rafmacalaba/data-use-mentions-extended huggingface dataset:
East Africa (Kenya, Uganda, Tanzania), we use the length of the rainy season ( _Length_ ) instead of _Rainfall_ since total precipitations are less of a limiting factor but the timing of precipitation greatly matters (Sultan _et al._ , 2010). Again, _Length_ is used in interaction with regional dummies to account for a different impact of the variable in the two different agro-ecological zones ( _Guinean_ and _East_ ). We also include the square of _Length_ , expecting decreasing returns. Table 9 and Figure 4, in the Annex, provide summary statistics characterizing precipitations in these four agro-ecological zones.

# _3.2.4 Other controls_

To account for the macroeconomic environment, we include the exchange rate against the dollar, in which cotton is traded internationally. This allows controlling for the different impact of world cotton and input prices across countries, depending on their macroeconomic situation. The fluctuation of the dollar value of the CFAF, the currency of several WCA countries, which is pegged to the euro, is, for example, said to play a key role in the profitability of cotton production in the region. The exchange rate data is taken from the Penn World Tables (Heston _et al._ , 2011). It is expressed as national currency units per one thousand US dollars, averaged annually.

We also include dummy variables to control for the effect of conflicts, which can disrupt production (e.g. Kaminski _et al._ , 2011, on the implications of the recent Ivorian crisis for cotton production). The data are taken from the UCDP/PRIO Armed Conflict Dataset (2009).<sup>16</sup>

# **4. Results**

# **4.1 Baseline Model Results**

The results of our baseline equation are given in Tables 1, 2 and 3. In Table 1, the institutional vector ( _I_ ) includes only _Post Reform_ . Columns (1) to (3) report results of regressions on yields and columns (4
The sugar industries of the Caribbean have been in decline since at least the 1970s, and repeated efforts to revitalize them have not been successful. Production and exports have declined by about half since 1970 (Figure 1).

**<u>Figure 1. Caribbean Sugar Production, Consumption, Exports and Imports, 1970-2003</u>**

<!-- Start of picture text --> Sugar Production and Consumption Sugar Exports and Imports<br>3.0<br>2.5<br>2.5<br>2.0<br>2.0<br>Production 1.5<br>1.5 Exports<br>1.0<br>1.0<br>0.5 Consumption 0.5<br>Imports<br>0.0 0.0<br>1970 1980 1990 2000 1970 1980 1990 2000<br>Million Tons Million Tons<br><!-- End of picture text --> Source: FAOSTAT data and authors calculations.

Domestic consumption has increasingly been satisfied by imports rather than production. Costs per unit of production have increased due to rising wages, deteriorating field and factory performance, and inefficiencies which have come with public sector control and management. Untimely strikes and high rates of absenteeism have further contributed to the deterioration of the sugar industries. Labor for undesirable tasks, such as cane cutting, is increasingly difficult to obtain and temporary workers for these jobs are often brought in from other countries. Losses accumulated over many years have led to large debts that further add to the cost of operation and limit capital for modernization.

While the situation varies by country, most of the Caribbean’s sugar producers would not be operating without preferential access to the EU and U.S. sugar markets that pay two to three times world market prices for imports from quota holders. These preferences have eroded over time as the quantities of imports have declined, especially in the United States
Mean|0.200|7.608|8.954|0.061|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|5,297|1,663|351|587|
|Numberof individuals|1,384|986|351|587|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Columns (1) and (2) show results of IV-Logit-FE (marginal effects) and IV-FE models, columns (3) and (4) show results of IV model using data from wave 3 of the BNLA. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

29
/sup> Comparing toilet ownership rates at SBM baseline and at survey baseline for VG households, we see in Table 8 that over half (83 out of 115) of vulnerable MF client households identified as owning a toilet in the 2012-2013 SBM data (making them ineligible for subsidy), did _not_ own a toilet according to our survey data 1-2 years later, in 2014.<sup>26</sup><sup>_,_27</sup> To examine which type of subsidy ineligible households the sanitation micro-credit supported we estimate a version of equation 1, now allowing for heterogeneity in impacts by three different sub-groups:

> 23In total, 3 subsidies were disbursed to subsidy ineligible MF clients in each of the treatment and control groups (6 in total).

> 24It is also possible that some toilets became dysfunctional. However, our survey would have captured this since households were asked to report on ownership of toilets regardless of whether they were functional.

> 25We note that also vulnerability status might change over time and might entail a degree of mis-classification. However, our survey data does not allow us to re-construct vulnerability status, which is why we take it as given and time-invariant in our analysis.

> 26Table 8 uses reconstructed baseline toilet ownership derived from endline reports of the date of toilet construction. Results are almost identical when using actual baseline toilet ownership observations using the reduced panel sample (results available on request).

> 27Our surveys asked households to report on ownership of any toilet, regardless of whether it was functioning. Thus, it is unlikely that we are missing out on households with a non-functioning toilet. It is possible that some households under-reported actual toilet ownership at our survey, in the hope of being considered eligible for a subsidy or any other form of support. Given the significant efforts that were spent in confirming toilet ownership status during our survey (for example through interviewer observations, random re-visits, random spot-checks, monitoring, flagging of inconsistent responses between survey baseline and survey endline, etc.
- 70 -

Second, related to the above, private financing in transport infrastructure has been limited. Private participation in infrastructure continues to play an important role even in developing countries. It has reached close to $160 billion per annum in 2009. However, private participation remains selective by sector and country. The transport sector accounts for about 15 percent of total private investments in infrastructure for the past two decades. The vast majority were invested in the ICT and energy sectors (Figure 43).

It is noteworthy that private financing cannot substitute for public financing. Governments’ strong commitment and other legal and institutional frameworks in the infrastructure sector are important to catalyze more private investments, as experienced in Latin America during the 1990s. While Chile and Colombia, in which public investment has remained high, attracted many private investments, private participation in infrastructure also tends to be marginalized, as in Argentina and Mexico where the public spending was also tightened under the fiscal pressure ( Calderón and Servén, 2004; Fay and Morrison, 2007).

**Figure 43. Private participation in infrastructure in developing countries**

<!-- Start of picture text --> 200<br>Water<br>180 Telecom<br>160 Energy<br>Transport<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>-<br>Source: PPI database.<br>Private participation in infrastructure in<br>developing countries ($ 2009 constant billion)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> However, the current public procurement systems may not work well to improve efficiency in spending resources in the infrastructure sector. Public procurement is normally sizeable in the economy. The public sector often account for 20 to 25 percent of GDP in developing countries. A large
Table 5: Potential TFP Gains with Different Industrial Weights

||Cen|sus||Enterprise|Surveys|
|---|---|---|---|---|---|
||Baseline|Partial|ES|Full Census|Partial Census|
|Ghana|75.7|71.1|146.6|32.8|54.3|
|Kenya|162.6|184.2|86.5|16.7|51.4|

Note: The TFP gains are calculated based on different industrial weights. The TFP gains under the column ‘Partial’ are calculated by restricting to the subset of industries that overlap with the ES. The columns “Full Census” and “Partial Census” refer to TFP gains across firms in the ES where industries are weighted based on the full and partial censuses, respectively.

Finally, table 5 reports the counterfactual gains in aggregate manufacturing resulting from reversing distortions in the the three types of data that we have been comparing: census, ES with census weights, and ES with survey weights. In the case of the Census, we report the baseline results introduced above together with the gains corresponding to the case where we restrict to the subset of industries that overlap with the industries in the ES.

The table conveys a clear message: once industries are weighted as in the census, and hence once we correct for biases in the sectoral distribution of production in the survey, gains from withdrawing distortions are significantly muted (column 4 vs column 2). They are less than half the census-based gains in the case of Kenya, and are reduced by 25% in the case of Ghana. The gains from the ES using its own implied weights are expectedly larger.

# **8 The Underlying Sources of Misallocation**

The results presented above clearly revealed that resource misallocation is quite large in our sample countries. Thus improving the allocation of resources is a critical element in
(2004) and take averages over the relevant years. The pre-war years are divided into two sub-periods (1992-1996 and 1997-2001) and the war years correspond to 2002-2007 and post war years 2008-2011.

The digital maps of the communes/departments and road network are used to compute the distances from each commune center to all six trade routes identified in Figure 1. The distance of a location is estimated as the ‘arc distance’ from the commune center to the nearest point on the road along a route. Each commune/department is then assigned to the route which is closest to it. Using this shortest distance, we compute distances from the centroid of each commune to all West African cities with population of 35, 000 or more in 1996 from the consolidated urban database by Blankespoor, Khan and Selod (2017), which draws upon population data from Brinkhoff (2018).<sup>16</sup> These arc distances are combined with city population to define a measure of market access which is elaborated in the empirical estimation section below.

# **(6) Empirical Issues and Strategy**

# **(6.1) Empirical Models**

# **Employment Pattern: A Triple-Difference Strategy with Location Fixed**

To investigate how the trade disruption caused by the civil war in Cˆote d’Ivoire may have affected the resource allocation across the Abidjan and non-Abidjan areas in Mali and Burkina Faso, we adopt an empirical model that takes advantage of the panel data by combining sub-district level fixed effect with a triple-difference set-up. With a two-period panel of employment shares, the regression specification is as follows:

> 16West Africa countries include: Benin, Burkina Faso, Cote d’Ivoire, Ghana, Mali, Niger, Togo, Mauritania, Senegal and Guinea Bissau.

18
built-up area shares. Higher build-up, especially in the top quartile, is strongly associated with increased urbanization; we find similar effects with other specification formats like linear shares. A plausible interpretation of these coefficients is that land availability is a strong governor of the urbanization process. While this interpretation would match anecdotal accounts of the constraints that land availability and real estate prices have for the location choices of manufacturing firms in India, this measure is indirect (e.g., compared to real estate price data) and thus caution in interpretation is warranted.

One effect of adding this control is that the coefficient on the infrastructure index declines somewhat and becomes insignificant when including state fixed effects. This is not too surprising given that the built-up metric in part captures higher infrastructure levels. Adding this variable, however, does not affect the results on our wage variables. We take from this set of results that cost factors that slow manufacturing urbanization in India are definitely present in the labor market and most likely present in the real estate market, too. The evidence further suggests that absolute cost levels and localized urban-rural cost ratios play a role in India’s urbanization.

Finally, Columns 9 and 10 alternatively model land use intensity by organized sector firms to measure the tightness of local land markets. Inclusion of this control does not affect our core variables. Interestingly, the coefficient is positive, suggesting greater urbanization is associated with tighter land markets. This counter-intuitive result is one factor suggesting caution regarding land availability variables.

Table 6a considers the robustness of these results over different time periods. The first two columns repeat the specifications in Columns 5 and 6 of Table 5 for 1994-2005. Columns 3 and 4 then consider 1994-2000, while Columns 5 and 6 consider 2000-2005. This disaggregation is interesting for several reasons. First, Figures 1a-1c showed the substantial flattening of the urbanization changes for 2000-2005 compared to 1994-2000, and it is interesting to identify what correlates with this slowdown. Second, as a basic robustness check on potential reclassification issues, the 1994-2000 change
as over the long-run they wouild be expected to have nearly the same inflation rate<sup>3</sup> . If, on the other hand, one were examining an economic outcome determined by local conditions, like expenditures on heating, little would be gained by aggregating cities like Minneapolis where the average January temperatire is 11.8F (-11.2C) and Miami which basks in 67.2F (19.6C) temperatures even in January simply because they are in the same country.

The value of disaggregating data on gender disparities to the subnational level depends on the extent to which they are "national" phenomena determined by conditions associated with a particular national government, versus the extent to which they are local phenomena, determined by social, cultural, environmental, economic or political conditions that vary sharply within a country. A,s documented below, our fnding is that while gender disparities in South Asia are partly "natioral", but there are also enormous variations within countries.

# I) The data

A perennial problem with cross-national comparisons is without doubt the degree of comparability and reliability of the data across countries. In order to avoid these problems, we use data drawn from a ccllection of household surveys that used a nearly identical survey instrument and methodology in each country (or area) -- the Demographic and Health Surveys (DHS) and the National Family Health Surveys (NFHS)<sup>4</sup> .

3 Between 1980 and 1992 cumulative inflation was 66 percent in Miami and 71 percent in Minneapolis (Statistical Abstract of the United States, table 759).

Details on the questionnaires and surveys are in Appendix 1.

4
it is not adequately captured by actions relating to debt, including debt forgiveness, debt rescheduling, and other actions (such as service payments to third parties, debt conversions, and debt buybacks) (OECD, 2000b). The debt forgiveness component measures the face value of total debt that is forgiven in a year rather than its present value (PV). Because the average concessionality of debt varies strongly across countries, this may be misleading (Depetris Chauvin and Kraay, 2005). For most types of debt rescheduling, the reduction in debt service in a given year as a result of present and past rescheduling is recorded. Again, this fails to capture the PV of current and future reductions in debt service as a result of debt rescheduling in the current year.<sup>16</sup> For these reasons, I omit action relating to debt as a regressor and instead control for the PV of public and publicly guaranteed long-term external debt as well as public and publicly guaranteed long-term external debt service. These variables should capture most of the effects of debt relief on social spending. Less debt service means that more resources are available to spend on other purposes, whereas a lower stock of debt means that the intertemporal budget constraint is loosened, which may increase the government’s appetite for spending. The PV of debt is obtained from Dikhanov (2004), which is updated through 2004.<sup>17</sup> The source for debt service is the Global Development Finance database (World Bank, 2006b). Again, I use current US$ GDP from World Bank (2006c) to express both variables as percentages of GDP.

Other control variables that are included in **_X_** _it_ are real GDP per capita (thousands of constant 2000 international dollars) and its growth rate, urbanization (urban population, % of total) and trade (% of GDP) (all 10
**4.2 Real Effects: Savings, Consumption, and Shock-Mitigation**

In this section, we report the impact of payroll accounts on real outcomes. While the main interest of our study is on how inexperienced individuals learn to use the financial technology, we also measure the extent to which engagement with accounts affects real outcomes, such as total savings, consumption, and the ability to cope with unanticipated economic shocks. Our analysis of real effects relies mainly on survey data, which allows us to capture a richer set of outcomes than administrative data alone. We report results for all outcomes that are available in administrative and survey data in the appendix and show that the results are consistent.

**Savings.** In Table 6, we examine the effect of payroll accounts on savings, remittances, and shock-mitigation. Panel A shows results based on endline survey data only, panel B reports results from the combined panel of follow-up and endline surveys. The results in column (1) reveal that the payroll account treatment had a strong positive effect on the extensive margin of savings and increased the share of participants who reported having any savings by approximately 4 percentage points at endline and 11 percentage points when we use the full panel of midline and endline surveys.

> 40We note that between baseline and endline, trust in both technologies in the control group increased substantially. This may be due to the increased prevalence of formal accounts in Dhaka or because of spillovers from the treatments onto the control group. In the presence of spillovers, the coefficients would be underestimates.

20
agencies do not generally track the domestic value added of goods that their countries trade, nor do they track the use of these traded goods, that is, whether they are used for sales to final consumers, whether they are used for further processing in a specific industry, and what share of this industry’s output is exported.

In the field of international economics, scholars have used a variety of approaches to gain insights into the structure of GPNs. One method has been to rely on the highly disaggregated product codes and descriptions in international trade statistics to classify traded goods according to their main use. Yeats (2001) and Ng and Yeats (2001), for example, categorized intermediate goods as those products whose description include the words “parts” or “components”. Lemoine and Ünal-Kesenci (2004) and Zebregs (2004) used the United Nation's "Broad Economic Categories" (BEC) classification to distinguish between intermediate and final goods. While this approach has been useful to demonstrate the large and growing role of GPNs in international trade, it faces two important shortcomings. First, classifying goods according to their product codes is somewhat arbitrary since product descriptions provide insufficient information to identify a product’s main use (Hummels, Ishii and Yi, 2001). Indeed, some goods, such as _tires,_ can be used both as a final good by consumers and as an intermediate good by car manufacturers. Second, even if traded goods were correctly classified as intermediate or final goods, international trade data do not identify in which sector intermediate goods are used, if it is processed for domestic consumption, or if it is used for export purposes. This makes it difficult to accurately link a trade flow with other trade flows within the same GPN.

An alternative approach used to map activities within GPNs is to combine international trade data with input-output (IO) table data. The advantage of IO data is that – for domestic activities – it unambiguously defines intermediate inputs by their use, i.
**Table S13:** Association of market access on outcome variables using OLS, panel data

||N|TL|Urb|an|Cropl|and|N|TL|U|rban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._003|_−_0_._0003|0_._001|0_._02|_−_0_._02|_−_0_._02<sup>_∗_</sup>|_−_0_._07<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._003|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._02)|(0_._03)|(0_._01)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 Low||0_._06<sup>_∗∗∗_</sup>||0_._02<sup>_∗∗∗_</sup>||0_._004||0_
local inflation was similar to international levels, but the real exchange rate had strongly appreciated, and the foreign debt had duplicated in three years. In this context of severe macroeconomic disequilibriums the economy faced the second oil shock.

# Pragmatic Export-Driven Growth 1982-1989

The second oil shock had three strong manifestations in the Chilean economy. First, capital flows that had reached 18% of GDP in 1981, were reduce to one half of that in 1982, and later one fourth in 1983. Second, the increase in the interest rates made more expensive the service of a recently doubled debt. Finally, the terms of trade were worsened, led by a strong decrease in the international price of copper. As a result, 1982 experienced a negative growth of GDP of 14%, and unemployment rates rose to almost 30%. The regime had to respond to the political turmoil that followed. Tariffs were momentarily raised to 35% and the exchange rate was devaluated in a crawling peg scheme. Imports responded immediately reducing in 1983 to less than one half of their 1981 level. Exports lagged, but started to grow specially after 1986. The economy grew at a fast pace, but most of the initial inertia was just recovery and full utilization of installed capacity. On the other hand, investment kept low like in the previous period, only starting to grow by the end of the regime.

An assessment of the military regime leaves many negative marks. For the whole 19731989 period the economy grew at a disappointing average of 2.6%. Industrial product practically did not grow. In 1989 real wages and minimum wages stood at a level lower than 1970. The "neutral" liberalization approach put the incentives on the export of goods were there was natural comparative advantages, not acquired, thus most of the exports since have been resource intensive. There was a severe worsening in the distribution of income, official consumer surveys for CPI construction reveal that from 1969 to 1987 the richest quintile increased its share of consumption from 44.5%