rafmacalaba/datause-encoder-data huggingface dataset:
important is the Bank for Agriculture and Agricultural Cooperatives (BAAC), which practices individual as well as group-based lending (mainly to support farming), mobilizes savings as part of financial intermediation, and is widely considered to be a successful rural finance institution (Yaron 1992, Fitchett 1999). Therefore it is legitimate to wonder whether the VRF has an added value to rural households that the BAAC could not provide – are they substitutes or complements? In other words, the relative effectiveness of both programs is an issue worth examining from the policy point of view, an issue to which we return in section 5.

In short, our main task is to measure the impact of the VRF program on three outcome variables of interest:

- **Expenditure per capita.** The measure of expenditure available is based on the Socioeconomic Surveys of 2002 and 2004, and includes 56 categories of expenditure (and home production), including the rental value of housing, but does not include the rental value of the household’s durable goods or vehicles (for lack of data).

- **Income per capita.** This measure includes 24 categories of income, and includes the rental value of housing (but not of durable goods).

- A number of measures of **household assets** , including whether the household has a washing machine, a VCR, or a motorized vehicle.  The SES-2004 did not collect information on the total value of household assets.

But now we are faced with a methodological problem: VRF borrowers do not represent a random sample of the households (or adults) surveyed in the Socioeconomic Survey of 2004 – among other things, they are poorer and more rural.

To get around the problem of non-random assignment, we are obliged to turn to a number of econometric techniques. These include propensity score matching (section 5) and instrumental variables (section 6), using data from the Thailand Socioeconomic Surveys of 2002 and 2004. These surveys also included a panel of rural households, which allows us to estimate the impact of the VRF using double differences, and instrumental variables with household fixed effects (section 7). But before discussing the impact evaluation techniques and results, some additional description of the data is in order.

# **4. The Data**

The data for the impact evaluation come from the Thailand Socioeconomic Surveys of 2002 and 2004. The 2004 survey interviewed 34,843 households (covering 116,444 people) throughout the country drawn from 2,044 municipal “blocks” and 1,596 villages in 808 districts. The data were collected in four rounds, spread throughout the year. The survey collected a wide variety of socio-economic data, including relatively detailed information on household income and expenditure. It used stratified random sampling with clustering; all the _Village Fund, draft of June 29, 2009_ _Page 6 of 34_
# Relative Deprivation and Migratior': Theory, Evidence, and Policy Implications

by Oded Stark and J. Edward Taylor

# Table of Contents

<!-- Start of picture text --> I. Absolute and Relative Ilcome Hypotheses of Migration 2<br>A. A Relative Deprivation Hypothesis 4<br>B. An Integrated Approach 9<br>C. Reference Group Substitution, Labor Market Discontinuities, 12<br>and Destination Choice<br>II. Evidence from Mexico 15<br>A. Data 15<br>B. Estimation 18<br>C. Logit Findings 22<br>III. Conclusions 27<br>Appendix 30<br>Notes 34<br>Tables 37<br>References 41<br><!-- End of picture text -->
**3**

depth ratios (some measure of money relative to GDP) rose, in some cases considerably, and quasi-liquid liabilities, which can be thought of as a non-transactions component of broad money, expanded rapidly following reforms (Figure 2), except in New Zealand, where the innovation of electronic funds transfer directly to the point of sale led to a temporary shift toward narrow money within an overall financial deepening. The countries' financial deepening typically involved a broadening of the menu of assets available to firms and individuals, as appears for example in Korea (Nam, 1992], which is at least suggestive of a g- ater use of firance to hedge risks.

Assessing improvements in the efficiency of resource allocation also is problematic: the presumption is that an effective financial system will maximize efficiency by allocating credit to industries and firms where it can be best used. However, total factor productivity is subject to a myriad of influences, is difficult to measure on an economy-wide basis, and ideally a number of years -- pre- and post-reform -- would be desirable to sort out its determinants. Moreover, in most countries financial reform has been carried out simultaneously with significant policy changes in other areas, in particular in foreign trade, which also are likely to increase aggregate efficiency; hence the impact of changes in financial policy are often difficult to isolate. As noted in Schiantarelli et al (1992), the standard measure of efficiency, the incremental output-capital ratio (IOCR), only gives a good indication of efficiency under the assumption that factors of production are used in fixed proportions. For those who accept these simplifications, gains in the IOCR were seen in five of the seven cases examined in that study from the pre-reform period to 1988-89. And in Malaysia, which, along with New Zealand did not experience efficiency gains over this period, the comparison is biased by including pre-1973 data; more recently there has been a recovery of the IOCR there.<sup>5</sup> More tellingly, Schiantarelli et al show that firm level data in a few countries reveal an association between financial reforms and significant increases in the allocation of credit to efficient firms, even when the firms' efficiency is judged solely on the basis of pre-reform data.' In Ecuador, not only does average efficiency rise but the dispersion of efficiency
**Table 3: Regression results quantifying the correlation between CRI and the logarithm of relative price North Macedonia 2011-2022**

||**Log(Re**|**lative price) is the**|**main dependent**|**variable**|
|---|---|---|---|---|
|**Independent variables**|**(1)**|**(2)**|**(3)**|**(4)**|
|CRI|0.069***<br>(0.008)|0.173***<br>(0.009)|0.175***<br>(0.009)|0.179***<br>(0.013)|
|Contract value deciles||Yes|Yes|Yes|
|Contract supplytype||Yes|Yes|Yes|
|Contract market(CPV division)||Yes|Yes|Yes|
|Buyer Locations(NUTS codes)|||Yes|Yes|
|Buyer types|||Yes|Yes|
|Year||||Yes|
|Month||||Yes|
|N|113,747|113,747|113,747|113,747|
|R<sup>2</sup>|0.006|0.107|0.109|0.114|

Standard errors clustered on CPV divisions shown in brackets below the coefficients. The stars represent the significance levels *** p<0.01, ** p<0.05, * p<0.1 We also perform a similar analysis as above using each individual corruption risk indicator. For models 1 to 7 in Table 4, we use each corruption risk indicator as the main dependent variable and estimate its impact on relative prices. All individual risk indicators have a positive relationship with relative prices.<sup>6</sup> Eliminating single bidding from public tenders is associated with a 7.2% decrease in relative prices whereas relying on open procedures instead of high risk, non-open procedure types is associated with a 5.7% decrease in relative prices.<sup>7</sup> Similarly, avoiding higher risk, that is shorter, advertisement and decision periods is associated with a 6.2% and 1.3% decrease in relative prices, respectively. While full, detailed identification of causal impacts is beyond the scope of this paper, the results suggest that corruption risks are likely to have a considerable impact on prices and reducing those risks can help increase the procurement authorities' savings in procurement.

**Table 4: Regression results quantifying the correlation between corruption indicators and the logarithm of relative price  in North Macedonia 2011-2022**

|**Independent**<br>**variables**||**Log(Relative price)**<br>**Coefficient**<br>**(Cluster Std. errors in**<br>**bracket)**|**is the main dep**<br>**N**|**endent variable**<br>**R**<sup>**2**</sup>|
|---|---|---|---|---|
|**Model 1**||<br>0.072***|||
|Single bidding|High Risk|(0.006)|113,747|0.123|
|<br>**Model 2**||<br>0.057**|||
|Procedure type|High Risk|(0.020 )|113,747|0.094|
|<br>**Model 3**||<br>0.062***|||
|Advertisement<br>period|High Risk|(0.005)|113,747|0.097|
|**Model 4**||0.013***|||
|Decision period|High Risk|(0.002)|113,747|0.089|
|<br>**Model 5**||<br>0.038|||

6 The coefficient in Model 5 using Tax haven registration as the main indicator was not significant as there were only 12 instances within the analyzed sample.

7 Procedure type risk was switched to binary indicator for the price impact analysis because there was only one instance of the high risk type within the filtered analysis sub-sample.

27
follow-up action from the government, and if so, what these actions may be. In particular, the link between mandated government action on vaccination and previous experience with drastic population control policies has often led to a fundamental “trust deficit” that impinges and defines survey evidence to this date. See for instance Jegede (2007) on the Nigerian boycott of the Polio Vaccination Campaign or Hussain et al (2012) on continuing misinformation about polio vaccines in India leading to population resistance.<sup>2</sup> Further, the act of surveying in itself could change the surveyed populations’ behavior in the future; a hypothesis for which there is now some evidence. See Zwane et al. (2011) and Das and Leino (2011).

Second, what does informed consent really mean in a low literacy setting? Is consent really informed consent when the respondent does not fully understand the context of the research at hand? The broad notion of informed consent has, to our minds, an implicit social contract where the researcher believes that survey information will provide a better basis for decision making or deepen our understanding of the needs of the surveyed populations in the future. Actions stemming from the research will arguably help improve the lives of at least a subset of the surveyed population. But what should we do in cases where the surveyed population itself engages in practices that are not beneficial—and even harmful—to a broader population of individuals? We have in mind here, for instance, surveys of service providers, including teachers, doctors and licensing organizations. A number of studies in low-income countries have revealed that the behavior of such agents deviate sharply from that which professional ethics in their chosen profession would dictate and the neutrality of the survey team

> 2 Hussain et al (2012) note for instance: “ _Though most respondents supported the eradication program and vaccinated their children, many did not seem informed why the program had intensified the frequency of vaccination. Families described that when they asked the door-to-door vaccination teams why they visited them so often, they were usually not given an adequate response. Though the presence of medical interns helped, members of the vaccination team were observed sometimes providing dubious etiological explanations to the families: telling them that polio was “special” and needed a constant boost which other vaccines did not. One clinician who worked with routine immunization services explained that many patients did not understand why, whereas they vaccinated with BCG at fifteen days, DPT at one and a half months, and received a measles injection twice, they had to vaccinate for polio almost twelve times a year until they were about six years old.“_

4
employers were required to pay severance at the time of separation, to be funded by employers depositing a percentage of wages into guaranteed individual accounts. Kugler’s question was whether part of this obligation was shifted to workers in the form of lower wages. Using a difference-in-difference-in-difference estimator, she concluded that 60-80% of the total SPSA contributions were shifted to workers in the form of lower wages, thus disproving the view held by some that such mandates would be “free” to the workers.

Another interesting example of using panel data to estimate the effects of a labor market policy change is the study by Ravallion et al. (2005) of Argentina’s Trabajar Program, a kind of workfare. The program provides short-term work at relatively low wages to retrenched workers. Workers who are recruited to approved projects may work for up to six months in Trabajar jobs. Using a difference-in-difference-in-difference estimator, the authors concluded that workers suffered large earnings losses from retrenchment and experienced sizeable income gains from participating in Trabajar, while those who left the program lost considerable amounts relative to those who stayed.

Another subject that has been explored fruitfully using panel data in developing countries is income and earnings mobility. (“Income” denotes income from all sources, while “earnings” denotes income from wage employment, salaried employment, and selfemployment only.) One question that has been asked is, how have economic growth and economic decline affected income or earnings changes for different groups in the population: men vs. women, better-educated vs. less-educated, initially high earners vs. initially lower earners, and so on? Fields et al. (2007) studied this question for Argentina, Mexico, and Venezuela. They found that notwithstanding the high and generally rising earnings inequality in all three countries, both in times of economic growth and in times of economic decline, the largest average earnings gains in currency units were experienced by those who were initially at the bottom end of the earnings distribution. They also found that a key explanatory variable was the change in employment status between formally employed, informally employed, and unemployed. An ongoing World Bank – Inter-American Development Bank project (Duryea et al., 2007) has looked further into employment transitions and earnings changes, not only in these three Latin American economies but also in six transition economies. Their results reveal 37
of a common component. In order to lobby, rms have to pay a xed cost, and as a result there is selection into lobbying activity where only a small fraction of rms engage in this activity.

The presence of lobbying in an environment with nancial frictions simultaneously generates positive and negative eects on misallocation. Consequently, the eects are not unambiguously determined and depend on which force dominates. To study the aggregate eects of the credit shock, I calibrate the model using micro-data on lobbying expenditure and eective tax rates that corporations paid before the crisis, and I calibrate the credit shock to replicate the observed decline in the ratio of external nance to capital for the non-nancial corporate sector.

One of the main ndings of the paper is that lobbying increases the misallocation of resources that arises with nancial frictions when the economy receives a nancial shock. The presented model accounts for 80% of the decline in output and almost all the decline in TFP observed in the data by the end of 2009. Compared to an economy without lobbying, I nd that the lobbying economy amplies the distortions produced by nancial frictions, leading to a one-third larger decline in output. The model is also able to capture the increase in lobbying activity observed in the data, as well as the impulse responses of rms according to size and industry nancial dependence.

A derived implications is that, not only it is important to have policy tools during these events, but it is even more important how the policy is designed in order to be eective. As we have seen, the government provides tax advantages, but most of those resources are assigned to unproductive and wealthy rms, enhancing the misallocation of resources. In this environment, and given the same scal cost, policies that subsidize credit to those rms in distress are more eective to foster the recovery.

Finally, the paper also discussed long run implications of lobbying and policy reforms, focusing on output and TFP. Banning lobbying implies that in the long run output is lower due to a decline in capital accumulation, and TFP increases as a result of lower misallocation of capital. From a welfare perspective, this institutional change implies a gain of 0.3%. In terms of policy, an elimination of all capital tax breaks while keeping the revenue neutral through reductions in the corporate tax rate have similar results in terms of signs, but the magnitudes are magnied. In this case, welfare increases by 1.1%.

One limitation of the analysis is the fact that rms can only adjust the production margin with the nancial shock, which is a direct result of the perfect competition framework. However, the empirical evidence suggests that market power is a relevant feature in modern economies and therefore rms can also adjust prices during a downturn. The interaction between nancial conditions and market power has been studied by Giuliano and Zaourak (2015) and by Gilchrist et al. (2015). The incorporation of market power in this framework is left for future research.

43
<!-- Start of picture text --> 15.0%<br>14.0%<br>13.0%<br>12.0%<br>11.0%<br>10.0%<br><!-- End of picture text --> <!-- Start of picture text --> 15.0%<br>14.0%<br>13.0%<br>12.0%<br>11.0%<br>10.0%<br>9.0%<br>8.0%<br>7.0%<br>6.0%<br>2008 2009 2010 2011 2012 2013 2014<br>—®— without deflation —@®—food deflator to all<br>—®— food to food, rent to rent —®— food to food/nonfood, rent to rent<br>0.40<br>0.39<br>0.38<br>0.37<br>0.36<br>0.35<br>0.34<br>0.33<br>0.32<br>0.31<br>2008 2009 2010 2011 2012 2013 2014<br>—®— without deflation =@— food deflator to all<br>—®— food to food, rent to rent —®— food to food/nonfood, rent to rent<br><!-- End of picture text -->
35

|Country Group/Country|Range|of Data|Wage|Fracti|on of Em|ployees|
|---|---|---|---|---|---|---|
||First|Final|Mal|es|Fe|males|
||Year|Year|First|Final|First|Final|
||||Year|Year|Year|Year|
|Nicaragua|1963|1971|.56|.57|.61|.69|
|Panama|1960|1980|.37|.60|.78|.89|
|Chile|1960|1982|.75|.58|.78|.66*|
|Uruguay|1963|1975|.73|.71|.77|.78|
|Bolivia|1950|1976|.46|.39|.16|.41|
|Brazil|1960|1980|.42|.63|.51|.76|
|Ecuador|1950|1982|.44|.49|.85|.64*|
|Guyana'|1946|1965|.64|.73|.66|.67|
|Paraguay|1972|:982|.36|.38|.52|.46*|
|Peru|1961|1981|.48|.48|.51|.41*|
|Venezuela|1961|1981|.57|.65|.75|.78|
|Africa (North mainly)|||||||
|Maritius|1962|1972|.84|.86|.91|.89*|
|Reunion'|1961|1982|.70|.78|.82|.93|
|Cameroon|1976|1982|.22|.22|.03|.03|
|Algeria|1966|1977|.67|.71|.74|.96|
|Egypt|1960|1976|.50|.64|.59|.83|
|Libya|1964|1973|.55|.61|.36|.58|
|Tunisia|1956|1975|.44|.66|.06|.43|
|Botswana'|1694|1981|.14|.50|.04|.35|
|East Asia|||||||
|Hong Kong|1958|1981|.65|.87|.62|.93|
|South Korea|1960|1980|.25|.47|.16|.37|
|Indonesia|1965|1978|.27|.40|.29|.36|
|Philippines|1960|1978|.27|.41|.35|.41|
|<br>Singapore|1957|1980|.74|.79|.74|.90|
|Thailand|1954|1980|.10|.26|.02|.17|
|South and West Asia|||||||
|Bangladesh|1961|1974|.43|.30|.30|.19*|
|<br>India|1961|1971|.47|.57|.26|.53|
|Iran|1956|1976|.45|.55|.58|.47*|
|Nepal|1961|1976|.20|.15|.10|.07*|
|Pakistan|1951|1981|.16|.26|.15|.38|
|Sri Lanka|1963|1981|.59|.59|.84|.79*|
|Bahraina|1971|1981|.80|.89|.96|.99|
|Kuwait|1965|1980|.83|.88|.97|1.00|
|Syrian Arab Republic|1960|1970|.52|.44|.53|.40*|
|<br>United Arab Emiratesa|<br>1975|1980|.90|.93|.97|.99|
|Cyprus|i976|1982|.62|.65|.59|.61|
|Israel|1972|1976|.77|.73|.84|.81*|
|Turkey|1975|1980|.36|.45|.09|.14|

'Excluded from decompositions because regression sample required real GNP figures, which were not available. Thus of the 75 cGuntries listed only 61 are used in theis paper.

*Female participation rate decreased over interval.
||Title|PPR Workinc Pauer Series<br>Author|Date|Oontmac<br>for paper|
|---|---|---|---|---|
|WPS31|Public Finance and Economic Development|Bela Balassa|August 1988|N. CAmpbell<br>33769|
|WPS32|Municipal Development Funds and<br>Intermediaties|Kenneth Davey|July 1988|R. Blade-Charest<br>33754|
|WPS33|Fiscal Policy in Commodity-<br>Exporting LDCs|John Cuddington|July 1988|R. Blade-Charcst<br>337S4|
|WPS34|Fiscal Issucs in Macroeconomic<br>Stabilization|Lance Taylor|September 1988|R. Blade-Charest<br>33754|
|WPS3S|Improving the Allocation and Manage-<br>ment of Public Spending|Stephen Lister|August 1988|R. Blade-Charest<br>33754|
|WPS36|Means and Implicationis of Social<br>Security Finance in Developing<br>Countries|Douglas J. Puffert|August 1988|R. Blade-Charest<br>33754|
|WPS37|Black Market Premia, Exchange Rate<br>Unification and Inflation in<br>Sub-Saharan Africa|Brian Pinto|July 1988|R. Blade-Charest<br>_33754_|
|W?S38|lntergovernmental Grants in<br>Developing Countries|Larry Schmreder|September 1988|R. Blade-Charest<br>33754|
|WPS39|Fiscal Policy in Low-Income Africa|Stephen_A._O'Connell|July 1988|R. Blade-Charest<br>33754|
|WPS40|Financial Deregulation and the<br>Globalizaaion of Capital Markets|Eugene L Versluysen|August 1988|R. Blade-Charest<br>33754|
|WPS41|Urban Property Taxation in<br>Developing Countries|William Dillinger|August 1988|R. Blade-Charest<br>33754|
|WPS42|Government Pay and Employment Peicies<br>and Government Performance in<br>Developing Economies|David L Lindauer|August 1988|R. Blade-Charest<br>33754|
|WPS43|Tax Administration in Developing<br>Countries: Strategies and Tools<br>of Implementation|Tax Administration<br>Division|August 1989|WDR Office<br>31393|
|WPS44|The Size and Growth of Government<br>Expenditures|David L Lindauer|September 1988|R. Blade-Charest<br>33754|

7 A _-._ '1 i.