rafmacalaba/datause-encoder-data huggingface dataset:
or policy reach. As a result, some evaluations may show that in some countries, at some times, anti-corruption efforts are not worthwhile either because the political opportunities do not exist or because the welfare gains are not likely to be significant.

# _Conclusion_

As noted earlier, path dependency is critical in determining the relative efficacy of various anti-corruption programs. For example in a largely corruption-free environment, anti-corruption agencies, ethics offices, and ombudsman serve to enhance the standards of accountability. In countries with endemic corruption, the same institutions serve a function in form only and not in substance. Under a best case scenario, these institutions might be helpful but the more likely outcome is that they help to preserve the existing system of social injustice.

Successful anti-corruption programs are those which address the underlying governance failures, resulting in lower opportunities for gain and a greater likelihood of sanctions.'<sup>3</sup> Thus, programs have to be targeted to a country's existing quality of governance. Past experiences of industrialized world confirm these conclusions since, without exception, these countries did not achieve reduction in corruption by introducing technocratic solutions but, rather, by encouraging a sense of public duty among officials though accountability for results. Such an accountability culture came about by empowering people and decentralizing decision making (see Shah, 1999). These conclusions suggest the following stylized presentation of anti-corruption measures based on the existing quality of governance.

**Table 5: Effective Anti-Corruption Programs Based on Governance Quality**

|**Incidence of**<br>**Corruption**|**Governance**<br>**Quality**|**Priorities of Anti-Corruption Efforts**<br>**(Based on Drivers of corruption)**|
|---|---|---|
|High|Poor|Establish rule of law, strengthen institutions of participation and<br>accountability; limit government interventions to focus on core<br>mandate|
|Medium|Fair|Decentralization and economic policy reforms; results-oriented<br>management and evaluation; introduction of incentives for<br>competitive public service delivery|
|Low|Good|Explicit anti-corruption programs such as anti-corruption<br>agencies; strengthen financial management; raising public and<br>officials awareness; no bribery pledges, fry big fish, etc.|

1' The SDC experience supports this point. The SDC finds (personal communications with Gerolf Weigel and Alexandre Widmer) that in highly corrupt societies, three forces are essential for a fight against corruption:

- a well organized and motivated community in the directly concerned area, ready to defend its interests, combined with;

- well targeted institutional development and reforms at the higher levels;

- - an organized group of "activists" from the media can be very helpful to suppor **t** he government reform process through well timed and coordinated newspaper, radio or TV publications for increasing the pressure on parliament and politicians to support reforms.

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# **_4.3 Non-migrant women in migrant households_**

What proportion of non-migrant women live in households with migrant members during the period covered by the surveys? Using information from the household roster, supplemented with the matched parent-child identification data provided by the CHNS, we can link migration information of parents, husbands, and children with non-migrant women resident in the same households. Table 2 gives, by age cohort and by survey years, the percentages of women who were not themselves migrants at the time of each survey round but lived in households with migrant members. For the 16 to 20 age group, Table 2 reports parents’ and siblings’ migration status, while it shows husbands’ and children’s migration for the older groups.

In all age groups, an increasing percent of non-migrant women live in migrant households over time. About 11% of the youngest women did so in 1997, while 37% did in 2006. Both the incidences of having parents and siblings who migrate for work increase substantially over the nine year period from 5 to 17% and 5 to 21%, respectively.

Around 85% of non-migrant women in the 21 to 35 age group are married. Pooling the married and unmarried, we see that the share of women living in migrant households in this age cohort increases from 6 to 21%. Conditional on being married, 13% live in a migrant household and 10% have a migrant husband in 2006 ─ a share that increased significantly from 3% in 1997. Note that the sample size for this age group declines from 649 to 186, which reflects the higher attrition of young women both through migration and marriage.

Women aged 36 to50 and 51 to 60 are far more likely to live in a migrant household than younger women. Although the husband migration rate increases steadily from 3 to 12% and 1 to 7% over time, the migration of children is the dominant phenomenon. It rises from around 15 to 40% for both groups. Although it continues to increase over time, spousal migration for non-migrant women in the 51-60 age group decreases significantly when compared to that for the younger cohorts. Having a migrant husband is far less common for women aged 61 and above (2.5% in 2006) ─ consistent with the fact that men’s migration declines with age ─ as is having migrant children 13
After a model is selected, household expenditure or income data are projected using the model in the remaining fold, and a poverty rate and mean squared errors (MSEs) are estimated with the projected data. At the cross-validation stage, we project household expenditure or income data assuming the error term and regression coefficients follow normal distributions.

More specifically, suppose �<sup>�</sup> is a vector of estimated coefficients and ��<sup>�</sup> is an OLS estimator of error variance. We first draw a random value � from a chi distribution with a degree of freedom, �����, where _N_ refers to the total sample size and _k_ refers to the number of variables selected by the stepwise regression procedure, and calculate ����������/�. We then draw �<sup>�</sup> from a normal distribution of ��<sup>�</sup> , ����<sup>�</sup> ��<sup>��</sup> � where X is a ����� matrix of (��, ... , ��, ... , ���′ . Finally, we draw a simulated household expenditure or income for household _h_ , ln ���, from a normal distribution of ����, ����∗�� where ��∗� refers to an ����� identity matrix. This simulation process is repeated for all households, typically 20 times.<sup>16</sup> A poverty headcount rate is calculated by comparing the simulated household expenditure or income with a poverty line for each of the 20 simulation rounds. The average poverty rate of the simulations is used as a poverty estimate. MSE is calculated in testing data by taking the average of the sum of squared differ� ences between �� and ��� ��� ∗��.

This analysis is repeated 10 times, each of which uses a different fold as testing data to test the performance in terms of mean squared errors and the absolute value of the difference between the projected and actual poverty rates. This test detects the over-fitting problem because all testing statistics are calculated from out-of-sample. Figure 11 shows an illustration of a three-fold cross validation exercise.

_Figure 11 Illustration of 3-Fold Cross-Validation_ Step 1:Randomly split data into three folds (C refers to consumption; X refers to non-consumptiondata) <!-- Start of picture text --> Household Survey data Randomly Split<br>by three<br>Step 2: Select two folds as training data, develop a model there, and test model performance in the test-<br>ing data<br>Household Survey Data Training Data Testing Data<br>modeling Compare<br>)<br><!-- End of picture text --> Step 2: Select two folds as training data, develop a model there, and test model performance in the testing data Step 3: Repeat the above procedure three times by changing the testing data

> 16 This process can be done using STATA’s command “mi impute regress”, or STATA Corp LP (2013).

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Maystadt, Jean-François, and Gilles Duranton. 2018. “The Development Push of Refugees. Evidence from Tanzania.” _Journal of Economic Geography_ : 1–36.

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Peri, Giovanni, and Vasil Yasenov. 2017. “The Labor Market Effects of a Refugee Wave: Synthetic Control Method meets the Mariel Boatlift.” IZA Institute of Labor Economics Discussion Paper No. 10605 Peters, Michael. 2017. Refugees and Local Agglomeration - Evidence from Germany’s Post-War Population Expulsions. Unpublished. <u>https://pdfs.semanticscholar.org/92c8/7655808506311c7fbed760f8c917e556e9b6.pdf</u> Rozo, Sandra, and Micaela Sviastchiy. 2018. “Are Refugees a Burden? Impacts of Refugee Inflows on Host’s Consumption Expenditures.” Working Paper Unpublished. <u>https://www.sandravrozo.com/uploads/2/9/3/0/29306259/rsw_july2018.pdf</u> Ruiz, Isabel and Carlos Vargas-Silva. 2013. “The Economics of Forced Migration.” _The Journal of Development Studies_ 49(6): 772-84.

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# **I. Introduction**

Governments of developing countries typically spend resources equivalent to between 15 and 30 percent of GDP. Hence, small changes in the efficiency of public spending could have a significant impact on GDP and on the attainment of the government’s objectives whichever these are. The first challenge faced by stakeholders is measuring and scoring efficiency. This paper attempts such quantification. Additionally it verifies statistically some empirical regularities that describe the cross country-variation in the estimated efficiency scores.

The paper has four chapters following this Introduction. The first one presents the methodology that defines efficiency as the distance from the observed input-output combinations to an efficient frontier. This frontier, defined as the maximum attainable output for a given input level, is estimated using the Free Disposable Hull (FDH) and Data Envelopment Analysis (DEA) techniques. The exercise focuses on health and education expenditure because they absorb the largest share of most countries’ budgets, and because of lack of data availability for international comparisons in other types of expenditures.

The second chapter estimates the efficiency frontiers for nine education output indicators and four health output indicators, based on a sample of 140 countries and data for 19962002 Both input-efficiency (excess input consumption to achieve a level of output) and output-efficiency (output shortfall for a given level of inputs) are scored. The chapter presents both the single input-single-output and the multiple-inputs multiple-outputs frameworks. In addition, this chapter explores how expenditure efficiency has changed over time.

The third chapter seeks to identify empirical regularities that explain cross-country variation in the efficiency scores. Using a Tobit panel approach, this chapter shows that higher expenditure levels are generally associated with lower efficiency scores. Similarly, countries in which the wage bill is a larger share of the total budget tend to have lower efficiency scores. Three other variables that explain the cross country variation in efficiency scores are the degree of urbanization (positively correlated with efficiency, the prevalence of the HIV/AIDS epidemic (negatively associated with efficiency scores), and inequality in income distribution (higher inequality associated with lower efficiency).

The fourth and last chapter summarizes the conclusions.

# **II. Measuring Efficiency: Methodologies and Overview of the Literature**

The object of this chapter is to briefly describe the specific empirical methods applied in this paper to measure efficiency and to survey the literature more directly related to the analysis of public expenditure efficiency. Empirical and theoretical measures of efficiency are based on ratios of observed output levels to the maximum that could have 1
O.A.M.D.G.

This paper examines the reaction of a household’s portfolio to violence. It explores whether the differing robustness of assets to different kinds of violence plays a role in a household’s portfolio decision. We want to examine for example whether the balance between fixed and likely less productive mobile assets shifts towards the latter in the presence of some kinds of violence, and towards the former for others. If yes, this would have implications for the discussion about violence. A change in household saving behavior in the form of a decline in total savings and a shift of the remaining savings towards less productive assets would constitute longer-term damage to the economy; i.e. a greater loss than the immediate measurable decline in the destroyed capital stock, which is often cited in the literature and discussed in the context of the Solow or related macromodels.<sup>2</sup> From a Solow perspective, a sustained downward shift in the savings rate would lower the steady state of the economy, whereas a loss in the capital stock would only be temporary, as savings could ‘fill the gap’.

The economic literature has so far paid little attention to the impact of violence, especially in relation to household investment. Stewart and Venieris (1985) are the first to study the reaction of aggregate savings to socio-political instability. The authors use Sandmo’s (1969) two-asset model to illustrate the effect of greater instability. Political instability is assumed to increase the perceived risk and to lower the future expected income and the expected value of the return from risky assets. As a consequence, domestic savings (i.e. the savings subject to instability) decline. The authors confirm their hypothesis from 1960s macro data. They do not examine the relative behavior of domestic and foreign assets or the consistency with individual level data. Bohn and Deacon (2001) show with macro-data that violence, via ownership risk, can lower

> 2 ‘Modelling the impact of war in the Solow model’ seems to be a preferred undergrad topic, compare for example www.glue.umd.ed/~mjpries or faculty.tcu.edu/jlovett/int_macro/exams. Also consider Brück (1997) who shows the destruction of capital stock as the most obvious cost of war.

2
Buzby and Roberts (2010) analyze similar data on US import refusals to that used in the present paper, but only provide descriptive statistics. Baylis et al. (2009) is the first paper to investigate US import refusals empirically. In order to test whether import refusal decisions are biased, and what are the reasons for it, they look at a set of potential factors triggering refusals. They find that contrary to prior expectations, countries with experience in exporting products to the US are actually subject to relatively more refusals, thereby suggesting some degree of “stickiness” in the refusal determination process. Their findings also suggest that refusals are influenced by political pressure.

Related work has also been conducted on European food standards. Baylis et al. (2010) use data on EU import alerts—closely related to refusals<sup>2</sup> —in a gravity model to show that they tend to decrease trade.<sup>3</sup> However, they do not examine the determinants of import refusals, and in particular the potential for reputation effects, which is the focus of our paper. Baylis et al. (2011) use information on EU notifications and find that increased use of notifications is linked with a decreased level of protection through tariffs. They also see that European countries that would intuitively be demanders of protection tend to be at the origin of more notifications than their EU partners. Finally, the first stage of the empirical approach taken by Cadot et al. (2009), which tests whether import alerts contribute to increase trade costs and thus supplier concentration, uses EU import alerts as the dependent variable, but the cross-sectional setting of their regressions means that they are unable to account for reputation effects of the type we are interested in here.

In this paper, we use newly collected data to focus on one important example of the de facto implementation of product standards: refusal of entry into the US market for imported foods on safety 2 The terminology for the European and US systems differs. US alerts designate particularly sensitive categories of products for which pre-determined automatic refusal will apply (we discuss this further below). Under the EU system, alerts encompass both market notifications for products that are already circulating in the European market (alert and information notifications) and border rejections.

3 It is unclear whether Baylis et al. (2010) use only border rejections in their work or the total number of rejections and notifications. Presumably we believe this is the latter as in Cadot et al. (2009).

3
to be markedly consistent across definitions of RCAs.

# **IV Conditionality of the relationship between product relatedness and exports**

## **Firm-ownership type and trade type heterogeneity**

We now assess whether the relationship between product relatedness and exports depends on the ownership type (domestic or foreign) of exporting firms and the trade regime (processing or ordinary). One interesting feature of the customs dataset is that it allows us to identify whether the export flows emanate from domestic or foreign firms,<sup>21</sup> and correspond to processing or ordinary trade.<sup>22</sup> Processing trade includes all trade from firms operating in the assembly sector that import inputs to process them in China and re-export the final products (these producers benefit from a preferential tax regime on imported inputs). In 2006, 53% of Chinese exports were from the processing-trade sector. The processing trade is dominated by foreign entities; in 2006, these accounted for roughly 80% of processing-trade exports.

A number of studies have emphasized the disconnection between ordinary activities and those based on imported technology and foreign affiliates (Lemoine and Unal-Kesenci 2004; Hale and Long 2006; Blonigen and Ma 2010). We suspect that firms engaged in the latter activity are less embedded in their local environment and, consequently, that their export performance relates less to how well their products conform to the local production structure.

Table 5 distinguishes between domestic firms and foreign firms when considering product spillovers and exports. In columns 1 and 2, we calculate two density indices according to 22
time. In this case, after the model parameters of the financial variables are computed internally, the DM can use the manual estimation option to produce financial scenarios based on his/her future views or current market based data, considering that estimations based on historical data are not ―desired‖ reflectors of the future state of the economy. Forward looking scenarios can be generated based on medium-term program targets of the government, central bank inflation targets, or market expectations such as forward rates and implied option volatilities. For example, in exchange rate scenario generation, one can adjust the drift term in line with rate of increase implied by forward rates. Once the ―estimated‖ or ―user calibrated‖ parameters are determined for all financial variables, these are used to create final simulation paths.

The simulation algorithms then produce the summary statistics (mean, 70<sup>th</sup> percentile, etc.) regarding the distributions of financial variables. The DM can also interfere at this stage if the summary statistics of these simulated paths reveal that the estimations based on historical data deviates significantly from his/her market expectations. The DM could then calibrate the model parameters by ―trial-error‖ method until he/she reaches the desired distributions.

Even though, the ―manual‖ option provides additional flexibility, in the implementation of TDSM, scenario generation based on automatic model estimation is considered as the default case. In practice, no user calibration is performed without taking into account the scenario trees generated by using the selected financial models described in section 4.2.1.

Once the simulation paths are generated, they are exported to MS Excel platform<sup>13</sup> automatically, where monthly forecasts are converted to quarterly<sup>14</sup> —granularity of the model—data and then these quarterly paths are fed into the cash-flow engine module of TDSM in order to estimate cost and risk profiles of alternative debt strategies.

# **4.3 Cash-Flow Engine**

The cash-flow engine is where computations are carried out with regard to the borrowing requirement, the bonds issued, the payments and finally the output metrics, given the scenario paths. The central feature of the engine is the debt-stock matrix. This is a matrix where each row represents a different financing instrument (i.e., Treasury bill, coupon bond, floating-rate note, etc.) and each column represents a different attribute of the corresponding financing

> 13 Using MS Excel platform to transfer simulation paths to Matlab enables users to create their scenarios manually (deterministic scenarios) and feed them easily to the Cash Flow Engine.

> 14 Although, TDSM performs mainly on quarterly granularity, financial scenario generator is based on monthly historical data and subsequently provides monthly forecasts. Since historical financial data was restricted to the beginning of 2002, this approach enables to extend the length of data set.

27