rafmacalaba/data-use-mentions-extended huggingface dataset:
# Displacement and Social Empowerment: Evidence from Surveys of IDPs in Iraq, the Philippines, and Uganda<sup>*</sup>

Patrick Vinck,<sup>†‡</sup> Thomas O’Mealia,<sup>§</sup> Carol Wei<sup>¶</sup> , Abdulrazzaq al-Saiedi,<sup>||</sup> Muslih Irwani,<sup>**</sup> and Phuong N. Pham<sup>††</sup>

## **_JEL Codes:_** D74, F22, C83, N47, N45, O15, R23

## **_Keywords:_** Displacement, IDPs, Social Cohesion, Political Empowerment, Surveys

> *Vinck and O’Mealia are co-first authors. Acquisition of the data used in this manuscript was supported by the Ministry of Foreign Affairs of the Netherlands (Iraq), the John D. and Catherine T. MacArthur Foundation (Uganda) and the World Bank and World Food Programme (Philippines). The funders played no role in the analysis, interpretation or writing of the results and decision to submit the manuscript.

This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the
, within-sector growth processes are also relevant to the overall outcomes for poverty. Various strands of the development literature have examined these factors. One strand of the literature has questioned whether the agricultural growth processes have helped the rural poor, many of whom are landless, while others have argued that the benefits of rising farm productivity are passed on in due course through higher wage rates.<sup>7</sup>

> 2  Overviews of the literature can be found in Fields (1980) and Ravallion (2016).

> 3  As in Robinson (1976), Fields (1980) and Anand and Kanbur (1985, 1993).

> 4  The existence of such a turning point requires certain conditions to hold, which are specific to the measure of inequality used; Anand and Kanbur (1993) derive those conditions for various measures.

> 5  This holds for all population-weighted measures, such as all those characterized by Atkinson (1987).

> 6  This is known from research on the effects of selective compliance in household surveys on measures of inequality; see the analysis of this closely related issue in Korinek et al. (2006).

> 7  Contributions to this debate are cited in Ravallion and Datt (1996) and Datt and Ravallion (1998, 2011).

2
the DCs, when available.

# **4.** **<u>The post-entry performances of entrepreneurial firms</u>**

Since entrepreneurs are driven by both progressive and regressive determinants and are intrinsically heterogeneous, the post-entry performance of newborn firms and their eventual contribution to economic development may be very diverse as well. In what follows, after a theoretical discussion dealing with the macroeconomic constraints to new firms’ growth (Section 4.1), we survey those empirical microeconomic studies which have 14
politicians (whose incentives differ from private shareholders and creditors). In particular, the following are potentially important for understanding SOEs behavior in developing countries.

- Cash flow rights. What tax rate does the SOE face? Does it adjust from year to year? Does the state charge different rates for different SOEs? Since the state may use tax rate as a transfer mechanism, it is useful to see the extent to which the government uses it for this purpose. How do tax rates of SOEs differ from those of comparable private firms? Does the government subsidize or bailout SOEs when they are in financial trouble?

- Control rights. Does the government appoint the top managers? Does the government appoint the middle managers? How were managers selected (bidding, election by workers, appointment by the government)? How long is manager tenure? What is the scope of managers' authority (e.g., decisions regarding production, investment, wages, staffing, and borrowing)? Does the government often interfere in production decisions? Often in wages? In hiring and firing? In new investment? In acquisition and consolidation? In borrowing?

- Incentives. How does the government align the manager's and government's objectives? Does it use state plans, performance contracts that spell out goals and incentives, private managers, who have control rights for a fee, or private parties bidding for concessions to operate and invest in SOEs and keep the profits (World Bank, 1995)?

To illustrate the usefulness of firm-level data to yield insights about policy questions, consider studies based on a 1980s panel data set of Chinese SOEs (Li, 1997; Xu, 1998; Groves et al. 1995a, 1995b). Using these data, researchers have found that decentralizing production decisions, allowing managers to determine wages, and allowing firms to retain a larger share of their profits all improved productivity. Most importantly, increased competition, as measured by lower markup ratios, substantially increased productivity.

18
ies in Kenya (e.g. Kremer et al. (2002) for primary school teachers, Chen and Kremer (2002) for preschool teachers). A possible explanation for this multiple evidence that teachers are little responsive to incentives is that, beyond a certain level, attendance starts to be very costly to the teacher due to factors like sickness or social obligations. For example, the Welfare Monitoring Survey (1994) ...nds that rural poor in rural Western Kenya missed an average of 5.9 days of work per month due to sickness. Over half of the respondents reported that they had fever or malaria in the preceding two weeks. In addition, people miss work for social reasons like funerals. For example, the parent representatives in the treatment schools were reported absent due to a funeral in 3 percent of the cases.

# 6 Price e¤ects and the generalizability of the ITT estimate

External aid can have substantial implications for school ...nance, which in turn can in‡uence outcomes like school participation. Assume an NGO enters a region and picks out a school for allocating aid to that school. Ceteris paribus, if the program has positive value to the parents, it will increase their valuation of school attendance in that school. If the market for schooling was competitive before the NGO started working with the school, the intervention can change competitive relations between the providers of education. A program that provides free school meals to the children attending school does not change the cost curve of supplying education, but it does change the demand for education in that school. For each unit of education provided, parents are willing to pay more, because their children receive a perk with each unit of education. By assigning pupils to their original treatment school rather than to the treatment status of their current school, the intention-to-treat estimator avoids upward biases that would arise from transfers into the treatment school One problem with the ITT estimator is that it does not take into account the general equilibrium e¤
Policy Research Working Paper 10048

# **Abstract**

This paper measures trends in factor misallocation in India between 1999 and 2014, using data from a rich panel of Indian firms. The misallocation of a factor is modeled as an adjustment cost, that is, an implicit variable cost incurred by a firm when using that factor. Trends in the adjustment cost are estimated using a new adaptation of the firm-level cost-minimization approach. The paper documents these trends for four factors of production (permanent labor, contract labor, land, and fixed capital) across Indian states and by firm size. Overall, the findings show that adjustment costs declined over time for labor and land but with significant heterogeneity with respect to state growth rate and firm size. Using these stylized facts on trends in factor adjustment costs, as well as in-depth field interviews with firms in two Indian states, the paper also discusses potential policy developments behind these trends, including a preliminary examination of the role of state-level governance in the implementation of relevant factor market policies.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ssharma1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
different groups of households based on their neighborhood status (informal or formal) and consumption quartiles. It is not clear a priori who benefits from better job accessibility between poor and non-poor households. Richer households may indeed choose to live at a further distance from the CBD and most job clusters, to enjoy larger houses—as is often assumed in urban economic theory—in exchange for longer commutes. They could however compensate longer distances with higher commuting speeds if they are equipped with cars and benefit from both good accessibility and larger dwellings. Yet, because only a tiny fraction of people commutes by car in Nairobi, even richer people may prioritize living closer to jobs. Poorer people are often constrained to live in informal settlements; so, their job accessibility depends on (a) the overall level of job accessibility in informal residential areas compared to formal areas and (b) the variation of job accessibility across informal neighborhoods. Thus, it is worth examining the JAI for different groups.

# _Hedonic models_

In the standard hedonic framework developed by Rosen (1974), the market prices of housing units represent the sum of expenditures in a bundle of characteristics that can be priced separately (see

> 12 The number of residents in Nairobi’s informal settlements varies depending on sources, and it is possible that the 2009 Census undercounted them. Even the population count of Kibera, the largest informal settlement in Nairobi, ranges from 170,070 to 270,000 (Lucci, Bhatkal, and Khan 2018).

10
on other village-level characteristics such as the Population Census 2011. Several variables were identified in consultation with state missions because implementation agencies have recently used these for identification and mobilization of target groups. The SECC data were given priority over the census data wherever data on a characteristic were available from both sources, as household level data were available for the SECC and it was a more recent survey. The choice of other variables in the village selection model was based on the literature review of similar interventions. Results from the village selection model for each state are given in the Annex (Tables A1-A3).

Once the matched list of treatment and control villages was identified, 6 households were randomly selected from each village from the SECC’s household list. Due to the nature of the rollout of the program, the sample was not evenly distributed across states. Program placement bias owing to state specific rollout heterogeneity was therefore addressed by specifying the village selection model at the state level. However, the household selection model was proposed for the entire sample. This was done to mimic power calculations and ensure availability of a larger group of comparison households to match from.

# **3.2 Survey Data**

In this section we present the summary statistics on key characteristics of the surveyed sample. The average age of the household head in the sample is approximately 47 years, and there are 10 percent female-headed households. The average education of the household head is approximately 4 years. Approximately 49 percent of household heads are married, with an average household size of 4.6. Only 33.1 percent of households have access to a

> 8 In a few cases, there were not enough sample blocks, so all the blocks were taken.

6
programs relative to conventional _-_

- MFIs strongly suggests that large numbers of poor people prefer Sharia compliant finance.<sup>1</sup> In addition, microfinance practitioners report that many of the Lebanese poor refuse financial services unless they are Sharia-compliant. (Hamze 2001)

- Microfinance practitioners working in certain regions in Afghanistan, Syria, and Indonesia report similar results from their market surveys. (The Dubai Islamic Bank)

Source: World Bank Memoriam 27
, regardless of other demands, is the outcomes that can be realized from such an increase.

# **_Defining Our Outcome of Interest: Learning-Adjusted Years of Schooling (LAYS)_**

The first step in answering these questions is to establish the outcomes and spending measures of interest. Should governments focus on increasing enrollments, improving learning, or both? Since we are interested in both access to education and learning, we used a measure that captures the levels of educational attainment and learning of the whole population (not just of those who are in school). We used the Human

> 13 See Annex Table A6.

25
<br>(US$/tonne)|**2.5**|**3.0**|NA|5.0|5.0|5.0|5.0|1.4|
|Average liquid bulk handling charge, ship to gate or<br>rail (US$/tonne)|**1.5**|**1.5**|1.0|4.0|NA|NA|NA|NA|

Source: Mundy and Penfold 2008. Derived from AICD ports database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

Across the region, there has been a tendency for aircraft fleets to be renewed and scaled down in size to facilitate consolidation of routes toward a hub and spoke system. The aircraft fleet serving Ghana has renewed rapidly in recent years, and the share of the fleet that comprises aircraft of recent manufacture rose from 52 percent in 2001 to 97 percent in 2007, which puts Ghana ahead of many of its neighbors. On the other hand, only about 16 percent of aircraft serving Ghana are in the small or medium size category, a share much lower than that of neighboring countries.

15
<!-- Start of picture text --> OUTWARD FDI FROM 30 OECD COUNTRIES<br>La Reported [_] Confirmed Zeros [__] Missing<br>2008 fo<br>2007 (oo<br>2006 |<br>2005 (oT<br>2004<br>2003<br>2002<br>20010 fo<br>2000<br>0 500 1,000 1,500 2,000 2,500<br>Source: OECD (2011)<br>Note 1: 85 IAB host countries, excluding Kosovo and Montenegro<br><!-- End of picture text -->
# **I Data and indicators**

## **Trade data sources**

The data used in this study come from Chinese Customs and provide export flows aggregated by province, year, product, and destination country over the 1997-2007 period.<sup>1</sup> We reaggregate the original eight-digit-level data into HS4-level data (more than 1,200 product lines). An interesting feature of this dataset is that it allows us to identify whether the export flows emanate from domestic or foreign firms<sup>2</sup> and whether they correspond to processing trade or to ordinary trade.<sup>3</sup> The processing trade includes all trade flows by firms operating in the assembly sector; that is, firms that import and process inputs in China and then re-export the final products abroad. Firms engaged in this type of activity might be less embedded in their local environment and might consequently generate fewer (and possibly benefit less from) externalities.

## **Explained variable: creation of new export linkages**

The creation of a new export transaction is measured by a dummy that takes the value 1 if the domestic firms in province _i_ begin exporting product _k_ to country _j_ at time _t_ + 1 and 0 otherwise. A specific database is constructed that incorporates the set of alternatives faced by

> 1We do not have firm-level data, but we believe that province/firm-type/trade-type/product/destination country data are suitable for the investigation of micro-phenomena such as export spillovers. The information that we have is very detailed. Feenstra and Hanson (2005) argue, for example, that their city/firm-type/tradetype/product/destination country dataset approaches the precision of a firm-level dataset. Moreover, with firm-level data, we would have information on the overall size or productivity of the firm, but we would lack information on the firm/product-specific ability. Finally, we have over four million observations for our regressions. For the analysis of the determinants of entry
closed during the second year); hence, outcome variables based on survey or administrative school data during this period would suffer from endogenous censoring.<sup>18</sup> By the spring of 2017, the total years of exposure to API _Plus_ range from zero to three depending on whether a school received the _Plus_ modality for two years during the second experiment, as well as on the API _Plus_ government assignment in the year after the experiment. The underlying assumption of this approach is that, once controlling for the official government criteria, the remaining variation in the program’s

> 18For this reason, in Section 6.2 we take advantage of the 2019 Census information that is not subject to this issue. The Mexican Census is available only every ten years, and hence we cannot use this information to estimate the model in equation (4).

33
We test in column 3 whether these gendered survey impacts vary across seasons when the self-report indicator interacts with the gender and agricultural seasons indicators. The magnitude of the coefficient on this interaction term is non-negligible and, as expected, higher in the main rainy season (2.3 percentage points, or 5%) and harvesting season (3.7 percentage points, or 8%) relative to the short rainy season, suggesting that the differential child/proxy discrepancies in child labor statistics for girls vary across seasons. However, we do not have enough power to reject the equality of these interaction coefficients as shown by the p-values at the bottom of the table.

The panel at the right of Table 3 depicts results at the intensive margin of work. While in column 4 we observe meaningful overall child/proxy discrepancies in reporting of worked hours equal to 1.99hr, or 11% from the mean hours reported by the proxy, we do not observe statistically significant differential survey impacts for girls relative to boys (column 5), or differential gendered impacts that vary across seasons (column 6).

Turning our attention to the 7-day recall period in Table 4, column 2 shows that girls have a statistically significant 5.5 percentage points higher discrepancy in the participation of farm activities report compared to boys, or a 10.3% higher rate of participation than the mean participation obtained from the proxy. Likewise, column 3 shows that the higher child/proxy discrepancies in reporting for girls that that for boys increases in the harvesting season for 5.7 percentage points (10.7%), relative to the short rainy season. Moreover, as the 30-day recall results, the point estimates for the interaction of self-report indicator, child’s gender, and season indicator are somewhat larger in the harvesting season than in the short rainy season but it is not measured with statistical precision. Furthermore, by looking at the intensive margin of work for the 7-day recall period in columns 4-6 in Table 4, we
.7|79.7|94.8|104.5|111.5|89.3|105.2|114.8|116.7|102.2|111.9|115.3|
|1 child|100.0|81.7|97.4|95.3|92.0|79.7|96.0|103.2|111.9|88.4|103.9|112.9|117.6|101.2|113.1|116.5|
|2 children<br>3 and more|100.0|79.5|95.7|93.3|93.0|75.7|89.1|103.5|108.8|89.0|105.3|119.2|115.2|104.4|109.5|117.2|
|children|100.0|81.8|115.3|110.7|120.4|93.3|107.5|118.3|119.2|97.4|114.6|111.7|115.2|101.3|111.2|98.0|
|singleparent|100.0|90.7|90.1|79.4|101.6|91.3|99.0|104.0|104.6|111.8|117.0|118.2|108.2|104.4|110.4|114.9|

Source: Authors’ estimates based on HSS data.

41
Table 4: Willingness to Pay (in Terms of Estimated Impact)

||Pooled<br>(1)|Policymaker<br>(2)|Policy<br>Practitioner<br>(3)|
|---|---|---|---|
|Quasi-Experimental|-1.55|0.66|-2.89|
||(1.61)|(3.10)|(1.84)|
|Experimental|-4.83***|-5.26|-4.70**|
||(1.84)|(3.42)|(2.18)|
|Diferent country, same region|-2.07|-1.03|-2.88*|
||(1.57)|(2.95)|(1.74)|
|Same country|-6.70***|-5.77*|-7.55***|
||(1.73)|(2.97)|(2.09)|
|Recommended by local expert|-3.11**|-4.99*|-2.01*|
||(1.23)|(2.84)|(1.12)|
|Small C.I.|-3.67***|-1.21|-5.13***|
||(1.24)|(2.21)|(1.49)|
|Observations|818|407|411|

This table reports the results of conditional logit regressions in terms of the implied willingness-to-pay for programs with certain attributes. “Impact” is implicitly included in each estimate. For example, policymakers would only be willing to accept a program that was not recommended over one that was if the
- 3 – and examine data from Indonesia over the period 1997-2009.

Indonesia offers an interesting case. First, Indonesia is one of the largest economies in Asia, currently the world’s most dynamic growth pole. Its rapid rate of economic growth achieved during the last ten years suggests that the services sector will continue to grow in importance. Second, while Indonesia is relatively open in terms of trade in goods, restrictions on foreign participation in the services sector (either through trade or through FDI) are pervasive. Third, over the period of our analysis, changes in policy restrictiveness toward FDI in the services sector have been largely driven by commitments that the Government of Indonesia made to the International Monetary Fund (IMF) during the East Asian Crisis in 1997-8, and by other commitments to the WTO and ASEAN. This provides some comfort in the assumption we make in our empirical approach that the policy changes are not a consequence of the performance of manufacturers, but a cause. In other words, that policy changes are largely exogenously determined.

To gauge the effect of restrictiveness toward FDI in services, we combine manufacturing firmlevel data, from which we obtain a measure of total factor productivity, with the OECD indicator of policy restrictiveness toward FDI in the services sector weighted by the respective manufacturing sector’s reliance on inputs from each services sector, the latter piece of information being gleaned from Indonesian input-output tables. We include a comprehensive set of controls for other channels that may link reforms conducive to liberalization with manufacturing productivity, such as policy restrictiveness toward FDI in the manufacturing sector itself, tariffs on imported intermediate inputs, tariff protection on the same manufacturing sector and the share of output in the sector produced by foreign firms, plus firm-level fixed effects aiming at controlling for unobserved firm heterogeneity, and at capturing time-invariant effects specific to the industry and province wherein which the firms operate.

Our main finding is that, in Indonesia, relaxing policies toward FDI in the service sector was associated with improvements in perceived performance of the service sector,