0.476***|0.461***|0.474***|0.459***|
||(0.035)|(0.069)|(0.035)|(0.069)|
|Lagged debt ratio|0.0146**|0.0202**|0.0140**|0.0191**|
||(0.005)|(0.009)|(0.006)|(0.009)|
|Inflation|0.0768|0.0534|0.0779|0.0553|
||(0.048)|(0.049)|(0.048)|(0.049)|
|Real GDP growth|0.062|0.0614*|0.0624|0.0617*|
||(0.042)|(0.035)|(0.042)|(0.036)|
|Elections|||-0.439|-0.501|
||||(0.400)|(0.483)|
|Fiscal rule|||0.243|0.389|
||||(0.657)|(0.745)|
|Number of observations|494|455|494|455|
|Number of countries|22|22|22|22|

Note: Dependent variable is primary balance as a percent of GDP. Lagged values (first and second lags) of inflation and growth are used as instruments in
captures whether a drought occurred in cluster _c_ during the growing months leading up to the harvest season ( _t_ ) that coincided with the child’s birth.<sup>21</sup> _Dc_ ( _t_ +1) is similar except it refers to the year after the birth year. _Wi_ is a vector of four dummy variables indicating whether the child was born in a household in one of the top four wealth quintiles (the lowest wealth quintile is the contrast variable). The month of birth x cluster fixed effect is captured by _θcm_ . _α_ is the constant. The error is clustered at the cluster level.

Wealth quintiles are relative measures and cannot be compared across countries. However, after controlling for cluster fixed effects, they may be used to estimate the relationbetween wealth status and the likelihood of for households in the same ship stunting cluster. Two points are worth noting. First, as expected, after controlling for geographic and spatial factors, differences in wealth are associated with differences in child anthropometric measures. When compared with the poorest quintile, every other quintile is significantly less likely to be stunted. Second, drought impacts are comparable to a household falling from the second wealthiest quintile to the poorest quintile. On average (for the five countries taken together), for stunting outcomes, the impact of a drought during a child’s birth season (10 percentage points) is similar to the difference between being born to a household in the poorest quintile instead of the second wealthiest quintile (8 percentage points).

While these numbers are large, three important caveats need to be kept in mind. First, there is limited variation in wealth status within a cluster. Second, the wealth quintiles are constructed (c.f. Rutstein (2015)) in a manner that suggests that this DHS wealth index is probably a very rough approximation of our idea of household wealth — in particular, no attempt is made to account for differences in the quality and price of the underlying assets, especially the
the 21 largest states and union territories. These large states and union territories are covered by the high‐quality household surveys whose data are needed for a proper stratification. Focusing exclusively on them further reduces the number of administrative units considered to 561,624.

The total population of these 561,624 polygons in 2011 was 1,174 million, or 3.0 percent less than reported by the 2011 Census for the country as a whole (ORGI 2011). Almost a third of this population gapabout 11.3 million peopleis accounted for by the small states and union territories excluded from the analysis. Some of these small states and union territories are richer than the Indian average and some are poorer, which suggests that their exclusion from the analysis may not bias results much. The rest of the gap is associated with the villages excluded because their boundaries or population figures were unavailable. These villages are most likely rural in practice, and their population size is small about 200  people at the median which implies that they too are unlikely to be an important source of bias.

# _A two‐stage stratification process_

A two‐stage disproportionate stratification approach is used to select the sample of administrative units to be inspected visually (figure 1). In the first stage, the universe is divided into eight strata by administrative category, following the definitions of the 2011 Census of India. Five of these categories state capitals, other municipal corporations, municipalities, industrial towns, and other townsare statutorily designated as urban. All state capitals are governed as municipal corporations; we use the term other municipal corporations to refer to the rest of the cities in this administrative category. Two of the   categories census towns and outgrowths are statutorily rural but recognized by the 2011 Census as 5