rafmacalaba/datause-encoder-data huggingface dataset:
low income countries is negatively affected by product standards more than those of higher income countries.

# **2 Data**

In an effort to measure standards restrictiveness we have collected import markets’ maximum residual limits of pesticides. Our source for this data is Agrobase-Logigram’s Homologa database. Agrobase-Logigram collects monthly changes in allowable pesticides for approximately 61 importing countries. They obtain their information directly from each country’s pertinent ministry and standardize it in terms of language, unit, and format.

Using this dataset we matched 243 agricultural products to their corresponding harmonized system (HS) codes at the six digit of disaggregation.<sup>4</sup> Table 1 displays the number of products that are covered by each importer in every year. We can see that there is a great difference in the coverage of products across countries. For example, in 2011, Brazil set pesticide limits on 75 agricultural products whereas the EU on 140 products. However, the coverage of each country is fairly constant across time. There are only 35 products regulated by all countries in the sample. Among these products we find: potatoes, tomatoes, peas, beans, apples, oranges, wheat, maize, sorghum, and ground nuts, among others.

Table 2 displays the number of pesticides regulated by each importer in our sample. Japan, Switzerland, and the EU are the importers that set standards on the greatest number of pesticides, whereas Thailand and other ASEAN countries only set standards for a limited number of pesticides. Countries greatly differ in the pesticides they regulate for a particular product. Take oranges in 2011 as an example. The EU set limits for 506 pesticides, Brazil for 102, and Russia for only 16. Thus the EU is far more restrictive in terms of number of standards.

Figure 2 displays the average number of pesticides per product in BRICS and non-BRICS countries between 2006 and 2011.<sup>5</sup> We observe several stylized facts. First, BRICS restrict fewer pesticides per product than non-BRICS. Second, non-BRICS countries are significantly increasing the number of standards they set per product going from an average of 25 pesticides in

> 4 Homologa’s product coverage is greater than 243 products; however we were unable to match all products directly to an HS code. In our analysis we will only use 66 out of the 243 available products because of the excessive number of missing data among the importers. In order to be included in the sample each product had to have a standards set for at least 50% of importers.

> 5 EU data is only available starting in 2008 which explains the kinks in Figure 1, 2 and 3 in that year.

5
the challenges posed by strategic reporting, given the sensitivity of registration status, and should not be viewed as precise measures. These are intended to summarize current official estimates of the nonregistered and temporarily registered populations, and do not imply any judgment as to their accuracy.

**Table 2: Estimated of Non-permanent Registration Status Mahalla Records and Implied Population Totals**

||Estimated<br>Share<br>Illegal|Implied<br>Population|Share of<br>National<br>total|Temporary/<br>Other<br>Register|Implied<br>Population|Share of<br>National<br>Total|
|---|---|---|---|---|---|---|
|Andijan|0.2%|2,716|<br>0.6%|0.1%|1,824|<br>0.4%|
|Bukhara|2.4%|45,011|<br>9.9%|0.0%|41|<br>0.0%|
|Fergana|0.6%|21,097|<br>4.6%|0.1%|4,821|<br>1.1%|
|Jizzakh|0.2%|3,274|<br>0.7%|0.4%|5,664|<br>1.3%|
|Namangan|0.3%|8,704|<br>1.9%|0.4%|11,527|<br>2.6%|
|Navoi|1.2%|11,952|<br>2.6%|0.9%|8,596|<br>2.0%|
|Karakalpakstan|0.9%|16,523|<br>3.6%|0.1%|990|<br>0.2%|
|Kashkadarya|0.3%|9,856|<br>2.2%|1.0%|28,374|<br>6.4%|
|Samarkand|6.4%|229,920|<br>50.5%|0.1%|5,322|<br>1.2%|
|Syrdarya|1.6%|13,215|<br>2.9%|0.2%|1,759|<br>0.4%|
|Surkhandarya|0.0%|-|<br>0.0%|0.5%|12,923|<br>2.9%|
|Tashkent city|2.8%|67,297|<br>14.8%|9.4%|230,527|<br>52.3%|
|Tashkent|0.7%|20,139|<br>4.4%|1.2%|33,633|<br>7.6%|
|Khorezm|0.3%|5,231|<br>1.1%|5.3%|94,764|<br>21.5%|
|Total|1.5%|454,936|<br>100.0%|1.4%|440,764|<br>100.0%|

_Notes: Author’s Calculations. Reported estimates derived from mahalla records and adjusted using population weights for the selected L2CU Baseline locations. Estimates are subject to sampling and measurement error. The share of the population that is officially registered is imperfectly measured due to the combination of a lack of census data, and the illegality of non-registration status._ There have been several changes to the propiska system since 2017 (see Appendix G for more details), and temporary registration has become more accessible and less bureaucratic according to many qualitative interviews conducted as part of this study. However, as of this writing, a person without a local propiska (either permanent or temporary) in Uzbekistan is not permitted to apply for identification documents, register a marriage, obtain pensions or other social benefits, send their children to public schools, obtain legal employment, or register a business, among other restrictions.<sup>15</sup> Purchasing housing in the city of Tashkent (with recent exceptions first for high-value newly constructed housing, later expanded to newly constructed housing more generally) is illegal for all

> 15 There is ambiguity about the use local hospitals or clinics for non-emergency purposes. Legally and according to most qualitative interviews, health care facilities are open to residents even if they are unregistered. However, some sources (including Tukmadiyeva, 2016) claim there are de facto many access restrictions.

19
# _ii) LIFEMOD_

LIFEMOD is an example of dynamic cohort microsimulation, simulating the life histories of a cohort of 2000 males and 2000 females. Each individual is born in the same year and is followed from birth through to death, experiencing major life events such as schooling, marriage, childbirth, children leaving home, employment and retirement as illustrated in Figure 1. There is no immigration or emigration into or out of the cohort, and the only way in which the cohort changes size is from attrition dup to mortality.

Ageing of the cohort is achieved through explicit modelling of the demographic and socio-economic process. Because the attributes of each person at time **t+1** are determined using the attributes at time t, the cohort is aged 'dynamically' rather than 'statically'. This ageing is based on the probabilities of the various demographic and other transitions occurring. These probabilities are estimated from official statistics, sample surveys and other data sources. Transitions between various states are then simulated by using the relevant probabilities allied with Monte Carlo selection processes.

A randomly generated number ranging from 0 to 1, drawn from a uniform distribution, is assigned to the record of each individual for every year (up to, and including, year 95). Taking mortality experience as an illustration, if the randomly generated number attached to an individual is less than the probability of dying in that year, given the age and sex of the person, then the individual dies and their records are terminaced. For example, the death rate for males aged 20 in 1985 was 0.93 per 1000. Since the random numbers are exactly uniformly distributed, two cohort males will be selected to die at age 20. However, where the random number exceeds or equals the mortality probability, the person survives to the next year of life. In this way, they become part of the pool 'at risk of death' in the following year where they are subject to the same procedure (with a new probability of death and different random numbers).

A similar approach is adcpted, for example, for entry into the 1-bour-force. The 9
# PRE Working Paper Series

||flt|Aubthr||Contact<br>taQWa<br>for per|
|---|---|---|---|---|
|WPS648|Who Paid the Bill? Adjustment and<br>Poverty in Brazil, 1980-95|M. Louise Fox<br>Samuel A. Morley|April 1991|WDR Office<br>31393|
|WPS649|An Observation on the Bias in<br>Clinic-based Estimates of<br>Malnutrition Rates|Margaret E. Grosh<br>Kristin Fox<br>Maria Jackson|April 1991|B. Diallo<br>30997|
|WPS650|Administrative Valuation of Soviet<br>Agricultural Land: Results Using<br>Lithuanian Production Data|Karen Brooks|April 1991|C. Spooner<br>30464|
|WPS651|Taxation of Financial Assets in<br>Developing Countries|Christophe Chamley|April 1991|A. Bhalla<br>37699|
|WPS652|Demographk Response to Economic<br>Shock|Kenneth Hill|April 1991|WDR Office<br>31393|
|WPS653|Tho Effects of Option-Hedging on the<br>Costs of Domestic Price Stabilization<br>Schemes|Donald F. Larson<br>Jonathan Coleman|April 1991|D. Gustafson<br>33714|
|WPS654|Reflections on Credit Policy in<br>Developing Countries: Its Effect on<br>Private Investment|Mansoor Dailami<br>Marcelo Giugale|April 1991|M. Raggambi<br>37657|
|WPS6S5|Interest Rate Policy in Egypt: Its<br>Role in Stabilization and Adjustment|Mansoor Dailami<br>Hinh T. Dinh|April 1991|M. Raggambi<br>37657|
|WPS656|Relative Deprivation and Migration:<br>Theory, Evidence, and Policy<br>Implications|Oded Stark<br>J. Edward Taylor|April 1991|M. Felix<br>33724|
|WPS657|Distributional Aspects of Debt<br>Adjustment|Ishac Diwan<br>Thierry Verdier|April 1991|S. King-Watson<br>33730|
|WPS658|Fiscal Policy with Fixed Nominal<br>Exchange Rates: C6te d lvoire|Christophe Chamley<br>Hafez Ghanem|April 1991|Raquet Luz<br>34303|
|WPS659|Inflation and Growth in the Transition<br>from Socialism: The Case of Bulgaria|Andres Solimano<br>|April 1991|E. Khine<br>37471|
|WPS660|The Development of the Colombian<br>Cut Flower Industry|Jose A. Mendez|May 1991|N. Artis<br>37947|
|WPS661|The Bretton Woods Agencies and<br>Sub-Saharan Africa in the 1990s:<br>Facing the Tough Questions|Richard E. Feinberg|May 1991|S. King-Watson<br>33730|
|ld market) both for the<br>of 1980-1997 and for the<br>1997, i.e. only countries<br>rative advantage”, with<br> exporting country and<br>ing country.<br>(2)<br>length of the spell|1.00<br>3.00<br>1.00<br>1.00<br>4.00<br>3.00|(2)<br>length of the spell|1.00<br>2.00<br>1.00<br>1.00|2.00<br>2.00|
|---|---|---|---|---|
|product _k_ from country _c_ to the wor<br>gn investors during the whole period<br>lization occurred between 1980 and <br>o our measure of “distance to compa<br>th the comparative advantage of the<br> comparative advantage of the export<br>(1)<br> length of the spell<br>median|1.68<br>7.44<br>1.68<br>1.66<br>7.78<br>6.46|(1)<br> length of the spell<br>median|3.21<br>5.33<br>3.35<br>2.87|5.40<br>4.94|
|e export spells (continuous exporting of<br>stock market was closed or open to forei<br>s where the event of stock market libera<br> to being fnancially open. DCA refers t<br>rresponding to products well aligned wi<br>sponding to products far away from the <br>average|h percentile of DCA<br>rcentile of DCA<br>centile of DCA<br>tile of DCA|average|25th percentile of DCA<br> percentile of DCA|percentile of DCA<br>centile of DCA|
|escriptive statistics for th<br>also countries where the<br>le including only countrie<br> being fnancially closed <br>ottom 25th percentile) co<br>op 25th percentile) corre<br> SUBSAMPLE|eralized<br>ized<br>eralized, bottom 25t<br>eralized, top 25th pe<br>ized, bottom 25th per<br>ized, top 25th percen||-liberalized<br>ralized<br>-liberalized, bottom <br>-liberalized, top 25th|ralized, bottom 25th <br>ralized, top 25th per|
|s d<br>ing<br>mp<br>om<br>, b<br>., t<br>G|-lib<br>ral<br>-lib<br>-lib<br>ral<br>ral|E|on<br>ibe<br>on<br>on|ibe<br>ibe|
|e<br>d<br>a<br>fr<br>.<br>g<br>N|n<br>e<br>n<br>n<br>e<br>e|L|n<br> l<br> n<br> n|l<br> l|
|id<br>lu<br>s<br>”<br>.g<br>e.<br>I|o<br>b<br>o<br>o<br>b<br>b|P|, <br>, <br>, <br>,|, <br>,|
|rov<br>inc<br> sub<br>ed<br> (e<br>s (<br>IZ|n<br> li<br> n<br> n<br> li<br> li|M|ies<br>ies<br>ies<br>ies|ies<br>ies|
|The table p<br>full sample<br>liberalizing <br>that “switch<br>lower values<br>higher value<br>LIBERAL|Switchers,<br>Switchers,<br>Switchers,<br>Switchers,<br>Switchers,<br>Switchers,|FULL SA|All countr<br>All countr<br>All countr<br>All countr|All countr<br>All countr|

37
with codes used for impact evaluation, external training, and other non-lending tasks, ESW and TA are part of what is known in World Bank jargon as Analytical Services and Advisory (ASA) work. Some of these codes, analytical in nature, support the generation of new knowledge on global issues and the discussion of regional and country policy issues. Others, more hands-on, fund technical inputs for developing country governments and capacity building efforts.

World Bank systems record relevant information about ASA budget codes. On the output side, a rating is reported at completion to judge the performance of the task against the objectives stated at its inception. The rating is proposed by the management of the unit responsible for the task, but adjusted – often downward – by the management of the unit that requested it and funded it. For a few of the budget codes, feedback from operational clients is also sought within the year that follows completion. However, for confidentiality reasons, information of this client feedback is available only with some level of aggregation, not for individual budget codes. On the input side, information is generally reported about spending, time to completion, the name of the TTL, the identity of the unit responsible for the product, and that of the requesting unit, among others.

One of the studies in this second group considered 1,331 ASA budget codes deemed completed between 2006 and 2012 (Doemeland and Trevino 2014). The assessment of these codes relied on the consolidated number of downloads for publications associated with them, although no correction was made for the size of the intended audience – for example, whether the budget code concerned a large region or a small island nation.

With this limitation in mind, the study showed that budget codes whose key reports had been released in English, and had been in the public domain for longer, had a higher number of downloads. Importantly, the distribution of downloads was highly skewed, with only 13 percent of the budget codes leading to more than 250 downloads, whereas a full 31 percent had none.

Studies tapping other information available about these budget codes tend to be more informal in nature. They are carried out mostly as part of regular operational management, with some of them covering the entire range of active ASA codes and others referring to a particular region or a particular sector. On occasion, however, some of these assessments go beyond monitoring and try to draw broader lessons for the knowledge work of the World Bank. For example, toward the middle of the period covered by this paper, one such study reviewed how ASA had evolved between 2003 and 2017 (World Bank 2018).

8
methodological. There can be many ways by which goods can end up in the right hands by travelling along pre-existing social ties. The proposed method empirically resolves this difficulty by approaching exhaustive data on dyadic flows from an aggregate point of view. This method is applicable well beyond factor markets in developing countries. It is potentially applicable to research questions as diverse as gravity models and international trade; labor markets; value chains; favor exchange; electrical power grid and diffusion of information on social networks. As argued more in detail in the appendix, all these questions have in common that flows take place on a network of links that are partially shaped by the geographical and social context. The same question arises in all of them: do restrictions on flows potentially imposed by social structure lead to an inefficient or inequitable allocation of resources, as evidenced by unequal marginal productivities or utilities – or ratios thereof? The main constraint to the applicability of the method is the availability of exhaustive information about dyadic flows, and sufficiently detailed information about social structure. While such datasets have largely been lacking in the past, they are becoming increasingly available. The methodology can also be applied to study whether differences in prices, costs, access or opportunities are due to social, geographical, or regulatory obstacles to exchange. The empirical application presented in this paper illustrates how the method can be used to identify situations in which social structure is an obstacle to an efficient or equitable resource allocation.

Second, the paper contributes to the empirical literature on the importance of pre-existing social networks for offsetting the negative effects of market imperfections. There is some evidence to this effect – but perhaps not as much as anticipated, based on the existing literature. Third, this paper provides empirical evidence on the efficiency properties of social networks in The Gambia, a country that is fairly representative of West African rural institutions. If pre-existing social networks are important for the selection of trading partners, this has direct implications for the prospects of reducing poverty and inequality as well as economic growth (Holden et al., 2009). This issue is particularly important in West Africa where access to land is an increasing concern as land scarcity increases due to population growth (World Bank, 2005). Land ownership is unequally distributed within villages of the Gambia, with some systematic advantage to members of the majority ethnic group in each village. Land transfers 5
of _SY Y_ ( _ω_ ), see (Uhlig, 2003); equation 3 demonstrates the maximization problem. Later, in the exercise identifying the primary business-cycle driver of investment, frequencies of 2 - 8 years are chosen.

Where _φi_ is an indicator vector that selects the _i_<sup>_th_</sup> impulse response vector. The solution to this maximization problem is the eigenvector associated with the largest eigenvalue of the denominator (which is _SY Y_ ( _ω_ )), evaluated over the relevant frequency _ω_ = [ _<u>ω</u> , ω_ ̄].

Given the limited sample size under consideration, and in a modification relative to existing implementations of spectral VAR methodologies, the MA-coefficient matrix D and _τ_ are constrained to the 1 - 10-year horizon, which has been shown to reduce estimation bias (Dieppe et al., 2019, 2021; Francis et al., 2014). That is, we instead maximize _f_<sup>_k_</sup> ( _α_ ), where _k_ reflects the horizon limit of the MA coefficients up to _k_ = 10 years.

## **3.2 Panel VAR Framework**

We extend this new spectral identification approach to a panel VAR, which takes the form: where _C_<sup>_n_</sup> , the constant, varies across countries, _n_ , while the slope coefficients _B_ , and the variancecovariance matrix of residuals Σ _u_ are both assumed to be common across economies. Additionally, dummy variables are included in certain economies during periods where inflation exceeds 20 percent. The estimated parameters _B_ and Σ _u_ can then be used to identify the effects of technology shocks using the spectral identification for each group.

# **4 Data and Estimation**

Typically, technology-identifying SVARs have been applied to quarterly datasets. Data shortcomings for EMDEs (with typically less than 10 years of quarterly data on employment or productivity) pose severe constraints. Hence, annual data are used to estimate the VARs. This choice significantly lengthens the period over which the VAR is estimated for many EMDEs. The time span of the data is critical for identifying technology shocks as those that drive long-term developments in productivity. Separately, the literature typically uses total hours worked. However, this type of data is largely unavailable for EMDEs for sufficiently long time spans. Instead, the estimations here rely on employment to ensure the comparability of results.

The VARs are estimated over the maximum length of data available for each country. Data on macroeconomic aggregates such as GDP and employment are taken from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database 8
# **4.3 Sales price regressions**

Results from land sales price regressions are in table 7 with columns 1 and 2 reporting results for the close to 175,000 and 100,000 sales of commercial and private land, respectively and the remainder focusing on commercial land purchases by legal entities the vast majority of which took place in 2024: regressions for some 26,000 sales with only registry information in columns 3 and 4 and some 8,500 and 2,000 with tenant without and with registry information in columns 5 and 6.<sup>20</sup> To interpret results from the land sales price regressions, note that the sale price is just the present value of the stream of rental payments, discounted at the market interest rate. If markets function well, we expect estimated coefficients from the land sale and rental price equations to be identical. Any differences thus point towards potential frictions that would be worth further investigation.

_Sale market competition depresses prices:_ With an estimated elasticity of -1.21 % for commercial and -4.3 % for personal land, higher levels of competition seem to be associated with lower rather than higher sales prices. This points to constraints on the demand (financial market frictions) or supply side (prices too low or an expectation of higher land prices in the future) that would be worth further investigation.

_Legal entities pay a premium, especially for personal land:_ While the 30% premium paid by legal entities for commercial land is in line with that for commercial land, a premium of 60% for personal land supports the notion that some of this land may be structurally different from commercial land, most likely as it is more suitable for non-agricultural uses. Use of satellite imagery to assess post-transfer uses of such lands is one way to explore if a possibility to ‘informally’ convert such land to residential or industrial use may have partly driven such a large premium.

_Informality results in a discount for commercial land:_ For commercial land, the magnitude of external effects of informality from unmapped land is similar to that observed in the rental markets (discount of 3.1% for a 10% increase in the share of unmapped area in the field where the transacted parcel is located). Non-registration and informality of lease contracts are both estimated to reduce sales prices: the discount of 6.3% for having more than 10% of a field’s area not registered and 1.9% for having more than 1%-10% of field area covered with informal lease contracts is much larger than in the rental regression, consistent with the notion that informality has the potential of creating more problems for owners and that the threat of, for example raider attacks, may be an impediment to investment.

_Field and parcel size are key price determinants:_ For commercial land, there is an optimum parcel size of 9.42 ha, which is more than three times the maximum observed in our data. The estimated coefficients

> 20 Coefficients remain remarkably stable even with a reduction of the sample by more than 98% in column 6 compared to column 1, results should be interpreted with caution.

19