rafmacalaba/datause-encoder-data huggingface dataset:
with some suggesting that transfers can generate equity gains outweighing efficiency costs (Gaubert : Schematic representation of barriers to spillovers and efficiency gains in lagging regions.

<!-- Start of picture text --> hinderedby<br>WEAK ECONOMIC REGIONAL<br>INSTITUTIONAL(QUALITY AND (DECLINESTAGNATIONIN LAGGINGAND geroniomrtiaRESILIENCE VULNERABILITYEXTERNAL TO<br>CAPACITY REGIONS MEGATRENDS<br><!-- End of picture text --> : Author’s elaboration based on Coenen et al., 2012; Martin, 2012; Rodriguez-Pose, 2013; The increasing realization, amid rising territorial inequality and social discontent, of the need to tackle the barriers to spillovers and efficiency gains in lagging areas has prompted a re-evaluation of existing territorial development policies. This has been conducive to expanding the focus of research into the areas of institutional quality, regional development paths, agency and resilience, and sustainable development practices (Rodriguez-Pose & Ketterer 2020; Venables, 2023). Research in these areas has become far more prominent and an important source of reflection to improve development interventions and design far more efficient territorial policies. In the following sections 3. The role of institutions in territorial development Until the turn of the century, territorial development primarily relied on two models: neoclassical offered policy makers a limited range of development strategies, primarily focusing on investing in physical capital (e.g., infrastructure) and promoting innovation and human capital. Even the more recent New Economic Geography highlighted the value of enhancing connectivity through
10 In an absence of distortions, TFPR should not vary across firms within each sector. In other words, in the absence of distortions, more capital and labor should be allocated to firms with higher physical productivity (TFPQ) to the point where their higher output results in a lower price, Psi, which also results in the TFPRsi equalizing across firms _i_ . Any dispersions of TFPR across firms within a sector imply distortions. A firm with TFPR higher than the sector average suffers from the effects of distortions. On the contrary, it is common for TFPQ to vary across firms because different firms may have different productivity levels.

From the revenue data, we can also derive TFPQsi as: Equation (7) shows that TFPQ is calculated from Psi , which contains elements of distortions, and κ is normalized to 1.<sup>14</sup> Hsieh and Klenow (2009) choose the elasticity of substitution, σ=3,<sup>15</sup> and R=10, assuming a real interest rate of 5% and a depreciation rate of 5%. Capital share, αs, and labor share, (1 − αs), are taken from the United States manufacturing sectors, where firms are assumed to operate in an environment of minimal distortions. Therefore, the shares of capital and labor of firms in the United States represent an efficient utilization of resources. Any deviations of the U.S. capital-labor shares suggest distortions.

Distortions represented by the output and capital wedges can be measured as follows: Firm _i'_ s wage bill is represented by wsiLsi , and PsiYsi represents the firm's value added. Both values are taken from the census data. Rewriting equation (10) to equation (11) shows that the relative utilization of factors will be affected by distortions in the capital market and 1−αs ~~,~~ the labor-capital ratio in the less distorted (United States) environment. αs

> 14 See Hsieh and Klenow (2009) for a detailed explanation.

> 15 Elasticity of substitution between products is related to the markups σsσ−1s = 1 + μs, where μs is the markup. An elasticity substitution of 3 corresponds to a markup of 50%.
<!-- Start of picture text --> A. Argentina<br>50<br>40 Shock<br>.30 \ -<br>‘ligt<br>ipa<br>20 ~ “<br>bt<br>iI (es<br>10 No shock<br>- ie<br>0 15— 160 O17 18 19 ok HH—<br>Age<br><!-- End of picture text --> <!-- Start of picture text --> B. Brazil<br>50<br>40<br>=~ *-"<br>.30 ~S‘XQ Shook Paih ae<br>eu eg<br>ie le<br>No shock<br>20<br>10<br>0<br>15_ «160617 18 19 TP HH—<br>Age<br><!-- End of picture text --> C. Mexico
estimated a logit model on the probability of being in the PSF sample, using region and urbanization dummies as explanatory variables, since these variables have been used to stratify the sampling frame. Estimation results are then used to compute the predicted probability of belonging to the PSF sample, _ps_ . Then ESPS observations are reweighted using _ps/(1-ps)_ as weights. This strategy gives much better results in that all strong imbalances between samples are corrected, with the maximum difference between the two surveys in the proportion of households living in a given region being lower than 0.5%. We also hold account of the general progression of prices between the two surveys, using figures provided by the Senegalese statistical agency (ANSD). PSF has been conducted between November 2006 and April 2007, almost exactly a year after ESPS. In order to estimate the average change of the price index between these two periods, we computed the mean of the annual inflation rates calculated over the 6 twelve-month periods going from November 2005 to November 2006, December 2005 to December 2006, January 2006 to January 2007, February 2006 to February 2007, March 2006 to March 2007, and April 2006 to April 2007. For total consumption, this leads to an average one year inflation rate of 4.7%. ESPS figures have been adjusted accordingly.<sup>3,4</sup> 3 A total of 1781 households are included in the original PSF sample. Three households have been dropped due to completely incoherent expenditure records. Three others have been excluded due to their head sharing his time between several households, which raised doubt on the correct allocation of his declared expenditure. One household was dropped due to incoherent recording of members’ relationship with the head and another five households, because though they include two cells, the surveyor recorded all expenditure in the head’s cell. Finally, seven households could not be included due to missing information on some key variable. We end up with a total sample size of 1762 households kept in the analysis.

4 PSF Households with exceedingly high levels of expenditures have been screened and, when possible, their records corrected case-by-case based on information provided by other variables in the survey. No such work could be done on the ESPS data since the available information is more limited. The maximum level of annual expenditure per capita in PSF is found around 5,810,000 CFA (that is about $11,500 in 2006-2007). In ESPS the maximum amount is around 13,780,000 CFA ($27,300), but only eight households out of 13559 have values higher than the PSF 11
# **1.2 The explanatory variables**

We begin with variables of interest that have often been cited in the literature as characteristics of establishments most likely to face informal competition. The variables of interest are:

1. **Size** – We hypothesize that small firms are more affected by informal competition. For each firm in the sample, we use the log of the firm employment as the size variable.

2. **Use of formal finance** – Limited access to financial services is one of the main constraints for informal firms (Morrisson 1995). We use a measure of the use of commercial lines of credit used as a way to assess the level of access to finance that firms have. In our sample, over 57.4 percent of manufacturing firms have an overdraft facility, 49.6 percent have an outstanding loan or line of credit from a financial institution, and 39.4 percent of firms said that they did not need a loan. Moreover, only 37.3 percent of our sample has _both_ a line of credit or outstanding loan _and_ an overdraft facility. When we take these firms and the 39.4 percent of those firms that said that they did not need a loan, we have 69.1 percent of firms that are neither credit constrained and/or are above average users of financial services. The remaining 30.9 percent are the firms most likely to be in head-to-head competition with informal firms. We create a binary variable where a value of one (1) indicates a firm that is credit constrained.

3. **Capacity utilization** – A recent World Bank study on informality in Latin America points out that “formality rises rapidly with firm size and productivity” (pg. 135; Perry et al, 2007). We use capacity utilization as a measure of productivity to see if the continuum between formality and productivity found in that study also holds with respect to the competitive pressure from informal firms that formal firms have to overcome. As before, we argue that firms that are less productive, therefore tending to be more like informal firms, face informal competition more directly than more productive formal firms.

4. **Buyer** – We hypothesize that firms that have large buyers as their customers are markedly different from informal firms and will therefore not be in direct competition with them. Having large buyers as customers requires that the firm supply at large volumes and at the lowest price since large customers have the bargaining power to push prices low. In order to satisfy both of these requirements, this implies economies of scale by the supplying firm. Furthermore, in order to satisfy their standards of quality and consistency, large buyers usually require stable, long-term business to business relationships most often associated with older, established suppliers. These requirements may not be easily satisfied by informal firms, making the informal sector
be eligible for VAT refunds. Once again, the delays can also be long for VAT refunds—delays between filing for refunds and receiving the refunds averaged 44 days in Mozambique and 113 days in Zambia.

# **III. EMPIRICAL RESULTS**

# **III.1 Data**

The data used in this study come from Investment Climate Surveys, surveys of manufacturing enterprises conducted by the Regional Program on Enterprise Development (RPED) unit of the World Bank, in collaboration with local partners within the countries. The surveys, which were conducted in 2002 or 2003, cover Ethiopia, Kenya, Mali, Mozambique, Senegal, Tanzania, Uganda and Zambia. Firms from eight industries were included in the surveys: Agro-industry; Chemicals; Construction Materials; Wood, Wood Products and Furniture; Metals; Paper, Printing and Publishing; Plastics; and Textiles, Leather and Garments.<sup>13</sup> The surveys were conducted in a uniform way across countries, using stratified random samples. The sampling frames were stratified across location within each country, sub-sector, and size.<sup>14</sup> When recent census data were available, the random samples was constructed using census data. If recent census data were not available, the lists were constructed using lists of enterprises from government agencies (e.g., from the National Bureau of Statistics in Tanzania). As a result, the surveys ultimately cover the ‘formal’ manufacturing sector—firms need to be registered with the government to be included. When firms could not be located or refused to participate in the survey, they were replaced with new firms with as similar characteristics (in terms of size, sector, and location) as possible. Table 7 presents summary statistics for the main variables included in the analysis by country.

> 13 In addition, some machinery firms were included in the sample for Kenya.  Since this sector was not covered in the other countries, they are dropped from the analysis.

> 14 The size categories are: very small (0-9 employees), small (10-49), medium (50-99), large (100-499) and very large (over 500 employees).

11
<!-- Start of picture text --> e<br>G2 4<br>re 6<br>as<br>g<br>5<br>Lie] Ae<br>g° oe<br>x] 0<br>iS ” -a bg<br>= ee e<br>a e%esoe<br>e 32° ogee<br>@ of° . eo". °<br>ic ° See<br>_aw bead 4<br>e . °<br>oO ya<br>2<br>0 2 4 6 8 1<br>Cumulative proportion of province regions<br>—— Full equality line ® Total per capita revenues before DAU<br>e Total per capita revenues after DAU ® Total per capita revenues Alternative 1<br>e Total per capita revenues Alternative 2 ® Total per capita revenues Alternative 3<br><!-- End of picture text --> <!-- Start of picture text --> =<br>[=<br>5a °<br>g-2<br>std]<br>c<br>2 J of 8<br>5 ee<br>ct<br>2<br>F<br>8 5 °. e,°<br>a :<br>= *<br>5 f<br>as .<br>5 :<br>oO of<br>ie<br>°<br>0 2 4 6 8 1<br>Cumulative proportion of C1 regions<br>——_ Full equality line ° Total per capita revenues before DAU<br>e Tatal per capita revenues after DAU e Total per capita revenues Alternative 7<br>e Tatal per capita revenues Alternative 2 ° Total per capita revenues Alternative 3<br><!-- End of picture text -->
various food items seem to increase jointly (so that it is not clear that households can offset the loss in purchasing power associated with the price increase by shifting to other foods). Also, the approach does not take into account any ripple effects of changes in the price of rice, maize and other cereals on other parts of the economy. More sophisticated methods could be used to measure the “general equilibrium” effect of a change in the price of rice (such as using a Social Accounting Matrix, as done for Ghana by Parra Osorio and Wodon, 2008), but such simulations require a much larger number of assumptions, some of which are the subject of debate (especially when more complex computable general equilibrium models are used). The estimations given here thus provide “first round” likely poverty effects from lower or higher food prices paid to producing households or paid by consuming households, assuming that households don’t change their consumption and production patterns for rice as well as for the other commodities after the change in their price.

Before providing the results, one more word of caution is required. As mentioned in the introduction, it remains an open question as to the higher prices paid by consumers translate into higher prices paid to producers. If one has doubts as to producers will really benefit in the short run from higher prices, one could consider estimates based on consumption impacts only as an upper bound for the impact on poverty, and estimates taking into account both consumer and producer impacts as a lower bound.

Key results from the simulations are provided in tables 5, 6 and 7. Consider first table 5, which is based only on data on the consumption of food. At the time of the survey, the share of the population in poverty was 28.5 percent. If the price of rice increases by 25 percent, and if we look only at the impact on the consumer side, the 13
**Figure 2:** Net aid inflows, 1993-2008 (USD million, current prices and percent of GDP)

<!-- Start of picture text --> 400 40<br>Net aid inflows<br>(USD million)<br>350 35<br>Net aid inflows<br>(percent of GDP)<br>300 30<br>250 25<br>200 20<br>150 15<br>100 10<br>50 5<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Source : Authors’ calculation based on IMF data.<br>USD million<br>percent of GDP<br><!-- End of picture text -->

**Figure 3:** Gross and net aid inflows, 2001-2008 (USD million, current prices)

<!-- Start of picture text --> 400<br>Gross aid inflows Average net aid inflows<br>2005-08<br>Net aid inflows<br>350<br>300<br>250<br>Average net aid inflows<br>2001-04<br>200<br>150<br>100<br>50<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Source : Authors’ calculation based on IMF data.<br>USD million<br><!-- End of picture text --> The pronounced jump in aid inflows between 2004 and 2005 is especially noticeable in the net aid figures, while gross aid inflows increased more smoothly over the last years (Figure 3).<sup>6</sup> The large difference between net and gross aid observable in 2004 can be attributed to the clearance of arrears of more than US$100 million with multilateral as well as bilateral creditors in that year. That arrears clearance consisted of repayment of arrears (28 percent of the total reduction of principal), rescheduling (41 percent) and cancellation (31 percent). The average net aid inflow between 2001 and 2004, the pre aid-surge period, was US$ 94 million or 15 percent of GDP.

> 6 Gross aid inflows are defined as the sum of project and program grants and loans. Net aid inflows = Gross aid inflows + debt relief – interest on public debt – amortization + arrears accumulation (with arrears clearance taking a negative sign). For further details see Berg et al. (2007).

8
tive and noncognitive outcomes. I also estimate this specification while restricting the sample to the eight students around the cutoff within each stratum.

# **4. Results**

In this section, I present reduced-form estimates of group composition and tracking effects on main outcomes of interest in the context of an ASP. I draw two main conclusions: first, better average effects emerge from mixing students by their initial propensity for violence than from segregating them on some behavioral and stress-related outcomes. Second, tracking has detrimental effects compared to integration, especially for students with a greater propensity for violence. Finally, being the marginal participant—that is, the least violent child within a highly violent group—negatively affects behaviors but does not necessarily harm academic performance.

## **4.1 GC average effect**

Table 3 shows estimations of GC in terms of violence using specification (2). In panel A, I present the effects of group composition on attitudes toward school as well as on learning and violencerelated outcomes. In panel B, I present the main neurophysiological outcomes estimated through electroencephalogram recordings. Finally, in panel C, I present the effects on children’s academic performance. Columns 1 and 2 present the estimated impacts of being assigned to participate in the ASP with either a heterogeneous (HT) or homogeneous (HM) composition of peers, compared to the C group. Column 3 shows estimated differences on the impacts between the HT and HM treatment arms.

First, from the comparison between each treatment arm with the C group, estimations indicate that any group composition can be more effective than no treatment. For example, in panel A, I find that any group composition improves behavioral outcomes in relation to not participating 24
The CMIP3 GCM ensemble projects mean-annual precipitation to increase in Tanzania over the course of the 21<sup>st</sup> century in response to increasing greenhouse gas concentrations (e.g. Christensen et al. 2007; Meehl et al. 2007). In accordance with this increase in mean-annual precipitation, we find that the occurrence of dry years is substantially reduced in Tanzania (Table 7). The ensemble-mean dry year occurrences range from 0.45 to 0.77 from the early 2000s through the 2050s, and from 0.32 to 0.36 in the 2060s, 2070s and 2080s.

In almost all cases, more than 50% of Tanzania’s dry years coincide with non-dry years in Tanzania’s selected African trading partners (Table 7). Exceptions include 36.00% co-occurrence of dry conditions in Tanzania and non-dry conditions in Kenya in the 2000s, and 41.82% co-occurrence of dry conditions in Tanzania and non-dry conditions in Mozambique in the 2050s. This means that extreme events in these three trading partners are expected to coincide more frequently. Of the African trading partners, Sudan exhibits the highest co-occurrence of non-dry years with Tanzania dry years, with at least 75% of Tanzania dry years co-occurring with a non-dry year in Sudan in all decades of the 20002089 period. Conversely, Kenya, Mozambique and Zambia exhibit the lowest co-occurrence of non-dry years with Tanzania dry years, with each partner-country exhibiting four decades in the 2000-2089 period in which less than 65% of Tanzania dry years co-occur with a non-dry year in that trading partner. The 2040s exhibit the lowest co-occurrence of non-dry years with Tanzania dry years across the African trading partners, with 5 of the 10 trading partners experiencing less than 65% co-occurrence of non-dry years with Tanzania dry years.

As might be expected simply from geographic proximity, Tanzania’s key trading partners outside of Africa more consistently exhibit co-occurrence of non-dry years with Tanzania dry years than do Tanzania’s key trading partners that are within Africa. Of those partners outside of Africa, Canada and the Russian Federation exhibit the highest co-occurrence of non-dry years with Tanzania dry years, with 100.00% of Tanzania dry years co-occurring with a non-dry year in those trading partners in all decades of the 2000-2089 period. China, India and the USA also exhibit high co-occurrence of non-dry years with Tanzania dry years, with at least 88% of Tanzania dry years co-occurring with a non-dry year in those countries in 9 decades of the 2000-2089 period. Conversely, Australia and Mexico both exhibit less than 76% co-occurrence of non-dry years with Tanzania dry years in the 2050s, 2070s and 2080s.
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