rafmacalaba/datause-encoder-data huggingface dataset:
_Table 8: Impact on Household Welfare_

|||Household ex<br>|penditure on:<br>||
|---|---|---|---|---|
||Food|Nonfood<br>Items<sup>20</sup>|Production<br>Assets<sup>21</sup>|Consumer<br>Assets<sup>22</sup>|
|round 3|43.114**|719.410***|491.314|927.430***|
||(20.071)|(182.553)|(305.688)|(243.909)|
|FIF*round 2|9.992|-109.109|-151.678|-39.243|
||(15.386)|(250.716)|(299.787)|(214.353)|
|FIF*round 3|21.213|3.951|-337.491|-160.438|
||(23.135)|(294.774)|(400.873)|(275.181)|
|WIF*round2|31.089|-138.285|-589.934*|-425.624|
||(40.016)|(333.434)|(331.368)|(458.609)|
|WIF*round3|60.343|227.114|-522.029|-556.553|
||(47.467)|(416.846)|(524.702)|(366.886)|
|WIF*FIF*round2|-33.814|387.243|158.585|568.251|
||(44.657)|(524.301)|(462.332)|(630.851)|
|WIF*FIF*round3|-67.018|1,127.125*|615.481|907.723|
||(58.757)|(589.203)|(642.087)|(637.544)|
|Observations|2,428|2,428|2,428|2,428|
|R-squared|0.067|0.105|0.221|0.240|
|Estimates: FIF2 + WIF2 + FIF_WIF2|7.267|139.800|-583|103.400|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.788|0.768|0.194|0.797|
|Estimates: FIF3 + WIF3 + FIF_WIF3|14.540|1358|-244|190.700|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.727|0.019**|0.675|0.730|
|Control Mean(BL)|161|4618|1412|889.200|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. Food exp is defined as total household food expenditure in the last seven days. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. The values of all the outcome indicators above are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **7 Robustness Checks**

We run three robustness checks, first with woreda fixed effects, second, with only male-headed households, and third, by controlling for land ownership and decision-making power of the female spouse.

# **_Results with Woreda Fixed Effects_**

While our main specification controls for regional effects, there may be concerns that more local drivers are affecting the results. Therefore, we rerun our regressions with woreda fixed

> 20 Nonfood expenditure includes school fees, clothing, transport agricultural service expenses, building materials, as well as batteries, soap, wood, electricity payments, haircuts, and so forth.

> 21 Production Assets include plow, sickle, axe, hoe, spade, beehive, water pump, tiller, and so forth.

> 22 Consumer Assets include table, chair, bed, radio, television, phone, stove, jewelry, bicycle, cart, car, firearms and so forth.

28
# **I.  Introduction**

It is almost impossible to “prove” discrimination in the labor market, yet most would acknowledge that it exists, and some would argue that the job you get and/or how much you are paid depends, in varying degrees, on who you are and the circles you move in. The insidiousness, the opaqueness, and the sophistication surrounding _processes_ of discrimination are complex and difficult to separate out. Those who feel that they have been discriminated against frequently, risk being accused of being paranoid or indulging in conspiracy theories. “It’s all in your head”, they seem to be told. Yet, the idea of differential labor market outcomes for different groups has long been the subject of theoretical and empirical work from across disciplines. It has for the most part focused on some social identity - race, ethnicity, gender, caste, religion and more recently, age, disability status and sexual orientation - as the major axis along which discrimination operates.

This paper moves from the assumption that discrimination is complex because it is often hidden and plays out through processes based on social codes. Karl Polanyi (1944) famously argued that the labor market is a crucial site for the play of social relations. In that, it reflects the existing and historical inequalities in a society. So, placing the analysis of the labor market within the overall context of social stratification and social norms provides a more nuanced understanding of the processes through which discrimination plays out, and also helps in refining the very notion of discrimination. This paper is a step in the direction of unpacking these subtle processes by understanding mechanisms that abet and enable labor market discrimination. In doing so, it provides a typology of processes that underpin discrimination. It is based on a review of the literature on processes of discrimination and the norms and attitudes that accompany them. It takes forward previous work on the Indian labor market<sup>1</sup> .

> 1 See World Bank 2011a and 2011b.

3
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Whalley, J. (1979) “A Simple Neutrality Result for Movements between Income and Consumption Taxes”, _American Economic Review_ , 69(5): 974-976.

Wiedmann, T., R. Wood, M. Lenzen, J. Minx, D. Guan and J. Barrett (2008) _Development of an Embedded Carbon Emissions Indicator – Producing a Time Series of Input-Output Tables and Embedded Carbon Dioxide Emissions for the UK by Using a MRIO Optimisation System_ , UK Department for Environment, Food and Rural Affairs, London.

World Bank (2008) _International Trade and Climate Change: Economic, Legal and Institutional Perspectives_ , Washington DC: World Bank.

Young, C.E.F. (1996) _Economic Adjustment Policies and the Environment: A Case Study of Brazil_ , Unpublished PhD thesis, University College London.

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- 31 -

|**Table 9. Summary statistics: Crop production data**|
|---|

|Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Crop production at each district (ton)|_q_||||||
|Cereal||1,292|1,125,720|1,487,816|<br>0.2|9,017,332|
|Potato||1,481|<br>189,000|236,465|<br>5.4|1,516,768|
|Oilseeds||1,133|<br>133,064|209,732|<br>0.2|1,741,450|
|Vegetables||1,550|184,637.5|413,477.0|<br>120.0|3,696,046|
|Transport time to the nearest city (hour)|_CITY2_|<br>5,456|<br>1.69|1.63|<br>0.05|<br>13.70|
|Transport time to the nearest border point (hour)|_BP2_|5,456|<br>13.25|7.69|<br>1.12|<br>48.55|
|Market Access Index based on transport time|_MAI2_|5,456|<br>0.06|0.06|<br>0.00|<br>0.55|

The dynamic panel regression is performed for each of the crops. First, the standard lagged value instruments are found to be largely valid, except for the model for cereal production ( **Table 10** and **Table 11** ). Second, cereal production is least sensitive to transport connectivity. The statistical significance of the estimated coefficients is generally low. It seems that the way of producing cereal in Kazakhstan would not change dramatically because of transport connectivity.

It is found that local market connectivity has almost always have a positive impact on crop production, meaning that these crops are produced more when farms are away from local populated areas. This can be interpreted as the fact that agricultural production normally takes place in nonurban areas.

The regional connectivity measured by transport time to the nearest border point is important to produce more potatoes, oilseeds and vegetables. These are among the important emerging export crops from the country. Particularly, the estimated coefficients are statistically significant for oilseeds and vegetables. The elasticity is about -0.3, meaning that a 10 percent reduction in transport time to the border point (including border crossing time) could increase oilseed and vegetable production by 3 percent. This impact is significant.

Last, the impact of domestic market accessibility measured by _MAI_ on crop production is heterogeneous across crops, possibly depending on the domestic consumption behavior. For potatoes and vegetables, the domestic market access is important. The coefficients are estimated to be positive and significant. It implies that these crops are important not only as export commodities but also for domestic consumption. Particularly, vegetables are often perishable. The
appreciate their exchange rate (Figure 1, Panel A). The converse is true during the periods of slow economic growth--capital flows slow down or reverse and the exchange rate depreciates. Since emerging economies hold liabilities denominated in foreign currency (Figure 1, Panel B), they are short in foreign currency and resist currency depreciation to limit valuation losses. The confluence of these factors is that the emerging countries have traditionally pursued procyclical monetary policy to resist the depreciation of their exchange rates, by tightening it when GDP growth is low, and loosening it when GDP growth is high.<sup>6</sup> In emerging market economies, the exchange rate appreciates when GDP growth is high and depreciates when GDP growth is low; and this correlation is stronger in countries which receive procyclical capital flows, (Figure 1, Panel C); the countries with procyclical capital flows are the ones more likely to pursue procyclical monetary policy, (Figure 1, Panel D).<sup>7</sup>

# **Figure 1: Emerging economies monetary policies seem to have been influenced in the past by procyclical capital flows and negative Foreign Currency Positions**

<!-- Start of picture text --> A. Emerging markets receive procyclical capital flows B. They maintain negative net foreign currency positions (data are for<br>1995)<br>0.80 0.80<br>Capital Flow Cyclicality Net Foreign Currency Positions<br>0.60 0.60<br>0.40 0.40<br>0.20 0.20<br>0.00 0.00<br>-0.20 ‐0.20<br>-0.40 ‐0.40<br>-0.60 ‐0.60<br>C. Capital flows influence the cyclical co-movement of exchange D. Countries receiving procyclical capital flows pursue procyclical<br>rate and GDP monetary policy<br>BelgiumCanadaFranceCyprusNorwayMaltaChina HKGermanyAustriaDenmarkItalyUnited KingdomIrelandLuxembourgSwitzerlandFinlandSwedenCzech RepubArgentinaJapanSpainPortugalNetherlandsMalaysiaMoroccoAustraliaChileKorea RepCroatiaIsraelThailandSlovakiaPeruSouth AfricaPhilippinesGreeceUruguayBrazilIcelandMexicoNew ZealandColombiaLatviaUnited StatesIndonesiaIndiaHungaryEstoniaTurkeyBulgariaUkraineCosta Rica BoliviaIndonesiaIcelandMoroccoPeruHungaryThailandMexicoPhilippinesFinlandNew ZealandMacedoniaTurkeyIndiaUkrainePolandIsraelAustriaUruguayDenmarkColombiaIranBrazilLithuaniaArgentinaSpainSwedenItalyKorea RepGreeceAustraliaSouth AfricaRomaniaPortugalChina MainlandChileLatviaGermanyMalaysiaSloveniaCanadaEstoniaJapanUnited StatesCzech RepubSlovakiaNorwayFranceUnited KingdomBelgiumIrelandNetherlands<br><!-- End of picture text --> 6 See Ghate et al (2013) for evidence on procyclical monetary policy in India in the post reform period, attributed to procyclical capital flows and managed exchange rate.

7 Examples abound from emerging countries, especially those with open capital accounts and flexible exchange rate regimes, where similar experiences have been observed. E.g., as noted in Calvo and Reinhart (2002), in the aftermath of the Russian crisis in August 1998, Chile and Mexico increased their interest rates to limit exchange rate depreciation despite a marked slowdown in their economies.

5
WPS4038

# Preferential Trade Agreements and Their Role in World Trade

## Denis Medvedev*

### Abstract

This paper investigates the effects of preferential trade agreements (PTAs) on bilateral trade flows using a comprehensive database of PTAs in force and a detailed matrix of world trade. We show that total trade between PTA partners is a poor proxy for preferential trade (trade in tariff lines where preferences are likely to matter): while the former amounted to one-third of global trade in 2000–02, the latter was between one-sixth and one-tenth of world trade. Our gravity model estimates indicate that using total rather than preferential trade to assess the impacts of PTAs leads to a significant downward bias in the PTA coefficient. We find that product exclusions and long phase-in periods significantly limit preferential trade, and their removal could more than double trade in tariff lines above 3 percent most favored nation (MFN) duties. We also show that the effects of PTAs on trade vary by type of agreement and are increasing in the incomes of PTA partners.

JEL classification: F13, F15, F53.

World Bank Policy Research Working Paper 4038, October 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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. Policy Research Working Papers are available online at http://econ.worldbank.org._

> *Development Prospects Group, World Bank and Department of Economics, American University. I am deeply grateful to Robert Blecker, Paul Brenton, Jeffrey Lewis, Kara Reynolds, and Hans Timmer for valuable comments and suggestions.
# **BOX 2: Real Estate Tax in Liberia (Okunogbe, 2021)**

**Problem:** Real estate taxes in Liberia, like many low-income countries, is characterized by high non-compliance rates. One significant hurdle in administering the real estate tax is the lack of a property cadastral. In the absence of comprehensive information on property location, ownership and values, tax enforcement cannot be conducted in a systematic way and many properties remain out of the tax net. Only about 5 percent of residential property owners were estimated to be on the tax roll (Olabisi, 2013).

**Technology intervention:** The Liberia Revenue Authority (LRA) initiated a low-cost technology investment to create a new property database. The tax authority recruited and trained young people to use open-source software on tablets to capture the location, ownership, photo and GPS of properties. The “foot soldiers,” as they were called, went door-to-door, conducting a brief survey with the resident of each property to collect ownership information and record property characteristics, including a photograph of the property. This resulted in the creation of a new property database that the tax authority could then use for property tax administration.

**Research design:** The tax authority used the information from this new database to send notices to taxpayers to request payment of real estate taxes. Four types of notices were sent out, with property owners randomly assigned to the different notices (Okunogbe, 2021). The first group received a “Plain Notice” that provided information on the property tax requirement and the procedures to pay. The second group received a “Detection Notice” that used information from the new tax database to personalize the notice: it was addressed to the property owner by name and included a photograph of the property. The third group received a “Penalty Notice” that provided information on the legal consequences of not complying. The fourth group received a “Detection and Penalty Notice” that combined the features of those two notices.

**Impact:** The Detection and Penalty notice tripled the property registration and tax payment rate among recipients, relative to the baseline registration rate of 4 percent and payment rate of 2 percent among those who received the Plain notice. The Detection notice and the Penalty notice alone did not have a statistically significant impact on registration or payment.

This intervention shows the effect of using technology to organize the publicly visible information on property tax liabilities to boost tax compliance. It highlights the complementary roles of communicating government detection capacity to taxpayers and alerting them of consequences for non-compliance. Nevertheless, it highlights the fact that additional factors, such as enforcement, may play an important role because even with perfect detection and information on penalties, less than 15 percent of those who received notices registered or paid.

Given the rapid increases in open-source software for geospatial database creation and 14
**Figure 5: TBM for single parents with 2 children**

<!-- Start of picture text --> 50000<br>45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>Percent of Average Wage<br>T_TAX_SS NETpl FBpl HBpl<br>SApl GROSSpl Net+500 500+<br>Source: Authors elaboration based on TMB for Poland 2016<br>Figure 6: Change in income accounting for childcare costs<br>160 Total income<br>+26.3?%<br>140<br>120<br>100<br>Transfers<br>80<br>income<br>60 -41.3?%<br>40<br>20<br>0<br>Earnings Composition before Composition at MW Composition of Earnings Lost<br>Single 2 Children<br>ITpl FBpl HBpl SCpl SApl 500+ Net Labor Income Child Care<br>Annual Income in PLN<br>0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99<br>% of Gross Minimum Wage<br><!-- End of picture text --> _Source:_ Authors elaboration based on TMB for Poland 2016 _. Note:_ Earnings’ composition before the single parent is working (left) and after taking a job at MW(center). Net labor income represents total labor income minus childcare costs, income tax, and social contributions.

13
# **1.  Introduction**

Changes in bank ownership during the 1990s and early 2000s substantially altered the governance of the world’s banking organizations. The developed nations of North America and Western Europe witnessed tremendous numbers of domestic bank mergers and acquisitions (M&As) and foreign acquisitions in response to deregulation, technological advances, and the globalization of nonfinancial economic activity. Ownership changes in other regions were even more dramatic. In the transition nations of Eastern Europe, the crisis nations of East Asia, many Latin American nations, and elsewhere, large shares of the banking systems had their ownership change from government control to private control and from domestic control to foreign control. These changes occurred as governments privatized many of their state-owned institutions and reduced entry barriers to foreign organizations, often in response to financial crises or to revisions in political philosophy.

These changes in the corporate governance of banks raise important policy and research questions. How do these changes affect bank performance? Are the changes permanent or do the banks tend to return to their previous conditions? How do banks that undergo governance changes reallocate their portfolios? To what extent do the dynamic changes following governance changes correspond to predicted effects: e.g., Do privatized banks tend to move closer to behaving like other privately-owned institutions? We address these questions using an econometric methodology that builds on some of the literature on the performance effects of various types of bank ownership in developed nations, and apply it to a unique data set on Argentine banks in the 1990s.

We refer to the different types of bank ownership – domestic ownership, foreign ownership, and state ownership – as forms of “governance.” Studies of U.S. corporations typically use the governance term to refer to the methods shareholders use to reduce managerial agency costs, such as board composition, voting rules, or stakes held by managers. Studies of governance outside the U.S., particularly in developing nations, often focus on the role of ownership in reducing these agency problems because of weak legal infrastructures that often do not adequately protect investors (e.g., Shleifer and Vishny 1997). In addition, the usual assumption in the U.S. context that all shareholders agree on the goal of value maximization may not necessarily apply to foreign and state owners. Foreign owners may be concerned with the value of the entire international organization, rather than an individual bank in a foreign nation, and state owners may be concerned with advancing other social or political
when most of the variation is driven by cross-country variation, i.e. when we include industry fixed effects but no country fixed effects.

Table 1: Correlation between upstreamness _Dik_ and the value-added-to-gross-output ratio

||(1)|(2)<br>(3)|(4)|
|---|---|---|---|
|Dependent var.:||VA/GO||
|Upstreamness|-0.025<br>[0.012]<sup>_∗∗_</sup>|-0.060<br>-0.019<br>[0.008]<sup>_∗∗_</sup><br>[0.008]<sup>_∗_</sup>|-0.028<br>[0.008]<sup>_∗_</sup>|
|Industry FE|No|Yes<br>No|Yes|
|Country FE|No|No<br>Yes|Yes|
|R2|0.106|0.429<br>0.152|0.542|
|N Obs.|478|478<br>478|478|

_Notes_ : Notes: OLS regression with robust s.e.; manufacturing industries, trimming observations with upstreamness above 4;<sup>_∗_</sup> significant at 5%;<sup>_∗∗_</sup> significant at 1%.

# **5.3 Calibration**

Our calibration exercise focuses on the 10 countries that are covered by the IDE-JETRO inputoutput tables. The general equilibrium model described above is calibrated so as to reproduce key features of the data. The parsimony of the model allows us to consider only a small number of parameters to calibrate, those listed in the left column of Table 2.

Thanks to Lemma 2 and the analytical results described in section 3.2, we can reduce the optimization problem described in equation (10) to a linear programming problem for each chain. Numerical simulations are performed in Matlab. We approximate a continuum of varieties by assuming 1,000,000 different final goods.

We now describe each calibrated parameter and its targeted moment, as described respectively in the left and right columns of Table 2.

**Labor supply:** Each country is endowed with an exogenous supply of factors. In the benchmark case, we consider only one factor of production: labor. For each country, we choose the labor force _Li_ to match aggregate value-added in tradeable goods sectors (i.e. excluding services) divided by the cost of labor (proxied by income per capita in our benchmark simulation).

**Labor productivity:** There is a tight link between wages and labor productivity in country _−_<sup><u>1</u></sup> _ξ i_ . As in Eaton and Kortum (2002), average labor productivity is driven by _A_<sup> ̄</sup> _i ≡ Ti_ where _Ti_ is a shift parameter for the Frechet distribution of _Ai_ ( _ω_ ) in country _i_ (equation 9). Given

42
# **TABLE 4: NON-EXPERIMENTAL ESTIMATES**

|Method:|Estimate|s.e.|Percent difference<br>compared to<br>experimental estimate<br>273.996|Testing<br>equality<br>with 273.996<br>T-stat|
|---|---|---|---|---|
|1) Using pre-migration income as the counterfactual|341.3|46.4|24.6|1.45|
|2) Selection on Observables: OLS regression|||||
|oldest member aged 18-45|383.5|46.4|40.0|2.36|
|all members aged 18-45|360.0|41.2|31.4|2.09|
|3) Difference-in-Difference Regression|||||
|oldest member aged 18-45|375.2|46.4|36.9|2.18|
|all members aged 18-45|328.5|42.8|19.9|1.27|
|4) Instrumental Variables using migrant network|498.8|209.6|82.0|1.07|
|5) Instrumental Variables using log distance to NZIS<br>office|305.0|93.5|11.3|0.33|
|6) FIML treatment effects model using log distance<br>to NZIS office as an instrument|298.0|69.3|8.8|0.35|

Notes: Experimental estimate is the IV estimate from column 6, Table 3.

44