rafmacalaba/datause-encoder-data huggingface dataset:
markets combined with an increasing productivity in that sub-sector. The contribution of livestock has also been increasing, although at a slower pace than non-cotton crops.

**Figure 2. Tajikistan: Trends in Gross Agricultural Output, 1999-2008**

(Billion Somoni, constant price) <!-- Start of picture text --> 4,500,000,000<br>4,000,000,000<br>3,500,000,000<br>3,000,000,000<br>2,500,000,000<br>2,000,000,000<br>1,500,000,000<br>1,000,000,000<br>500,000,000<br>-<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Non-cotton crops Cotton Livestock<br>Somoni (real prices 2007=100)<br><!-- End of picture text --> Source: Background paper for the Tajikistan CEM (2009) This production shift put the issue of freedom to farm at the center of the country’s growth strategy. Indeed, given that Tajikistan is constrained by its stock of arable land (estimated at about 500 thousands hectares) and that the productivity of cotton sector has been decreasing lately, a more efficient use of such scarce resource could make a significant difference in the country’s growth prospects.

# _Shifting trade patterns_

The easiest way to see how Tajikistan’s trade patterns have been shifting is to cluster its exported goods by production sector.<sup>3</sup> As can be seen in Figure 3, the bulk of Tajikistan export is coming from the manufacture of precious and non-ferrous metal (mainly aluminum). As exports started expanding after the independence of the country in 1992, the share of aluminum in total export declined from more than 80 percent to less than 25 percent in 1997, mainly due to the civil war as world prices for aluminum were trending up between $1,200 and $1,600 per ton during that period. Then the share of aluminum started to increase again, to stabilize at around 70 percent of total export since 2008. Add to that farming activities and you cover more than 80 percent of the country’s total trade.

Other sectors contribute moderately to Tajikistan exports: manufacture of textile, manufacture of chemicals and chemical products, manufacture of food and beverages and scrap metal. The manufacture of textile is mainly yarns and cotton fabrics produced from cotton mainly grown in the 3 We use the third revision of the STIC trade classification along with its corresponding ISIC sector classification. The 5-digit STIC and their corresponding 4-digit ISIC classifications are used.

4
The research presented in this paper is based on twelve months of desk and field work that was undertaken to provide the empirical basis for designing options for improving procurement performance in the short- and medium-term.<sup>1</sup> Our case study narratives of public procurement reform in six districts and two provinces present a more detailed and nuanced picture of the dynamics underlying the adoption and sustainability of the reforms than this overview paper. The focus of other forthcoming analytical work (see Turkewitz et al 2013) is on the relationship between the uptake of procurement reform and subsequent improvements in procurement performance.<sup>2</sup> Section 2 examines the main features of the public procurement system in Indonesia, including a discussion of its historical antecedents. Section 3 provides a justification for using Indonesia as a case study to examine subnational variation in administrative reforms. Section 4 describes the study’s methodology and fieldwork. Based upon a scan of existing procurement data, Section 5 profiles procurement performance at the local level. Section 6 discusses the features that we expect to be associated with the uptake and implementation of procurement reform at the sub-national level in Indonesia and Sections 7 and 8 present the main findings of our case studies. We conclude the paper in Section 9 by offering a set of short-term policy recommendations for the GoI and pressing empirical questions for policymakers within Indonesia and research on procurement more broadly.

# **2 Background on Procurement Reform in Indonesia**

Following the demise of the New Order regime in 1998, comprehensive procurement reforms were instituted as part of a broader effort to improve In-

> 1This paper draws on background work that was commissioned by the World Bank to inform the overall project, “Strengthening Reform and Improving Public Procurement Performance in the AFR and EAP Regions” (Buehler, 2012; The Asia Foundation, 2012). The Asia Foundation case studies are a joint product of the World Bank and The Asia Foundation.

> 2In this companion piece, we discuss the availability and limitations of of sub-national procurement data including: the scale of procurement; the quality of the contracting process; the quality and value of procurement outcomes; and the equity (including compliance) of the procurement process.

3
**Table 6. Effect of modern technology inputs on productivity through depletion of the resource stock**

|**_Resource qualitypararneter_**<br>**_Resouce_**<br>**_qalityparaeterelasti_**|**_Production_**<br>**_city_**<br>**_(I%)'_**|_Effect of 1%_<br>**_technologyinpu_**<br>**_stc_**|**_increase in_**<br>**_tsonresource_**<br>**_%2()3_**|_Effect of_**_1% increase in_**<br>**_technologyinputsonproductivity_**|
|---|---|---|---|---|
|||**_Water_**|**_Fertilizer_**|**_Water_**<br>**_Fertilizer_**|
|Available<br>soil<br>organic<br>matter|<br>0.060|-0.053|-0.265|-0.003<br>-0.016|
|Available soil phosphorus|0.024|-0.159|-0.136|-0.004<br>-0.003|
|Total soluble salts in soils<br>|-0.015|0.224|0.103|-0.003<br>-0.002|
|Electroconductivity<br>|-0.006|0.077|0.095|-0.000<br>-0.001|
|**Total**||||**-0.011**<br>**-0.021**|

t The production elasticities of resource stock variables _(alnPlalnS,_ where P represent productivity, and S a stock variable) were obtained from the cost function coefficients by evaluating them at the mean values of all other variables involved, and then inverting their signs, i.e. _alnPIalnS=-alnC1lnS_ 2The percentage effect of a technology variables on a resource stock variable (alnSIalnl, where I represents a technology variable) was obtained by regressing the logarithm of each resource stock variables on the logarithm of technology inputs (i.e., fertilizer and water).

> 3The effect of technology variables on productivity _(dlnPd61nl)_ was estimated by multiplying the production elasticity of a resource stock variable with the effect of technology variable on the resource stock, i.e. _(6InPlo/nf) = (alnPlalnS)* (alnSIalnl)._

_Source: Authors' own calculation_

# **Conclusions**

Although results achieved in Pakistan with Green Revolution technologies are impressive, the results of this study raise important questions about the sustainability of those gains, especially in light of growing evidence of degradation of land and water resources. In this study, the assembly and analysis of a very comprehensive data set on both crop and livestock production, as well as a number of soil and water-quality variables, provided the opportunity to understand some of the underlying trends in major production systems of Pakistan's irrigated Punjab.

On the surface, overall output growth in the sector of over 3% annually for nearly three decades, and TFP growth of 1.26% per year, suggest a fairly dynamic sector backed by significant technical change. However, growth in land productivity has slowed since the Green Revolution while labor productivity has jumped with acceleration of tractorization so that much of the recent growth in TFP is due to adoption of labor-saving technologies. A closer examination also shows considerable variation in productivity growth by production system. Crop sector TFP growth was relatively high in two production systems (wheat-cotton and wheat mungbean), modest in one system (wheat-mixed), and negative in the wheat-rice system.

TFP is a composite measure of the effects of changes in technology, resource quality, and improvements in human and physical infrastructure. As such a positive trend in TFP as observed 22
- 7 -

where _y_ is the amount of a crop produced at location _i_ , which depends on an exogenous agroecological factor and the amounts of inputs used. _ui_ is an error term. _z_ is a measurement of crop suitability or productivity of land. _x_ ’s are production inputs: _k_ = { _L_ , _R_ , _I_ , _F_ , _T_ } . _L_ denotes labor. Land is divided into two types: rain-fed ( _R_ ) and irrigated ( _I_ ). _F_ and _T_ denote fertilizer and transport accessibility, respectively. The logarithms are taken for all dependent and independent variables.<sup>4</sup> As is often discussed, spatial autocorrelation is an important empirical issue for estimating the equation. Our primary data is spatial, and the unit of analysis is approximately10 x 10 km parcels of land for each crop. Therefore, one observation may not be independent of another, i.e., _Cov_ ( _ut_ ,<sup>_u_</sup> _s_ ) ≠ 0 . By nature, agricultural land is a continuum of various characteristics, such as soil fertility and water availability. Weather conditions are continuous across locations. On the infrastructure side, public infrastructure, such as transport networks, is a typical network industry, which also creates autocorrelation among neighboring areas.

Another issue is that our agricultural production data are censored. As in many empirical spatial studies, an actual value for the dependent variable—in our case, crop production _y_ —is only available for a subset of the observations. As discussed below, some crops are grown everywhere, and others are produced in specific areas. As a result, there are a large number of locations (i.e., 10 x 10 km of land parcels) with zero production of a given crop.

To deal with these two problems, the spatial tobit (SPTobit) regression model is used. Denoting the latent variable of the output produced by _y*_ , the following censoring mechanism is considered:

> 4 A small positive number (one) is added if the amount of input used is zero to avoid taking the logarithm of zero. For instance, fertilizer is not used in many observations of our sample.
- 40 -

# Table 3 - Continued

||Estimate|d coefficient|
|---|---|---|
|Variable|Internal<br>migration|Mexico-to-U.S.<br>migration|
|AGESQ|-0.006**|-0.009**|
||<br>(0.002)|(0.002)|
|ED|0.266**|-0.129*|
||<br>(0.06)|(0.09)|
|HEAD|0.067|-3.129**|
||(0.82)|(1.09)|
|MEXEX|0.467**|0.144*|
||(0.08)|(0.11)|
|USEX|0.141*|0.487**|
||(0.10)|(0.09)|

Note: Log-likelihood -194.22; * indicates significance at below the 0.10 level; ** indicates significance at below the 0.05 level; standard errors appear in parentheses.
region, growth in the extensive margin – through increased exports of the same products to new markets - has been more important than growth in the intensive margin. And this has been similar, although to a lower extent, when looking at export growth with EU-27. It seems undisputable that over the period considered, small ECA countries have diversified the market destinations taking advantage of the fast growing neighboring markets. Here again, for the relatively larger countries (Russia, Kazakhstan, Belarus, Uzbekistan), growth at the intensive margin is more important than at the extensive margin.

Figure 15a: Decomposition of Export Growth to EAP, 2000-2010 <!-- Start of picture text --> RUS UKR KAZ<br>BLR UZB AZE<br>TKM GEO ARM<br>TJK MDA KGZ<br>Net old Extinction New New Old<br>products in of products products in products in products in<br>old markets new old markets new<br>markets markets<br><!-- End of picture text --> Figure 15b: Decomposition of Export Growth to EU-27, 2000-2010 <!-- Start of picture text --> Extinction New<br>of products<br>products in new<br>markets<br><!-- End of picture text -->

# **Growth Orientation of Export Products & Export Markets**

To complement the indicators of diversification above and inform on how ECA countries’ most important export products and export markets are faring in international competition, we compare their export growth to the growth in world demand for their exports, over the period 2005-2010, now excluding oil and gas from the export basket, following (Reis & Farole, 2012).<sup>11</sup> Figures 15a and 15b plot the annual averages growth rates of world demand for the top 10 most important products for each of the ECA countries (in the X-axis) against the average export growth of those products in the country in question (in the Y-axis). Figures 16a and 16b plot the annual average growth rates of world exports to the top 10 most important destinations for each of the ECA countries considered (in the X-axis) against the average export growth of the ECA countries to those destinations. These graphs are informative on the country’s performance within the intensive margin of export growth in two respects: (i) The degree of diversification of a country within the top ten most important products (markets), which is illustrated by the relative sizes of the bubbles that represent the importance of the product (market) in the export basket of the country.

> 11 To identify the products, we consider the top 10 most important at the beginning of the period (2005).

15
- 4 -

# II. INDEXED MONETARY BALANCES IN COLOMBIA

2.01 For households, the transaction costs of expenditures can be minimized if transaction balances can be placed into highly liquid deposits that are not affected by changes in the inflation rate. For the most part, the savings and time deposits offered by the Colombian Caja Ahorra de Vivienda (CAVs), savings and housing corporations, have been such instruments. While in recent years ceilings on the amount of adjustment for inflation heve reduced the real return on these deposits, the record of their largely positive ex post return since 1972 has built confidence in the use of such instruments.

2.02 The CAVs were created in 1972 by a series of decrees by the Pastrana government. The government relied upon Constitutional authority relating to the disposition of personal savings and their development played a major role in the government's development strategy.11 Their design and implement&tion were placed under the direction of Laughlin Currie.A1 These institutions were introduced into a financial system which had allocated a contracting share of national resources to the formal financial sector and which operated parallel to a thriving and growing informal sector. For example, over the 1965-69 period credit outstanding as a share of GDP averaged 15 percent, whereas it averaged 19 percent in the preceding five year period. In contrast, by the 1980-84 period, the net credit outstanding as a share of GDP averaged 36 percent, and there is indirect evidence that the level of economic activity in the informal sector had secularly contracted.21 i/ See Berry and Soligo (1980) for a discussion of the development strategy and Sandilands (1980) for a discussion of the CAVs creation and their early regulation.

A/ Currie was formerly the chief advisor to both Mariner Eccles, Chairman of the Federal Reserve Board, and President Roosevelt. He also directed the first World Bank mission to Colombia in 1949.

5/ The informal sector generally has three important dimensions: finance, employment and housing production. In contrastt to many other Latin American economies, each of these aspects of the inform.al sector appeared to contract during the 1970s. See the World Bank (1987), p. 70 for further discussion of the expansion of the formal financial sector in the 1970s .mentioned in the text. With respect to housing, Urrutia (1985) p. 64 documents the very significant formalization of the housing stock that took place over the 1970s. The World Bank (1983) p.<sup>19</sup> -<sup>23</sup> provides a discussion of labor participation over the 1960s and 1970s saying that in the latter period the employment expansion would appear to be the highest achieved by a big country over a comparable period. After 1972 employment in the informal sector continued to grow more rapidly than did employment in the formal sector. However, it grei. at a less rapid rate than in the 1958-72 period even though total employment expanded much more rapidly in the second period. Over the period 1958-72 the share of the labor force in the informal sector showed an increasing trend, expanding in all the years except
the other hand, South‐South GVC‐trade is mostly affected by WTO+ provisions. For North‐North agreements, the coefficients on WTO+ and WTO‐X are not significant.<sup>20</sup> While there is no formal theory to guide the analysis, the differential effects of deep agreements across countries’ levels of development may have a simple intuitive explanation. Deep trade agreements affect GVC trade directly, as they lower trade barriers between members, and indirectly, as they improve institutions through commitments to reform. Deep PTAs may matter less for developed countries as trade is already liberalized and domestic institutions are robust. On the contrary, weak institutions in developing economies are likely to be a constraint for GVC integration with developed countries. Deep provisions in PTAs can offer a commitment device to implement reform that contributes to boost GVC‐related trade. Finally, since tariffs and other border barriers are often still high between developing countries, PTAs may affect GVC trade mostly through traditional trade liberalization in South‐South relationships.

# 5. Robustness

In this section, we undertake three robustness tests. First, we consider indirect effects of PTAs. The second robustness exercise allows for slow adjustment in trade policy. Finally, we account for dynamic effects in the regressions.

## a. PTAs indirect effects

In a world where production is fragmented across countries, the level of depth of preferential trade agreements signed by third countries along the supply chain could indirectly affect GVC‐ related trade between two countries. Intuitively, deeper trade agreements in third countries lower trade costs along the entire supply chain, thus encouraging trade in intermediates also among countries that are not part of the agreement. To control for the indirect effect of deep trade agreements, we follow Noguera (2012) and estimate the impact of deep PTAs on the level of integration in GVCs using the following modified gravity framework:

> 20 Estimations using WIOD, which covers only a few developing countries, are similar to this last set of results.

15
# **Summary**

Recent survey evidence suggests that pesticide traders or retailers are an important source of information on the potential health risks of pesticides to farmers. This is particularly true in developing countries where access to such information may often be concentrated in relatively few hands. As conduits of this information, it would be important to better understand the behavior, knowledge and training habits of traders such that policymakers can easily identify and target informational gaps for more effective pesticide information campaigns. More specifically, it would be of value to know what shapes a traders’ risk perception of pesticides and to then to focus capacity building efforts in these areas.

In this study we surveyed 110 Bangladeshi pesticide traders (shops) for pesticides in stock, knowledge and training in pesticide use and handling, sources of information, protective measures and health effects. With this information, we proceeded in two steps. First we constructed an equation to determine what may explain the health status of pesticide traders. We then constructed a second equation of risk perception, containing the health status variable, and estimated this simultaneously with the first. Results for the health effects equation reveal that health status is significantly associated with the amount of toxic inventory, number of years in business, trader experience and the interaction between highly toxic stock and training. This suggests that increases in exposure to toxic pesticides, on a repeated basis, for many years, leads to adverse health conditions experienced by pesticide traders. However, as a traders’ experience with pesticides increases, as does his awareness about the potential risk hazards, this tends to decrease the prevalence of adverse health outcomes; perhaps through averting behavior.

Estimation of the second equation of pesticide risk perception revealed significant associations with a traders’ health status, the amount of toxic pesticides in stock, the number of hours spent in the shop per day, pesticide use training and the interaction between highly toxic stock and training. This suggests that a trader is more aware of the inherent risks the greater the number of health impairments experienced, the greater the 2
|P128950|2013|2017|6/19/2017|10 Active|Bosnia and Herzegovin|
|---|---|---|---|---|---|
|P128950|2013|2018|3/7/2018|11 Active|Bosnia and Herzegovin|
|P128950|2013|2019|12/6/2018|12 Active|Bosnia and Herzegovin|
|P128950|2013|2019|6/28/2019|13 Active|Bosnia and Herzegovin|
|P128950|2013|2020|12/18/2019|14 Active|Bosnia and Herzegovin|
|P128950|2013|2020|6/2/2020|15 Active|Bosnia and Herzegovin|
|P128965|2016|2012||0 Closed|Thailand|
|P128965|2016|2013||0 Closed|Thailand|
|P128965|2016|2016|6/30/2016|1 Closed|Thailand|
|P128965|2016|2017|4/26/2017|2 Closed|Thailand|
|P128965|2016|2018|5/17/2018|3 Closed|Thailand|
|P128965|2016|2019|6/17/2019|4 Closed|Thailand|
|P128965|2016|2021|7/15/2020|5 Closed|Thailand|
|P128966|2013|2013|5/15/2013|1 Closed|Pakistan|
|P128966|2013|2014|12/8/2013|2 Closed|Pakistan|
|P128966|2013|2014|6/15/2014|3 Closed|Pakistan|
|P128966|2013|2015|1/5/2015|4 Closed|Pakistan|
|P128966|2013|2015|6/25/2015|5 Closed|Pakistan|
|P128966|2013|2016|1/12/2016|6 Closed|Pakistan|
|P128968|2015|2015|1/26/2015|1 Closed|Brazil|
|P128968|2015|2016|8/28/2015|2 Closed|Brazil|
|P128968|2015|2016|5/13/2016|3 Closed|Brazil|
|P128968|2015|2017|12/9/2016|4 Closed|Brazil|
|P128968|2015|2017|6/15/2017|5 Closed|Brazil|
|P128968|2015|2018|12/19/2017|6 Closed|Brazil|
|P128968|2015|2018|6/22/2018|7 Closed|Brazil|
|P128968|2015|2019|11/12/2018|8 Closed|Brazil|
|P128968|2015|2019|5/9/2019|9 Closed|Brazil|
|P128968|2015|2020|12/13/2019|10 Closed|Brazil|
|P129000|2012|2012||0 Closed|South Sudan|
|P129000|2012|2013|11/13/2012|1 Closed|South Sudan|
|P129000|2012|2013|5/27/2013|2 Closed|South Sudan|
|P129000|2012|2014|12/5/2013|3 Closed|South Sudan|
|P129000|2012|2014|6/3/2014|4 Closed|South Sudan|
|P129000|2012|2015|12/30/2014|5 Closed|South Sudan|
|P129000|2012|2015|6/30/2015|6 Closed|South Sudan|
|P129000|2012|2016|12/24/2015|7 Closed|South Sudan|
|P129000|2012|2016|6/28/2016|8 Closed|South Sudan|
|P129000|2012|2017|11/9/2016|9 Closed|South Sudan|
|P129098|2012|2013|5/23/2013|1 Closed|Poland|
|P129098|2012|2019|5/9/2019|2 Closed|Poland|
|P129112|2012|2013|4/2/2013|1 Closed|Moldova|
|P129112|2012|2015|12/14/2014|2 Closed|Moldova|
|P129112|2012|2015|4/27/2015|3 Closed|Moldova|
|P129119|2012|2012||0 Closed|India|
|P129119|2012|2013|9/14/2012|15 Closed|India|
|P129119|2012|2013|12/1/2012|16 Closed|India|
<!-- Start of picture text --> Dhaka - Population (1000s)<br>by Skill level<br>25000 + | |<br>20000 | |<br>15000 | 14460 |<br>10000 - | 8874 |<br>5000 - | 10099 |<br>5228><br>0<br>2011 2035 - Scenario A<br>ee<br><!-- End of picture text -->

<!-- Start of picture text --> Dhaka - Population (1000s)<br>by housing<br>250002000015000 14543 ||<br>5915<br>1000050000 XEN 10017 ||<br>2011 2035 - Scenario A<br><!-- End of picture text -->

<!-- Start of picture text --> Dhaka - Employment (1000s)<br>by sector<br>8000 - | |<br>6000 - | 4707 |<br>4000 -<br>2812 _<br>2000 4 413 9964<br>1541<br>o-<br>2011 2035 - Scenario A<br>HE Manufacturing Hs Tradable Services<br>HE Non-Tradable Services<br><!-- End of picture text -->

<!-- Start of picture text --> East-Dhaka - Population(1000s)<br>by Skill level<br>1500<br>986<br>1000 | 600<br>500<br>580<br>333<br>0<br>2011 2035 - Scenario A<br><!-- End of picture text -->

<!-- Start of picture text --> East-Dhaka - Population (1000s)<br>by housing<br>1500<br>1000<br>858<br>312<br>500ty) 62021 708<br>2011 2035 - Scenario A<br><!-- End of picture text --> <!-- Start of picture text --> East-Dhaka - Employment (1000s)<br>by sector<br>400<br>300 321<br>200<br>156<br>100 5<br>568 111<br>0<br>2011 2035 - Scenario A<br>HE Manufacturing HE Tradable Services<br>RR) s(Non-Tradable Servicees<br><!-- End of picture text -->