rafmacalaba/datause-encoder-data huggingface dataset:
Turning to the crisis period 2007-2009, which saw a large fall in consumer demand in developed and emerging countries, and a subsequent fall in world trade, the decomposition analysis indicates that although the net effect of firm churning continued to remain positive, on average fewer firms entered and exited the market and the period was associated with less churning. This result is true no matter what dimension of export decomposition is studied – in short, entries and exits by new firms, existing firms in new countries, sectors and products, all fell over the recession years. In addition, data for the latest period available (2009-2010) seems to indicate that Pakistani exporters are yet to recover from the effects of the recession years and that their performance continues to be less dynamic than in the pre-recession period.

Remarkably, however, although the extensive margin of exports has seen a fall over the recession years, the contribution of the intensive margin to export growths has rebounded nicely and has even grown. Valuation effects linked to the cycle of commodities markets and the spectacular fall and recovery in foreign demand are likely contributors of these developments.

# **4. Determinants of firms export growth at the intensive and extensive margins**

The remainder of this paper will concentrate on exploring econometrically the key leads from the descriptive analysis, with a particular focus on identifying the factors that may determine the extensive and the intensive margin of export growth. In other words, the main interest lies in teasing out the effect of factors that might be external to Pakistani firms, such as changes in demand from foreign markets, or exchange rate movements, and of other factors, such as market concentration and existing performance, that could have a direct impact on the probability of new exports and of survival.

Our baseline equation regresses the mid-point growth rate of exports at the firm-destinationproduct-year level, which we used for the export growth decomposition of Table 6, on measures that track developments in foreign demand, firm characteristics, and changes in a number of measures of export costs over the period 2002-2010. The regression is carried out by means of ordinary least squares (OLS).

Our dependent variable, the mid-point growth rate of firm i exports is constructed as in Bricongne et al, 2011, to which the reader is referred for a definition. It has three dimensions: year t, 8-digit HS product k, and destination c. It is computed from export flows in Pakistan’s Rupees. We observe the export performance of all firms that exported at least once over the period of reference. This is possible because the mid-point growth rate allows computing rates also for entries and exits. We explain the change in exports with the following variables.

41
ii

|V.4 Health and Safety Provision .......................................<br>..............|37|
|---|---|
|V.5. Knowledge of Standard Working Conditions, Wages, and Benefits..|41|
|V.6. Migration and Poverty Alleviation .....................................................|47|
|V.7. Labor Union (SPSI) .....................................................|49|
|V.8. Workers' Aspirations .............................<br>........................|50|
|VI. MAIN FINDINGS AND POLICY RECOMMENDATIONS ....................|51|
|VI. 1. Minimum Wages ...........................................................|51|
|VI. 2. Compliance With Labor Legislation .............................. ,.,.,,.....|53|
|VI. 3. Health and Safety Provisions ......|54|
|VI. 4. Knowledge of Standard Working Conditions, Wages and Benefits ..............|54|
|VI. 5. Role of Trade Unions and Workers' Aspirations .......................................|55|
|VII. CONCLUDING NOTES<br>...........................................|55|
|References ..........................................................|58|
|Survey of Women Workers Questionnaire ..........................................................|70|
- 32 -

Figure 8. Implied Price Cost Margins, 1990–2004 (1990=100) <!-- Start of picture text --> 180<br>160 Non-beneficiaries<br>(average)<br>140<br>120<br>100<br>80 Beef protocol<br>countries (average)<br>60<br>40<br>Source: Author’s calculations.<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> The above evidence may raise a general question of why trade preference programs may be an ineffective instrument to assist the agriculture sector in developing countries. One main reason, which seems plausible in the current beef preference context, is that a commitment to the temporariness of granted preferences is not credible enough.<sup>41</sup> As suggested by the MillBastable test, the protection must be temporary. Although the Cotonou Agreement is transitory and supposed to be replaced with more WTO-compatible arrangements in 2008, the expectation may persist that the commodity preferences would be carried over to the next trade regime.

# **_Counterfactual examples of infrastructure developments_**

One of the important policy implications of the estimation results is that accumulated infrastructure stocks could facilitate livestock production and trade. For illustrative purposes the impact of infrastructure development is inferred from the estimated equations. Suppose that infrastructure would be developed to the 75 percentile level of the sample.<sup>42</sup> The case of road quality improvement measured by the share of paved roads is examined as an example, because it yielded one of the significant estimation results.

> 41 Another reason for unpromising performance of trade preferences is that high import tariffs to keep privileged countries producing would cause large welfare losses in terms of consumption and production distortion costs. In theory, the infant industry literature suggests that direct production subsidies are rather better (Corden, 1997).

> 42 This is assumed an exogenous shock; thus, all other relevant consequences are ignored, such as investment costs.
# **The Lack of Empirical Support for the Conventional Theory of Collective Action**

In a major, book-length effort, Poteete, Janssen, and Ostrom (forthcoming) review the empirical support for the theory of collective action related to natural resource problems. They examine the evidence generated by in-depth case studies, meta-analyses of cases, large-scale comparative field studies, laboratory experiments, and agent-based models. The major finding is that the unambiguous predictions of the conventional theory are not supported. While many instances of free riding are observed in the array of empirical research, a surprisingly large number of individuals facing collective action problems do cooperate.

Contrary to the conventional theory, many groups in the field have self-organized to develop solutions to common-pool resource problems at a small to medium scale (Baland and Platteau, [1996] 2000; Agrawal, 2000, 2002; McKean, 2000; Wade, 1994; Schlager, 1994; Schlager et al., 1994; Ostrom, 1992, 2001; Ostrom et al., 1994; NRC, 1986, 2002; Dietz et al., 2003). Rational choice theory was used as a foundation for the conventional theory of collective action. Predictions from this theory are well supported when applied to the analysis of the provision and production of private goods in a highly competitive environment. Predictions from the theory are not well supported when applied to situations involving social dilemmas where participants trust one another to be effective reciprocators (Ostrom, 1998). Thus, before analyzing efforts to reduce the threat of massive costs related to climate change, it is essential to update the theory of collective action so that future policies are not made on the basis of a theory that appears to be so reasonable but has not received strong empirical support.

# **Updating the Theory of Collective Action Related to Climate Change**

For future analyses of how individuals relate to natural resources at multiple scales, the conventional theory of collective action needs revision based on a behavioral theory of human 10
learning. Even the most interesting activities are unlikely to lead to learning without those links established clearly and explicitly through direct instruction. Research shows that teachers who establish clear and specific learning goals for each lesson use class time more effectively (Brophy, 1999; Clearinghouse, 2009; Shield & Dole, 2013). Teachers who clearly communicate lesson objectives in relation to classroom activities help students know what is expected of them at the end of the lesson, which in turn can help them focus their effort on the goal (Ribera et al., 2012) and improve academic performance (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013; Hattie, 2009; Seidel & Shavelson, 2007).

# _2. The teacher’s explanation of content is clear_

In addition to clear lesson objectives tied in to relevant learning activities, teachers’ ability to explain concepts clearly to students also promotes academic learning outcomes (Reeves, 2009). Classic work from the 1960s and 1970s, known as “clarity studies” which looked at different types of lessons, demonstrated that when teachers lack verbal fluency, known as teacher vagueness (Hiller, Fisher, & Kaess, 1969; Smith, 1977; Smith & Edmonds, 1978), or commonly have false starts or discontinuity (i.e., teacher interrupts the lesson by injecting irrelevant content), students perform less well on tests of achievement because they were distracted from important class content (Rosenshine, 1968). Further research also identified clear communication of classroom process to be as important as clear content (Civikly, 1992). These results highlight the importance of clear, focused instructions and explanations in helping students recognize what is important for learning.

Another teaching method found to be helpful is clarifying content to illustrate complicated concepts in the form of logical and/or visual representations using simple concept-maps and graphs (i.e., infographics). These methods of visualizing concepts can help synthesize important information and make clear to students how concepts are related. A vast number of studies have shown that visual information is easier to encode and understand (i.e., lower cognitive load on the brain) compared to large amounts of text or even information simply presented verbally (Klingner, Tversky, & Hanrahan, 2011), and visual information has been shown to be more easily understandable for students especially when learning about complex concepts (Boakes, 2009).

18
**Table 3.7. Sectoral structure for base scenario in 2012 and 2030 (Percent)**

||**Shares(%) in**|**national ...**|**.**|||**Shares (%) for**|**...**|
|---|---|---|---|---|---|---|---|
|||||||**exports in**<br>**sector**<br>|**imports in**<br>**sector**|
|**Year Sector**|**VA**|**Output**|**Employment**|**Exports**|**Imports**|<br>**output**|<br>**demand**|
|**2012 Agriculture**|40.6|25.0|50.4|22.2|0.4|16.1|1.5|
|**Industry**|11.1|25.9|7.8|22.8|71.8|15.1|70.4|
|**Mining**|3.6|3.8|0.7|20.3||98.5||
|**Manufacturing**|2.3|5.9|1.5|2.5|43.0|3.7|82.9|
|**Other**|5.2|16.3|5.6||28.8||54.4|
|**Services**|48.3|49.0|41.8|55.0|27.7|20.6|29.7|
|**Private**|35.6|36.8|32.9|55.0|26.1|27.4|36.7|
|**Government**|12.6|12.2|8.9||1.6||7.1|
|**Total**|100.0|100.0|100.0|100.0|100.0|18.1|46.3|
|**2030 Agriculture**|34.5|22.1|46.0|11.5|0.5|18.2|1.5|
|**Industry**|16.5|24.0|6.6|33.9|61.2|48.6|72.9|
|**Mining**|12.9|11.5|2.9|32.5||99.7||
|**Manufacturing**|1.5|4.8|1.1|1.3|41.1|5.2|81.0|
|**Other**|2.1|7.8|2.7||20.1||58.7|
|**Services**|49.0|53.9|47.4|54.7|38.3|35.9|32.9|
|**Private**|34.4|40.6|36.6|54.7|35.9|47.6|42.8|
|**Government**|14.6|13.3|10.8||2.4||7.4|
|**Total**|100.0|100.0|100.0|100.0|100.0|35.0|44.6|

23
2

# **Novel Indicators of the Trade and Welfare Effects of Agricultural Distortions in OECD Countries**

Empirical indicators of farm support by governments and their effects on consumer prices, called Producer and Consumer Support Estimates (PSEs and CSEs), have been estimated in a consistent way since 1986 by the Secretariat of the OECD (2009) for its 30 member countries. The indicators provide policy transparency, contribute to a better understanding of the various dimensions of agricultural support measures in high-income countries, and have been used extensively as inputs into economic models of agricultural markets. The OECD (2006) has also released PSEs for Brazil, China and South Africa, as well as for several East European countries; and it will soon add them for Chile.

A recent global World Bank study (Anderson 2009) complements and extends the OECD’s efforts by providing similar estimates for a longer time period (back to 1955) and for individual member countries of the European Union. It also has comparable estimates for 45 other countries at different stages of economic development and includes a time series of rates of assistance to producers of nonagricultural goods, to compare with agricultural distortion estimates.

The OECD and World Bank measures for each product are aggregated using the value of production and consumption as weights to obtain an annual average PSE and CSE for each country. That traditional aggregation method provides a reasonable indicator of the average price distortion across that country’s product set, but it is not necessarily a good indicator of the distortion to the volume of trade in farm products because that depends also on the responsiveness of domestic supply and demand to price changes (that is, price elasticities), and on whether there are any negative PSEs that are offsetting positive ones in the aggregating process. It is an even poorer indicator of the national welfare cost of that country’s farm price and trade policies, because for each product that cost is related to the _square_ of the rate of price distortion and so the total cost depends on the extent of dispersion in product PSEs and CSEs.
with head office and other branches. This also adversely affects the smooth and efficient operation of a payment system. Much of the country is covered by gravel roads and where roads are paved, away from the main line of rail, they are in bad need of repair. All these factors contribute to significantly raising the costs of operating in these areas. In most cases, they prove prohibitive for investment into the provision of financial services.

# **1.3 Credit to Households and the Private Sector in Zambia**

Access to credit remains extremely limited in Zambia. As illustrated in the following chart, between 1990 and 2004, bank credit to the private sector, measured as a percentage of GDP, dropped slightly from 8.8% to 8.1%, while at the same time banks increased their holding of government securities.

<!-- Start of picture text --> Figure 6. Zambia. Domestic Credit to Private Sector<br>by Banks<br>15%<br>10%<br>5%<br>0%<br>Credit to Private Sector/GDP<br>Source: WDI Database<br>1990 1992 1994 1996 1998 2000 2002 2004<br>GDP<br><!-- End of picture text --> The number of firms and households borrowing from banks in Zambia also remains extremely low. In 2005, authorities estimated that the total number of outstanding loan accounts amounted to only 46,908, which includes 39,074 of accounts for individual persons and the rest are accounts for firms, government entities, etc. In other words, this means that in Zambia only 0.37% of the population has a credit (or loan) account with a commercial bank.

As illustrated in the following figure, approximately 50% of all loan accounts are represented by credits and advances below $100 dollars.<sup>7</sup> It is interesting to note that only 5,687 borrowers (4,028 individuals and 1,659 firms) had loans that represented 91% of the total loan portfolio of commercial banks in Zambia, reflecting the high concentration of wealth in the country.

> 7 The large number of loans below US $100 dollars can be attributed to the large number of nonperforming loans that are still in the books of banks and which have become small over time.

15
0.05% of receipts **Construction, Installation, and Engineering Contracts:** 0.03% of contracted amount **Property Leasing Contracts:** 0.1% of lease amount; amounts less than 1 Yuan to be stamped as 1 Yuan. **Commodity Shipping Contract:** 0.05% of transport fees. **Warehousing Contracts:** 0.01% of warehousing & storage fee. **Loan Contracts:** 0.005% of loan amount Property insurance contracts 0.003% of amount insured **Technology Contracts:** 0.03% of indicated amount **Property Transfer Documents** 0.05% of indicated amount **Business Accounting Documents:** 0.05% of recorded capital **Permits and Licenses:** 5 Yuan<sup>1)</sup> All securities trading: 0.4% stamp tax on both sides for central and local. Change to 0.2% in 2001<sup>2)</sup> **Major Exemptions:** Tax exemptions may be granted on duplicates or copies of documents on which Stamp Tax has already been paid; documents executed when property is donated to the government, social welfare units supporting the widowed, the aged, the injured and the handicapped, or schools; noninterest bearing or discounting loan contracts; preferential loan contracts concluded between foreign governments or international financial institutions and the Chinese government or State financial institutions; and insurance contracts for agriculture and forestry products, and animals in animal husbandry **Hong Kong Rate of Stamp Duty on Property Transfer:** http://www.freshfields.com/places/asi 3.75% of purchase consideration for the properties a/publications/newsletters/asia_sfina which transferred into Real Estate Investment & nce/6306.pdf Trust (REITs). **Rate of Stamp Duty on Transfer of Share:** 0.2% where the Special Purpose Companies (SPVs) whose shares are transferred is incorporated in 39
# **Table C3: Ordered probits**

||**Deendent**||**Order**|**ed Probit Mo**|**dels of Sev**|**erity of Co**<br>**bli**|**nstraints**||**rn**|**ritrti**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**p**<br>**Variable**|**electricity**|**finance**|**transport**|**market**|**puc**<br>**utilities**|**labor**|**land**|**gove**<br>**ance**|**egsa**<br>**on**|**taxes**|
||**Explanator**|||||||||||
|**Country**|**y**<br>**variable**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Banglades|mngr is male|0.276*|-0.317|-0.024**|0.251|0.504|0.156|-0.109|0.548|1.093*|0.618|
|||(0.208)|(0.255)|(0.214)|(0.273)|(0.320)|(0.434)|(0.487)|(0.223)|(0.463)|(0.490)|
|Ethiopia|mngr is male|0.237|-0.247|0.300|-0.210|-0.049|1.553***|-0.065|0.685**|0.559**|0.794**|
|||(0.248)|(0.196)|(0.220)|(0.270)|(0.213)|(0.430)|(0.236)|(0.204)|(0.273)|(0.378)|
|Indonesia|mngr is male|-0.122|-0.058|-0.058|-0.009|-0.196*|-0.010|0.071|-0.049|-0.046|0.053|
|||(0.131)|(0.115)|(0.133)|(0.118)|(0.115)|(0.166)|(0.134)|(0.125)|(0.131)|(0.143)|
|Sri Lanka|mngr is male|0.264*|0.086|0.148|-0.004|0.355**|0.162|0.214|-0.082|0.150|-0.112|
|||(0.139)|(0.143)|(0.131)|(0.136)|(0.123)|(0.196)|(0.207)|(0.218)|(0.183)|(0.182)|

**Controls** hh enterprise, services, trade, log firm age, electricity,  lnK, lnL  mngr age,  mngr age<sup>2</sup> /100, mngr primary, mngr secondary, mngr **included but** tertiary, town, ln dist market, credit institution, ln local wage **not**

## **<u>presented:</u>**

Note: *** p<0.01, ** p<0.05, * p<0.1. Regressions are weighted. Standard errors are heteroscedasticity robust and clustered at the village level.

56
Rwanda, interviewees point out that congestion in online systems at peak times is likely to increase, without adequate (and costly) investments in storage capacity and server upgrades, as more and more taxpayers are now brought into the tax net (also see Mascagni, Dom and Santoro, 2022).

On the bright side of availability of tax infrastructure and systems, the tax administration in Rwanda set up automatic cross-checks of different data sets, such as VAT, income taxes and EBMs, which imply that the self-reported turnover for income taxes is now benchmarked to the total of sales as transmitted by the EFD. Much more can be done in this direction, eventually enabling the abundant pre-filling of income tax returns now common in high income countries.

Third, another important precondition is the preparedness and incentives of revenue authority staff that interface with a new technology. In Sierra Leone, concerns exist around change management, which will determine whether the upcoming ITAS turns out to be op– timal or not since tax officials are used to one specific way of operating which could be difficult to change in a short time. A similar story comes from Malawi, where, as technology evolves, it closes loopholes previously used by some staff for illegal practices. As a result, there was significant resistance and delay in the launch of ITAS. Eventually, the top management provided clear directions of change and started imposing strict deadlines on the staff.

In the case of Uganda, in line with Mayega et al. (2019) and the challenges discussed in section 4, there seems to be weak incentives for staff to change. Since adapting to the new technology is inconvenient to them, they tend to resist it. Staff are very familiar with Excel, where they have more discretion on what to input, while ITAS modules are more complicated, with a variety of often-sophisticated checks and controls, and require more training and commitment. Similarly in Ethiopia, manual practices co-exist with digital ones and are difficult to erase. There are many returns which are not captured into SITGAS, but inputted manually by tax officials, especially outside the capital. This is mostly due to poor 26
19 of antitrust policies or of their implementation. In addition, in the context of planned economies, the requirements for a central gathering of information, its processing, and the final dissemination of instructions to individual enterprises put limitations on the number of enterprises within each industry so as to make the system manageable.'5 There is also, in any case, inconsistent incentives between those who want information and those who have to reveal i,.

In fact, there is no strong evidence that increasing returns to scale are responsible for this market structure. An anecdotal evidence of the scope of scale economies, which is often cited, is that of the automobile industry. It is generally believed, from studies of developed countries, that the minimum efficient scale of production is in the range of 200,000-300,000 cars per model per annum."1 But most developing countries have average production runs of only around 20,000 imnplying the presence of unexploited scale economies.' These studies, however, do not take into account the less capitalintensive and low technology techniques of production, the reduced expenditures on R&D, advertising and marketing, and the long life of each model. Nor have the economies of scale inherent in some of the heavy capital-intensive industries manifested themselves in national comparative advantage in those industries as the scale of output increased. In fact, calculations of the domestic resource costs of these industries have found them to be higher than the exchange rate (i.e., the domestic resources utilized to produce a dollar's worth of import-substitutes have been higher than the domestic resources utilized for a dollar's worth of imnports). Moreover, the extent of the scale economies depends fundamentally on the technology of production which in the developing countries with their advantage in lower labor costs is

**IS/** Newberry. D.M. and Kamuman, P. in **Market** Concentfaion and Competition in Eastern Europe', The **World** Economy, Vol. **15,** No.3, May 1992, provide figures from 1988 to show that 'enterprises in the Soviet-type **economies** are **more** than ten times the average size of those in the developed **market** economies'. 16/ **See** Owen (1983). **table** 4.12.

17/ **See** Rodrik (1989). **table** 5.2.