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28 industry (72849-excluding Estonia and Latvia), refrigerating equipment (74149-excluding Bulgaria and Estonia), and packing electric machinery (74523-excluding Estonia and Lithuania). There are five product groups in which seven CEEC have RCA indices larger than unity; two in which six CEEC share comparative advantage; nine in which five CEEC simultaneously have comparative advantage.

# 4.3. **Specialization and trade in parts**

The share of part groups with a double comparative advantage in total exports and imports of parts provides indication of a country's participation in global networks of production and distribution. The intuition behind is that RCA above unity for a product group means specialization in both exports and imports. As can be seen from Table 12, these products accounted for significant portions of exports and imports of only a few CEEC in 1997. Slovakia had the highest share of parts with a double comparative advantage in both exports (69%) and imports (52%), followed by the Czech Republic (61% and 45%). Hungary had a lower share of parts with a double comparative advantage in exports (51%) than Slovenia (60%), but a larger share in imports-43 percent against Slovenia's 15 percent. Estonia had large shares in both exports (49%) and imports (50%). The shares for remaining countries were significantly lower than 50 percent.

**Table 12: The share of products with a double comparative advantage, comparative advantage in exports and imports in** CEEC **exports and imports of parts in 1997 (million of US dollars and percent)**

|Country|Value of<br>exports<br>with double<br>RCA>1|The share<br>in exports<br>of parts (in<br>percent)|Value of<br>exports<br>with export<br>RCA >1|The share<br>in exports<br>of parts (in<br>percent)|Value of<br>imports<br>with double<br>**RCA>1**|The share<br>in imports<br>of parts (in<br>percent)|Value of<br>imports<br>with import<br>RCA >1|The share<br>in imports<br>of parts (in<br>percent)|
|---|---|---|---|---|---|---|---|---|
|Bulgaria|9|18|16|32|11|7|99|60|
|Czech R.|1,247|61|1,279|63|1,322|45|2,709|92|
|Estonia|111|49|207|91|166|50|175|53|
|Hungary|816|51|968|60|1,343|43|2,992|95|
|Latvia|1|3|9|47|2|1|59|40|
|Lithuania|<br>5|22|10|47|3|1|133|49|
|Poland|168|14|493|41|125|3|3,644|89|
|Romania|<br>51|27|83|44|37|7|359|69|
|Slovakia|344|69|402|80|543|52|977|94|
|Slovenia|416|60|442|64|110|15|471|64|

Source: Computations based on partners' data from UN COMTRADE Statistics.

The share of exports of parts with 'export' RCA indices exceeding unity indicates the extent of specialization in production of a part as well as concentration of exports. By this measure Estonia-with 91 percent of exports of parts to EU markets having RCA indices exceeding unity in 1997-appears to have reached the highest level, followed by Slovakia (80%),
On the other hand, if the claimant reports an instance of non-compliance, the team calls the responsible authority for an explanation. Calls are repeated until the CJC has a specific answer about the case’s status; however, after five calls, the case is registered as an instance of noncompliance. All calls and answers are recorded in the system in order to have a detailed track record for each case.

The final step in the process involves the CJC lawyers grading the status of compliance based on the specifics of the court’s order and the information collected. The CJC sends a detailed monthly report to the Sala’s Presidency and to each of the Justices for follow-up in the event that responsible authorities refuse to comply. Legally, the Sala IV can accuse the defendant of desacato, but is up to the Prosecutor General to take the case into the criminal justice system; and this has proven challenging.

Critically for our purposes, the CJC records detailed information on the parties to the case, the nature of the complaint and order, the response of the responsible authority, as well as a number of additional features of the process, including the timing of votes and responses.

# **Analysis**

Our analysis makes use of the CJC monitoring data, beginning in October, 2009, and ending in December, 2011. We seek to estimate two kinds of information. Given enough time, the vast majority of amparos are implemented per the Sala IV’s requirements;<sup>8</sup> however, there is considerable variation in the time it takes to implement an order. In so far as delayed implementation is a form of non-compliance (Staton, 2010), we focus our analysis on estimating

> 8As of May 2, 2013, the CJC had tracked 11,052 of the 11,363 orders issued by the Sala IV between October, 2009, and March 25, 2013. Among the orders that were tracked, the CJC was able to verify compliance in 9,391 instances (85%).

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- 24

Table 6. Financial Ratios of the Deposit Money Bank in Indonesia

||RES|NFA|TRES|CPS|NETCG|K|OT|LIABIL|CCB|
|---|---|---|---|---|---|---|---|---|---|
|1972|0.286|-0.225|0.061|1.191|-0.055|0.148|0.275|1.423|0.479|
|1973|0.281|-0.451|-0.17|1.433|0.12|0.178|0.656<br>|1.834|0.393|
|1974|0.42|-0.444|-0.02|1.328|0.192|0.174|0.766|1.94|0.373|
|1975<br>|0.38<br>|-0.221<br>|-0.159<br>|0.477<br>|0.227|0.211|-0.127|1.084|0.461|
|1976|0.36|-0.145|0.215|0.458|0.252|0.188|-0.118|1.07|0.397|
|1977<br>|0.36<br>|-0.126<br>|0.234<br>|1.093<br>|0.228<br>|0.216<br>|0.464<br>|1.68<br>|0.333<br>|
|1978<br>|0.274<br>|-0.087<br>|0.187<br>|0.584<br>|0.205<br>|0.229<br>|-0.166<br>|1.064<br>|0.383<br>|
|1979|0.313|0.001|0.314|0.604|0.189|0.178|-0.071|1.106|0.409|
|1980|-0.295|0.19|0.486|0.612|0.152|0.188|-0.128|1.06|0.383|
|1981|0.243|0.24|0.483|0.569|0.161|0.172|-0.199|0.973|0.442|
|1982|0.3|0.034|0.334|0.636|0.283|0.191|0.028|1.219|0.587|
|1983|0.205|0.115|0.32|0.702|0.26|0.184|-0.017|1.166|0.481|
|1984|0.203|0.106|0.308|0.628|0.318|0.197|-0.049|1.149|0.629|
|1985<br>|0.194<br>|0.132<br>|0.326<br>|0.675<br>|0.239<br>|0.168<br>|-0.059<br>|1.108<br>|0.464<br>|
|1986|0.168|0.153|0.321|0.802|0.234|0.175|0.029|1.204|0.444|
|1987|0.149|0.085|0.235|0.866'|0.178|0.159|0.035|1.194|0.352|

# Variables

All variables are expressed as a fraction of total deposits (demand and time).

RES -serves NFA Ne6 foreign assets TRES Sum of RES and NFA cPS Net banks' credit to the private sector (the difference between the banks' credit to the private sector and the BI rediscounting).

NETCG Sum of credit to the government and to public enterprises minus the government's deposits. K Banks' capital OT Net other liabilities minus loans to other financial institutions LIABIL Total liabilities (equal to total assets) CCG BI loans to DNBs (rediscounting) The variable NETCG does not represent the net liability of the public sector since it does not include the deposits of the public enterprises. (The net position of the government with DMBs has been always positive).

The level of total liabilities (of assets) exceed that of deposits. Therefore the value of LIABIL is greater than one.
processing ...rms’imports of capital are recorded separately from material imports, implying �<sup>K</sup> i<sup>= 0.16Thus, the only necessary adjustmenthere is to remove foreign content in domestic</sup> materials, �<sup>F</sup> i<sup>, which causes EXPi�IMPito overestimate DV Aiin exports.From (3), ...rm</sup> i’s ratio of domestic value added in exports to gross exports (DVAR) depends only on the share of imported materials in total revenue (P<sup>I</sup> Mi<sup>I=PYi)withadjustmentsfor�F</sup> i<sup>=EXPi:</sup> Without ...rm-level information on �<sup>F</sup> i<sup>=EXPi,werefertoKWW12fortheindustryes-</sup> timates for 2007 and impute the estimates backward for each industry-year between 2000 and 2007, using the weighted average of the growth rates of the number of ordinary (nonprocessing) importers across upstream industries.<sup>17</sup> These industry estimates range from 0.4 to 5.7 percent, which we use to proxy for �<sup>F</sup> i<sup>=EXPiin(4)toconstructa...rm’sDVAR.18</sup> in imports used by Chinese exporters ( EXP�<sup>D</sup> <u>i</u> i<sup>)increasedfrom0.1%in1995to1.3%in2007.Theyalsoshow</sup> that these estimates can vary widely across sectors, ranging from 2.5% for the Chemical Products sector to 0.2% for the Leather and Footwear sector. Unfortunately, such estimates are not available separately for processing and ordinary exports. Nevertheless, given the low estimated domestic content in imported materials at the aggregate level, adjusting for it is unlikely to have a signi...cant e¤ect on both the levels and the trends of our aggregate DVAR estimates. Our approach therefore may underestimate the DVAR for sectors that use imported material with high domestic content. Given that our DVAR estimates for most sectors are already higher than the existing estimates based on IO tables, accounting for returned domestic content in imports will only strengthen our point that the existing estimates are subject to a downward aggregation bias.

16The Chinese customs data record material and capital imports separately from a ...rm’s total imports, in a category called “Equipment for Processing Trade”(code number = 20). We thank a referee for pointing this out.

17The rationale is that the net entry of ordinary importers, stimulated by China’s continuous trade liberalization, may increase the supply of intermediate inputs that embody more foreign content. This assumption is grounded on the ...ndings in Brandt et al. (2015), which shows that while the cost share of imports in total materials has been stable, the aggregate import share has increased substantially due to a large entry of new importers since China’s accession to the WTO in late 2001.

18Table A7 in the appendix reports the estimates of �<sup>F</sup> i<sup>=EXPiby industry-year.Notice that our approach</sup> does not double count DVA as long as we exclude indirect trade between processing ...rms and focus on mesauring DVA of the processing trade regime. We need additional assumptions to deal with the double8
122,000 hectares of the 4.6 million hectares under cultivation<sup>13</sup> . In 2007, tobacco contributed 53 percent to the volume of agricultural exports, compared to 9 percent each from sugar and tea, 3 percent from cotton, and 4 percent from edible nuts.

**Figure 19: Agricultural Exports Composition in Malawi(2000-2007)**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br>tobacco tea sugar edible nuts<br>cotton coffee pulses knitted apparel<br>unknitted apparel other exports re-exports<br>merchandise exports(%)<br><!-- End of picture text --> 78. The analysis of the tobacco value chain covers two levels of burley production, differentiated on the basis of management levels. The low management scenario assumes low fertilizer application (4 bags each for basal and top-dressing) and low labor intensity on a per hectare basis. The high management scenario assumes high fertilizer application (8 bags each of basal and top-dressing) and high labor intensity (370 persondays). The assembly costs include curing and baling costs as well as levies and cesses at the Auction Floors. At the processing level, we assume a 55 percent conversion from dried leaf to tipped and threshed tobacco that is ready to be packed in boxes for delivery at the international market. The main assumptions for the burley tobacco farm models used in the analysis are shown in Tables 16 and 17.

**<u>Table 16: Burley tobacco assumptions</u>**

|**Sector**|**Yield**<br>**(MT/Ha)**|**Key Inputs**|**Assembly**|**Process**|**Distribute**|
|---|---|---|---|---|---|
|FAM-<br>low<br>FAM-<br>high|0.9<br>1.25|4x4 fertilizer on lands,<br>some insecticide, hired<br>sprayer, delivery to<br>TAMA depot, 325 days<br>labor(150 daysfamily)<br>8x4 fertilizer on lands,<br>more insecticide, own<br>sprayer, delivery to<br>TAMA depot, 370 days<br>labor(175days family)|Transport to floor,<br>deductions for<br>TAMA, ARET,<br>ATC, and AHL<br>cesses and levies|Convert to<br>tipped and<br>threshed<br>tobacco at<br>55%|Delivery boxed<br>T&T tobacco to<br>Europe|

Fertilizer use expressed in 50kg bags basal (NPK) x 50kg bags top dressing (urea) per ha.

> 13 Otanez, M.G. H. Mamudu and S.A. Glantz. Global Leaf Companies Control the Tobacco Market in Malawi. _Tob. Control 2007; 16: 261-269_

36
# **Product space analysis**

While RCA highlights comparative advantage by examining export shares, it does not indicate the direction a country could take so as to upgrade or diversify its product mix. One problem with any technique attempting to identify products that could be candidates for upgrading is difficulty with the definition of upgrading. The simplest definition calls for the export of goods that are more sophisticated than the ones currently exported. The product-space analysis proposed by Hausmann, Hwang and Rodrik (2007) enables researchers to identify such goods. Their analysis relies on two quantities, PRODY and EXPY. PRODY is calculated at the commodity level and is the weighted average of income of exporting countries. The intuition behind this is that if a rich country is exporting more of a particular commodity, it should be more sophisticated. Thus, each commodity is assigned a PRODY value which represent the sophistication of a product (Hausmann, Hwang and Rodrik 2007).<sup>3</sup> EXPY is defined as the weighted average of PRODY for each country. Therefore, EXPY is a proxy for the overall quality of the export basket of a country. EXPY for Penang in 2006 was $13,768.47, much higher than the average income for Penang residents. The positive difference between PRODY and EXPY signifies ‘upgrading’ from the average sophistication of exports. This is represented by the vertical axis in Figure 1.

Diversification possibilities are measured along the horizontal axis (inverse of DENSITY). This is based on the ‘capabilities’ already present in Penang to export a product. Construction of this measure is based on the assumption that exporting a product requires certain ‘capabilities’ and some of such ‘capabilities’ are shared among different products. Therefore, if many countries export both goods A and B, a country that exports only good B may have enough capabilities to start exporting good A. For each commodity, then there are many related commodities (i.e. high probability of a country exporting both goods). DENSITY shows as how many of these related products that a country exports (more specifically, goods enjoying comparative advantage). High DENSITY means that a country is exporting a large number of related goods in which a

> 3 There are other ways to measure a sophistication of a product such as unit value.  Often unit value is hard to calculate because not all the countries report consistent quantity units.  It is well-known that unit value is a highly unreliable measure.

8
Figure 2: Quantile Treatment Effects on Earnings Panel A: Empirical Distributions of Earnings in Control and Public Certification Groups <!-- Start of picture text --> Control<br>Treated<br>0 2 4 6 8 10<br>Earnings (inverse hyperbolic sine)<br>B: Quantile Treatment Effects of Public Certification on Earnings<br>.6 .7 .8 .9 1<br>Percentile<br>1<br>.9<br>.8<br>Empirical CDF<br>.7<br>.6<br>6<br>4<br>2<br>0<br>Treatment effect on earnings (IHS)<br>−2<br><!-- End of picture text --> Panel B: Quantile Treatment Effects of Public Certification on Earnings Panel A shows the empirical distributions of earnings in the control and public certification groups. Earnings are the inverse hyperbolic sine transformation of earnings in South African rand, with 1 rand _≈_ 0.167 USD in purchasing power parity terms. Earnings are coded as zero for candidates who are not working. The vertical axis in Panel A is truncated below at the 60<sup>th</sup> percentile because earnings below that value are zero. Panel B shows the quantile treatment effects (QTEs) of public certification. These are unconditional QTEs, estimated without controlling for any covariates or stratum fixed effects. The 95% pointwise confidence intervals allow heteroskedasticity and clustering by treatment date. The confidence intervals exclude zero at all percentiles except 73-74, 86, and 93-99.

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# **Figure 21 Time Fixed Effects of Momentum Statistics**

This figure presents the time fixed effects obtained from the regression of the Kaminsky et al. (2004) momentum statistics on time and PFA fixed effects. Panel A presents the Contemporaneous Momentum Statistic M1 and Panel B presents the Lagged Momentum Statistic M0. Coefficients that are statistically significant at the ten-percent level are marked on the figure. T-tests are one-tailed. Numbers represent percentages (coefficients are multiplied by 100). Significant events that occurred during this period of time are also highlighted on the chart: the Asian crisis (July 1997), the Russian crisis (August 1998), the introduction of Fund E and the widening of the minimum return band (October 1999), and the establishment of the multi-fund regime (September 2002).

Panel A. M1 Time Fixed Effects (1996 - 2005) <!-- Start of picture text --> 1000<br>M1<br>800 Significant at ten percent<br>600<br>400<br>200<br>0<br>-200<br>-400<br>-600<br>-800<br>Multi-Fund<br>Asian Crisis Russian Crisis Fund E<br>-1000<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05<br>Panel B. M0 Time Fixed Effects (1996 - 2005)<br>1000<br>800<br>600<br>400<br>200<br>0<br>-200<br>-400<br>M0<br>-600 Significant at ten percent<br>-800<br>Multi-Fund<br>Asian Crisis Russian Crisis Fund E<br>-1000<br> Time Fixed Effects from Momentum Regression<br> Time Fixed Effects from Momentum Regression<br><!-- End of picture text --> Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05 Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05
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identical across the consumption experiment as well as the TZNPS 020/21 and 2019/20 rounds. The sample sizes were 4,644 in 2020/21 (following up with a panel sample that was first interviewed during the 2014/15 round) and 1,179 households in 2019/20 (following up with a subset of an older panel sample that had been interviewed as part of the TZNPS 2008/09, 2010/11 and 2012/13). As discussed above, there are differences in terms of food and non-food consumption modules that were introduced in TA 2 and TA 3 to understand the potential for using lighter version of these modules to obtain accurate poverty predictions.

Finally, in TA 1 and the TZNPS 2020/21 and 2019/20 rounds, the total consumption is taken to be the sum of food (consumed at and away from home) and non-food consumption (health, education, utilities, furnishing and household expenses, transport, communication, retreats, and other). We provide more detailed discussion on the food and non-food consumption expenditure aggregates for the TZNPSs and the three TAs in Appendix B.

# **2.2. Descriptive statistics**

We spatially and inter-temporally deflate all the consumption aggregates in the three TAs and the TZNPSs. The spatial and temporal price differences in nominal household consumption expenditures within all survey rounds are corrected using Fisher price indices. These price indices are estimated within each survey round by stratum and quarter (or month, in the case of the experiment), and the base period in each estimation comprises the entire period of each round.

The across-survey intertemporal deflation is in turn conducted using the annual inflation series for various consumption groups, as obtained from the World Bank Global Database of Inflation (Ha _et al._ 2023).<sup>7</sup> Specifically, food expenditure is deflated using the consumer price inflation for

> 7 To access the database, visit: https://www.worldbank.org/en/research/brief/inflation-database.

10