rafmacalaba/datause-encoder-data huggingface dataset:
In conclusion, full liberalization generally increases poverty more than the Doha agreement. However, poverty actually falls among urban households. This is primarily due to the adverse rural impacts as Philippine agricultural producers are confronted with lower priced imports, in the wake of full liberalization. The introduction of an income tax instead of an indirect tax to compensate lost tariff revenue results in greater poverty increases, as household bear the full weight of this tax.

# **5. Disaggregating the Full Liberalization Scenario**

In simulations 4 and 5, we break down the effects of eliminating all tariffs in the rest of the world (free world trade) and in the Philippines (domestic liberalization) from simulation 2.

## **5.1  Macro and Sectoral Effects**

We observe dramatically opposing price effects in these two scenarios (Table 3). While prices uniformly increase under free world trade, primarily as a result of increased export demand and prices, they fall under the domestic liberalization scenario as a result of falling import prices. However, both simulations result in increased trade, due to increased export demand under free world trade and increased import competition and real exchange rate devaluation under domestic liberalization. Whereas ROW free trade boosts trade through increased world export prices and demand, domestic liberalization does so through reduced domestic import prices. These contrasting price effects generally offset each other when ROW and domestic liberalization are combined in simulation 2, whereas the export, import and consumption volume effects reinforce each other.

Contrasting results are also found in the sector analysis (Table 4). ROW liberalization leads to a reallocation of production from services to industry with agricultural output practically 23
# **<u>Wereda Dummies</u>**

Ethiopia is divided into different regions. A region is composed of zones, which in turn consists of weredas, which cover a number of kebeles, the smallest administrative unit in Ethiopia. A wereda is an administrative unit comparable to a district. The enterprises in the RICS-AgSS dataset are from 4 different regions, 50 different zones, 264 different weredas and 480 different kebeles, in total. The enterprises in the RICS-Amhara dataset are from 4 different zones, 45 different weredas and 175 different kebeles, in total.

# **APPENDIX 1B: DEFINITION OF SELECTED VARIABLES RICS-AGSS**

**Annual profits:** annual profits are calculated by multiplying monthly profits with the number of months the firm was functioning in the previous years.

**Annual costs:** annual costs are calculated by multiplying monthly costs by the number of months the firms was functioning in the previous year.

**Costs:** costs are calculated by multiplying the share of operating costs in sales with the level of sales. **Profits:** Profits are computed as sales*(1-share of cost in sales)

**Profits per day:** profits per day were computed by dividing monthly profits by the number of days the enterprise had been open in the past month multiplied by the number of employees.

**Annual profits:** annual profits are calculated by multiplying monthly profits with the number of months the firm was functioning in the previous years.

**Annual costs:** annual costs are calculated by multiplying monthly costs by the number of months the firms was functioning in the previous year.

**Seasonality:** A dummy variable which is coded 1 if enterprise managers answered ‘yes’ to the question – “Are the enterprise activities seasonal?”

-48-
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Page **24** of **25**
|Russian|Mature|20%|18%|28%|64%|-|
|---|---|---|---|---|---|---|
|Federation|Young|22%|12%|28%|82%|-|
||All|5%|6%|9%|63%|-|
|Turkey|Mature|6%|7%|9%|65%|-|
||Young|1%|1%|2%|27%|-|

Source: Author’s elaboration from Enterprise Survey (2014).

<u>Differences across trader-status groups: Trader firms i.e., exporter, importer or both, tend to be more</u> innovative in both dimensions (technological and non-technological) than non-trader firms (Table 6), with relatively larger gaps between groups for process innovation. However, these differences are statistically different in few cases. Further, the proportion of innovative firms within the non-trader group in leading countries is larger than the proportion of innovative firms in laggard ones. The overall innovation dynamic of the region is consistent with what is the average behavior in other regions such as ECA and Africa.

<u>Table 6. Firm Innovation by Trade Status (% of all firms in each category)</u>

||**T**|**echnologic**|**al innovation**|**Non-techn**|**ological**|
|---|---|---|---|---|---|
|**Country**|**Product**|**Process**|**Product & Process**|**Organizational**|**Marketing**|
|**Bangladesh**||||||
|No Trader|42%|59%|77%|34%|89%|
|Trader|48%|68%|79%|43%|95%|
|**Pakistan**||||||
|No Trader|7%|8%|13%|14%|69%|
|Trader|15%|9%|21%|17%|85%|
|**India**||||||
|No Trader|53%|59%|83%|55%|82%|
|Trader|55%|66%|83%|56%|82%|
|**Nepal**||||||
|No Trader|12%|9%|20%|33%|87%|
|Trader|14%|21%|31%|54%|92%|
|**ECA average**||||||
|No Trader|16%|10%|22%|52%|-|
|Trader|26%|20%|36%|57%|-|
|**Africa average**||||||
|No Trader|25%|30%|44%|41%|-|
|Trader|31%|34%|50%|48%|-|
|**Russian Federation**||||||
|No Trader|18%|14%|25%|62%|-|
|Trader|37%|33%|47%|87%|-|
|**Turkey**||||||
|No Trader|2%|3%|4%|46%|-|
|Trader|10%|12%|15%|72%|-|

Source: Author’s elaboration from Enterprise Survey (2014) <u>Differences across importer and exporter groups: In all the SAR region except in Pakistan, exporters are</u> more innovative than importers in terms of creating new products; but importers and two-way traders are 14
measurement error most likely renders any analysis useless. Our solution to this problem is to consider years for comparison which are less affected by inflation. This is most relevant for the 1980s, where we consider 1980 and 1986 the most appropriate base years. Apart from that we have no means of analyzing if and how measurement error has changed over time in the EPH and thus assume it constant.

# **5. Data and Estimation Issues**

The data used for this analysis is the household survey _Encuesta Permanente de Hogares_ (EPH) of Argentina which has been carried out by Argentina’s statistical office (INDEC) since 1972 and is used as the primary source of generating official unemployment rates. The survey includes comparable labor market information from 1980 through 2003 for the province of Gran Buenos Aires (GBA). The GBA sample encompasses the capital city of Buenos Aires and the surrounding Province of Buenos Aires. According to the Argentine Census, 46 percent of the Argentinean population lived in this area. As Argentina is mostly urban, trends observed in Buenos Aires are often considered representative for Argentina as a whole.

More urban centers of Argentina were later added to the sample over time, totaling 28 major provincial cities in the most recent incarnations of the survey. There is data with comparable coverage since 1992 for 16 main urban conglomerates in Argentina (henceforth ARG16). Until 2003, the survey was conducted on a semi-annual basis (May and October) before the questionnaire and methodology changed substantially.

We investigate the time series for GBA from 1980-2002, always using the October round of the survey<sup>9</sup> . For the wage analysis we focus on real hourly wages of workers with one single job only as reported in the EPH questionnaire<sup>10</sup> . To convert nominal wages into real wages we use INDEC’s historic general consumer price index (IPC) for Gran Buenos Aires and deflate all values to constant October 2000 pesos.

> 9 The May round of 2003 could be used to expand the data by another half a year, however in an analysis of variance this might be rather misleading due to seasonality effects on employment and wages. Data from INDEC clearly shows that there is considerably higher economic activity in May than in October.

> 10 To avoid effects stemming from changes in the incidence of multiple-job holders this paper focuses on wages from the principal occupation, only. In order to do that one has to discard workers with more than one occupation in order to establish consistency of the data series over time. Before 1995, hourly wage data is only available for those workers with one single job. Even though this may be a minor point, to our knowledge this adjustment to guarantee consistency has not been done yet in any empirical research using EPH data.

13
# **Figure 1. Emerging Economy Imports Affected by TTBs through 2011 (cont.)**

<!-- Start of picture text --> a. By policy<br><!-- End of picture text -->

## **b. By category of affected trading partner**

<!-- Start of picture text --> percent Chile<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1991 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Colombia<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1992 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Malaysia<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Pakistan<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2004 05 06 07 08 09 10 11<br>Stock: imports subject to any imposed TTB in effect<br>Stock: imports subject to AD only in effect<br>Flow: imports subject to any newly initiated TTB investigation<br>Flow: imports subject to newly initiated AD investigation only<br><!-- End of picture text --> <!-- Start of picture text --> Chile<br>percent<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1991 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> <!-- Start of picture text --> Colombia<br>percent<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1992 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Malaysia<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Pakistan<br>percent<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>2004 05 06 07 08 09 10 11<br>All trading partners' exports under any TTB in effect<br>China's exports under any TTB in effect<br>Other emerging economies' (non-China) exports under any TTB in effect<br>High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

21
_Mexico: The 1997 Pension Reform_ _24_

|1997|0.42|2011|0.61|
|---|---|---|---|
|1998|0.83|2012|0.60|
|1999|0.82|2013|0.59|
|2000|0.79|2014|0.58|
|2001|0.77|2015|0.58|
|2002|0.77|2016|0.57|
|2003|0.75|2017|0.56|
|2004|0.73|2018|_0.55_|
|2005|0.70|2019|0.54|
|2006|0.68|2020|0.54|
|2007|0.67|2021|0.53|
|2008|0.65|2022|0.53|
|2009|0.64|2023|0.52|
|2010|0.62|2024|0.52|

Source: SHCP.

The difference between this result and the 141.5% of GDP (in 1994) occurs because of the way the new law states that the Government will have to face the new systems' obligations as opposed to those of the old system. For instance, the new system includes new obligations such as the Social Quota, the new MPG indexed to prices as opposed to the old pension guarantee indexed to the minimum wage, and a redistribution of the former contributions to the system between the government, the worker and the employer. Another important difference has to do with the fact that pension contributions will go to individual accounts, which will be capitalized at a market rate of return. As a result, fiscal costs are lower since workers who opt for benefits under the old PAYG system will have to surrender the accumulated balances in their individual accounts to the Government.

# **_Fiscal_** _Costs:_ **_Assumptions and Scenario Analysis_**

**Estimating the size of contributing population.** The first step in the estimation of the fiscal costs was to project the size of the future population, which would enroll in the IMSS scheme. IMSS does not publish this information and demographic projections prepared recently by the Luis Donaldo Colosio Foundation were used. These projections show that, in the long run, the population affiliated to the IMSS as a proportion of economically active population (EAP) would grow from 28% to 38% by 2031.

The second step was to determine the contributors age distribution. For the transition population this was obtained using the IMSS tables of number of affiliates (1982-1993) broken down by age. The proportion by chronological age was established by applying moving averages to the 1982-1993 data, which show very small year-to-year variation. For new entrants, IMSS' _Age Frequency Table_ for new affiliates was used which shows that entrants are predominantly under 30 years old. The age distribution of the contributing population will change as function of new entrants' age. Entrants' age distribution is assumed to be the same throughout the projection period.
However, the majority of these works do not solve the endogeneity bias affecting the povertyisolation relationship. In fact, road location is non arbitrary and people do not randomly settle next to roads once they have been constructed. Moreover such studies make comparisons and generalizations difficult (Estache, 2009). In particular, Van de Walle (2009) points out the fact that "`Knowledge about [roads] impacts and the heterogeneity in those impacts continues to be limited"'. Roads projects evaluations performed by Khandker, Bakht and Koolwal (2009) in Bangladesh and Mu and van de Walle (2007) in Vietnam provide good examples of a willingness to provide more robust and reliable evaluations of roads’ impact that tackle efficiently the endogenous nature of road placement. The former use a difference-indifference methodology associated with household-level fixed effects. The later combine the difference-in-difference with a propensity score method to yield unbiased estimates under the assumption that a time variant selection bias due to initial observables is at work.

Such methodologies require panel data that are often hard to obtain. Household surveys are generally available but their lack of temporal dimension explains the direct modeling of poverty as a function of road access. Robust estimates on cross-section data require the use of instrumental variables. Gibson and Rozelle (2003) provide the only example to our knowledge with their instrument that "measures the year in which the Papua New Guinea (PNG) national highway system penetrated into each of PNG's districts". They assume that any newly created national highway stimulates the feeder roads network, and thus reduces the traveling time to the nearest road. As the national highway building in PNG was from coast to inland, without any wealth considerations, the authors argue that their instrument is uncorrelated with poverty at the household level. However, we consider this direct modeling not completely relevant because we believe that is not the road _per se_ that affects poverty but the fact that the road leads to some services or facilities. As Njenga and Davis (2003) claim "isolation reduces physical access to vital services such as markets, information sources, social and political networks as well as health and educational services while access to these services is crucial for improving poor people's livelihoods".

The explicit recognition of the indirect impact of road access on poverty, using cross-section data for the year 2001 in Cameroon, constitutes the first contribution of this paper. The literature on poverty and isolation defines three channels through which road access contributes to reduce poverty: access to inputs and output markets, access to education and 3
the Productive Safety Net Programme (Berhane et al. 2014; Knippenberg and Hoddinott 2017). Households were asked whether, in the last 12 months, they faced a situation when they did not have enough food to feed the household for five or more days. Those who did were prompted to list in which months they lacked sufficient food. The measure of Months Hungry is the sum of those months.

The CSI measures the intensity of hunger in the past week. It is a composite weighted score of various strategies households engage in when faced with shortterm food shortages _s_ (Maxwell 1996). It is a measure of the intensity of hunger. Coping strategies c are a set of 8 questions which reflect undesirable activities households are forced to engage in due to food insecurity, a set of strategies _c_ .<sup>9</sup> As these strategies are unpleasant, unhealthy and socially stigmatizing, resorting to them is an indicator of short-term food stress (Maxwell et al. 2003). The survey asks the number of days in the past week a household engaged in each of these activities, then multiplies those days by a weight _wc_ indicating its severity. The scores are then compiled into the following index: 8 Coping Strategy Index = � _daysc ∗ weightc_ (6) _c_

|9. Coping strategies and corresponding weights:|
|---|

|“In the past 7 days, how many days have you<br>or someone in your household had to...|Number of Days|Weight|
|---|---|---|
|Rely on less preferred foods?||1|
|Limit the variety of foods eaten?||1|
|Limit portion size at mealtimes?||1|
|Reduce number of meals eaten in a day?||2|
|Restrict consumption by adults for small children to eat?||2|
|Borrow food, or rely on help from a friend or relative?||2|
|Have no food of any kind in your household?||3|
|Go a whole day and night without eating anything?”||4|

13
While the exact realization of the climate system over the next two decades is unknown, the poverty results from the overall the CMIP3 GCM ensemble suggest slightly increasing poverty vulnerability in Tanzania. However, if the real climate system displays behavior similar to GCM 08 over the next two decades, then poverty vulnerability could instead decrease by some measures. Further development of decadal‐scale climate prediction techniques could help to resolve the climate‐based uncertainty, although it is possible that the temporal (and spatial) scales being considered exceed the limits of predictability.

15
We estimate these differences in budget outcomes within a regression framework to control for other factors that matter for these outcomes. The identification strategy follows (Loayza, Teran, and Rigolini 2013), which here consists of just comparing budget outcomes in _communes_ where mines are located with neighboring _communes_ with similar characteristics while controlling for other factors. This is based on the presumption that at the lowest administrative level, location of mines is determined by exogenous geological factors. Therefore, comparing nearby similar _communes_ will minimize the issue of omitted variables biases. While gold mining is taking place in only three regions of Mali, our sample includes all 705 _communes_ , including those in the capital city, which allows us to control for a range of factors such as urbanity and rainfall.

A clean econometric identification of the impact of mining activities on local government budget outcomes would require data on budgets for both mining and non-mining _communes_ during preand post-mining. However, we have annual budget data available for only 2006 to 2008, during which most of the mines had already opened. Industrial gold production started with the first site in Sadiola in 1996, just two years before the second population census of Mali. Fortunately, we are able to use the 1998 population census to fully control for some initial differences across communes, such as poverty and education levels prior to the mining boom. We also test whether larger mines have bigger impacts on budget outcomes by using the cumulative level of production measured in metric tons as an alternative measure of mining activity.

The baseline specification considers three groups of communes: _communes_ where mines are located and their neighboring _communes_ within the same region, which are the “treatment” because of their spatial proximity and possible institutional similarities. The control group is all other communes located in the rest of the country. The regression equation is as follows:

# _Outcomerc_ =  β0* _miningrc_ + β1* _neighborsrc_ + α _Xrc_ + εrc

Our outcome variables are communes’ fiscal revenues, capital, and current expenditures, all in per capita terms. Per capita capital expenditures is further broken down by education, health, water, and others, while per capita current expenditures has salaries and others as components. The variable _mining_ is an indicator equal to 1 if the commune has an industrial mine site within its boundaries, and 0 otherwise. It can also represent the continuous cumulative gold production levels within the producing communes. The variable _neighbors_ is an indicator equal to 1 if the commune directly shares a border with a mining commune, and 0 otherwise. _Xrc_ controls for pre-boom differences such as poverty and education, as well as for average rainfall within the _communes_ . Without-region fixed effects, _β0_ and _β1_ give the difference between mining and neighboring _communes_ with all other control _communes_ . We expect _β0_ to be positive and significant and _β1_ not 19