rafmacalaba/datause-encoder-data huggingface dataset:
# **Short- and Long-Run Impacts of Food Price Changes on Poverty**

In the recent period of high and volatile food prices, there has been considerable concern about impacts of high food prices on global poverty. A number of analyses have concluded that, in most developing countries, higher food prices raise the poverty headcount in the short run because not enough poor farming households benefit sufficiently from higher selling prices to offset the negative impacts of higher food prices on net food consumers (Ivanic and Martin 2008; De Hoyos 2011; Ivanic, Martin, and Zaman 2012). This is despite the well-known stylized fact that three-quarters of the world’s poor live in rural areas, and most depend on agriculture for their livelihoods. A simple explanation for this apparent contradiction is that many of the poorest farming households are actually net buyers of staple foods.

Deaton and Laroque (1992) describe the price behavior of storable commodities as being characterized by long periods in the doldrums, punctuated by intense but short-lived price spikes. Much of the concern about high food prices in recent years has arisen in a context of intense price spikes such as those in 2007–8 and 2010–11. If the poor are adversely affected by such surges in food prices, and if they have little ability to buffer the effects of these price shocks, then these periods of high prices—whether seen as a problem of high prices or as a problem of volatility—would appear to be a serious concern.

For some types of price changes, such as those resulting from sustained changes in the global agricultural supply-demand balance of the type that have occurred since 2000, the focus of attention needs to be on the longer-run impacts of price changes. In this case, if supply responses are sufficient and/or wage rates for unskilled labor change substantially, the poverty impacts of food price changes might be reversed. Clearly, this could have major implications for policy, with policy makers focused on reducing poverty perhaps welcoming, rather than fearing, high food prices. Whether this is the case is likely to depend heavily on the income sources of households and the responsiveness of household farm output to price changes, and can only be determined empirically.

Most of the available analyses of the impacts of food price impacts on poverty follow the classic Deaton (1989) study in taking into account the first-order impacts of higher food prices on household incomes as determined by the initial net-sales position of each household. Some, in addition, consider second-order impacts of food prices through changes in the quantities of food 2
|1|164|Slovenia|1994|-2.9%|-0.7%|
|---|---|---|---|---|---|
|1|224|Suriname|1985|-0.9%|-1.3%|
|1|166|TFYR Macedonia|1979|-1.7%|-0.2%|
|1|103|Thailand|1990|-0.6%|-0.4%|
|1|136|Ukraine|1976|-4.0%|-0.9%|
|**2. Countries w**|**ith peak bet**|**ween 2000 and 2010**||||
|2|38|Algeria|2005|-1.3%|0.8%|
|2|213|Argentina|2030|0.3%|0.1%|
|2|107|Azerbaijan|2007|-1.3%|0.7%|
|2|46|Botswana|2008|-0.2%|0.0%|
|2|215|Brazil|2024|-0.3%|-0.4%|
|2|216|Chile|2009|-0.1%|0.0%|
|2|205|Costa Rica|2008|0.0%|-0.2%|
|2|188|Dominican Republic|2027|0.2%|-0.2%|
|2|238|Fiji|2002|-0.2%|-0.8%|
|2|191|Haiti|2030|0.0%|0.1%|
|2|97|Indonesia|2002|-0.3%|-1.0%|
|2|83|Iran|2004|-2.5%|0.9%|
|2|192|Jamaica|2011|0.4%|-0.7%|
|2|85|Kyrgyzstan|2008|-0.1%|-0.5%|
|2|116|Lebanon|2012|0.4%|0.0%|
|2|47|Lesotho|2006|-0.8%|-0.1%|
|2|40|Libya|2030|-1.1%|0.1%|
|2|209|Mexico|2012|0.2%|-0.7%|
|2|247|Micronesia (Federated States of)|2024|-0.5%|-1.3%|
|2|76|Mongolia|2006|-0.9%|-0.6%|
|2|41|Morocco|2029|-0.2%|0.0%|
|2|100|Myanmar|2009|-0.1%|-0.4%|
|2|132|Republic of Moldova|2002|-3.6%|-0.3%|
|2|198|Saint Lucia|2006|-0.7%|-0.1%|
|2|199|Saint Vincent and the Grenadines|2001|-2.2%|-1.3%|
|2|36|Sao Tome and Principe|2029|0.5%|0.1%|
|2|49|South Africa|2014|0.3%|-0.4%|
|2|89|Sri Lanka|2000|-1.1%|-0.4%|
|**Classific**|**U.N.**|**Country**|**Year of**|||

**Average annual**

48
worthwhile since the costs to be apportioned resulted<sup>from an</sup> estimation procedure and were thus<sup>approximate. The criteria</sup> by which costs were distributed<sup>to direct service departments</sup> are shown in Table 3.

# Main methodological problems

At this point it is useful to point out the main<sup>problems</sup> encountered so that the results can<sup>be viewed in the light of</sup> these problems.

Staff costs. The use of average salaries<sup>to calculate total</sup> salary and wage costs may introduce inaccuracies.<sup>This was</sup> checked by comparing the total salary and<sup>wage bill according</sup> to this analysis (excluding the pension contribution) with total actual expenditure. In general,<sup>actual and estimated expenditure</sup> were extremely close. Subjective estimates had<sup>to be relied on</sup> to allocate the time of staff who had<sup>duties in more than one</sup> cost centre. This was, however, a relatively<sup>small proportion</sup> of total staff.

Vehicle runting costs. Time permitted<sup>the analysis of logbooks</sup> for only one sample month, and<sup>logbooks for all vehicles in use</sup> i;i 1987/8 could not always be located.<sup>The majority of logbooks,</sup> however, ~ere analysed.

Drugs. Again, time constraints meant<sup>that the time period used</sup> to analyse **drug** supplies was very<sup>short. It did seem, however,</sup> that wards ordered what they needed<sup>on a day-to-day basis and</sup> did not keep large stocks. The period studied<sup>should therefore</sup> be reasonably representative, at least of that time of year.
# Barrier or Opportunity? How Trade Regulations Shape Colombian Firms’ Export Strategies<sup>*</sup>

## Samuel Rosenow

_International Finance Corporation, World Bank Group World Trade Institute, University of Bern_

**JEL Classification** : F13, F14, L11, O19.

**Keywords** : Trade Policy, Colombia, Non-Tariff Measures, Firm Heterogeneity

*I thank Angella Montfaucin, Ana Fernandes, Miriam Manchin, Joseph Francois, Mahdi Ghodsi, Massimiliano Cali, Ralf Peters, Doug Nelson and participants in the Galbino project summer school for their comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent. All errors are mine.
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positively and significantly associated with the implied privatization sales value.

In an earlier 1999 paper, Wallsten estimated non-sequentialized models for 30 Latin American and African countries. The effect of having a regulator was never significant at the 5% level or better for any of the telecom indicators. However, a variable interacting privatization and regulation was positive and significant for mainline penetration, number of payphones and amount of connection capacity. This model included a variable intended to capture competition in fixed line services. However, the proxy – the number of wireless operators owned by a company other than the incumbent – is a relatively poor one.

# **5.3.3 Gutierrez (2003)**

Gutierrez (2003) concentrates on estimating the impact of regulation on telecom outcomes in 22 Latin American and Caribbean countries. As with the other telecom papers discussed above, he uses panel data techniques which he applies to the experience over the period 1980-1997.

The main importance of this paper is that:

- (a) the regulatory variables used are an <u>index</u> (or sub-indices) derived from a number of characteristics of regulatory governance;

- (b) it reports tests for endogeneity of competition, privatization and regulation; and

- (c) it reports results for static and dynamic panel models as well as testing for fixed versus random effects

The index used in this paper is a 7-item index with each element being given a 16.7% weight. The index is made up entirely of legally defined measures. There are no items on regulatory practice (e.g. turnover of regulatory commissioners) or quality, not least because of the problems of collecting such data for other than a very small sub-sample of countries.

The measures in the index are:

- 1) A Legal factor – whether the telecom regulator (for countries where an independent regulator is present) was established by a primary law or a decree;

- 2) A Separation factor – whether operating and regulatory factors were separated, irrespective of whether the regulatory activities were carried out by an autonomous regulator;  and

- 3) A group of 5 factors labelled “ITU/Ten” incorporating issues of autonomy, accountability and clarity of roles.  These last were factors generally identified with good regulatory practice identifiable from legal and related documents.

The specific factors included in 3) were:

- Autonomy – measured (i) by budgetary independence and (ii) by legal limits on government freedom to replace regulators;

41
sensitivity analysis to the methods used to estimate the contributions and benefits is required to obtain robust results.

To study the effects of government’s transfers we present the incidence across pre-fiscal income deciles in columns [1] through [10] of Table 6. In addition, **Figure 6** shows the concentration coefficient of the programs analyzed in the study, as well as the Gini index of different income concepts. All programs are progressive and most of them have negative concentration coefficients.

The most progressive programs are the Food transfers, the _Plan de Equidad_ and the noncontributory pensions. In fact, these programs were conceived for the lower-income population and both are mean-tested, so the results are in line with the programs’ designs. The rest of direct transfers are progressive though they are not specially designed for redistributive purposes among all the population: they comprise benefits that cover risks of formal workers such as illness or unemployment.

As shown by the Kakwani index, progressivity of all _in-kind_ benefits is lower to the progressivity observed of all direct transfers. However, in-kind benefits are more heterogeneous. The concentration coefficient of educational programs ranks from -0.457 for childcare to 0.316 for tertiary level. Finally, the concentration coefficient of health services is close to zero.

**Table 6.** Incidence of Taxes and Transfers on Income Distribution. Percentages. Uruguay, 2017.

|Decile|Direct<br>taxes<br>[1]|Contributi<br>ons to<br>Health<br>System<br>[2]|_Plan de_<br>_Equidad_<br>[3]|Non<br>contribut<br>ory<br>pensions<br>[4]|Food<br>transfers<br>[5]|Other<br>direct<br>transfers<br>[6]|All direct<br>transfers<br>[7]|Education<br>[8]|Health<br>[9]|All in-<br>kind<br>transfers<br>[10]|
|---|---|---|---|---|---|---|---|---|---|---|
|1|0,1|2,7|12,2|9,0|10,2|8,6|40,0|46,9|35,1|82,1|
|2|0,2|5,0|3,9|2,9|1,7|3,6|12,1|20,6|17,0|37,6|
|3|0,4|5,9|1,6|1,5|0,5|2,8|6,4|13,1|12,3|25,4|
|4|0,7|6,3|0,6|0,9|0,2|1,8|3,5|8,3|9,5|17,8|
|5|1,0|6,5|0,2|0,4|0,1|1,5|2,2|5,9|7,9|13,7|
|6|1,5|6,5|0,1|0,3|0,0|1,1|1,5|4,2|6,5|10,7|
|7|2,3|6,6|0,0|0,1|0,0|0,8|1,0|3,2|5,4|8,6|
|8|3,3|6,6|0,0|0,1|0,0|0,7|0,8|2,1|4,3|6,4|
|9|5,3|6,4|0,0|0,0|0,0|0,4|0,5|1,4|3,1|4,5|
|10|9,9|4,9|0,0|0,0|0,0|0,1|0,2|0,5|1,5|1,9|
|All<br>|5,2|5,8|0,4|0,4|0,2|0,9|1,9|3,8|5,0|8,8|
|_Kakwani_<br>_Index_|_0,331_|_-0,021_|_-1,151_|_-1,002_|_-1,231_|_-0,585_|_-0,869_|_-0,692_|_-0,451_|_-0,556_|

Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: Deciles are defined by per capita Pre-fiscal Income; the Kakwani index uses Pre-fiscal Income 22
**Table 14: Cell consumption levels, by current marital status.**

||Once<br>married|Remarried<br>widows|Remarried<br>divorcees|Widows|Divorcees|
|---|---|---|---|---|---|
|Log of total cell consumption per capita<br>(CFA francs per year)<br>_In a leviratic union (N=71)_|12.31|12.22<br>12.06**|12.33|12.43**|12.55***|
|_In a non-leviratic union (N=70)_||12.36||||
|N|2153|155|247|430|170|

Note: 1 dollar = 522.9 CFA francs in 2006. Divorced includes separated women. All significance tests are relative to once-married women, where *** p<0.01, ** p<0.05, * p<0.1. For 14 remarried widows, we have no information on whether the current husband is a relative of the deceased husband. Their mean log consumption is 12.26.

<mark>Source: Authors’ calculations using PSF1.</mark> 44
definition that may differ substantially from one’s ideal. Any pair of assessment methodologies will measure a different (if unknown) mix of these various dimensions of corruption. For example, what weight should be given to central, state and local governments when assessing “corruption” for federal countries such as the United States or India?<sup>2</sup> These sorts of questions typically are not explicitly answered in the methodology of existing country-level indicators. Table 1 provides examples of different methods for generating country-level corruption measures.

The strength of nationally-representative surveys of firms or households is in measuring the incidence of corrupt behaviors encountered by users of government services. This approach emphasizes administrative corruption. However, firm surveys can measure some aspects of state capture, by including questions about improper influence over laws and regulations affecting business. Surveying firms and households is less effective in assessing the prevalence of corrupt transactions that occur entirely within the state, for example when politicians bribe bureaucrats or when funds are illegally diverted. Many types of conflicts of interest also are not easily captured by firm surveys, for example equity stakes of public officials, or employment promises to them by firms (World Bank, 2000).

The Business Environment and Enterprise Performance Survey (BEEPS) is a nationally-representative survey of business firms assessing corruption and other problems faced by businesses in the ECA region. The BEEPS is sponsored by the European Bank for Reconstruction and Development (EBRD) and the World Bank, and has covered almost every country in the region, in each of three survey waves: 1999,

> 2 The impact of corruption should be lessened if firms and households in corrupt cities or states (e.g. Louisiana or Bihar) can readily move to less corrupt areas (e.g. Minnesota or Kerala).

6
# **Figure 4. Differences in Equity Issuance Behavior: Connected versus Unconnected Domestic Listed Firms**

This figure shows differences in equity issuances between connected and unconnected domestic listed firms. The figure plots the difference-in-differences (DiD) coefficients (and the 90% confidence intervals) obtained by estimating Equation (1). The DiD coefficients show, for each year, the average differences in equity issuances between connected and unconnected firms (relative to the 2012 difference). The 2012 coefficients show the differences between connected and unconnected firms in 2012. Panel A uses the amount of equity raised over 2012 assets as the dependent variable. Panel B uses the cumulative amount of equity raised over 2012 assets as the dependent variable. Left-side panels use the full sample of connected and unconnected firms. Right-side panels use the propensity score matched (PSM) sample of connected and unconnected firms. The shaded areas capture the implementation of the Stock Connect program and the incorporation of domestic listed stocks into the MSCI Emerging Markets Index. Table 4 reports the coefficients shown in this figure.

## **A. Equity Raised over 2012 Assets**

<!-- Start of picture text --> Stock<br>Connect<br><!-- End of picture text --> <!-- Start of picture text --> MSCI<br>Incorporation<br><!-- End of picture text --> <!-- Start of picture text --> Stock<br>Connect<br><!-- End of picture text --> <!-- Start of picture text --> MSCI<br>Incorporation<br><!-- End of picture text -->

## **B. Cumulative Equity Raised over 2012 Assets**

DiD Connected versus Unconnected
11 (quality-contingent social reward). A key element of the incentive design was the expectation that community members would engage with the certificates to a degree sufficient to provide feedback (mainly in the form of recognition) to facility staff, although the intervention did not directly communicate with community members.

# _RESULTS_

We begin with results from the RCT, reporting separately in Ekiti and Niger. Conditional on having any nonzero transactions (one of the auxiliary questions on the checklist; note that most items on the checklist cannot be meaningfully completed if no relevant transactions took place over the previous week), we compare the checklist scores in the Social Recognition arm to those in the Control arm during Weeks 1 – 4. The results are displayed in Table 3. A visualization of those trends in both states is provided in Figure 1 (upper and lower panels, respectively; Week 0 = Baseline performance pre-RCT).

As shown in the Table 3, Social Recognition—having a Certificate of Excellence displaying the facility’s performance in public on a weekly basis—improved the checklist score by 12.4 percentage points in Ekiti State: a roughly 18-percent increase over baseline. The p- value for this effect is 0.005. In Niger State, however, there was no detectable effect of the Social Recognition intervention.<sup>5</sup> To be sure that the results are not sensitive to our choice of the simplest possible specification, we provide three alternative empirical specifications in Table 3. In the second (and fifth) column, we add the facility’s score in the week prior to intervention as a control variable. This increases precision slightly, but does not substantially change the main coefficient estimate. In the third (and sixth) column, we include the week prior to intervention to estimate a difference-in-differences model; this yields a higher point estimate, but with slightly wider standard errors. Finally, in the fourth (and eighth) column, we include scores from weeks when there were no relevant transactions at the facility, and while this mutes the effect somewhat, the coefficient remains statistically significant. Because the logarithm of the number of outpatients

> 5 We have just one main outcome, but two states in which we test the intervention’s effects on it. With an unadjusted p-value of 0.005, our finding is robust to the Dunn/Bonferroni multiple test correction (in its simplest form, multiplying the p-value by the number of tests), which yields an adjusted p-value of approximately 0.01.
# <u>Table 11:  Other Determinants of Trade Credit Supply:  Collateral Value, Customer Creditworthiness and Legal Institutions</u>

The reported estimates are from logit (Colums 1-3) and OLS (Columns 4-6) regressions. AR_d is a dummy indicating the use of accounts receivable and AR_per is the percent of monthly sales sold on credit. We transform AR_per in a continuous variable by using the inverse function of the logistic model. See Table 1 for all variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||_AR_d_|_AR_d_|_AR_d_|_AR_per _|_AR_per _|_AR_per _|
|L_Age|-0.13<br>[0.14]|-0.11<br>[0.18]|-0.14<br>[0.13]|-0.67<br>[0.01]***|-0.65<br>[0.00]***|-0.67<br>[0.01]***|
|L_emp|0.04|0.05|0.07|0.25|0.25|0.33|
||[0.35]|[0.28]|[0.12]|[0.04]**|[0.03]**|[0.01]**|
|D_Foreign|0.43<br>[0.05]**|0.39<br>[0.07]*|0.34<br>[0.13]|0.34<br>[0.63]|0.20<br>[0.77]|0.39<br>[0.60]|
|D_State|0.01|0.01|0.00|0.18|0.14|0.17|
||[0.97]|[0.96]|[1.00]|[0.66]|[0.72]|[0.68]|
|D_Export|-0.35<br>[0.09]*|-0.31<br>[012]|-0.31<br>[0.14]|0.28<br>[0.67]|0.35<br>[0.59]|0.20<br>[0.76]|
|AP_d|1.42<br>[0.00]**|1.41<br>[0.00]***|1.29<br>[0.00]***||||
|AP_per||||0.09<br>[0.00]***|0.091<br>[0.00]***|0.08<br>[0.00]***|
|LC_unused|0.28|0.20|0.28|0.87|0.72|0.97|
||[0.30]|[0.45]|[0.31]|[0.33]|[0.39]|[0.28]|
|Uniqueness|0.00<br>[0.68]|||-0.00<br>[0.88]|||
|Contracts||0.43<br>[0.02]***|||0.53<br>[0.23]||
|Property_rights|||-0.00<br>[0.57]|||-0.00<br>[0.83]|
|Constant|-2.49<br>[0.00]***|-3.28<br>[0.00]***|-2.89<br>[0.00]***|-12.60<br>[0.00]***|-12.07<br>[0.00]***|-11.62<br>[0.00]***|
|Observations|1,766|1,865|1655|1,756|1,853|1,640|
|PseudoR-squared|0.13|0.13|0.12|0.18|0.18|0.18|

48
# **Table 3: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012**

|**The Determinants**<br>|**of Greenfield FDI**<br>_Dependent Variable: Lo_<br>_Different Est_|**in the MENA r**<br>_g GFI (in millions o_<br>_imation Methods_|**egion Q1 2003**<br>_f USD)_|**– Q4, 2012**||
|---|---|---|---|---|---|
|Estimation Method|Difference<br>GMM|Systems<br>GMM|OLS|LSDV|Han-Philips|
||(1)|(2)|(3)|(4)|(5)|
|Political Instability t-1|-1.65***|-0.37***|-0.32***|-0.99**|-1.26***|
||(0.14)|(0.11)|(0.09)|(0.31)|(0.31)|
|GFIt-1|0.05|0.25***|0.27***|0.10*|0.02|
||(0.07)|(0.06)|(0.04)|(0.04)|(0.08)|
|ln Industrial Productiont-1|0.82***|0.14***|0.11***|0.79**|0.94**|
||(0.26)|(0.02)|(0.01)|(0.37)|(0.38)|
|Δ ln Industrial Productiont-|0.38|0.98|1.21#|0.62|0.56|
||(0.72)|(0.92)|(0.65)|(0.65)|(0.63)|
|Inflation t-1|-10.62***|-14.44***|-11.87***|-10.36***|-11.03***|
||(2.88)|(3.22)|(3.78)|(3.72)|(3.65)|
|Δ Exchange Ratet-1|0.88|-2.37|1.37|3.36|3.83|
||(3.36)|(3.28)|(4.94)|(4.80)|(4.91)|
|Country FE|Yes|Yes|No|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|587|604|604|604|604|
|Number of Countries|17|17|17|17|17|
|Number of Instruments|502|521||||
|AR-2 (p-value)|0.288|0.134||||
|Sargan test(p-value)|0.171|0.723||||
|Difference-in-Sargan(p-value)||0.273||||

Robust standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10 28