rafmacalaba/datause-encoder-data huggingface dataset:
23

# **Table 8: Decomposition of Predicted Change in Real Credit (Disequilibrium Hypothesis)**

||||**S**|**upply**||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Coeff**|||**Growth**||||**M**|**ultiplicati**|**on**||
|||**Jul.01-**<br>**Jun.04**|**Jul.04-**<br>**Jun.08**|**Jul.08-**<br>**Jun.09**|**Jul.09-**<br>**Sep.10**|**Oct.10-**<br>**Jun.11**|**Jul.01-**<br>**Jun.04**|**Jul.04-**<br>**Jun.08**|**Jul.08-**<br>**Jun.09**|**Jul.09-**<br>**Sep.10**|**Oct.10-**<br>**Jun.11**|
|**Real Lending rate**|2.706|-1.5%|-1.1%|-4.1%|2.1%|0.6%|-4.06%|-2.87%|-11.16%|5.58%|1.62%|
|**Real T-bill rate**|-2.191|-1.3%|-0.8%|-4.2%|2.9%|3.6%|2.78%|1.65%|9.25%|-6.44%|-7.85%|
|**Ln(Lend. Cap.)**|0.139|11.5%|5.3%|-5.9%|-1.6%|-2.6%|1.60%|0.73%|-0.82%|-0.22%|-0.36%|
|_Shifts in the curve_|||||||**4.38%**|**2.39%**|**8.43%**|**-6.67%**|**-8.22%**|
|_Total changes in Supply_|||||||**0.32%**|**-0.48%**|**-2.74%**|**-1.09%**|**-6.60%**|
||||**D**|**emand**<br>||||||||
||**Coeff**|||**Growth**|||||**Multiplicati**|**on**||
|||**Jul.01-**|**Jul.04-**|**Jul.08-**|**Jul.09-**|**Oct.10**|**Jul.01-**|**Jul.04-**|**Jul.08-**|**Jul.09-**|**Oct.10**|
|||**Jun.04**|**Jun.08**|**Jun.09**|**Sep.10**|**Jun.11**|**Jun.04**|**Jun.08**|**Jun.09**|**Sep.10**|**Jun.11**|
|**Real Lending rate**|0.746|-1.5%|-1.1%|-4.1%|2.1%|0.6%|-1.12%|-0.79%|-3.08%|1.54%|<br>0.45%|
|**Ln(Industrial Prod.)**|0.638|12.9%|10.1%|-4.2%|-7.0%|-3.2%|8.21%|6.42%|-2.70%|-4.46%|<br>-2.04%|
|**Ln(EGX 30)**|-0.113|21.5%|81.3%|-39.9%|21.7%|-7.5%|-2.42%|-9.16%|4.50%|-2.45%|<br>0.85%|
|_Shifts in the curve_|||||||**5.78%**|**-2.74%**|**1.80%**|**-6.91%**|<br>**-1.19%**|
|_Total changes in Demand_|||||||**4.67%**|**-3.53%**|**-1.27%**|**-5.37%**|<br>**-0.75%**|
|_Estimated Total shifts in Supply and Demand_|||||||**10.2%**|**-0.35%**|**10.23%**|**-13.57%**|<br>**-9.41%**|
|_Estimated Total changes Private credit_|||||||**4.98%**|**-4.01%**|**-4.01%**|**-6.45%**|<br>**-7.34%**|
|_Observed Total changes Private credit_|||||||**4.16%**|**-0.29%**|**-5.70%**|**-6.13%**|<br>**-7.13%**|

Source: Constructed by the authors using the regressions results. Notes: 1= change in real T-bill rates + change in lending capacity.

2= 1 + change in lending rate 3= change in industrial production and stock market index

- 4= 3+ change in lending rate + change in T-bill rate

5=1+3 6= 2+4

- 7= observed change in claims on private sector.
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MALAPIT, H., QUISUMBING, A., MEINZEN-DICK, R., SEYMOUR, G., MARTINEZ, E. M., HECKERT, J., RUBIN, D., VAZ, A., YOUNT, K. M. & PHASE, G. A. A. P. 2019. Development of the project-level Women’s Empowerment in Agriculture Index (pro-WEAI). _World development,_ 122 **,** 675692.

RUDMAN, L. 1998. Self-Promotion as a Risk Factor for Women: The Costs and Benefits of Counterstereotypical Impression Management. _Journal of personality and social psychology,_ 74 **,** 629-45.

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RYAN, R. M. & DECI, E. L. 2000a. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. _Contemporary Educational Psychology,_ 25 **,** 54-67.

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2
effect of improved trade facilitation on trade flows. The methodology proposed by Wilson, Mann, and Otsuki (henceforth WMO) (2003) was the first to measure the impact of trade facilitation on trade performance using a gravity model. They focused on four dimensions of trade facilitation: port infrastructure, customs environment, regulatory environment, and e- business infrastructure. They constructed four indicators for Asia Pacific Economic Cooperation (APEC) countries for a single year by applying single averages to 13 primary variables, which were mostly collected from the WEF. They estimated a gravity model that included the four indicators as well as other typical controls, such as the income level of individual countries, geography, and tariffs. Using model estimates, WMO find that intra-APEC trade could increase by $254 billion, or 21 percent of intra-APEC trade flows, if APEC members with below-average indicators improved capacity halfway to the average for all members, about half the increase being derived from improved port efficiency.

Subsequently, Wilson, Mann, and Otsuki (2005) expanded their four indicators to a larger set of countries. Using simulations based on their gravity model, they find that the total gain in trade flows in manufacturing goods from trade facilitation of the “below-average” countries “halfway” to global average levels yields an increase in global trade of $US377 billion. This can be decomposed as follows: $US157 billion and $US107 billion yielded by e-service infrastructure and port efficiency, respectively, as well as $US83 billion and $US33 billion due to improvements in the customs environment and the regulatory environment, respectively.

To derive our indicators, we not only collect the WEF variables used by WMO and newly defined WEF variables<sup>6</sup> , but also incorporate recent indicators from other sources available for at least the period 2003-07. In addition, the factor analysis methodology is used not only to construct the indicators but to define the sub-groups to be considered.

Other regional studies have suggested the importance of infrastructure and institutional indicators for trade facilitation. Njinkeu, Wilson, and Fosso (2008) analyze the impact of reforms in port efficiency, the customs environment, the regulatory environment, and service infrastructure. They find that improvements in port efficiency and service infrastructures are the

> 6 Some of the original WEF primary variables found by WMO (2003, 2005) are no longer constructed or were modified. We are thankful to Margareta Drzeniek Hanouz for making World Economic Forum (WEF) data available for this research.

7
support stronger conclusions ‐‐ in either direction ‐‐ on this important issue. Similarly, there may very well be other applications of SYS‐GMM in cross‐country growth empirics in which weak instrument problems do not arise, or even if they do, that weak instrument‐consistent confidence sets nevertheless are meaningfully small and support strong conclusions about the sign and size of the growth effect of the variable under consideration.

More generally, this paper underscores the importance of routinely documenting instrument strength in applications of the SYS‐GMM estimator to cross‐country growth empirics, and of relying on weak instrument‐robust approaches to inference as appropriate. Moreover, the results in the last section of this paper suggest the importance of also systematically documenting the sensitivity of conclusions about parameters of interest to a reasonably broad set of choices for constructing the instrument set within SYS‐GMM. While users of these techniques should probably avoid using this sensitivity analysis to “mine” the universe of possible instrument sets to isolate those that are strong, the results of Section 4 do suggest that taking care to limit the proliferation in the number of instruments (by limiting lags and by “collapsing” the instruments) is likely to lead to stronger identification of the parameters of interest.<sup>14</sup> There probably also is scope to apply recent techniques from the literature on many weak instruments to the specific setting of the SYS‐GMM estimator to improve instrument strength as well as to remove some of the discretion in the choice of the instrument set (see for example Hansen and Kozbur (2014) and references therein). Finally, consumers of cross‐ country empirical studies using the SYS‐GMM estimator should expect authors to report weak instrument diagnostics, weak instrument‐consistent approaches to inference, and sensitivity analysis over alternative instrument sets, in order to help them to assess the reliability of the evidence reported in these studies.

# **References**

Anderson, T. W. and H. Rubin (1949). “Estimation of the Parameters of a Single Equation in a Complete Set of Stochastic Equations”. The Annals of Mathematical Statistics”. 20:46‐63.

Arellano, Manuel and Stephen Bond (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”. Review of Economic Studies. 58(2):277‐97.

> 14 This recommendation echoes a similar point made in Roodman (2009), but from the perspective of avoiding the risk of overfitting in the first stage regressions when the number of instruments is large relative to the number of observations.

24
<!-- Start of picture text --> Graph 6<br>Foreign Direct Investment<br>-<br>12,000<br>o 10,000-<br>CD)<br>D 8,000-<br>0<br>° 6,000-<br>: 4,000-<br>2,000<br>0<br>CD CD CO CD CO CD CO CD CO to (O CO O CO D D CO C CD CD<br>ao ao OD CD CD a 0 0 OD a OD C CO (D CD CD CO CD CD CD CD<br>Source:INEGI 0<br>Data from 1980-93 and 1994-98 are not strictly comparable do to change in the methodology<br><!-- End of picture text --> _96_
# **Appendix C (continued): Bank Ownership, Costs, Employment, Wages and Provisions (Weighted)**

Regressions are estimated via OLS at bank level for the year 2001 to 2008. Robust t statistics in brackets and bank level clustered t statistics in parenthesis. All regressions control for time and country dummies. Inverse of number of banks in a country in a given year is used as weights. ***, ** and * represent significance at 1, 5 and 10% level respectively.

||7|8|9|10|11|12|
|---|---|---|---|---|---|---|
||**Overheads**<br>**to Assets**|**Personal Expenses**<br>**to Assets**|**Employment**<br>**to Assets**|**Wage (Personal**<br>**Expense)**|**Wage**<br>**(Overheads)**|**Loan Loss**<br>**Provisions to**<br>**Gross Loans**|
|**Dummy Public Ownership**|**0.375**|**0.204**|**0.039**|**-8.368**|**-16.895**|**1.405**|
||[3.38]***|[2.83]***|[8.32]***|[5.82]***|[4.59]***|[2.94]***|
||(2.22)**|(1.73)*|(5.26)***|(3.64)***|(4.40)***|(2.09)**|
|**Dummy Foreign Ownership**|**0.204**|**0.126**|**0.012**|**-1.035**|**-3.801**|**0.304**|
||[2.09]**|[1.75]*|[3.08]***|[0.88]|[1.51]|[1.22]|
||(1.31)|(1.03)|(1.62)|(0.45)|(0.95)|(0.84)|
|**Dummy Listed**|**0.288**|**0.065**|**0.003**|**0.893**|**3.667**|**-0.6**|
||[2.50]**|[0.77]|[0.69]|[0.77]|[1.28]|[2.12]**|
||(1.96)*|(0.64)|(0.56)|(0.58)|(1.22)|(1.73)*|
|**Lag Total Assets (Log)**|**-0.161**|**-0.118**|**-0.011**|**4.078**|**9.214**|**0.067**|
||[3.01]***|[3.13]***|[8.31]***|[9.20]***|[7.51]***|[0.72]|
||(2.43)**|(2.55)**|(5.23)***|(6.50)***|(6.46)***|(0.61)|
|**Non-Interest Income to Assets**|**0.285**|**0.101**|**0.004**|**1.802**|**3.47**|**0.404**|
||[5.81]***|[2.70]***|[1.50]|[2.58]**|[1.73]*|[2.67]***|
||(4.78)***|(2.09)**|(1.13)|(2.98)***|(1.65)|(2.19)***|
|**Deposits to Assets**|**-0.012**|**0.003**|**0.0003**|**-0.252**|**-0.794**|**0.046**|
||[1.44]|[0.73]|[1.86]*|[4.01]***|[4.40]***|[3.35]***|
||(1.28)|(0.61)|(1.20)|(2.32)**|(3.44)***|(2.89)***|
|**Loans to Assets**|**0.007**|**0.004**|**0.0002**|**0.029**|**-0.035**|**-0.009**|
||[1.98]**|[1.87]*|[1.83]*|[1.05]|[0.58]|[0.91]|
||(1.37)|(1.28)|(1.03)|(0.67)|(0.43)|(0.71)|
|**Observations**|574|384|387|270|384|488|
|**R-squared**|0.41|0.52|0.57|0.73|0.65|0.43|
|**Number of Countries**|9|9|9|9|9|9|
|**Number of Banks**|120|92|102|77|104|115|

46
administration and growth in the formal sector. It is noteworthy to emphasize that the non-oil revenues were twice the level of oil revenues in 2004 and surpassed again oil revenues in 2005. In 2006, non-oil revenues still substantial compared to direct oilrevenues and are likely to remain more so given the fairly weak projected oil production trend. This implies that the government of Chad should continue its effort to modernize and to reform and promote the agricultural and pastoral sectors together with the rural sector development so as to promote a sustained growth, widen the fiscal space and secure additional revenue from the real sector of the economy and for poverty reduction.

<!-- Start of picture text --> Evolution of Chad Current Expenditure 1998 - 2006<br>(in percentage of non-oil GDP)<br>12<br>10<br>8<br>Current expenditure<br>6 Wages and salaries<br>Good and Services<br>4<br>2<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Sources: Chadian authorities; Bank staff estimates and IMF.

Oil<sup>10</sup> almost doubled government revenue, from 9 percent of non-oil GDP in 2003 to 17 percent in 2005. Primary current expenditure rose from 10 percent of non-oil GDP to 11 percent, and domestically financed investment from 2 percent of non-oil GDP to 4 percent, resulting in an increase of 3 percent of non-oil GDP in the non-oil primary deficit. In 2006, due to the deterioration of the security situation, exceptional military spending are estimated at 4.3 percent of non-oil GDP. However, priority sector expenditure is expected to increase by about 70 percent in nominal terms.

<!-- Start of picture text --> Chad Government Revenue<br> 2002 - 2006<br>30<br>25<br>20 Resources<br>Oil Revenue<br>15<br>Non-oil revenue<br>10 Budget Support<br>5<br>0<br>2002 2003 2004 2005 2006<br>Years<br>% of non-oil GDP<br><!-- End of picture text --> Source: Chadian Authorities and Bank’s staff.

> 10 Information on oil revenue is published routinely. The _Collège_ publishes quarterly reports on the execution of oil-financed programs.

8
blackboards, and toilets. The school-level outcomes of student-teacher and student-classroom ratios are constructed by us using the data on total number of students, teachers, and classrooms. These databases also serve as the source of baseline data on school-level covariates, namely location, gender type, level, and registration status.

The SLQAT serves as the source of data on the school’s student pass rate, our treatment assignment variable _z_ . Separate electronic databases have been constructed by PEF for phase-3 and phase-4 SLQAT takers. These databases contain the total score for each student that took the test. In the databases, student test scores are organized by school and, within school, by grade. The school identification information provided in the databases comprise of the school’s name (at times, with some address elements) and location (tehsil<sup>22</sup> and district). Although PEF constructed the SLQAT pass rate for each school, we used the student test score data in order to construct our own measure of school SLQAT pass rates. The match rate between their calculation of school SLQAT pass rates and ours’ in both phases is nearly perfect at 99.5%. While it makes virtually no difference, we use our measure of school SLQAT pass rates as the treatment assignment variable Constructing a single electronic database for our analysis required that the application data are linked with the SLQAT data at the school level. Unfortunately, the same unique school identification variable was not used across databases. Consequently, we linked the application databases to the SLQAT databases using an iterative visual-matching process. First, schools were matched across databases using the district name and school name variables.<sup>23</sup> Exact matching failed in a number of cases as PEF did not maintain consistency in the spelling, word ordering,

> 22 Tehsil is the spatial unit of government administration one tier below district. There are 127 tehsils in Punjab. 23 Although information on the school’s tehsil was also available in both the application and test databases, this information was error-ridden. Consequently, we did not use this information in the cross-database matching exercise.

24
# **1. INTRODUCTION**

Most researchers would agree that export diversification matters and it is especially important for developing countries. The literature has, so far, focused on identifying transmission channels. For example, developing countries’ exports tend to be concentrated on a few products, often commodities, with very volatile demand. This translates into high income instability, which in turn provokes high growth volatility. Export diversification in this setting has the advantage of creating a more stable income inflow. Others view the benefits from diversification in terms of the spillovers in the economy as a result of having a more diversified production structure, Hausmann and Klinger (2006).

In our view, in order to be able to understand export diversification we need first to understand its microeconomic determinants. We are still lacking the necessary understanding of these determinants but recent theoretical and empirical contributions to the trade literature offer new tools to investigate this issue. In this regard Melitz (2003) develops a theoretical framework that provides a simple explanation for the presence of many of the zeros in the trade matrix. Empirical work has already been done on these grounds. For instance, Baldwin and Di Nino (2006) studied how the implementation of the Euro has affected the extensive margin of trade (the number of varieties traded) in European countries. In another recent paper, Amurgo-Pacheco (2006) focuses on the Euro-Mediterranean FTA to investigate how preferential trade agreements affect the number of varieties traded among its members (through the reduction in trade costs).

Following this line of work, the objective of the paper is to investigate the differences in export diversification patterns between developed and developing countries at a highly disaggregated level (we use the HS 6-digit trade data). For instance, a common observation is that the degree of diversification varies depending on destinationspecific factors such as trade costs and the size of the market. In particular, we observe that trading with larger markets implies a higher probability of finding demand for more and new products, and thus, exporting more product varieties. We first disentangle some of the determinants of trade growth and investigate the relevance of diversification. Then, if diversification accounts for a non trivial share of export growth, we want to know what determines it and what can be done from a policy perspective. We address these points throughout the paper.

We organized the paper as follows: Section 2 introduces the dataset and discusses some stylized facts on export diversification and the margins of trade for both, the developed and developing nations. In Section 3, we present the theoretical framework used to investigate export diversification patterns. In Section 4 we present our formal empirical analysis using gravity equations to estimate trade flows. We conclude in Section 5 summarizing the main findings of the paper, discussing some policy recommendations that can be drawn from them, and providing some avenues for future research.

2
**Table 15. Simple average tariffs on edible groundnuts, unprocessed and processed**

||Unprocessed<br>MFN|Processed<br>Tariffs|Unprocesse Processed<br>GSP Tariffs|Unprocesse<br>LDC|Processed<br>Tariffs|
|---|---|---|---|---|---|
|EU|0%|13%|0%<br>9%|0%|0%|
|Japan|3.7%|19%|3.7%<br>19%|3.7%|15%|
|Canada|0%|4%|0%<br>4%|0%|3.2%|
|Korea|243%|65%|243%<br>65%|243%|65%|

Source: WTO

# **IV. IMPACT OF GROUNDNUT PRODUCT POLICY REFORMS ON WORLD PRICES, TRADE FLOWS AND WELFARE**

We summarize key findings of our quantitative analysis of distortions in groundnut markets (see Beghin, Diop, Matthey, and Sewadeh 2003). A full description of the model and further details can be found in the latter reference. We first report results obtained under the most plausible assumptions underlying the model. We later discuss sensitivity analysis which concerns US policy and the uncertainty on China’s protection (VAT and protection of processed groundnuts).

# **Policy Reform Scenarios**

We analyze multiple scenarios. First, we consider full multilateral trade liberalization for groundnuts, meal, and oil, with and without the removal of the U.S. peanut program. We call these two scenarios FMTL&US, and FMTL. Then we consider multilateral groundnut trade liberalization, again with and without the removal of the U.S. farm peanut program (GMTL&US, and GMTL scenarios). We then consider full trade liberalization in the two largest and most distorted groundnut markets, China and India (CIFTL scenario). We report results on these key five scenarios in Tables 16 to 21. All results regarding changes in price and physical flows are reported in percent change from the baseline, Tables 16-20. Changes in welfare are reported in 1995 PPP U.S. dollars (purchase power parity holding in 1995) in Table 21. The baseline and simulations were run for 3 years (1999-2001), and averaged. We report the 3-year average impacts in the last column of the tables.

# **Results**

In countries characterized by high groundnut protection, the combined effect of the world price increase and removal of their own protection is beneficial to final users of groundnuts, other things being equal. For countries with moderate or no protection prior to reform, the net impact (tariff removal and terms of trade) is an increase in domestic groundnut prices, handicapping groundnut users (final consumers, crushers), other things being equal. A similar logic and dichotomy of cases carries through for groundnut oil and meal for which the combined effect of world price and tariff removal has to be assessed. These substantial terms-of-trade effects have a large impact on trade and welfare. Allocative efficiency gains in domestic markets can be offset by large price increases originating in post-reform world markets.

Further, in countries with high protection of the oil and/or meal sectors (e.g., India), the oil and cake tariff removal, net of the world price hike, induces lower domestic prices for these two products and reduces crush margins. As a result, the domestic excess supply of groundnut crushed into oil and cake decreases, drawing imports. By contrast, countries with moderate or no 22