rafmacalaba/data-use-mentions-extended huggingface dataset:
37

|Table A3: A Brazilian Spatial Price Index (RM Sao Paulo = 1.0)|
|---|
|PNAD Region<br>Spatial Price Deflator|
|RM Fortaleza<br>1.014087|
|RM Recife<br>1.072469|
|RM Salvador<br>1.179934|
|Northeast (other urban areas)<br>1.032056|
|Northeast Rural<br>0.953879|
|RM Belo Horizonte<br>0.958839|
|RM Rio de Janeiro<br>1.002163|
|RM Sao Paulo<br>1.000000|
|Southeast (other urban areas)<br>0.904720|
|Southeast Rural<br>0.889700|
|RM Porto Alegre<br>0.987001|
|RM Curitiba<br>0.987001|
|South (other urban areas)<br>0.904720|
|South Rural<br>0.889700|
|RM Belem<br>1.088830|
|North (other urban areas)<br>1.032056|
|RM Brasilia<br>1.037915|
|Center West (other urban areas)<br>0.968388|

Note: This regional price index is based on the consumption pattems and implicit prices from the PPV 1996 survey, for the Northeast and Southeast regions, and extrapolated to the rest of country according to a procedure specified in Ferreira et. al. (1999), where the exact derivation of the index is also discussed in detail.

We assume, largely due to the lack of earlier comparable regional price information, that the structure of average regional cost-of-living described above remained constant over the period. Temporal deflation was undertaken on the basis of the Brazilian consumer price indices IGP-DI (for 1976), and INPC-R for the three subsequent years. For 1996, the INPC-R was upwardly adjusted by 1.2199, so as to compensate for the actual price increases which
with more than 100 employees, bribes as a percentage of the output of small plants and big plants, among the plants who paid bribes, and bribes as a percentage of the total output of small plants and big plants regardless of bribes being paid. I use the average of the 2009-2014 data for the plant size distribution moments and I use the 2013 WBES for bribery moments. Tables 6 and 7 summarize the parameter values and the calibration performance.

The assumed distribution function is able to generate the U.S. plant size distribution. Moreover, although there is some room for improvement with more data and careful parametrization, the model is able to generate not only the plant size distribution of Türkiye but also the relative bribery payments of different size groups. The estimated average effective tax rates for small and big plants are 28.3% which implies that bribing is optimal for all plants at the Türkiye’s benchmark calibration. Even if one cannot directly relate the fraction of the plants that accept that they paid bribes in the data and the probability of being assigned to the corrupt official in the model, the inferred probabilities are different from the fractions that are calculated from the Enterprise Survey data.

# **6 Results of the Experiments**

In this section, the results of the experiments are presented. First, I introduce the size dependent distortions and the bribery opportunities to the U.S. economy. Then I run experiments with Türkiye’s economy, which is calibrated with the size-dependent distortions and the bribery opportunities. These involve analyzing the effects of changing the size dependency of the distortions, the return on bribes, and the probability of meeting with a corrupt official. Then I discuss the consequences of removing bribery and distortions. I compare the new steady-state values of the output, mean size, the employment share of big enterprises, the tax wedge, average effective tax rate, and bribery expenditure with the benchmark steady-state values. Specifically, the tax wedge is defined as following: where _y_ ̄ and _b_
# **Table 2: Enumerator’s demographics, enumerator’s attrition, and length of tenure in survey operation**

||Long-term<br>enumerator|Number of days of<br>work|
|---|---|---|
|Enumerator gender (male)|-0.019|-2.769|
||(0.104)|(2.675)|
|Enumerator age|0.013|-0.421|
||(0.012)|(0.367)|
|Enumerator education|-0.034|-2.27|
||(0.198)|(4.415)|
|Enumerator from urban locality|0.026|-0.851|
||(0.101)|(2.791)|
|Observations|54|54|
|R-squared|0.335|0.362|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : Team (language group) fixed effects and a constant are included in all regressions but not shown. _Long-term enumerator_ is a dummy which takes value 1 if the enumerator worked at least 25 days (which corresponds to the 75<sup>th</sup> percentile in the distribution of the number of work days during the survey) and 0 otherwise. _Number of days of work_ is the total number of days the enumerator worked during the survey period. Robust standard errors in parentheses are clustered at the enumerator level.

27
generated by stepwise selection – both approaches produce remarkably similar results in a simulation of a 40% programme coverage simulation with data from Indonesia, despite relying on different mechanisms. Compared with a policy baseline PMT that requires 22.8 questions to achieve an EER of 26.44%, truncation only needs 14 questions for a similar EER, and the early stopping method requires 18. A combined method that leverages their respective advantages achieves a superior length-accuracy trade-off at any point, and requires a mere 8.9 questions for an equivalent EER.

The methods presented here provide policymakers a choice on cost versus accuracy to suit their context and constraints. When selecting a point that maximizes incidence in a rudimentary simulation of an urban setting, a considerable improvement (to [78.3 %] incidence) appears feasible over both current practice ([65.6%]) and a more streamlined approach based on 28
/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their Portuguese achievement in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1) in the paper, where the dependent variable in all columns is the Portuguese test scores. In column (1) we show results for treated schools with high Portuguese test scores in 2007, in column (2) schools with medium Portuguese test scores, and in column (3) schools with low Portuguese test scores. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weightsts. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these
||(0.67)|(0.61)|(0.55)|(0.60)|(0.52)|(0.38)|
|---|---|---|---|---|---|---|
|Number of children 14 to 17|0.70|0.58|0.45|0.30|0.26|0.21|
||(0.79)|(0.67)|(0.60)|(0.58)|(0.51)|(0.45)|
|Urban|0.84|0.86|0.85|0.83|0.86|0.84|
||(0.37)|(0.35)|(0.36)|(0.38)|(0.35)|(0.37)|
|Other adult female in the household|0.35|0.31|1.00|0.19|0.19|1.00|
||(0.48)|(0.46)|(0.00)|(0.39)|(0.40)|(0.00)|
|Number of people working at|||||||
|household|1.65|1.62|1.78|1.38|1.43|1.56|
||(0.96)|(0.88)|(0.88)|(0.76)|(0.78)|(0.84)|

_Source:_ Underlying data are from the Chilean socio-economic household survey (CASEN) and administrative data from the Ministry of Education for 1990–2011.

32
Total expenditures are the sum of direct expenditures and intergovernmental expenditures and represent final sector expenditures. All sector expenditures can be broken down into recurrent expenditures used to pay for wages and salaries to employees, purchase goods and services, pay for repairs, utilities, etc., and capital outlay expenditures including spending on capital repairs, purchase of fixed assets, land, etc.

The education and healthcare expenditure figures can be further broken down by sub-functions and rayons. To align better the BOOST/Treasury data with the information available in the Household Budget Survey for Moldova, the analysis allocates regionally disaggregated sectoral spending to custom variables, following the logic of the survey. Each variable in Table 6 can be briefly summarized as follows:

- “ **Health: in-hospitals** ”: the category covers government spending on in-patient care. The category generally includes government spending on in-patient care services provided in rayons, as well as general and specialized inpatient care services provided in the Municipality of Chisinau. About 63% of overall in-patient care expenditures took place in the capital city, being driven primarily by the budgets of in-patient care providers of republican significance (e.g. national hospitals, institutes, etc.).

- “ **Health: out-of-hospitals** ”: the category is comprised of government spending on several budget sub-functions, namely – “30 Outpatient specialist care”, “31 Laboratories/diagnostics”, “40 Outpatient primary care”, “41 Home care”, and “70 Emergency care”. About 48% of overall combined expenditures on these sub-functions were spent in the Municipality of Chisinau, largely due to the providers of national significance located in the capital of the country.

- “ **Education: preschool** ”: the variable captures government spending on pre-school education institutions. About 27% of expenditures on pre-school education in 2017 were spent in the Municipality of Chisinau, mainly due to the greater number of preschoolers residing within the boundaries of the municipality.

- “ **Education: school”
the circumstance, different theoretical motivations may drive FDI. Our innovation is to frame these distinct cases in terms of emerging versus advanced economy target acquisitions.

While a small number of papers have relied on cross-border M&A data (di Giovanni 2005; M ́eon & Delannay 2006; Rossi & Volpin 2004), these have tended to be relatively limited in terms of time period and/or country coverage, and none have explicitly focused on M&A _by_ emerging economies. Finally, unlike several recent papers, we eschew an explicit focus on policy- or politically-related factors driving FDI—such as political risk (Busse & Hefeker 2007) or investment agreements (B ̈uthe & Milner 2008; Neumayer & Spess 2005)—and instead embed these factors in our overall gravity framework.

The paper is organized as follows. In the following section, we describe some broad stylized patterns of M&A activity originating from emerging economies (Section 2). This is followed by an overview, in Section 3, of the different theoretical streams that have informed economists’ understanding of bilateral M&A (and FDI, more generally). Section 4 follows with a description of the dataset, econometric specification, and estimation methodology. Benchmark results and robustness checks follow (Section 5), before a final section concludes with some brief thoughts on policy and future research directions.

3
# **Annex 2: Selection bias analysis**

As detailed in section 2, the average characteristics of firms included in the survey differ substantially from those of the sample frame, a fact which suggests the presence of non-random selection into the survey. In table A.2 we present the results of a regression estimated on the whole set of firms in our sample frame in which the dependent variable is a dummy that indicates whether the firm was successfully interviewed or not. The regressors correspondent to the firm’s age, its size in terms of employment, its export activities according to customs data and the federal district of location. The results show that older, bigger and exporting firms had a higher probability of being successfully interviewed.

<u>Table A.2 Selection bias in survey response</u>

|<br>Dep. var.: ==1 if successful interview|<br>(1)<br>OLS|<br>(2)<br>Probit|
|---|---|---|
|Log firm age|0.012<sup>**</sup>|0.088<sup>***</sup>|
||(0.005)|(0.032)|
|Log employment|0.043<sup>***</sup>|0.269<sup>***</sup>|
||(0.006)|(0.031)|
|Exporter in 2016, customs data|0.021<sup>**</sup>|0.125<sup>***</sup>|
||(0.008)|(0.041)|
|Central federal district|-0.004|-0.019|
||(0.016)|(0.096)|
|South federal district|-0
, Watermelon. ∗∗_ Basket consists of 1 unit of each: _Aibika, Amaranthus-Aupa, Banana-Cooking, Banana-Ripe, Broccoli, Cabbage-English, Capsicum, Carrot, Cassava, Choko-Tips, Cucumber, Ginger, Lemon, Lettuce, Onion Bulb, Orange, Pakchoi, Pawpaw, Peanut, Pineapple, Sweet Potato, Pumpkin-Tips, Taro True, Tomato, Watermelon. ∗∗∗_ Basket consists of 1 unit of each: _Aibika, Amaranthus-Aupa, Banana-Cooking, Banana-Ripe, Broccoli, Cabbage-English, Capsicum, Carrot, Cassava, Choko-Tips, Cucumber, Fern, Ginger, Lemon, Lettuce, Onion Bulb, Orange, Pakchoi, Pawpaw, Peanut, Pineapple, Sweet Potato, Pumpkin-Tips, Taro True, Tomato, Watermelon, Wongbok. Source:_ The statistics have been prepared by the author for this paper based on end-of-July (2022) food price data from IFPRI. The FCS average has been reproduced from table A1 in Andr ́ee (2021) who presented identical estimates for 25 FCS countries (excluding PNG).

The first selection tries to optimize for what benefits historical studies and the key challenge is the lower data availability, the third selection tries to optimize for monitoring purposes of current trends and the key challenge is the shorter time dimension which makes trend modeling more difficult. In all cases, the foods are all fresh produce vegetables, and the baskets have strong item overlap. The application keeps the item weights fixed at 1 KG of each item when constructing the basket price. The resulting price index is a Laspeyres index that does not seek to adjust for changing consumption behavior that could in part be induced by relative price rises. This is appropriate in an extreme-poverty or food-insecurity context in which food accounts for the majority of household expenditures and shifts toward less nutritious foods may keep basket prices equal and at the same time create food and nutrition security risks.

Taken together, the data selection simulates the real-world challenge: when a new survey program is deployed, it is possible to design it so that a high number of food items will
excluded since their initial enrollment decisions would have depended on conditions in their natal homes rather than in their current residence. These criteria yield a sample of 4,699 children, of which 2,192 (47%) are girls and 2,507 (53%) are boys.

Table 1 provides some descriptive statistics for the sample, highlighting, in particular, the importance of the school entry margin in rural Pakistan. Also evident is the substantial gender gap in schooling, one that varies tremendously by region. In the more developed North Punjab, 92% of boys and 85% girls had enrolled in school by age nine, whereas the poorer southern part of Punjab and the entire province of Sindh have much lower enrollment rates (just above 50% for girls) and huge gender gaps favoring boys (17 and 24 percentage points, respectively). By contrast, dropout rates from primary school are much less differentiated by gender and region, perhaps because many girls who would have dropped out never entered school to begin with. Conditional on having enrolled in school and not completed the primary grades, 88% of 9-15 year-old boys are still in school compared to 81% of girls, with gender gaps of 5, 5, and 9 percentage points in North Punjab, South Punjab, and Sindh, respectively.<sup>3</sup>

# 3 Context

The term "village" in much of Pakistan, and indeed in large parts of South Asia, has a dual connotation. On the one hand, the administrative or revenue village is the lowest tier of the government’s administrative structure, at which public services, such as schools and health workers, are allocated. Most administrative or revenue villages, however, consist of several distinct clusters of habitations or hamlets; though most residents would also refer to these as "villages", we will call them "settlements." Among the 165 PHRS-II villages, for example, our village census records 1031 named settlements. However, villages of North Punjab
0141|-0.0136|
|3rd wealth quintile||||||-0.0036|-0.0017|
|4th wealth quintile||||||-0.0168|-0.0126|
|5th wealth quintile (richest)||||||-0.0149|-0.0092|
|Rural area|||||||0.0205*|
|Central province|||||||-0.0353|
|Coast province|||||||-0.0124|
|Eastern province|||||||-0.0343|
|Nyanza province|||||||-0.0166|
|Rift Valley province|||||||-0.0206|
|Western province|||||||-0.0258|
|North Eastern province|||||||-0.0137|
|Constant|0.0302***|0.0161|0.0063|0.0078|0.0085|0.0117|0.0088|
|Number of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0009|0.0014|0.0074|0.0085|0.0138|0.0148|0.0166|
|F-statistics|0.3099|0.5431|1.6654|1.3013|1.2799|1.1153|0.9228|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother
more prominent in smaller farmers. Figure 4.1 illustrates graphically the results from Table 4.2 and shows how beyond a certain point increasing the use of fertilizer has an adverse effect on yields.

**Figure 4.1 Urea and rice yields in 2007 –2SLS model estimation**

<!-- Start of picture text --> Rice yields (kg/ha)<br>2SLS Model Estimation<br>Actual urea use (kg/ha)<br>0 200 400 600<br>0<br><!-- End of picture text --> _Source_ : World Bank staff calculations.

The analysis in this section supports the notion that urea usage has a positive impact on yields, with the exception of the larger farms, but it still leaves some questions unanswered. It does not answer whether the costs at which this improvement in yields is achieved outweigh the benefits. Neither does it discuss whether alternative public spending (on irrigation, improved R&D or extension services) may actually have a larger impact on productivity than the subsidization of fertilizer.

To assess the benefits of fertilizer subsidies in terms of rice production, one can look at the elasticities estimated in the model in Tables 4.2 and 4.3 and calculate the decline in urea usage and in yields that an increase in the price of the fertilizer would cause. A removal of the fertilizer subsidy and therefore an increase in prices from the average urea price paid by rice farmers according to the 2007 survey (Rp 1,524/kg) to the estimated market price of Rp 2,170.5/kg would translate into lower consumption of urea and a decrease of 6.3 percent in yields per hectare. (That is, assuming the effect of all other inputs is constant.) If one translates this decline to the total rice production for 2008, then, production would have been 3.8 million tons lower (from the estimated production of 60.3 million tons by
# **2. Conceptual Framework**

Public officials in developing countries are often viewed as unskilled, poorly educated and poorly motivated to perform their official tasks. The assumption is frequently made that public officials are primarily motivated to exploit any official privileges that their positions give them to engage in opportunistic behaviors. Given the poor performance of public bureaucracies in many developing countries, and the undeniable reality of the low public sector salaries, such an assumption may not be wholly unrealistic. Yet, there are many public officials who, despite such a poor incentive environment, may perform their tasks without shirking, stealing or subverting in spite of overwhelming incentives for opportunism. For every rent-seeking or poorly motivated public official, there may be another who is providing a valuable service under extremely adverse circumstances. There is perhaps more to be learnt from an examination of why some public officials perform against the odds than from reciting the standard litany of failing public services.

This paper starts from the premise that public officials' actions - and hence the performance of their organizations - depend upon the institutional environment in which they find themselves. In this way the analytical framework avoids the anti-government bias that pervades much of the literature on the public sector in developing countries, without attempting a defensive justification for poor performance.<sup>4</sup> The analytical model allows verification of the assumption that it is the institutional environment that impacts on the performance of organizations in the public sector rather than the waywardness or malfeasance of officials. Accordingly, this model suggests that reform interventions that improve any or all aspects of the institutional environment will result in higher perfornance of some or all of the areas in which performance is measured.

# **Figure 1. Analytical model for BNPP-funded surveys**

- Institutional environment: Performance: * policy credibility i * results focus * rule credibility * accountability . resource adequacy and * employee morale predictability

# _2.1 What is meant by institutional environment?_<sup>_5_</sup>

Institutions are the humanly devised
# **IV. SERVICES**

# **A. Conceptual and Measurement Issues**

There are no comparable international data that permit a systematic examination of the state of policies regarding restrictions on trade in services in developing or developed countries. The reasons are well known: because many of the modes of delivery of services are intangible, there are no barriers that take the form of tariffs. Barriers take the form of quantitative restrictions -- sometimes involving complete bans, and many times government regulation.

Some of the latter may be explicitly discriminatory against imports of services or foreign providers; others may be of a general regulatory nature that applies equally to national and foreign services providers. And to date no general description of the barriers to trade in services that cuts across all four modes of delivery (cross border supply, consumption abroad, commercial presence, temporary entry) and all sectors has been attempted -- and may not be feasible with the present state of data collection.

Some general impressions can be obtained as to what the actual policy situation tends to be by using a recently developed World Bank data base whose results are summarized in Table 13. This database describes a new initiative to collect comparable information on services trade policies for 103 countries, five sectors (telecommunications, finance, transportation, retail and professional services) and the key modes of service supply. It contains richly textured policy information as well as a preliminary quantification of policy measures across a range of service sectors and the relevant modes of service delivery (Borchert, Gootiiz and Mattoo, 2012). The first impression from the existing data is that developed countries have liberalized far more than developing countries, especially in the financial, telecom and retail services sectors. The differences are smaller in the transport sector and even less in the professional services—which 59