rafmacalaba/datause-encoder-data huggingface dataset:
through general agglomeration effects (e.g., Audretsch and Feldman, 2004, Delgado, Porter, Stern 2014, Ketels 2017, Matano et al. 2020, Bernado and Patrick 2017, Thi Bich et al. 2017, Mukim 2011). Notably, the ACS methodology is applicable even if clustering is not distinctly prevalent, and it provides larger efficiency gains over other methodologies as clustering increases.

To study informal businesses, ACS is applied as follows. The geographic area of each covered city is divided into square blocks, each side 150 meters long. These blocks represent the primary sampling units. The methodology starts with a random selection of blocks, each of which is thoroughly enumerated. An enumerator canvasses the whole block and lists all informal businesses. Systematic listing captures information on the type of activity, physical location, and the number of workers, collected through an enumeration form. Refusals to the enumeration are recorded, and the corresponding information is captured by observation, including the data that could indicate whether the businesses are informal. Since businesses can operate within household premises, enumeration requires identifying informal business activities in all homes located in each block. The enumeration process is repeated adaptively, where blocks that surround the ones with more than a pre-defined number of informal businesses are enumerated (Salehi and Seber 1997).

In the process of enumeration, a sub-sample of informal businesses are randomly selected to conduct an interview. This interview lasts around 25 minutes and contains approximately 150 questions covering a wide range of topics related to business operations. The selection of businesses to interview is randomly assigned and pre-programmed in the software application used for data collection. Given that the whole process is random with known probabilities of selection for both the blocks and businesses within blocks to interview, ACS generates a representative sample of informal businesses in the respective city. In addition to efficiency gains in fieldwork, the sampling method provides greater precision in the population estimates as compared to alternative sampling methods such as non-adaptive area sampling.

# **2.2 Questionnaire Design to Survey Informal Businesses**

Surveying informal businesses requires a well-developed instrument to be used for the interview. The questionnaire used in the Informal Sector Enterprise Surveys is a result of trial-anderror through implementation. The World Bank Enterprise Analysis unit has explored various approaches of surveying informal businesses around the world for several years. The process 6
- Do, Quy-Toan, Andrei A Levchenko, and Martin Ravallion. 2013. “Coping with Food Price Volatility: Trade Insulation as Social Protection.” In _The Economics of Food Price Volatility_ , ed. Jean-Paul Chavas, David Hummels, and Brian D Wright. University of Chicago Press.

- Dorosh, Paul A. 2009. “Price Stabilization, International Trade and National Cereal Stocks: World Price Shocks and Policy Response in South Asia.” _Food Security_ 1 (2): 137–149.

- Eaton, Jonathan, and Gene M Grossman. 1985. “Tariffs as Insurance: Optimal Commercial Policy When Domestic Markets Are Incomplete.” _The Canadian Journal of Economics_ 18 (2): 258–272.

- Fafchamps, Marcel. 1992. “Cash Crop Production, Food Price Volatility, and Rural Market Integration in the Third World.” _American Journal of Agricultural Economics_ 74 (1): 90–99.

- ———. 2003. _Rural Poverty, Risk and Development_ . Edward Elgar Publishing.

- Feenstra, Robert C. 1987. “Incentive Compatible Trade Policies.” _The Scandinavian Journal of Economics_ 89 (3): 373–387.

- Galtier, Franck. 2009. “How to Manage Food Price Instability in Developing Countries?” _MOISA, INRA, Working Paper_ 5.

Gardner, Bruce L. 1979. _Optimal Stockpiling of Grain_ . Lexington, Mass.: Lexington Books.

- Gardner, Bruce L, and Ramón López. 1996. “The Inefficiency of Interest-Rate Subsidies in Commodity Price Stabilization.” _American Journal of Agricultural Economics_ 78 (3): 508–516.

- Gilbert, Christopher L. 1996. “International Commodity Agreements: An Obituary Notice.” _World Development_ 24 (1) (January): 1–19.

- ———. 2011. “International Commodity Agreements and Their Current Relevance for Grains Price Stabilization.” In _Safeguarding Food Security in Volatile Global Markets_ , ed. Adam Prakash, 202–230. Rome: FAO.

- Gilbert, Christopher L, and Wyn Morgan. 2010. “Food Price Volatility.” _Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences_ 365 (1554) (September 27): 3023–34.

- Giordani, Paolo E, Nadia Rocha, and Michele Ruta. 2012. “Food Prices and the Multiplier Effect of Export Policy.” _CESifo Group Munich, Working Paper_ 3783 (April).

- Glewwe, Paul, Hanan G Jacoby, and Elizabeth M King. 2001. “Early Childhood Nutrition and Academic Achievement: a Longitudinal Analysis.” _Journal of Public Economics_ 81 (3): 345–368.

- Goodwin, Barry K, Ashok K Mishra, and François Ortalo-Magné. 2012. “The Buck Stops Where? The Distribution of Agricultural Subsidies.” In _The Intended and Unintended_

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Figure 4. Correlation<sup>of BLL and Agility in 109 Children in Mexico City (after Secretaria de</sup> Salud 1990) <!-- Start of picture text --> 140<br>130<br>I _<br>tr 120<br>'-4 0<br>*14<br>> o b~, , *,. S<br>.0 10 20 30 40 so<br>Blood Lead Level (pdl)<br><!-- End of picture text --> H aertens **i** on n adults. A relationship<sup>that a 1 pg/m3 increase in the concentration</sup> of lead in the air<sup>will result in an average increase of 2 ug/dl in BLL of adults (and between 3 and</sup> _5_ ug/dl in children)<sup>has been proposed in USAID (1990) a_ud EPA (1986). In a study recently made</sup> in Mexico (Rothenberg<sup>et al. 1990), estimates of such elasticity are not explicitly provided, but</sup> running a regression<sup>on the data presented lead to an estimated relation coefficient of over 6. We</sup> assumed that a<sup>change of 1 ug/m3 in lead concentration in the air leads to a 3 ;g/dl change in BLL.</sup> It has also been<sup>proposed that a 1 percent change in BLL leads to a 0.8 percent change in the</sup> probabDity of blood<sup>pressure being greater than 90 mm Hg (above which the person is considered</sup> to be hypertense)<sup>(EPA 1985). This means that the elasticity of the probability of being hypertense</sup> to the lead concentration<sup>in air can be deternined if the average BLL in the population is known.</sup> Between 1989 and<sup>1990 there has been a significant decrease from 1.98to 1.37 g/m3</sup> in the average concentra.ion<sup>of lead in the air of Mexico City (the legislation standard is 1.5 ,ug/m3).</sup> Because the harmful<sup>effects from lead on blood appear not to present threshold levels (that is, even</sup> below legislation<sup>standards there are negative health effects), in order to compute the costs associated</sup> to lead levels in<sup>the air we considered the effects of 1.37 ag/m3 (the 1990 average concentration).</sup> This corresponds<sup>to a 4.11 Ag/dl decrease in average BLL of the population. The average BLL of</sup> the population of<sup>Mexico City is not known, but there are scattered samples (Secretaria de Salud</sup> 1991 and Rothenberg<sup>et al. 1990). We made the conservative assumption (in this case by increasing</sup> it) that the average<sup>male adult population BLL was 15 ,g/dl. This means that eliminating the 1.37</sup> jsg/m3 of lead concentration<sup>in the air would mean a 27.4 percent change in BLL (4.11 divided by</sup> 15). Using the<sup>0.8 elasticity, there would be a 21.9 perce</sup><sup>**At**reduction in the probability of blood</sup> pressure being greater<sup>than 90 (that is, of hypertension). Finally, according to the Encuesta Nacional</sup> 18
rates and the reduction in the money supply mitigates rising inflation and aggregate demand, thereby muting REER appreciation.<sup>24</sup> Alternatively, the government may decide to build up reserves in the central bank. The government may do this because it is concerned about potential loss of competitiveness or as form of insurance against future shortfalls in aid inflows. While the level of aid inflows to Tanzania has been relatively high over the entire sample period they have been subject to wide fluctuations. The average annual level of net aid inflows jumped from 10 percent of GDP over the 1970-86 period to 24 percent over the 1987-94 period before falling to 15 percent over the 1995-2004 period. Moreover, annual aid disbursements have been unpredictable as donor commitments differ significantly from actual disbursements. One possible response to the volatility of aid inflows is for the central bank to build reserves during aid surges and draw down reserves when aid flows decline.<sup>25</sup> Such a strategy would tend to depreciate (appreciate) the nominal and, in the short run, the real exchange rate in the presence of on aid surge (decline), all else equal.

Figure 10 shows the relationship between net aid inflows and reserve accumulation in Tanzania over our sample. There appears to be no relationship between the series until 1990, at which point changes in BoT reserves become highly correlated with net aid inflows (correlation coefficient 0.73). Since 1995, reserve accumulation has been dramatic, increasing from

**Figure 10. Aid Inflow and Change in Reserves**

<!-- Start of picture text --> 600 2000<br>500 1800<br>400 1600<br>1400<br>300<br>1200<br>200<br>1000<br>100<br>800<br>0<br>600<br>-100 400<br>-200 200<br>-300 0<br>1976 1980 1984 1988 1992 1996 2000 2004<br>Change in Reserves Aid (US$)<br>Source: OECD DAC and IFS<br>Aid Inflow (mil US$)<br>Change in Reserve (mil US$)<br><!-- End of picture text -->

1.5 months of imports to 6.5 months of imports in 2004. The reserve accumulation combined with significant unemployed resources in the economy may be explaining part of the negative relationship between aid inflows and the REER, especially in recent years. The short-run specification below will include changes in BoT reserves to capture this possible influence on the REER over the short-run.

# **ii) Tanzania’s REER: An error correction model**

Stage two of Engle and Granger (1987) consists of running a short-run specification of the REER which includes changes in the key explanatory variables

> 24 If capital market is free from controls, rising domestic interest rates may actually attract international capital flows and appreciate the currency (IMF 2005).

> 25 Improving tax revenues is also an option, but the ratio of tax revenues to GDP has been virtually constant over the last thirty years at less than 14 percent.

21
39

# **<mark>Annex Table</mark>** **<u><mark>3: Coverage of measures for “Key restrictions”</mark></u>**

|**Annex Table 3: Coverag**<br>**Sectors**<br>**Modes**|**e of measures for “**<br>**Categories**|**Key restrictions” **<br>**Measures**|**Key restrictions**|
|---|---|---|---|
|||**Standardized measures**|YES (onl in|
||Form of Entry|Greenfield branch- allowed|y<br>banking)|
||Form of Entry|Greenfield subsidiary – allowed|YES|
||Form of Entry|Greenfield subsidiary - max. ownership allowed|YES|
||Form of Entry|Acquisition domestic private entity – allowed|YES|
||Form of Entry|Acquisition domestic private entity -max. ownership allowed by a single entity|YES|
||Form of Entry|Acquisition domestic private entity -max. ownership allowed by a group of entities|YES|
||Form of Entry|Acquisition domestic private entity - controlling stake allowed|YES (only in<br>telecom and<br>transport)|
|**ALL SECTORS,**|Form of Entry|Acquisition domestic state-owned entity –allowed|YES|
|**EXCEPT FOR**<br>**PROFESSIONAL**<br>**SERVICES**<br>**MODE 3**|Form of Entry|Acquisition domestic state-owned entity -max. ownership allowed by a single entity|YES (only in<br>telecom and<br>transport)|
||Form of Entry|Acquisition domestic state-owned entity -max. ownership allowed by a group of<br>entities|YES|
||Form of Entry|Acquisition domestic public entity -controlling stake allowed|YES (only in<br>telecom and<br>transport)|
||Form of Entry|Joint Venture –allowed|Not covered|
||Form of Entry|Joint Venture -max. ownership allowed by a single entity|Not covered|
||Form of Entry|Joint venture -max. ownership allowed by a group of entities|Not covered|
||Form of Entry|Joint Venture -controlling stake allowed|Not covered|
# **Table 5c Absolute trade gains of European partner countries resulted from regulatory policy improvement (half-way to EU15) of each new member and candidate member countries (unit: million $)**

|Regulatory policy|EU|15|Newm|embers|Candidat<br>cou|e member<br>ntries|
|---|---|---|---|---|---|---|
||Export gain|Import gain|Export gain|Import gain|Export gain|Import gain|
|_New members_|||||||
|Czech Republic|746.96|1611.31|117.87|351.57|8.60|38.92|
|Estonia|30.99|59.62|4.87|10.79|0.33|0.14|
|Hungary|850.51|2063.06|85.68|203.19|24.21|82.23|
|Latvia|40.54|61.00|15.96|20.03|0.60|0.05|
|Lithuania|44.84|59.55|14.44|13.84|0.51|2.78|
|Poland|1602.45|2586.69|181.20|345.11|29.29|59.40|
|Slovakia|272.83|579.37|107.04|231.18|3.63|18.56|
|Slovenia|189.25|347.55|21.53|48.60|3.36|9.56|
|**_Subtotal_**|**_3778.36_**|**_7368.15_**|**_548.58_**|**_1224.32_**|**_70.53_**|**_211.63_**|
|_Candidate member countries_|||||||
|Bulgaria|73.46|152.59|8.45|8.88|6.41|14.81|
|Romania|343.71|694.49|51.81|46.27|17.60|34.56|
|Turkey|1279.26|2804.18|48.30|152.08|37.46|88.80|
|**_Subtotal_**|**_1696.43_**|**_3651.26_**|**_108.56_**|**_207.23_**|**_61.48_**|**_138.17_**|
|**TOTAL**|**5474.79**|**11019.41**|**657.14**|**1431.55**|**132.01**|**349.80**|

**Table 5d Absolute trade gains of European partner countries resulted from IT infrastructure improvement (half-way to EU15) of each new member and candidate member countries (unit: million $)**

|IT infrastructure|EU|15|New m|embers|Candidat<br>cou|e member<br>ntries|
|---|---|---|---|---|---|---|
||Export gain|Import gain|Export gain|Import gain|Export gain|Import gain|
|_New members_|||||||
|Czech Republic|880.45|2294.29|138.94|500.59|10.13|55.42|
|Estonia|NA|NA|NA|NA|NA|NA|
|Hungary|1926.69|5645.56|194.10|556.04|54.85|225.02|
|Latvia|200.72|364.88|79.01|119.82|2.98|0.28|
|Lithuania|456.31|731.97|146.92|170.10|5.19|34.19|
|Poland|4020.49|7839.71|454.62|1045.96|73.50|180.02|
|Slovakia|449.16|1152.18|176.21|459.75|5.97|36.91|
|Slovenia|342.45|759.69|38.96|106.23|6.07|20.89|
|**_subtotal_**|**_8276.27_**|**_18788.27_**|**_1228.77_**|**_2958.49_**|**_158.71_**|**_552.73_**|
|_Candidate member countries_|||||||
|Bulgaria|412.26|1034.49|47.44|60.22|36.00|100.43|
|Romania|666.29|1626.30|100.44|108.35|34.13|80.93|
|Turkey|1911.45|5061.39|72.16|274.50|55.97|160.27|
|**_subtotal_**|**_2990.01_**|**_7722.19_**|**_220.04_**|**_443.07_**|**_126.10_**|**_341.63_**|
|**TOTAL**|**11266.28**|**26510.45**|**1448.81**|**3401.55**|**284.80**|**894.36**|

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# **Conflict countries and employment growth**

**South Asia is a conflict region, with varying degrees of security issues.** According to the United Nations Security Levels System, all countries in the South Asia Region are facing security and conflict issues. However, the extent of the conflicts varies. The UN Security Levels System objectively classifies regional security into six categories: minimal, low, moderate, substantial, high, and extreme. The system identifies Bangladesh, Bhutan and Nepal as minimal to low. Sri Lanka is classified as minimal or in some regions moderate, India is similar aside of the Indian administered Kashmir region. Two countries have several regions which are heavily affected by security issues - Afghanistan and Pakistan. Following this classification, we grouped Afghanistan and Pakistan as countries with a severe ongoing conflict, and describe India, Bangladesh, Bhutan, Nepal and Sri Lanka as countries with a moderate security environment. This picture is confirmed by qualitative reports such as the Conflict Barometer (2010).

**Differences between severe conflict regions and others are vast, because conflict areas miss out on urban growth.** Countries in regions with moderate conflicts grow at an annual average of 11%, which is significantly faster than the 7% relative growth on an annual basis in conflict countries. While growth differences in non-urban areas are not significant, the overall difference is driven by urban areas, where the difference in the average growth rate is 8%.<sup>11</sup>

**Firms in non-urban conflict regions are smaller and younger.** Companies in rural areas are much smaller and younger in conflict countries than in countries with little or moderate conflict. More generally, firms in the sample that are located in conflict areas are younger than firms in regions with a moderate conflict. Interestingly, the opposite holds for urban firms in conflict areas – these are older than urban firms in non-conflict areas.

**Table 5: Firm growth in urban and rural areas in moderate and severe conflict environments**

|**Intensity of**<br>**conflict**|**Urban**|**Employment**<br>**(median)**|**Share of**<br>**investing**<br>**firms**|**Age**<br>**(median)**|**Annual**<br>**relative**<br>**employment**<br>**growth**<br>**(median)**|**Annual**<br>**relative**<br>**employment**<br>**growth**<br>**(mean)**|
|---|---|---|---|---|---|---|
|Moderate<br>conflict|Non-<br>urban|16|36%|15|0%|9%|
|Moderate<br>conflict|Urban|25|47%|13|4%|13%|
|Moderate<br>conflict|All|22|42%|14|2%|11%|
|Severe conflict|Non-<br>urban|12|53%|6|0%|9%|
|Severe conflict|Urban|15|42%|15|0%|5%|
|Severe conflict|All|12|46%|8|0%|7%|

_Source: Interim Enterprise Survey, 2009/10; Own calculations_

> 11 Only results that are significant at the 95% level are reported in the text.

16
# **2.2 Data on BLAs**

The data we use for the BLAs is from Chilton and Woda (2022). This dataset contains a comprehensive set of 1,222 BLAs between two countries since World War II. The dataset contains information on the year the BLA was signed and the two countries between which the treaty was signed. The dataset is the most comprehensive dataset on BLAs to date and builds on the data compiled by Chilton and Posner (2018), Peters (2019 _a_ ) and Peters (2019 _b_ ). A richer description of the dataset is presented in Appendix A.

We transform the dataset to match the format of the migration dataset. That is, for each BLA in this dataset, we identify the country of origin, the country of destination, and assign it to the subsequent year in migration data.<sup>4</sup> That is, a BLA signed between 1970 to 1979 is assigned to _t_ = 1980. To simplify categorization, we label the country with a lower GDP per-capita as the origin country and the other as the destination country. The origin-destination-year cells without a BLA is assumed to have no BLA.

# **2.3 Descriptive statistics**

The combined dataset used in this paper has information on BLAs and migration from 198 countries to 197 potential destinations (i.e., 39,006 origin-destination corridors) for 7 periods from 1960 to 2020 at ten-year intervals. A common feature of the bilateral migration dataset, as seen in table 1, is that most (61 percent) of the corridors do not have any migration. About 12 percent of the corridors have at least 10 observed migrants across all time periods; we term these as _regular_ corridors. The shares of non-empty corridors and regular corridors are similar across the key subsets used in the analysis.

Likewise, BLAs are also relatively rare in the data. About 1.6 percent of corridors have a BLA signed between them, with the share lower for African origin countries (0.80 percent) and higher for GCC destinations (3.4 percent). BLAs are more common in regular corridors with close to 9 percent of the regular corridors having a BLA. Similar pattern holds across the various sub-samples used in the analysis.

Of the 261 million migrants in our combined dataset in 2020, 82 million (31 percent) are in corridors with a BLA. The proportion of migration accounted by corridors with a BLA is

> 4To address the issues of countries and names that changed over time, we use the same principles and coding applied in the migration data (Ozden<sup> ̈</sup> et al., 2011).

8
firms report the same answer (yes or no) to the question about whether money was spent on R&D is 34 percentage points higher for firms that trade with each other than for firms that do not trade with each other. However, because of the small number of direct links in the Sudanese data (see table 2), the estimated coefficients of direct trade should be interpreted with caution. Furthermore, we find that Sudanese firms with a common client tend to differ _more_ than other firms with respect to R&D and IT usage. This finding is not consistent with the notion that network proximity tends to result in similar practices regarding innovation.

Next, we consider the role of geographical distance between firms. For Ethiopia, the distance coefficient is negative in all five of the specifications shown in table 3, and this coefficient is statistically significant at least at the 10 percent level in four of these specifications. Hence, geographical proximity tends to be associated with greater differences in innovation practices. The results are similar for Sudan: the distance coefficient is negative and highly statistically significant in the models for R&D (column [8]), IT usage (column [9]), and the first principal component (column [10]). These results suggest that for technology, strategic substitution effects dominate strategic complementarities for firms that are located near each other.

The control variables in these regressions have explanatory power. The estimated coefficients of the same-sector dummy are negative in all of the specifications except [6], and these coefficients are often statistically significant. This result indicates that, as expected, firms in the same sector tend to have similar innovation practices. Differences in firm size, which are measured as the absolute difference in the log of employment, are positively associated with differences in innovation practices in all of the specifications, suggesting that firms of similar size tend to adopt similar practices. There is also some evidence that managers of the same gender or with similar levels of education select similar innovation practices. The coefficients 20