rafmacalaba/data-use-mentions-extended huggingface dataset:
17

# **4. The Specific Benefits and Costs of Foreign Entry**

Openness to foreign entry is an important element in determining the degree of contestability in the financial system (Levine 1996 and Claessens and Glaessner, 1998). Entry policies for foreign financial institutions are determined by a variety of domestic regulations, some of which can be bound in international agreements such as the WTO Financial Services Agreement of December 1997. Table 3 presents a comparison of the degree to which some key countries have committed themselves under the Financial Services Agreement to permitting foreign commercial presence in the banking system and insurance sector (see further Qian, 1999). On this index (ranging from closed, 0, to 1, completely open), Japan and Argentina score the highest, 0.8, followed by Germany, US, and the UK. At the lower end of the spectrum are Chile, the Philippines and Thailand with a score of 0.2.

While liberal entry to foreign banks potentially lowers the franchise value of (domestic) banking institutions, empirical evidence suggests that foreign entry can provide important benefits to the domestic banking system. A study of 2,000 banks (of which 500 foreign) in 80 countries shows that larger ownership share of the banking system is associated with reduced profitability and lower overall expenses of domestically-owned banks (Claessens, DemirgucKunt, Huizinga 1998). These results suggest that foreign bank entry improve the fimctioning of national banking markets, with positive welfare implications for banking customers. Openness to foreign competition also puts additional pressure on domestic firms to improve their productivity and services, and allows firms access to foreign technologies and ideas to help them raise efficiency.<sup>2 2</sup> Argentina, Colombia, Hungary, Spain, Ireland, Portugal, and others both opened up internationally and deregulated rapidly domestically, and reaped substantial gains.<sup>23</sup> Moreover, cross-country experience indicates that increased foreign entry can bolster the framework for financial services provision: entry creates a constituency
# IV. **World Bank Lending for Private Sector Development in Education**

According to data from the World Bank's review, _Priorities and Strategies in Education_ (World Bank 1995b), the Bank was at the time of publication, the largest single source of external assistance for education in developing countries, accounting for about a quarter of all external support. In the span of 30 years from the first project in 1963 through 1994, about $19.2 billion worth (over 500 projects in more than 100 countries) was lent to developing countries to finance education projects. Both in absolute and relative terms there was significant expansion in lending for education between 1963-95. Bank lending for education increased from an average of US$914 million in 1988-89 to US$1.9 billion in 1995-96, a nominal increase of 108 percent (Bennell and Furlong 1997). Increase in lending has not been uniform in all regions. Bennell and Furlong (1997) report that in sub-Saharan Africa, for instance, Bank lending for education declined markedly from about US$400 million annually in 1992 and 1993 to a third of this level by 1996, representing barely 5 percent of total Bank lending in the region and less than 10 percent of total Bank lending for education.

In the span of the three years (1995-97) the Bank has provided about $4.9 billion to assist education reform efforts in its client countries. Overall Bank lending for education dropped from US$2.1 billion in fiscal year 1995 to US$1.0 billion in fiscal year 1997. This indicates a 51.5 percent decline in total lending. The trend varies from a sub-sectoral perspective: levels of lending are increasing for pre-primary and higher education; the scenario appears to be more or less constant for secondary general lending; lending for primary education declined from 38 percent in 1995 to 26 percent in 1997. The drop in lending could be attributed to better targeting.

12
‐output ratioK�/Y�|_(4)_|2.560|||
|Public capital share of total capitalK<sup>�</sup>/K|_(5)_|0.450|||
|Initialpublic capital‐to‐output ratioK�<br>�/Y�|_(6)_|1.152|||
|Initialprivate capital‐to‐output ratioK�<br>�/Y�|_(6)_|1.408|||
|**C. Projected Variables, C**<br>|**entral Pa**<br>|**th (2010/11—**|**30)**<br>||
|Investment‐to‐output ratioI/Y|_(7)_||0.215||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(7)_||0.086||
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(7)_||0.129||
|Human capital growthg�|_(8)_||0.010||
|TFP growthg�|_(9)_||0.014||
|Population growth rateg�|_(10)_|||0.016|
|Working age‐to‐population share, growthg�|_(10)_|||0.006|
|Labor force participation rate, growthg�|_(11)_||0.000||

- *Multiply by 100 to obtain parameter/variable values in percent share or growth terms (%). Note (source and calculation):

- _(1)_ Penn World Tables (PWT) 9: share of labor compensation in
monitoring, where Russian firms particularly lag behind other countries, are the ones more closely associated with firm performance. Finally, the link of management practices in affecting firm performance is particularly heightened for the globally linked firms.

The paper beyond this point is organized as follows: Section 2 introduces the methodology of the management survey and some descriptive results. Section 3 presents the methodology for computing management scores, and the main descriptive results on the relationship between various firm characteristics such as size, age with the observed management scores. This section also explores the drivers of better management practices. Section 4 presents a simple regression framework for assessing the link of management practices with firm performance. Section 5 concludes with policy discussion.

# **Section 2: The Management Survey**

# _2.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcomes on business performance, these practices are not well measured in administrative data on firms or in commercial data on companies’ accounts. Earlier studies have used information on publicly traded firms to link managerial style of senior managers and company performance (e.g. Bertrand and Schoar 2003), these data are rather limited to a country such as the US. Furthermore, evidence suggests that a company is far more than simply the identity of its most senior employee (Bender et al, 2015). Critically, this type of data on its own does not tell much about how firms are managed or organized. Therefore, over the last decade Bloom and co-authors have attempted to fill this gap in data by collecting comprehensive information on management practices (see Bloom et al. 2014).

Bloom et al. (2016) propose the following three broad management and organizational capabilities to be important for firm productivity: (i) performance monitoring: collecting and analyzing information on daily activities of the firm for continuous improvement, (ii) target setting: using and stretching short and long run targets, tracking outcomes, and taking
industries and increased in 11 of the 13 industries. The share decreased only in beverages and wearing apparel. The only industries in which Greater Accra did not have the largest share were again textiles, leather, wood, and not in wearing apparel, where Kumasi had the largest share. The region with the largest share of employment in furniture making became Greater Accra in 2014. This analysis suggests that the general trend was towards an increasing share of total manufacturing employment in Greater Accra, the most urbanized and most densely populated region in Ghana. The regression analysis below sheds further light on the processes that have contributed to this trend.

## **Industry‐district panel data**

The above descriptive analysis used the firm level data on regions and manufacturing subsectors to provide a broad overview of the changes in manufacturing in Ghana between 2003 and 2014. In this section we provide some descriptive statistics at the district and subsector level, since this is the unit of analysis in the regression work below.

Table 4 shows the 10<sup>th</sup> percentile, median and 90<sup>th</sup> percentile growth rates for each manufacturing subsector across all districts. Growth rates are calculated (in this table only) as suggested by Davis and Haltiwanger (1995):

# Emp growth= empt‐empt‐1)/(( empt+empt‐1)/2).

This is one way of avoiding very large growth rates having a disproportionate effect, but still making the resulting growth rates easily interpretable, which is not so easy when using the dependent variable used in the regressions. The growth rates calculated in this manner are bounded by ‐2 and 2. The median for all industry‐districts is 0.4, whilst the minimum is ‐1.83 and the maximum is 1.96. Table 4 shows that growth rates generally vary substantially within each industry across districts, even in industries that were shown in Table 2 above to have either grown or shrunk substantially
precipitation for each grid cell<br>from 1900-2014.|Matsuura and Willmott, (2012b)|
|Standardized Precipitation Index (SPI)|Gamma distribution fitted to precipitation<br>data to correct for the fact that<br>precipitation data is not normally<br>distributed.|Computed from Matsuura and Willmott,<br>(2012b) precipitation data.|

# **<u>Table A2: Country Composition</u>**

|**Country**|**Year**|**Country**|**Year**|
|---|---|---|---|
|Afghanistan|2014|Madagascar|2013|
|Albania|2013|Malawi|2014|

31
additional effort to gather the disaggregated empirical data necessary for estimating the various relationships, which is an important issue for future research. This requires additional surveys to be done of farmers’ water use and pumping practices; willingness to pay e.g. in terms of higher water prices for better service, including the value of water meters; surveys of additional features such as how crop choice might depend on effective water prices; and various features of pumping equipment. It would also be important in such surveys to include groups of farmers with water metering, wherever feasible. Such data should also include supply side data, and data for water table depth and magnitude of groundwater extraction externality, which are likely to vary significantly by location.

> 10 See also e g Bardhan and Mookherje (2006), Muhairwe (2009), and Walker et al (1999).

15
was stamped on a bus pass unique to that household.<sup>11</sup> Bus ridership records were kept by a supervising member of the field team and we report on these here.

In addition to collecting data on individuals and households, we also conducted in-depth interviews among minibus owners and drivers, of which two were providing bus services to the market town (not from the cluster of villages included in this study). Minibus owners and drivers near the market town were interviewed in order obtain a broader understanding of the market for transportation in Ntchisi district.

# **3. Results**

Results are presented in two parts: the first one relates to the take-up of the bus and presents what is the affordability level in this area and bus use when it is subsidized. The second part tries to assess if there is any price level, at which the bus provider would recover operational costs and then make private bus provision a viable option for transport firms.

## **Take-up of the bus service**

At the household level, more that 60 percent of households used the bus at least once with an average of 2.85 inbound rides. We focus on individual take-up in Figure 1. This figure presents proportion of individuals who used the bus at least once by randomly assigned price. There is a sharp decrease in ridership among those required to pay a positive price, especially prices above 100 Kwacha. These results are similar among men and women (not shown).

> 11 One bus pass was valid for all adult members of that household. The bus pass contained a photo and name of the adult members of the pass holder’s household and the price of each round trip to the market town.

7
## **3.2.1 A statistical validation of the instrument**

We test the validity of our instrument by assessing the probability that labor productivity performance has led to the installation of coal-fired power stations. This is done by assessing the effect of labor productivity on coal-fired stations. We include lagged and lead effects to rule out endogeneity. Lead effects are included to rule out the anticipated productivity effects on an expansion in electricity provision by building new plants, while lagged effects are used to rule out the impact of previous growth effects on the supply of electricity from coal-fired power plants. We also control for other effects, such as city sales and wages. We use a standard logit regression: The vector _X_ contains fixed effects, as well as the various lags and leads of labor productivity, while our dependent variable is a dummy ( _Dj,k,t_ ), taking the value of 1 in the year the power plant was built in city _j_ of region _k_ at time _t_ , and 0 otherwise.

Table 1 summarizes the results for our orthogonality test. Both lead and lagged labor productivity do not significantly impact the choice of building power plants. We conclude that our narrative regarding the exogeneity or the validity of the instrument is supported by the standard F-stat statistic (see IV first-stage estimates below), as well estimates from labor productivity on building power plants.

**Table 1:** Impact of labor productivity on building power plants

||Dummy|Dummy|
|---|---|---|
|log <sup>_Yi,t−_1</sup><br>_Ni,t−_1|-0.06<br>(0.03)||
|log <sup>_Yi,t_+1</sup><br>_Ni,t_+1||-0.06<br>(0.03)|
|FE (Year)|Yes|Yes|
|FE (Sub-Sector
where there is a risk that the randomization may not get implemented in practice or a risk that take-up of a program may be lower than anticipated.

However, when the outcome has low autocorrelation, the baseline does not reveal very much about likely future outcomes, and thus can be an expensive undertaking. Researchers attempting to implement large-scale evaluations with governments or NGOs, where there is reasonable uncertainty as to whether randomization will actually be carried out and whether they will have sufficient take-up. For example, consider a micro-savings intervention, where a government or NGO agree to randomly choose villages in which to introduce a new savings product. There is a risk that after doing baseline surveys in the treatment and control villages that the government changes its mind, and decides to introduce the product in all villages (or decides not to introduce the product at all). There is also a risk that no one uses the new product, in which case there is no possibility of measuring the impact of savings accounts on household outcomes. In such a case, practical considerations may suggest it is optimal to not carry out a baseline, and then wait and only carry out post-treatment surveys if the product is indeed randomly introduced and take-up is high.

# **5. Conclusions**

This paper has shown that when the autocorrelation in outcome data is low, as is common with outcomes of interest like business profits, expenditure, and income, the standard paradigm of single baseline and follow-up, followed by difference-in-differences analysis, is unlikely to be optimal. Large improvements in power can be obtained from multiple post-treatment measures in experiments with fixed treatment and control group sizes, and from using ANCOVA instead of difference-in-differences. Researchers choosing how to allocate a fixed budget over multiple surveys may find they can obtain more power by not conducting a baseline at all, and if they use a baseline, will often get more power doing two follow-up waves with a smaller cross-sectional sample size than a single follow-up with a larger cross-sectional sample.

These findings are particularly likely to be of
|**No.**|**City**|**Population, urban area**|**Population, city**|
|---|---|---|---|
|14|Kanpur|3,132,844|2,823,249|
|15|Kochi|3,123,271|604,696|
|16|Thrissur|3,122,168|325,110|
|17|Indore|3,053,112|1,837,041|
|18|Nagpur|2,908,845|2,228,018|
|19|Coimbatore|2,814,268|959,823|
|20|Thiruvananthapuram|2,624,797|784,153|

_Source:_ UN World Urbanization Prospects 2018, https://population.un.org/wup/Download/Files/WUP2018-F12-Cities_Over_300K.xls.

# Appendix 5: Participants in focus group discussions and interviews

## Focus group discussions

### Profile of participants

Female and male owners of property were recruited to take part in focus group discussions (FGDs). Since the research aimed to understand the two groups’ differences in their choices concerning home ownership and recurrent property tax compliance, to ensure a meaningful comparison, the groups’ profiles were designed along the following minimum parameters.

#### _1. Property ownership_

- All participants should have a residential property in their name, either single ownership or joint ownership (with a member of the opposite gender).

- Wherever possible, each FGD group would have an equal mix of single and joint owners to facilitate understanding of decisions around joint and single ownership.

- For male joint property owners, the joint ownership should be with a female. This was to understand more clearly decisions on property purchase/ownership in light of gendered discounts and the influence of these discounts on having a female co-owner.

- For male property owners
state, the estimated model generates a probability of becoming pregnant conditional on being alive at that state. I do need an estimate of the actual hazard process for my counterfactual analysis, however. To study the eect of dierent policy interventions on outcomes of lifecycle fertility and child mortality, I include the infection and mortality processes in the simulations.

I assume an actual HIV infection hazard rate with a functional form similar to that of the perceived infection hazard described in equation (2). Information about the hazard process is contained in the HIV test results, the age in which the tests were taken, and the ages in which a woman is last observed (regardless of testing histories). I dene _Hi_ to be a vector of a woman _i_ 's testing and survival information: the oldest age in which she had a negative test result (if ever), the earliest age at which she received a positive test result (if ever), and the latest age in which she is observed in the data. The probability of observing a woman with testing survival history _Hi_ , conditional on her type and observable elements of her initial state is given by I present the details of how this probability is computed in the appendix.

# 4.4 Type Distribution

A multinomial logit specication is used for the type probabilities. The probability that woman _i_ is of type _j_ is given by where _<u>wti</u>_ is a vector of woman's initial conditions, including region of residence, schooling level, age of marriage, whether she is married to a polygamous husband, year of birth, and the number of children she had in the rst period observed interacted with the age she was 25
novel high-frequency phone data used in the paper and present our empirical strategy. In Section 4 we present our main findings on how refugees experienced the pandemic over time as compared to nationals. In Section 5 we discuss the results and their policy implications.

# **2. Background**

## **2.1 The Kenyan refugee context**

Kenya is the second largest refugee-hosting country in Africa after Ethiopia, with an estimated 534,622 refugees and asylum seekers as of September 30, 2021. Refugee inflows started in the early 1970s and reached a peak in the 1990s with refugees today living in Kenya primarily stemming from Somalia (53%), South Sudan (25%), the Democratic Republic of Congo (9%), and Ethiopia (6%) (UNHCR Kenya, 2021). Refugees are hosted in three main locations: Dadaab camps in Garissa county (43%), Kakuma camp and Kalobeyei settlement in Turkana county (41%) and urban areas, mainly Nairobi (16%). As more refugees have entered the country, camps have grown to considerable sizes, with Kakuma and Dadaab camps hosting more than 200,000 refugees each in July 2020 (UNHCR Kenya, 2021; UNHCR Kenya, 2021). In both counties the camps serve as commercial hubs in an otherwise semi-arid infertile place and thus have grown to fulfill a central cultural, social, and economic role, for both the refugees and the national community.

With the influx of refugees in the 1990s, Kenya changed its aid-delivery approach to one based on temporary protection and containment. Instead of integrating refugees into the 4
Such ethnic inequalities matter for poverty reduction policy and income mobility. Two recent studies examine the interaction between ethnic inequality and poverty in Malaysia based on analysis of tabulated nationally-representative survey data for the period 1970–2016 (Ravallion, 2020a, 2020b). An important insight emerging from this work is that between-group relative inequality decreased over this time period, as a result of Bumiputera households having higher income growth rates than the other ethnic groups. Inequality at the national level also declined. However, Ravallion (2020a, 2020b) observes that the growth differential was not sufficient to reduce absolute differences in income, so that ethnic income inequality in absolute terms is higher now than 50 years ago. The difference is relevant, given that popular perceptions of inequality may well track absolute rather than relative inequality measures.<sup>2</sup> Ravallion (2020a, 2020b) further indicates that only about 10 percent of absolute poverty reduction in Malaysia, and 17 percent of the decline in relative poverty, can be attributed to reduced inequality between ethnic groups over this time. Growth of overall mean income has thus played a much more important role in reducing poverty than the fall in relative inequality between ethnic groups. Nevertheless, Ravallion (2020b) does emphasize the role of reduced ethnic inequality and notes that reducing it further may still matter for reducing poverty as measured by his weakly relative poverty line. Our study will show, however, that Ravallion’s conclusions should be viewed alongside findings about spatially differentiated poverty dynamics and inequality, between rural and urban areas, and between Peninsular and East Malaysia.

Conventional efforts to monitor poverty typically involve tracking measures such as the headcount ratio over time. Such studies provide insights into poverty trends at some aggregate level such as the country, community or sector, but are unable to shed light on the dynamics of poverty at the level of individuals or households. Yet, from both a theoretical and policy perspective, such micro-level poverty transitions are of considerable interest. If we observe chronic poverty, where individuals or