rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

# V. **3 Public Finances and Fiscal Policy**

As we indicated in the opening of the paper, since Turkey opened up its economy through a series of reforms in the 1 980s, establishing fiscal discipline has been a key policy challenge. Capital flows to T urkey have been associated with large public sector borrowing requirements and over time external flows became increasingly important in financing the deficits. This situation distorted the flow of funds in the domestic financial system in favor of the public sector. The large borrowing requirements have also been associated with growing share of interest payments, and large turnover of domestic debt. While a significant portion of capital account have been short term borrowing and short term portfolio flows, a predominant share of domestic public debt has been of short term maturity. This problem in turn has been affecting all dimensions of policy management in Turkey's open economy. Charts 5-7 summarize the evolution of key variables.

**Chart 5**

**Turkey: Public Sector Borrowing Requirement**

<!-- Start of picture text --> 16.<br>10<br>1990___ 1991___ 1992__ 1993___ 1994_________1996__1997_<br>12 16 8<br>4<br>-2<br>1989 1990 1991 1992 1993 1994 1995 1996 1997<br>E= Consolidated Budget X Social Security<br>UinNon-Financial SEEs -_Total PSBR<br><!-- End of picture text --> Note: Consolidated budget balances were before transfers to SEEs and Social Security institutions
||(0.939)|(0.933)|(0.938)|(1.089)|(0.918)|(0.992)|(0.989)|(1.000)|(0.996)|
|---|---|---|---|---|---|---|---|---|---|
|Conscientiousness (Z score)|0.070|0.199|0.138|-0.086|0.265|0.138|0.193|0.081|0.128|
||(0.906)|(0.832)|(0.870)|(0.951)|(0.915)|(0.943)|(0.942)|(0.930)|(0.936)|
|Openness (Z score)|0.159|0.172|0.166|0.004|0.125|0.081|0.138|-0.088|0.006|
||(0.923)|(0.866)|(0.893)|(1.043)|(0.928)|(0.972)|(0.977)|(1.010)|(1.003)|
|Emotional stability (Z score)|0.248|-0.064|0.084|0.396|-0.147|0.050|0.412|-0.174|0.070|
||(0.911)|(0.871)|(0.903)|(0.953)|(0.969)|(0.997)|(0.909)|(0.924)|(0.962)|
|Agreeableness, cooperation (Z score)|0.031|0.134|0.085|-0.208|0.114|-0.003|0.016|-0.007|0.003|
||(0.928)|(0.897)|(0.913)|(1.071)|(0.966)|(1.017)|(1.009)|(0.990)|(0.997)|
|Grit (Z score)|0.111|0.175|0.145|0.006|0.098|0.065|0.140|0.093|0.113|
||(0.882)|(0.855)|(0.868)|(1.036)|(0.935)|(0.973)|(1.014)|(0.915)|(0.957)|
|Decision making (Z score)|0.007|0.162|0.088|-0.115|0.198|0.084|0.058|0.015|0.033|
||(1.009)|(0.893)|(0.952)|(0.999)|(0.908)|(0.953)|(1.018)|(0.983)|(0.998)|
|Hostile attribution bias (Z score)|--|--|--|0.056|-0.007|0.016|0.055|0.020|0.034|
||--|--|--|(1.080)|(0.947)|(0.997)|(1.001)|(0.982)|(0.990)|
|Risk taking (Z score)|--|--|--|0.133|-0.101|-0.016|0.024|-0.029|-0.007|
||--|--|--|(1.076)|(0.918)|(0.984)|(1.011)|(0.997)|(1.003)|
|Time preference (Z score)|--|--|--|0.039|0.001|0.014|-0.040|-0.078|-0.062|
||--|--|--|(1.018)|(1.002)|(1.007)|(0.976)|(0.956)|(0.965)|
|Metropolitan area of residence|0.284|0.335|0.311|0.264|0.200|0.223|0.499|0.504|0.502|
||(0.451)|(0.472)|(0.463)|(0.442)|(0.400)|(0.416)|(0.500)|(0.500)|(0.500)|
|Observations|412|454|866|254|446|700|713|995|1,708|

Notes: The sample is of men and women between the ages of 25-54 in urban Armenia, Boliva, Colombia, Georgia, Ghana, Kenya, Serbia, Ukraine and Vietnam for whom observations for all variables included in the full regression model are available.

28
**3. Model**

The general model we wish to test, for both air and water pollution, is similar in nature to the one developed by MV (1990) and LR (1996).'5 These authors have developed a structural model of pollution whereby inspections along with a number of structural variables (such as industrial sector of activity, size, location, and time) are determinant of pollution emissions. In the current paper, an important difference is the inclusion of the pollution levy as a determinant of environmental performance. In China, the probability of inspection and effective pollution levy together determine the expected penalty for noncompliance with the existing regulatory standards.

While we are generally interested in estimating the impact of inspections on the pollution emissions, a difficulty is that inspections at any given time t may themselves be a function of pollution at time t (that is, the regulator may observe pollution at time t, and then decide whether or not to inspect at time t). In other words, inspections may themselves be endogenous and correlated with the same variables which determine current pollution levels. If this is the case, least-square estimates will be biased in general. In fact, both MV (1990) and LR (1996) have rejected the hypothesis that inspections were exogenous. As a result, MV (1990) have included in their analysis a vector of only past inspections. LR (1996) on the other hand have preferred to estimate an inspection equations and to reestimate their basic inspection model by instrumental variables using expected inspections as instruments. Interestingly, these last authors have found that the probability of an inspection in any given period is a decreasing function of past inspections - the regulator's monitoring strategy thus being akin to one of sampling without replacement.

Given these results, the basic system of equations of interest in this paper is of the following nature:

> **_15_** Our previous work with the Industrial Pollution Projection System (IPPS) does not suggest that there is a need to be concerned with a potential correlation between air pollution and water pollution.

12
Table 3: Program impacts on antisocial behaviors

|||||I|TT regres|sion (N= 94|7)||
|---|---|---|---|---|---|---|---|---|
||||Ther|apy only|Cas|h only||Both|
|Outcome|Round|Control|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||mean|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Antisocial behaviors, z-score|2–5w|0.151|-0.249|[.088]***|-0.079|[.091]|-0.308|[.089]***|
||12–13m|0.032|-0.083|[.093]|0.132|[.097]|-0.247|[.088]***|
|Usually sells drugs|2–5w|0.166|-0.077|[.027]***|-0.041|[.029]|-0.076|[.028]***|
||12–13m|0.135|-0.034|[.029]|0.035|[.030]|-0.059|[.029]**|
|# of thefts/robberies in past 2|||||||||
|weeks (sum of 8)|2–5w|2.577|-0.841|[.400]**|-0.770|[.409]*|-1.236|[.407]***|
||12–13m|1.839|0.073|[.395]|0.352|[.388]|-0.728|[.363]**|
|Disputes and fghts in past 2|||||||||
|weeks (mean of 9), z-score|2–5w|0.076|0.013|[.092]|0.027|[.091]|-0.132|[.076]*|
||12–13m|-0.060|-0.026|[.091]|0.100|[.090]|-0.100|[.077]|
|Carries a weapon on body<sup>_†_</sup>|2–5w|0.157|-0.086|[.034]**|-0.045|[.037]|-0.093|[.035]***|
||12–13m|0.148|-0.059|[.031]*|0.043|[.035]|-0.066|[.033]**|
|Arrested in past 2 weeks|2–5w|0.139|-0.011|[.027]|0.006|[.027]|-0.013|[.029]|
||12–13m|0.118|-0.006|[.024]|0.007|[.025]|-0.033|[.024]|
|Aggressive behaviors (mean of|||||||||
|19), z-score|2–5w|0.102|-0.208|[.081]**|0.008|[.085]|-0.196|[.087]**|
||12–13m|0.188|-0.153|[.110]|-0.043|[.107]|-0.339|[.109]***|
|Verbal/physical abuse of partner|||||||||
|(mean of 4), z-score<sup>_†_</sup>|2–5w|-0.035|-0.087|[.111]|0.091|[.114]|-0.032|[.115]|
||12–13m|-0.071|0.142|[.100]|0.233|[.113]**|0.059|[.104]|

_Notes:_ The table reports intent to treat estimates of the effect of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. For instance, thefts/robberies is the sum of 8 kinds of crimes; disputes/fights is the standardized mean of 9 kinds of physical or verbal altercations with peers, community, and authorities; aggressive behaviors is the standardized mean of 19 possible types of aggression and hostility; and verbal and physical abuse of partners is the standardized mean of 3 forms of verbal abuse of intimate partners plus one form of physical abuse. (For the latter two cases, we report standardized indexes since the incidents are measured on a 0–3 frequency scale, and the absolute sum itself has no interpretation.) The overall summary index is the standardized mean of these seven composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1

> _†_ These variables were not collected during every phase/round, so their regressions have a smaller sample size.

19
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countries in Latin America, are in this category. Thirteen countries had a minimum loan size requirement of ten times or more GNI per capita. Saudi Arabia (USD 1.3 million), Jordan (USD 1.4 million) and Germany (EURO 1.5 million) are the countries with the largest cutoff requirements where the credit registry is used mostly for bank supervision and systemic risk monitoring. Regarding the countries analyzed in this paper, Argentina has a very low minimum cut-off, of 50 Argentine pesos (previously US$50 and now roughly US$18), Brazil has a minimum of US$ 2,100 down from US$ 50,000 several years ago and Mexico begins at US$ 20,000.

The supervisory agenda is also often apparent in the type of data collected in a PCR. The most obvious example of this is the lack of transparent data on payment status for loans, which is the heart of most private credit bureaus. Only about one-third (18/57) of PCRs collect a straightforward borrower payment history which shows the number of on-time and late payments and defaulted loans and which indicates the extent of arrears (30 days, 90 days, 180 days late, etc.). To some extent this data is contained in ratings as described above, since payment status is a key determinant of a rating. However, since other more subjective factors can bear on the rating, such as a bank’s estimate of the probability of default, ratings data are less transparent and more difficult to verify than objective payment data. Another variable which is very valuable for credit evaluation but often neglected by public bureaus is address information, which is lacking in about 40% of PCRs. Other data which are important for assessing creditworthiness, such as employment data and demographic data are also absent from a significant proportion of PCRs.

# **_3. Economic research, macroeconomic policy and control_**

Credit bureau information can also be important for macroeconomic intelligence. Credit bureau information can be considered by region, by sector, by quality of borrower and used to analyze the credit market and interest rate developments in detail. Moreover, the dynamics of assetquality can be highly illuminating in assessing activity and growth, by region or sector and macroeconomic risks. For example, an increase in the movement of loans to lower grades in a particular sector or region may highlight particular economic problems and risks in that area or sector. This information may be invaluable for a Central Bank to understand the transmission of 19
have lost 19 percent of their predisplacement earnings (which accounts for both lost hours and lowered wages once the worker has re-entered the workforce) as compared to the 28 percent loss experienced by unskilled workers. Additionally, Hummels et al ...nd that it is essential to control for endogeniety of trade events in such an analysis.

An additional ...rm-level study by Sethupathy (2009) shows that over the period in which the North American Free Trade Agreement (NAFTA) came into force, lowering costs of o¤shoring within North America, US ...rms that already o¤shored to Mexico signi...cantly increased: (i) their o¤shoring to Mexico; (ii) their operating pro...ts per US worker; and (iii) the wages they paid to their US workers –without, apparently, reducing their US workforce. This suggests that some of the productivity bene...ts of the Grossman and Rossi-Hansberg (2008) model have been realized by those ...rms, and bene...tted those workers, but it should be noted that this ...nding does not imply any bene...t to workers outside of those ...rms (unlike the Grossman/Rossi-Hansberg model).

In sum, recalling the account in Section 2.3.1, there are two main stories that have emerged from the theoretical literature, ...rst, that o¤shoring can raise wage inequality in both countries as in Feenstra and Hanson (1996); and second, that o¤shoring can raise the real wages of unskilled workers by enhancing their e¤ective productivity, as in Grossman and Rossi-Hansberg (2008). These two predictions are distinct, but not mutually exclusive. There is now fairly strong evidence for the ...rst story, the income-distribution e¤ect, from multiple data sets and approaches. The second story has done less well when confronted with data, at least when the focus is on o¤shoring to low-wage-countries.<sup>5</sup>

# 3.4 Implicit Contracts.

Empirical tests of implicit-contract models with trade are rare. Bertrand (2004) points out that if implicit contracts are very e¤ective, then a workers’wages will be a¤ected by labor-

> 5It is worth noting that in the Bernard, Jensen and Schott (2006) and Ebenstein et al (2009) papers, Mexico is not classi...ed as a low-wage country.

40
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average access statistics. Additional data on quality, costs and institutional aspects should also be collected in this way. Finally, econometric issues linked to the use of this type of data are also discussed, one key aspect being the endogeneity of firms’ technological choices.

In summary, this paper’s recommendations converge to the combination of three main components at the macro-, micro- and economic geography level. A drive to strengthen collection of microeconomic data through both household and firm-level surveys, considering the need to take into account institutional constraints (ownership of surveys) and limitation in their current design, should support major theoretical and empirical efforts at the macro-level (especially with regards to the assessment of the impact of political, institutional and regulatory aspects on the delivery and efficiency of infrastructure services), and at the economic geography level.

Infrastructure is understood in this paper to include the following sectors: Energy, transport, telecommunications, water and sanitation. Although some of the material discussed bears on developed countries, the focus of the conclusion is on low and middle income transition and developing countries. The structure of the paper is as follows. Section 2 presents the theoretical foundations of the effect of infrastructure on growth and other development outcomes in the context of growth theory and of the new economic geography literature. It then discusses to what extent the literature reviewed provides answers to the two broad set of issues mentioned above. Section 3 reviews how these insights have been taken to the data. It starts with an overall assessment of the empirical literature based on the analysis of 140 specifications from 64 papers between 1989 and 2007, looking in details at the type of data used, the level of aggregation, the technique, the nature of the sample, etc. It then discusses macroeconometric contributions, looking at the questions addressed, the main methodological issues and the limitations of this approach. Next, it reviews studies including geographical insights. Microeconometric contributions are mentioned, and finally a review of lessons according to the two set of issues above is performed. Based on this, Section 4 spells out what appear to be the most promising directions for future research, highlighting key short to medium term working objectives. Suggestions of priorities in data development are presented.

5
**Figure 5:** Living below the USD 1.90 per day poverty line.

- **(a)** Predictions from interaction model.

<!-- Start of picture text --> .9<br>.8<br>.7<br>.6<br>.5<br>Men Women<br>Host IDP<br>E[Poor | gender, X]<br><!-- End of picture text -->

- **(b)** Average marginal effect of being an IDP.

<!-- Start of picture text --> .3<br>.25<br>.2<br>.15<br>.1<br>Men Women<br>AME (IDP)<br><!-- End of picture text --> 26
**<u>Table 5: Returns to education allowing for nonlinearity and endogeneity</u>**

|||Men|||Women|
|---|---|---|---|---|---|
||(1)<br>lnw|(2)<br>lnw|(3)<br>lnw|(4)<br>lnw|(5)<br>(6<br>lnw<br>ln|
|Years of schooling 1 to 7|0.13|0.14|0.12|0.05|0.06<br>0.|
||(0.101)|(0.102)|(0.107)|(0.080)|(0.083)<br>(0.0|
|Years of schooling 8 to 11|0.18*|0.19*|0.15|0.13|0.15*<br>0.1|
||(0.097)|(0.098)|(0.103)|(0.083)|(0.087)<br>(0.0|
|Years of schooling 12 to 17|0.19*|0.20**|0.18*|0.18**|0.19**<br>0.|
||(0.101)|(0.102)|(0.105)|(0.086)|(0.088)<br>(0.0|
|**Years of schooling 1to 7 X short**|||0.06||**0.1**|
||||(0.042)||**(0.0**|
|Years of schooling 8to 11 X short|||0.05||‐0.|
||||(0.036)||(0.0|
|**Years of schooling 12 to 17 X short**|||**0.06****||**0.0**|
||||**(0.027)**||**(0.0**|
|Years of schooling 1 to7 X proxy|||0.00||0.|
||||(0.054)||(0.0|
|Years of schooling 8 to 11 X proxy|||0.06||‐0.|
||||(0.044)||(0.0|
|Years of schooling 12 to 17 X proxy|||0.01||0.|
||||(0.034)||(0.0|
|�<sup>�</sup>: Treatment variables Short, Proxy|no|yes|yes|no|yes<br>ye|
|� : control function term m/f|yes|yes|yes|yes|yes<br>ye|
|� : District dummies|yes|yes|yes|yes|yes<br>ye|
|Observations|192|192|192|99|99<br>9|
|R‐squared|0.486|0.496|0.517|0.453|0.464<br>0.5|

Standard errors, clustered at the household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1. All regressions include control variables: age, age squared, mean distance to nearest all season road, and a constant

**Table 6: First stage to obtain control function term**

||Men<br>Years ofschooling|Women<br>Years ofschooling|
|---|---|---|
|Community mean distance to primary school|‐0.21|‐0.55**|
||(0.203)|(0.244)|
|Community mean distance to secondary school|‐0.17**|‐0.15*|
||(0.078)|(0.088)|
|Community mean distance to nearest all‐season road|0.05|0.06|
||(0.106)|(0.094)|
|� : District dummies|yes|yes|
|Observations|192|99|
|R‐squared|0.324|0.391|

Standard errors, clustered at the household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1. All regressions include control variables: age, age squared, and a constant.

32
basic parameters and the mechanisms for disbursing funds. Project implementers are also extremely important to the effectiveness of projects. Frontline staff who work directly with beneficiary groups are especially critical actors in building participatory processes. They are expected to mobilize communities, build the capacity for collective action, ensure adequate representation and participation, and, where necessary, break through elite domination. They must be culturally and politically sensitive, charismatic leaders, trainers, anthropologists, engineers, economists, and accountants. Despite their centrality, however, there is virtually no generalizable evidence on their role. What evidence there is comes from case studies, which present a somewhat dismal picture.

Jackson’s (1997) analysis of field worker diaries in India indicates that field staff tend to be driven by the incentives they face, which are often not well aligned with the needs of the project. In particular, Jackson notes that field workers tend to gloss over local power relations in a rush to show results. Vasan (2002) shows how facilitators in the forest management projects she examined overlooked project goals when their personal incentives were not aligned with them. Botchway (2001), studying participatory projects in Ghana, notes that project facilitators, who are typically young, inexperienced, and poorly paid, are often vulnerable to manipulation by locally powerful elites. Michener (1998) echoes this and points out that senior members of the implementing agency are often former bureaucrats with little experience in community empowerment. Their vision of development, guided by a lifetime of work with line ministries, is closer to “supply driven demand driven development” (Serrano 1996; Tendler 2000; Tendler and Serrano 1999).

44
tification more seriously in the following section, using an SVAR approach.

An alternative tack would be to use a narrative of changes in tax policies in the spirit of Romer and Romer (2010). To date, however, a historical narrative of the changes in tax policy, together with the rationale for their change (endogenous/exogenous to economic conditions) is not available for a large sample of countries.‘ We estimate the following equation Ayn = @+ BAT rH + YZnt + Ents (1) where Ay,,; is the growth rate of (the change in log) real GDP, AT,4-1 is the lagged change in the tax rate in country n, and Z,,; is a vector of controls, some dated t and some t — 1. We include the lagged unemployment rate (UR(—1)); Barro and Redlick (2010) also argue for including an exogenous shock as a covariate. In their case they include the (squared) spread between long-maturity Baa-rated bonds and Treasury bonds of similar maturity as a measure of tightness of credit conditions. For our sample, including smaller countries, we found it more appropriate to consider an external shock. We include a trade shock variable, defined as the weighted average of the GDP growth of each country’s trading partners. Each country’s trade shock is then scaled by the country’s exports-to-GDP ratio, as a shock to trade might have a larger effect on countries more open to trade. Results are also robust to inclusion of the lagged growth of government expenditure, although unlike Barro and Redlick (2009), who include military expenditure only, inclusion of total government expenditure raises concerns of reverse causation. We estimate (1) in a panel of 28 countries, of which 19 are developing and 9 are high-income, based on World Bank classifications. Estimates reported <u>~ 4Tp addition to the US narrativbye</u> Romer and Romer (2010), Cloyne (2011) compiled a similar narrative for the UK.

9