rafmacalaba/datause-encoder-data huggingface dataset:
# 3. SUBSECTOR AND SAMPLE CHARACTERISTICS

- This study focuses on the three industries garments, leather products (other than shoes), and nonelectrical machinery products. Interviews with SME finns were undertaken in the cities of Bogota (71 firms), Medellin (27 finns), and Bucaramanga (27 firms). Bogota and Medellin are the country's largest cities and most important industrial centers, while Bucaramanga is middle-sized, with an important history as a metal products center as well as significance in the garments industry.

Firms were considered candidates for inclusion in the survey if they had exported or were currently exporting and if present size fell in the range of [0 to a few hundred workers. The few relatively large firms included in the samples had typically been small or medium in size in the recent past. With the advice and assistance of the broad-based industry association ACOPI, and the footwear and garments associations, (ASOCUEROS and ASOCONFECCIONES), firms to be interviewed were selected principally from the national Directory of Exporters, 1991. Appointnents were requested with the owner or manager of the firm. Some firms were dropped from the sample when it was ascertained that they did not meet one or another of the criteria established with respect toexport experience, size, or products. A few others were discarded after part or all of the interview when it became clear that the quality of the information obtained was low. Te sample includes a significant share of the totl number of SME direct exporters in the leather goods and non-electrical machinery industries (probably over half and about a third, respectively), and a smaller share of those in the garment industry. The high share of interviews undertaken in Bogota could be a source of non-representativeness, and we attempt to keep this possibility in mind in the analysis.

While the three industrial sectors examined in depth in this study vary widely in many respects, seveial features are more or less common to all of them:

- SME firms produce the bulk of output in each sector. Even when very small firms are not taken into account, the output of large plants (200 or more workers) ranges only between a quarter (garments and non-electrical machinery) and a third (leather goods) of the sector total as of 1990.

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**<u>Table 1: Descriptive statistics for large farms above 50 ha</u>**

||**Total**|**Maize**|**Sorghum**|**Wheat**|**Teff**|
|---|---|---|---|---|---|
|**Size & utilization**||||||
|Size cultivated (ha)|266.66|377.90|234.56|551.74|237.60|
|.. of which maize (%)<br>|10.34<br>|43.82<br>|5.27<br>|9.07<br>|36.47<br>|
|.. of which sorghum (%)|14.34|6.30|30.12|3.80|4.50|
|<br>.. of which wheat (%)|3.34|1.43|0.29|60.08|2.32|
|.. of which teff (%)|0.71|2.77|0.31|1.19|7.65|
|.. of which other (%)|71.27|45.68|64.01|25.87|49.06|
|**Year of establishment**||||||
|Before 91 (%)|4.68|5.73|2.60|9.53|6.69|
|1991-92 (%)|3.49|0.82|6.75|0.00|2.08|
|<br>1992-2002 (%)|21.50|11.85|22.91|37.34|4.33|
|<br>2002-2006 (%)|22.61|19.70|27.26|8.52|18.70|
|07-2008 (%)|24.98|33.16|18.90|22.67|41.29|
|09-2010 (%)|14.54|21.15|15.45|17.11|16.63|
|11-2013 (%)|8.20|7.59|6.15|4.83|10.28|
|<br>**Ownership type (%)**||||||
|Government|2.74|3.02|0.93|3.87|5.67|
|Private|92.39|86.26|94.74|93.90|82.92|
|Cooperative|4.61|10.08|4.24|2.15|10.17|
|Ethiopian|96.62|95.87|98.30|92.49|96.91|
|Foreign|2.67|2.39|1.24|6.94|1.50|
|Joint|0.71|1.74|0.47|0.57|1.59|
|**Type of acquisition (%)**||||||
|Direct negotiation|6.83|5.02|1.31|1.40|7.24|
|Woreda|51.32|30.81|66.43|47.95|31.31|
|Region|37.17|55.42|30.24|42.26|53.45|
|<br>Fed’l Gov’t|4.68|8.77|2.02|8.39|8.00|
|**Input use**||||||
|Used fertilizer (%)|54.77|81.70|63.77|82.74|91.23|
|.. if yes, kg/ha|71.86|112.77|38.89|85.96|99.60|
|Used chemicals|68.49|64.11|85.31|82.41|64.08|
|Used improved seed|25.98|84.39|19.86|64.02|89.47|
|<br>.. if yes kg/ha|32.86|29.96|11.51|69.99|25.73|
|Hectares per perm. worker|56|55|83|254|32|
|Temp. workers/ha|3.56|2.82|3.70|1.32|1.70|
|**Fees, investment, credit**||||||
|Lease fee reported (%)|36.74|58.54|29.96|73.31|37.24|
|.. if yes Birr/ha|530|327|274|1518|203|
|Other payments. (%)|11.37|24.09|9.11|19.01|12.53|
|<br>.. if yes Birr/ha|376|464|218|180|170|
|<br>Any investment (%)|93.11|90.18|98.71|80.45|89.43|
|.. if yes Birr/ha|11,952|13,276|10,217|15,549|15,449|
|... share on roads (%)|6.86|8.95|3.31|7.91|8.29|
|... share on land clearing|32.61|23.61|33.83|22.40|21.14|
|<br>... share on buildings|18.05|23.03|13.48|18.28|25.23|
|<br>... share on machines|42.48|44.40|49.39|51.42|45.35|
|An loan last 5 ears (%)|2068|1710|2462|1492|1698|
|y y<br>.. if yes Birr/ha|.<br>18,195|.<br>16,548|.<br>17,039|.<br>39,997|.<br>12,099|
|Has loans (%)|5.74|11.30|5.60|9.34|15.20|
|.. if yes Birr/ha|9,394|10,790|7,961|9,654|10,376|
|Provides local benefits|67.18|79.50|59.45|73.76|88.25|
|Number of obs.|3484|822|1659|194|323|

_Source:_ Own computation from 2013/4 CSA large farm and smallholder farm surveys. _Note:_ Although data are from a census of all farms > 50 ha, weights are applied to adjust for non-response.

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**Figure 1. 8  Breakdown of loans in the Colombian banking system**

<!-- Start of picture text --> Gross Commercial Loans Gross Consumer Loans<br>100 100<br>80 27120 22 0 22 0 190 21180 21150 80 2113 4 1914 4 1712 5 154 3 9 173 3 7 173 3 8<br>25 26 25 5<br>60 13 9 7 8 8 9 60 13 7<br>40 40<br>69 69 69<br>20 47 43 45 48 53 56 20 49 56 62<br>0 0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03 Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Private Domestic Banks Public Banks Private Domestic Banks Public Banks<br>Private Foreign Banks Financial Cooperatives Private Foreign Banks Financial Cooperatives<br>Others Others<br>Gross Mortgage Loans Microcredit<br>100 2 2 1 1 2 2 2 3 100 9 8<br>10 10 8 7 7 5 1<br>2<br>80 80<br>49 49<br>60 60<br>88 88 89 91 91 92<br>40 40<br>20 20 39 42<br>0<br>0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Dec 02 Dec 03<br>Private Domestic Banks Public Banks<br>Private Domestic Banks Public Banks<br>Private Foreign Banks Financial Cooperatives Private Foreign Banks Financial Cooperatives<br>Others Others<br>% %<br>% %<br><!-- End of picture text --> _Source: Superintendencia Bancaria_

# **1.4 BASIC ACCESS INDICATORS**

26. **On the basis of simple access indicators, access to financial services in Colombia has been declining.** Figure 1.9 shows that both the number of current and savings accounts per 100 inhabitants decreased over the past years. Equally, the number of cashed checks decreased from 3 checks per inhabitant in 1998, to 1.6 in 2003. This reflects, among other factors, the impact of financial sector taxation (discussed in Chapter 2), although it could also be a consequence of the increased use of electronic payments. Declining intermediation and available credit to the economy also demonstrate the negative trend in access to financial services in Colombia.

27. **At the same time, growing use of electronic banking services shows increased acceptance of alternative banking technologies by the banked** (see Figure 1.9). The penetration of debit and credit cards in Colombia has been increasing over the past few years. At the end of 2003, one in five inhabitants (or one out of two banked persons) had access to a debit card, and 74% of active savings accounts had an 27
for Latin America. It suffices to say that due to sampling and measurement issues, one can get a distorted view of over-time trends by looking just at the household expenditures and incomes between cross sections. What is needed is a methodology that removes the effect of changes in household samples on the average household consumption and income shares derived from commodities.

To build the 2004-2008 pseudo-panel, we focus on households observed in 2004 and estimate commodity income shares for these households in 2008. This procedure requires the estimation of a regression model for commodity income shares separately for the 2004 and 2008 samples. We then use the estimates from the 2008 sample, together with the residuals from the 2004 regression to produce counter-factual panels. Intuitively, we predict average income shares using the observed covariates in 2004 and the estimated parameters for 2008.<sup>3</sup> The results are reported in Table 5. The first row reproduces the observed income shares in 2004 that were reported in Table 3, while the second row shows the predicted 2008 shares. A key result is that income dependence on commodities increased in Chile, El Salvador, Honduras, Peru, and Uruguay, which is expected given the rise in global commodity prices. However, dependency remained roughly constant in (urban) Argentina and in the Dominican Republic; Brazil and Mexico experienced a slight decline in commodity income dependence; while in Costa Rica, Ecuador, Panama, and Paraguay, the decline was large. – – This international heterogeneity might be surprising given the commodity price boom but differences are expected due to various factors discussed below.

It is also noteworthy that the dynamics of income dependence on commodities varies across quintiles within countries. In El Salvador, Honduras, and Uruguay, income dependence increased not only on average, but also for all quintiles. In Chile, average dependence rose, but it decreased for lower quintiles and increased for higher quintiles.

> 3 Let _si,t_ be the commodity income share of household _i_ at time _t_ . The cross-section model for _t_ is _si,t_ = **x**<sup>_′_</sup> _i,t_<sup>_βt_+</sup><sup>_εi,t_.</sup> For _t_ + 1, the model for household _j_ is _sj,t_ +1 = **x**<sup>_′_</sup> _j,t_ +1<sup>_βt_+1+</sup><sup>_εj,t_+1,whereweuse</sup> _j_ instead of _i_ to highlight the fact that these are repeated cross-sections, not panels. Denote _β_<sup>�</sup> _t_ +1 the � OLS estimates of _βt_ +1. From the empirical distribution of the residuals, let _εi,t_ +1 be the draw attached to household _i_ . With these estimates, the prediction for the income commodity shares for household _j_ at time _t_ � is _si,t_ +1 = **x**<sup>_′_</sup> _i,t_<sup>_β_�</sup><sup>_t_+1 + �</sup><sup>_εi,t_+1.Notethat,since weare takingaverages,theresiduals vanish(or areverysmall).</sup> Consequently, the lower and upper bounds in Dang, Lanjouw, Luoto and McKenzie (2011) are very close in our experiment. We report below the lower bound.

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Many of the conceptual issues regarding coverage rates mentioned in Section II apply to occupational pension schemes as well. For example, strict comparisons of pension coverage should take into account differences in the quality of the pensions being provided by employers in each country. Also, alternative concepts of pension coverage, such as the percentage of the wage bill covered by occupational and/or privately-managed pension schemes can be used to make cross-country comparisons.

Note that in Switzerland, while coverage is mandatory, it is not quite universal since certain workers below age 25 and of low income are exempted from the mandate, resulting in the 92 percent coverage rate shown. In Australia, where occupational pension provision was made mandatory in 1993, coverage is expected to have risen significantly since the mid-1980s figure which is presented in the table.

Unfortunately, data on the actual number of contributors and pensioners of occupational schemes were not available for most of the OECD countries shown in Table 5.1. Instead, estimated coverage rates were taken from the OECD report entitled Private Pensions and Public Policy, Paris, 1992, which present the figure as an estimated ratio. Other estimates were made by dividing the reported number of contributors and pensioners by the labor force and population over 65, respectively. For Australia, Canada, the Netherlands and the United States, column 2 data refer to the percentage of households headed by persons older than 65+ receiving occupational pensions taken directly from tables produced by Deborah Mitchell using Luxembourg Income Study data for the mid-1980s. These tables are included here as Annex Appendix III of this document.

# B. **Assets and Investment Returns of Occupational Schemes and the Chilean AFP System**

Assets and Portfolio Allocations. Occupational pension scheme assets, including those managed by private insurance firms, are shown in Table 5.1 and 5.2. For Table 5.1, all figures in column (3) are taken from Davis (1994) and not from Davis (1993) as stated in the footnote. In some cases, such as the US, pension plans for state and local employees are included. Assets for South Africa are taken from South Africa (1992). For Brazil, data were obtained from an unpublished World Bank report from the Latin America and Caribbean Department (LAC) under the supervision of Cheikh Kane. As explained in the notes to Table 5.2, data on the share of pension fund assets managed by insurance firms are assumed 45
world, these results suggest policy makers should be cautious in expecting such programs to have large impacts.

# **Appendix A: Definitions of Key Variables**

# **_Employment outcomes_**

The following employment outcomes are defined based on data collected in the follow-up survey.

_Working at all:_ An indicator variable which takes value one if the individual has worked for cash or in-kind income at all in the past four weeks.

_Employed 20 hours +:_ An indicator variable which takes value one if the individual is currently working for 20 hours a week or more.

_Weekly hours:_ Hours worked per week in the last month employed. This is coded at 0 for individuals currently not working, and top-coded at 100 hours per week (the 99<sup>th</sup> percentile of the baseline response) to reduce the influence of outliers.

_Monthly income:_ Total monthly income from work in the last month. This is coded as 0 for individuals not working, and top-coded at the 99<sup>th</sup> percentile of the control group earnings distribution (2500 Turkish Lira) to reduce the influence of outliers.

_Transformed monthly income:_ The inverse hyperbolic sine transformation of monthly income from work in the last month, log�y+ (y<sup>2</sup> + 1)<sup>1/2</sup> �. This is intended to be more robust to outliers than levels of income and is similar to the logarithm transformation, but is also defined when income is zero (Burbidge et al, 1988).

_Occupational status:_ this is coded based on work occupation using the international measures of socioeconomic occupational status of Ganzeboom and Treiman (1996). This is a continuous measure ranging from 16 (e.g. domestic helpers) to 90 (e.g. judges), and is coded as zero for individuals not working.

_Formal work:_ this is an indicator variable coded as one if the individual is currently working in a job covered by social security.

_Formal income:_ this is monthly income earned in jobs covered by social security.

_Aggregate employment index:_ A standardized index obtained as the average of each of the above variables, after each has been standardized by subtracting the mean and dividing by its standard 26
has a positive impact on exports in the full sample, but a negative impact in a sub-sample of developing countries, suggesting that in poorer countries EPAs efforts should focus on on-shore activities.

The rest of this paper is organized as follows. Section 2 reviews the existing literature on export promotion. Section 3 describes our global survey of EPAs and provides some descriptive statistics to help understand the objectives, activities, and institutional structures that exist in EPAs around the world. Section 4 describes the econometric strategy. Section 5 provides the empirical results, and section 6 concludes.

# **2 EPAs: What do we know so far?**

As far as we are aware, there has been no cross-country statistical analysis of the impact of EPAs on exports. The exception is perhaps Rose (2007), who estimates the impact that the presence of an embassy or consulate may have on bilateral trade using a gravity model. Rose argues that as communication costs fall, foreign embassies and consulates have lost much of their role in decision-making and information-gathering, and therefore are increasingly marketing themselves as agents of export promotion. In a sample of twenty-two exporting countries –of which eight are developing countries– and around 200 potential trading partners he finds that for each additional consulate abroad, exports increase by 6 to 10 percent.

The bulk of the empirical literature that has looked more closely at the effectiveness of export promotion has focused on agencies in developed countries, particularly the United States. There have been two broad approaches. One relies on surveys of random samples of exporters and potential exporters, asking which programs they have made use of, their opinions of these programs, and the success they have had in exporting. Kedia and Chhokar (1986), for example, found that export promotion programs in the United States have little impact, largely because of a lack of awareness of their programs. Seringhaus and Botschen (1991) surveyed the opinion of nearly 600 firms in both Canada and Austria, and found that export promotion service use was low, and the programs were not addressing the needs of exporters.

4
concentrated right after the harvest. This is the channel emphasized by Khandker (2012) and Bellemare, Barrett and Just (2013) in studying the welfare loss from food price seasonality and volatility.

An indicative answer to the question of price induced seasonality in food consumption is given by the seasonal gap in consumption obtained from estimating (9), at least to the extent that the estimated seasonality related to the monthly fluctuations in consumption can be attributed to movements in the prices of food staples. Controlling for possible demand shifters such as the religious holidays in the estimation of the seasonal factors makes this plausible. Nonetheless, it cannot be excluded that other factors, unrelated to staple prices, are driving the results. Also, the very limited number of observations for each month, remains a concern. If unobserved common factors have affected food consumption in a particular year across an entire sample or sub‐sample, this may give rise to misleading seasonality estimates when samples are short. Examples of such factors in the Tanzanian data include increasingly severe drought conditions during the 2008‐09 LSMS‐ISA wave and severe flooding which may have impacted December and January food consumption in the 2010‐11 wave. Inclusion of household fixed effects does not control for the effect of such unobserved time variant factors.

To address these difficulties the effect of staple prices on food consumption is estimated directly, conditioning on the month of interview and religious holidays (i.e. conditioning on other potential sources of seasonality in food consumption). In particular, equation (9) is extended to: The price‐based seasonal factors for food consumption are then given by the seasonal factors in the average price series _p_<sup>_av_</sup> multiplied by the estimate of the price elasticity . Given that the price seasonal factors are obtained from longer time series market data (19 years in the case of Tanzania), they will be less subject to the short sample bias problems which potentially affects the consumption seasonality estimates of equation (8). By controlling for the month of interview and religious events, the estimated price elasticity is also purged from other seasonal 12
31

**Table 9**

# Tests of weak exogeneity

Test of exogeneity of: For long-run parameters only/ For both short and long-runk/

||Statistic|p-value|Statistic|p-value|
|---|---|---|---|---|
|_kg_|0.242|0.623|1.699|0.428|
|z|0.508|0.476|0.509|0.775|
|_crpr_|0.305|0.581|0.647|0.724|
|_Pk_|0.025|0.875|_0.554_|0.758|
|_kg, z, crprandPk_|1.545|0.819|3.365|0.909|

- Notes: a/ The statistics for the individual tests are distributed as x<sup>2</sup> (1); the statistic for the joint test, as X 2 (4).

- _I_ The statistics for the individual tests are distributed as x<sup>2</sup> (2); the statistic for the joint test, as X (8).2
In 2015, with presidential elections approaching, the economy stagnated, inflation remained high, and foreign exchange pressures continued. Fiscal policy remained without a clear medium-term framework and without quantitative fiscal rules at the federal level. Political budget cycles re-emerged. Monetary policy had multiple and expansive objectives and central bank financing of the government was institutionalized. The broader framework, or direction, for macroeconomic policy making was unclear, other than general political statements on “not repeating the mistakes of the 1990s.” Even understanding the size of fiscal and monetary problems was difficult, given doubts about macro statistics, and increasing use of off-budget fiscal mechanisms. In terms of the definition of credibility, and the characteristics of credible policy, outlined in the introduction, the path forward on credible macroeconomic policy could be challenging, to say the least.

# **IV. Microeconomic policy**

In this section, a brief historical survey will be organized by sector, or policy area, rather than chronologically. The areas covered are: trade and foreign investment; business climate; labor markets; financial markets; public utilities; and the new “supply law.” Social sector policies have a critical role in both economic performance and socioeconomic outcomes, as well as the political economy of reform; however, they are beyond the scope of this paper.

# _Trade and foreign investment_

Argentina is a member of the Mercosur customs union, which started functioning in 1994, and Argentina is a member of the World Trade Organization (WTO) since its inception in 1995. There continue to be long lists of exempted goods within Mercosur, so that it does not function as a true customs union. Common tariffs on manufactured goods remain high, compared to other emerging market economies. External trading partners also note high costs associated with customs clearance.<sup>44</sup> Argentine non-tariff measures have increased in recent years, as noted above, in the macroeconomic policy section. More specifically, the current dispute before the WTO concerns:

- <mark>The procedure connected to the Advance Sworn Import Declaration (</mark> _<mark>Declaración Jurada Anticipada de Importación</mark>_ <mark>, DJAI), required by the Argentine Government since February 2012 for most imports of goods into Argentina; and,</mark>

- <mark>The imposition by Argentine authorities on economic operators of one or more of the following trade-related requirements (TRRs) as a condition to import into Argentina or to obtain certain benefits: (a) to offset the value of imports with, at least, an equivalent value of exports; (b) to limit imports, either in volume or in value; (c) to reach a certain level of local content in domestic production; (d) to make investments in Argentina; and, (e) to refrain from repatriating profits. The requirements are in some cases contained in</mark>

> 44 See “Assessment of Barriers to Trade and Investment between the EU and Mercosur,” May 2011, Copenhagen Economics prepared for the European Commission, DG Trade.

21