rafmacalaba/datause-encoder-data huggingface dataset:
can be used to estimate demand elasticities and second order The estimation of labor income effects is harder due to lack of price variability at the household level. The simple idea of using unit values as a proxy for prices, which would deliver enough price variation, is problematic because of endogeneity of unit values, biases due to proxy variables, and measurement error. In this paper, I have proposed a method that uses unit values as measures of prices, but that is free from these problems. The method combines Deaton’s model of demand with an equation that describes the agricultural wage income of the household. By estimating the demand model together with the quality shading model, I was able to extract the right price signal from unit value data. These data can then be plugged into the wage equation to identify the relevant elasticities.

When a fraction of household income is allowed to be endogenous, there are additional effects to consider in the estimation of the demand parameters. First, there is the “profit effect”whereby a change in an agricultural price causes a change in household income and additional income effects in consumption. Second, the change in household income brings about further quality shading responses that affect the elasticities of demand. In this paper, I have derived formulas to correct for these factors.

The method was applied to the estimation of consumption and agricultural wage income responses to agricultural prices in rural Mexico. It was found that the corrections suggested in this paper can make a difference and should be preferred to a simpler model that uses average cluster unit values as regressors. Failing to control for endogeneity, biases, and measurement errors may lead to inconsistent estimates of the price elasticities and to an incorrect or misleading evaluation of policy changes. In addition, I have assessed the role of consumption and income responses in the evaluation of trade reforms. I have shown that the proposition that net consumers will be hurt by price increases whereas net producers will be benefited is only true in a static scenario. When consumers adjust quantities, farmers adjust production, and local labor markets react to the changed prices, so that net consumers can become net producers, thus benefiting from price increases. Data from rural Mexico support these findings.

29
# **Patterns of Export Diversification in Developing Countries: Intensive and Extensive Margins**

_Alberto AMURGO-PACHECO_<sup>_1_</sup> _and Martha Denisse PIEROLA_<sup>_2_∗</sup>

**JEL** . **Keywords** : export diversification, intensive margin, extensive margin.

**JEL Codes** : F13  F14  F15

**Acknowledgements:** We are very grateful to Richard Baldwin for his valuable contributions and to Richard Newfarmer and Paul Brenton for their comments on a previous draft. We are also indebted to Mombert Hoppe, Heiko Hesse, Roberta Piermartini and Rosen Marinov for helpful discussions.

> 1 Graduate Institute of International Studies, Avenue de la Paix 11A, 1202 Geneva, Switzerland, email: <u>amurgop1@hei.unige.ch</u>

> 2 Graduate Institute of International Studies, Avenue de la Paix 11A, 1202 Geneva, Switzerland, email: <u>pierola3@hei.unige.ch</u>

<u>Disclaimer:</u> The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
<!-- Start of picture text --> Volume in Monitored Outlets: Apples<br>(01/21/92-08/25/92)<br>120.<br>40 ....... _ .<br>C.. .<br>20, --------------- 0 ----- -------- --A- '''''''''''''''''''''''--t'''I I I I I.<br>:>~~~~~~~~~~~~~~~~~~~.............. *.e ". \<br>01/21 02(04 02118 03t17 04107 04/21 05(05 05/19 06/0 06/23 07/07 07/21 08104 08/18<br>Date<br>|_ Market Volume State Volume |<br><!-- End of picture text -->

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# **Online Appendix**

# **A. Timeline**

November 2012-February 2013: Communication campaign and application window October 2013-December 2013: Baseline survey April 2014: Training interventions May 2014-August 2014: Once a month mentoring sessions _Short-term follow-up survey rounds:_

- September 2014: First follow-up survey    ( _4 months after training_ )

- January-February 2015: Second follow-up survey _(9-10 months after training)_

- August-September 2015: Third follow-up survey _(16-17 months after training)_

- August-September 2016: Fourth follow-up survey ( _28-29 months after training)_

_Long-term follow-up survey rounds:_

- March-April 2021: Phone follow-up surveys ( _7 years after training)_

- September-October 2021: In-person follow-up survey ( _7.5 years after training_ )

# **B. Data Definitions**

To adjust for inflation we converted all nominal values of financial variables to real September 2021 CFA using the consumer price index published at the Institut National de la Statistique et des Etudes Economiques et Démographiques (INSEED-TOGO), a public establishment attached to the Togolese Ministry in charge of statistics. For ease of interpretation of magnitudes, we then converted these to USD at the exchange of 550.5 CFA per USD. We combined the phone and in-person surveys into a single longterm survey round, taking the in-person response where available, and the phone survey response for those who did not answer the in-person. Table A5 shows results are robust to the inclusion of survey month fixed effects.

The main outcome variables in Table 1 are defined as follows:

- <u>Real last month profits:</u>

- <u>Main business: Profits in the last full month in the main business operated by the</u> entrepreneur. This variable was winsorized at the 99th and 1st percentiles by survey wave, and coded to 0 for individuals who are not running a business.

- <u>All businesses: Profits in the last full month for the main business added to those in any</u> other businesses run by the entrepreneur. This variable was winsorized at the 99th and 1st percentiles by survey wave, and coded to 0 for individuals who are not running a business.

- `o` <u>All businesses conditional on survival: Profits in all businesses as defined above, but coded</u> as missing for those no longer running a business.

- <u>Real last month sales:</u>

- <u>Main business:</u> Revenues in the last full month in the main business operated by the entrepreneur. This variable was winsorized at the 99th and 1st percentiles by survey wave, and coded to 0 for individuals who are not running a business.

24
of the financial institution.<sup>18</sup> Banks partnering with MNOs need to take proper measures for back-up and replication of data, KYC requirements, handling complaints, and liquidity management by agents.<sup>19</sup> In a bid to strengthen controls around the mobile money transfer services, a national working group was formed comprised of Bank of Uganda and Uganda Communication Commission (UCC) to enhance cooperation and joint oversight of the mobile money services. UCC is in the process of drafting new rules that will oversee the Mobile Money trade in the country.<sup>20</sup> Based on evaluation by Porteous' framework and when compared with other countries, Uganda's regulatory environment appears to be relatively open but ranks below the average in terms of certainty (see Figure 1). The legal framework does not prevent nonbank financial institutions from issuing mobile money albeit there is not a clear regulation on mobile money itself as mentioned above. Mobile financial services such as deposit and withdrawal through agents are possible with partnered banks and while there is no strict oversight on agents, banks have to ensure that agents properly manage liquidity, confidentiality of data, etc. There is no interoperability of platforms, which forces users of mobile money services to affiliate with multiple mobile providers. In fact about 43 percent of Ugandan customers use more than one operator.<sup>21</sup> Mobile number portability has not been introduced based on the perception that the time is not yet ripe. There are no specific KYC exemptions for small accounts or indication of a risk based approach to KYC.

As per factors related to certainty, an electronic signature Act was legislated in 2011 providing certainty for e-contracting. Customer protection is not always guaranteed and in some cases

> 18 Ssonko (2010).

> 19 Bank of Ugand, (2011).

> 20 During the Uganda Mobile Money Agents Association stakeholders meeting held at Serena Hotel in December 2011, Godfrey Yiga Masajja, Deputy Director of Commercial Banking at Bank of Uganda, remarked that the BoU, together with the UCC, is drafting a set of rules including self-regulating mechanisms, quality assurance standards and monitoring systems for mobile money transactions.

> 21 - <u>http://www.gsma.com/mobilefordevelopment/wp content/uploads/2012/06/mmu_interoperability.pdf</u>

13
# **Table 6: Alternative questions and enumerator characteristics for placebo test**

||Age|Alcohol and tobacco<br>consumption|
|---|---|---|
|Enumerator gender (male)|-0.003|0.011|
||(0.514)|(0.009)|
|Enumerator education|-0.048|0.003*|
||(0.064)|(0.002)|
|Enumerator age|0.556|0.010|
||(0.952)|(0.016)|
|Enumerator from urban locality|-0.025|-0.002|
||(0.418)|(0.013)|
|Work experience as enumerator|-0.004|0.000|
||(0.005)|(0.000)|
|Observations|6,693|6,687|
|R-squared|0.315|0.454|

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

_Notes_ : This table presents OLS regression results for equation (1). For each column, the outcome variable is indicated in the first row. _Alcohol and tobacco consumption_ is a dummy that takes value 1 if the respondent consumes alcohol or tobacco and 0 otherwise. _Work experience as enumerator_ is the number of months previously worked as an enumerator. Each regression includes as additional control variables: _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ (for the definitions see Table 5). as in Table 3. Each regression includes community fixed effects and a constant. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

31
**<u>Table 2. Types of disabilities included in definition of “person with a disability”</u>**

||_Type of_<br>_survey_|_Visual_<br>_disability_|_Hearing_<br>_disability_|_Speech_<br>_disability_|_Physical_<br>_disability_|_Mental_<br>_disability_|
|---|---|---|---|---|---|---|
|Burundi 2000|MICS2||||X||
|Cambodia 1999|SES|X|X|X|X|X|
|Cambodia 2000|DHS||||X||
|Indonesia 2003|SES|X|X|X|X|X|
|Jamaica 1998|LSMS||||X|X|
|Jamaica 2000|LSMS|X|X|X|X|X|
|Mongolia 2000|MICS2|X|X||||
|Mozambique 1996|NHS|X|X|X|X|X|
|Myanmar 2000|MICS2|X|X||||
|Romania 1995|LSMS|X|X|X|X|X|
|Sierra Leone 2000|MICS2|X|X|X|X||

_Note_ : See Annex Tables more precise wording and disaggregations.

Despite the fact that all 11 surveys have an impairment definition of disability, non comparable definitions remain an issue in any effort to compile data across countries. Table 2 summarizes the items covered in each survey that define a person as having a disability. Clearly the definitions are non-comparable, even across surveys within the same country. Six of the surveys use an “extensive” definition that includes visual, hearing, speech and physical disability. But even in this group of six surveys, the definition of each type of impairment varies. For example, in Cambodia 1999 the physical disability category contains a detailed list of potential cases—“amputation of one limb; amputation of more than one limb; unable to use one limb; unable to use more than one limb; paralyzed lower limbs only; paralyzed all four limbs”—whereas in Jamaica 2000 there is simply one category described as “physical disability (legs and arms)”. More generally, in some countries the definition is stricter than in others. In Mongolia and Myanmar sight and hearing are described as “problematic” whereas in other surveys they are characterized as “blind” and “deaf”.<sup>5</sup> The second main data constraint in carrying out this analysis is the fact that surveys do not identify large numbers of individuals as having a disability. Therefore, any subsequent analysis such as the correlation between disability and poverty, or disability and schooling, will suffer from imprecision. Table 3 highlights this point by showing the number of youth identified in each survey and the subset with a disability. For some surveys the small sample problem is especially acute, for example the Jamaica 2000 survey identifies only 14 youth as having a disability, the Sierra Leone survey identifies only 28.

> 5 Note that another non-consistent aspect of the data is the coverage in terms of age: the upper age limit is sometimes 14 in Burundi and Myanmar.

4
disposition of the property, and; 3) The right of redemption (to keep the property after paying the outstanding obligation).

The MENA region is almost exclusively dependent on court-based enforcement. Even the DIFC in Dubai, which adopted relatively modern laws within its small area of jurisdiction, relies exclusively on judicial enforcement. As mentioned earlier, only Bahrain, Iran and Qatar allow out of court enforcement.<sup>35</sup> In some civil law countries like Tunisia, the fast track processes in court for the recovery of debts (―injonction de payer‖) which are ―certain, liquid and due‖ (like checks for example), seem to be working quite well, so it could serve as a model for other countries in the region. In Egypt there are expedited court procedures for certifying collection matters, in order to allow a creditor to begin the process of seizure more quickly than if a full court proceeding was required. However, after the court order, if indeed it works and the order is granted relatively quickly, the procedure to seize and sell assets can be as problematic as elsewhere in the region.<sup>36</sup> In Palestine, as well, there are expedited proceedings for urgent matters, but they are rarely used in practice since the matter is subject to review by a regular first instance judge. Neither are there specific courts in Palestine to consider summary proceedings or competent commercial circuits. Morocco, Jordan, Lebanon, Oman and UAE all report court enforcement that is time-consuming, expensive and unpredictable.<sup>37</sup> Delay tactics by debtors are common, and opportunities for objection are numerous.<sup>38</sup>

**Table 11: Percentage of Banks in MENA that Consider Enforcement of Secured Assets a Major Constraint**

|**Region**|**Problems in Enforcing Rights on Assets**<br>**Immovable**<br>**Movable**|
|---|---|
|**MENA Average**|58%<br>36%|
|**GCC Countries**|59%<br>29%|
|**Non GCC Countries**|56%<br>43%|

_Source:_ World Bank MENA SME Lending Survey, 2010 _2.7 Enforcement of security interests: sale of assets in cases of default_ Disposition of the seized property by the secured creditor is an important element in efficient enforcement mechanisms. The secured creditor, rather than a court enforcement officer, should

> 35 _Doing Business 2010_

> 36 Observations based on interviews with Egyptian attorneys and judges.

> 37 _Hawkamah Report_

> 38 Delay by debtors, and numerous opportunities to object are common problems reported worldwide by systems attempting to reform their enforcement systems.  These problems are symptomatic of court-centered enforcement/debt collection.

30
they are based on the largest sample of available data and provide the most conservative estimate. Outliers also haunt the AIRS data for some criteria air pollutants, like fine particulates. Therefore, for PM10, LB is the most conservative

# 5.2 Different Measures of Activity

Medium-specific intensities were calculated for each of the following measures of activity:

- ® total value of shipment (TVS) in millions of 1987 US $; @® value added (VA) in millions of 1987 US §$; ® total employment (TE) in thousands of persons.

The advantages and disadvantages of each measure have already been discussed in section 3.2.3. By developing all three, we provide more options for areas where data are scarce. Table 5.2 shows that the intensity rankings are almost perfectly correlated in any case. Therefore, the choice of measure should be ditven by the availability, reliability, coverage and detail of the corresponding production data. The more disaggregate the available information, the more robust the intensity measure will be, irrespective of which scaling variable is used.

*7The LB air pollution intensity estimates incorporate all the AIRS observations in the numerator; total ativity levels from the 1987 LRD were used in the denominator.

39
The paper builds on the logic presented in Adam and Bevan’s (2006) careful consideration of aid’s supply-side productivity effects in a model calibrated to Uganda. In a migration-free model with Engel curve attributes, they focus on public-infrastructure-induced productivity spillovers and learning by doing in the export sector. Their model shows that welfare effects and real exchange rate dynamics are highly sensitive to the location of productivity effects and the composition of domestic demand. It emphasizes aggregate linkages to rural productivity in agricultural sectors, but does not explore these dynamics in detail.

We take up that challenge by building a subsistence threshold-based framework that shows how a poverty-trap dynamic can take shape in the presence of low-input agriculture and soil nutrient depletion. The model presented here does not aim to provide specific empirical results or point estimates. Instead, in line with the arguments of Robinson and Lofgren (2005), it aims to outline directions of medium-term structural shifts. Some aspects are similar to the nontradable agriculture analytical model in Matsuyama (1992), although here staple foods are treated as nontradable due to the reality of subsistence food economies with low private and public capital stocks, rather than as a function of overall economy openness. Indeed, one important part of our model is the ability for labor to shift easily between nontradable (food) and tradable (cash crop) sectors while remaining on farm.

The approach presented here differs from Lofgren and colleagues (2002), who follow the Dervis, de Melo and Robinson (1982) tradition of a standardized, mixed-complementarity computable general equilibrium (CGE) model, including a monolithic and exogenous public sector.<sup>2</sup> It also differs from the Poverty and Economic Policy Research Network (PEP) standard model (Decaluwé et al. 2009), which has a nested production structure and excludes home consumption, and from other Africa-focused macroeconomic models that have emphasized 5
Broadly speaking, there are two fundamental approaches to identify the effects of NTBs on trade: a direct and an indirect one. The direct approach collects observable data on NTBs and estimates how their trade-inhibiting role is altered by RTAs. Measuring the stringency of NTBs over time and space is notoriously hard, and relating such changes to RTAs even more so (De Melo and Nicita 2018; Anderson and Van Wincoop 2004). To overcome these difficulties, an alternative approach consists in taking the definition of NTBs literally by assuming that the entire trade effect of RTAs that is not attributable to changes in tariffs must be due to the change in NTBs. Assuming that importers’ preferences for foreign versus domestic goods and services are unaffected by signing an RTA, that view takes NTBs as _residual_ barriers to trade. Using structural gravity, their change can then be inferred from changes in trade flows—hence, one may talk about an indirect effect.

Much of the gravity literature includes a dummy variable for the existence of an RTA in the estimated equation. Interestingly, however, many papers actually do not control for tariffs and so the dummy measures the full effect of RTAs (thereby confounding changes in tariffs and in NTBs). Studies that apply the indirect approach and do control for tariffs typically find that RTAs do reduce NTBs and boost trade. Often, the effect is almost exclusively driven by deep trade agreements (e.g., Aichele et al. (2016), Dhingra et al. (2021), and Felbermayr, Gröschl, et al. (2018)).

Like the direct approach, the indirect (or residual) approach is sensitive to data issues: the RTA effect can only correctly identify changes in NTBs when changes in tariffs are accurately measured; otherwise, the estimated coefficient of the RTA variable combines the NTB effect and the tariff effect. However, as shown by Teti (2020), tariffs from standard sources, such as the World Bank’s World Integrated Trade Solutions (WITS), suffer from substantial measurement error. As deep RTAs also feature the most comprehensive tariff cuts, it is impossible to say if the RTA effect identified in a gravity equation measures the NTB reduction caused by the new RTA or instead only captures mismeasured tariff reductions.

This paper uses a new database containing carefully cleaned tariff data to apply the indirect approach. Do RTAs really lower NTBs, and if yes, by how much and through which provisions? Or is all the talk about NTBs only distracting from the lowering or elimination of tariffs? We proceed in two steps. First, we estimate the average effect of RTAs on NTBs across many agreements using the newly available Global Tariff Data (new GTD) provided by Teti (2020) that deals with measurement error. In this exercise, we highlight the important role of general globalization trends. Omitting these overstates the role of NTBs. The second part of the paper sheds light on the heterogeneity between RTAs and on the role of specific provisions implemented in those deals. Following Baier, Yotov, et al. (2019), we estimate a separate coefficient 2