rafmacalaba/datause-encoder-data huggingface dataset:
financial crises, and some of that includes precise measures of the occurrence and magnitude of the crises; in particular, see Caprio and Kinglebiel, 2001. There is also an important literature on “early warning” indicators, which focuses on identifying macroeconomic and financial variables whose changes precede debt crises, including those related to balance of payments, banking, and public debt. Leading examples of this line of research are Kaminsky, Lizondo, and Reinhart (1998); and Goldstein, Kaminsky, and Reinhart (2000).

Finally, on stronger monetary stability, the most obvious indicator consists of low and stable domestic price inflation. Nonetheless, a more complete analysis requires considering foreign exchange rate and interest rate movements. Fluctuations in exchange and interest rates are to be expected in dynamic and flexible capital markets; therefore, by themselves, these fluctuations do not indicate lack of monetary stability. In order to detect monetary instability even before it manifests itself in inflation shocks, it is necessary to analyze the degree of misalignment in real exchange and interest rates with respect to their equilibrium values. As the recent Argentinean crisis painfully illustrated, low inflation and motionless exchange rate do not mean monetary stability but, far from it, may hide large misalignments. Some seminal work on equilibrium and exchange rates are Edwards (1988), Williamson (1993), Elbadawi and Soto (1997), and Hinkle and Montiel (1999).

# **B. INTERNATIONAL FINANCIAL MARKETS**

**_1. General Principles._** Market-oriented reforms in international financial markets basically consist of removing restrictions on cross-border flows of debt, equity, and financial services.  As in the case of domestic financial markets, market-oriented reforms dictate that financial liberalization is accompanied by prudential regulations directed at minimizing market failures.

The globalization of financial markets is possibly the area of reform whose benefits and costs are most hotly debated (see World Bank, 2001). Weary of its potential dangers, Keynes advised, “above all, let your finance be national.” It is argued that the main disadvantage of financial globalization for developing countries is that they may become vulnerable to external shocks that are difficult to manage and control. The contagion of capital market volatility and crises is a major concern not only for its effects 10
year primary classes at year .<sup>22</sup> i is a vector of municipal controls including the number of pre-schools, total male to female sex ratio, total and per-recipient Bolsa Família transfers, and—tt−5 XX both in logs and four-year log-differences—municipal GDP and public spending (welfare, education,<sup>23</sup> health, transportation, and housing). Unobservable heterogeneity is captured by a year trend, , a municipal fixed effect, , and a time-varying ss ii ii ii municipal component, yyf yy , a state × year specific shock, δδi . ηη The resulting estimate, εε , reflects the intention-to-treat effect of a one unit increase in secondary school density on teenage birthrates. Municipal differences of the within-variation in αα� the secondary school expansion between the years 1997 and 2005 identify this coefficient.<sup>24</sup> For an unbiased estimate of we assume strict exogeneity between the error term and school introductions. This assumption is supported by the analysis in Section 4. We subject this αα assumption to further tests and specifications in sections 5 and 6. The remarkable stability of our results across specifications indicate to us that this assumption is plausible.

**5. Results** Estimates for Equation (3) are presented in Table 3. The coefficient of interest, , is reported in the first row [ _Secondary school density (t-4)_ ]; estimates for selected control variables in αα i and P XX ii,ii−5<sup>are reported in the remaining rows. All regressions include a year trend and municipality</sup>

> 22 The first observation for P 23 This largely captures municipal spending on pre-primary and primary education, rather than on secondary education ii,ii−5<sup>(at tt= 2001) is constructed from the 1996 wave of the Brazilian School Census.</sup> (which is the responsibility of the states). Excluding education expenditure leaves the estimates virtually unchanged. Results available from authors.

> 24 In an earlier version of this paper, we instrumented secondary school enrolment using the expansion of secondary schools in year t-4. This has the benefit of normalizing the estimates to be in terms of per student enrolled, rather than “school” which is not a well-defined unit of measurement and can vary in size across municipalities. It has the disadvantage in that it may ignore important effects of introducing schools into a municipality that arise through channels other than school enrolment. The results reported here are qualitatively similar to our previous findings (Foureaux Koppensteiner and Matheson 2019) .

16
22 points increase in severity. The state of Kano shows 2.5 percentage points in head count, 3.5 percentage points decline in depth and 2.8 percentage points decline in severity. State of Sokoto shows 5.7 points increase in headcount, 7.1 percentage points increased in depth and 6.4 percentage points increase in severity.

Table 4.13 shows that Sokoto was the highest contributor to all indicators in 1985, with a contribution of 12.5 percentage points to headcount, 14.1 percentage points to depth and 14.7 points to severity respectively. The values were very closely followed by Kano with 11.3, 11.5 and 10.9 percentage points for headcount, depth and severity respectively. Some states (Bauchi and Plateau) show large increases in contribution to all three indicators for the seven years covered in this study. These two states contribute the highest to poverty incidence, depth and severity even in 1992/93.

**Table 4.12: Poverty Incidence in Nigeria by State, 1985-93 (Poverty Line=N395.41)**

|||1985/86|||1992/93||
|---|---|---|---|---|---|---|
|**STATE**|**P0**|**P1**|P2|Po|**P1**|**P2**|
|Lagos (Urban)|0.240|0.070|0.032|0.279|0.009|0.003|
|FCT|0.000|0.000|0.000|0.495|0.245|0.157|
|Anambra|0.309|0.104|0.045|0.163|0.059|0.031|
|Bauchi|0.587|0.247|0.132|0.556|0.236|0.133|
|Bendel|0.417|0.146|0.067|0.162|0.061|0.033|
|Benue|0.460|0.183|0.093|0.368|0.176|0.114|
|Borno|0.421|0.168|0.087|0.418|0.185|0.108|
|Cross River|0.421|0.139|0.061|0.330|0.156|0.094|
|Gongola|0.488|0.191|0.098|0.317|0.129|0.068|
|Imo|0.322|0.101|0.045|0.144|0.066|0.040|
|Kaduna|0.498|0.180|0.087|0.247|0.097|0.053|
|Kano|0.483|0.164|0.076|0.508|0.199|0.104|
|Kwara|0.413|0.130|0.054|0.314|0.136|0.082|
|Lagos (Rural)|0.361|0.116|0.052|0.361|0.145|0.076|
|Niger|0.566|0.285|0.182|0.444|0.200|0.122|
|Ogun|0.454|0.145|0.062|0.263|0.097|0.048|
|Ondo|0.445|0.179|0.093|0.266|0.112|0.066|
|Oyo|0.315|0.072|0.024|0.229|0.088|0.047|
|Plateau|0.475|0.172|0.087|0.426|0.188|0.113|
|Rivers|0.358|0.097|0.036|0.379|0.158|0.088|
|Sokoto|0.469|0.185|0.094|0.526|0.256|0.158|
|**All Nigeria**|**0.430**|**0.157**|**0.079**|**0.341**|**0.147**|**0.085**|

Source: NCS, 1985/86 and 1992/93.
# **Table A.1 Local Networks of Rural-Urban Migrants at Time of Migration Five-City China Urban Labor Survey (CULS) Migrant Survey***

||Source C<br>Loc|ommunity<br>ation|
|---|---|---|
||All<br>Provinces|4 RCRE<br>Provinces|
|Share of Migrants with:|||
|Job Arranged Before_First_Migration Experience|0.52|0.57|
|Job Arranged Before_Current_Migration Experience|0.53|0.56|
|Some Acquaintance from Home Village in City Before Migrating|0.91|0.94|
|**Close Family Member in City Before Migration|0.35|0.35|
|**Extended Family Member in City Before Migration|0.52|0.58|
|**Hometown Acquiantances|0.65|0.67|
|Five or Fewer Hometown Acquaintances|0.39|0.44|
|More than Five Hometown Acquaintances|0.27|0.24|
|At Least One Local Acquaintance|0.09|0.08|
|Number of Migrants|2,463|481|

*Respondents are holders of rural registration ( _hukou_ ).  The survey was conducted in Fuzhou, Shanghai, Shenyang, Wuhan and Xian during late 2001.  Sample frames were assembled using information on distribution of migrants within cities from the 2000 Population Census.  After selecting neighborhoods through a proportional population sampling procedure, sample frames were assembled using residents’ committee records of migrant households and registers of migrants living on construction sites and held by local by police stations.  Very short-term migrants, who lack a residence that falls under the jurisdiction of either of these authorities, are unlikely to have made it into the sample frame.

**A _close family member_ is adult sibling or member of nuclear family (e.g., spouse, child, parent).  An _extended family member_ refers to cousins or other relatives. _Hometown acquaintances_ are unrelated, but known by the respondent. Note that migrants may have acquaintances in several categories, so that subcategories of acquaintances will add to more than 100.

64
income levels. This is a very different approach from those discussed in the preceding paragraphs. The household survey data are used in the estimation process, but they are not used in the simulation model itself.

This has several advantages. Firstly, since all households in a given country have the same preferences, there is also a well-defined poverty level of welfare, and the analysis of consumption impacts is clean and straightforward. Secondly, to the extent there are measurement errors in the household survey, these will likely be eliminated in the process of estimation of the demand system. Finally, it permits the authors to adjust predicted consumption levels in cases where the household survey data and CGE model refer to different base periods. On the other hand, one important drawback of this econometric approach to assessing the consumption impacts of trade reform is that one can no longer refer to the impacts on particular households in the survey.<sup>12</sup>

**_Transactions Costs:_** In their survey of the empirical evidence on trade liberalization, Winters, McCulloch, and McKay (2004) highlight the important role of transactions costs between the border and the consumer (for imports), and between the border and the producer (for exports).  Assuming these marketing costs are specific (as opposed to _ad valorem_ ), they tend to dampen the impact of world commodity price changes on domestic consumers, while exaggerating the impact of such price changes on producers of export products. As a result, the presence of such marketing costs can sharply alter the predicted impact of trade policy changes on remote rural households.

Recent work by Arndt et al. (2000) in Mozambique underscores the empirical significance of marketing margins in low-income countries. The authors report producer-consumer margins as high as 300% (cassava). The biggest margins reported in their study appear to be for food products, which tend to dominate both the consumption and production bundles of the poor in that country. So the existence and behavior of these margins is critically important for any poverty study.

The only study in this survey to seriously come to grips with this issue is that of Nicita (2003), who estimates a modified version of the popular exchange rate pass-through model (e.g., Goldberg and Knetter, 1997). This model offers differential pass-through of Mexican tariff changes, by region,

> 12 Furthermore, this approach needs to be more extensively validated. While the authors have found that it does a good job of predicting expenditure patterns by commodity across the income spectrum in Indonesia, more work is required to ensure that its predictive performance is satisfactory across a range of developing countries.

28
percent by 2007, as the share of assets invested in fixed cash deposits declined over the period from 12 percent to 7 percent. Zambia’s pension system asset allocation in real estate decreased overall over the 2001–07 period, from 28 percent to 26 percent, although there was an increase of two percentage points (up from 24 percent) from 2005–07.

In Kenya’s pension system, there has also been a greater emphasis on holdings of equities over the 2001–06 time series for which asset allocation data are available, with the investment allocation increasing from 9 percent to 24 percent. At the same time, holdings of government securities declined somewhat over the period from 50 percent to 42 percent. The share of the Kenyan pension system’s assets invested in real estate remained within the 6–8 percent range over the 2001–06 period.

In Ghana, available time-series data for SSNIT indicate a shift of assets away from real estate over the 2000-06 period, with holdings reported as 11 percent in mid-2006, down from 31 percent in 2000 and 25 percent in 2001. Most of the remaining holdings in real estate projects as of mid-2006 (8 percent of SSNIT’s total asset allocation) were projects under construction, which do not generate cash flow. SSNIT’s holdings of cash and deposits also increased significantly over the period 2000–06, from 7 percent to 36 percent and total asset holdings in the form of corporate and student loans, although down by five percentage points, to 20 percent at mid-2006, still represented a significant share of the total portfolio invested in assets typically generating weak returns. SSNIT further increased its total asset holdings in equities, from 21 percent in 2001 to 30 percent by mid-2006.

In Madagascar, pension system assets held in real estate also declined over the 2000–05 time series, from 21 percent in 2000, to 16 percent in 2001, to 6 percent by 2005. But because alternative investment vehicles were severely limited, total pension system assets invested in nongovernment-issued securities remained low at just under 4 percent while asset holdings in government securities increased over the period, from 73 percent in 2000, to 80 percent in 2001, to 90 percent as of 2005. Pension fund asset allocation patterns for South Africa reflected that country’s better-developed capital markets. Asset holdings in cash/deposits and government securities stayed around 6–7 percent and 9–12 percent, respectively, and holdings in real estate fell further, from 4 percent to just under 1 percent.

35
These results show that while the differences in performance metrics between different modeling scenarios are not very large, small differences can multiply over space leading to substantial differences in maize area estimation. Hence, there is value in achieving small performance gains anchored in better training data.

Finally, after evaluating maize classification performance in Malawi and Ethiopia, we generated 10-meter resolution rasters of area cultivated with maize for both countries over the period of 2016-2019. Table 10 provides an overview of these rasters. After creating the rasters of probability of maize cultivation, we generated binary maizeland masks for each country and season in two steps. We first used our country- and season-specific cropland rasters to remove all pixels that were not cultivated with any crops. Pixels with probability of (any) crop cultivation less than 40 percent were assumed to be non-cultivated. Subsequently, we used our country- and seasonspecific maizeland rasters to identify which of the cultivated pixels were cultivated with maize. In Malawi, pixels with probability of maize cultivation greater than or equal to 60 percent were assumed to be cultivated with maize. The comparable threshold was 50 percent in Ethiopia.

Table 10: Specifications of predicted maizeland rasters in Malawi and Ethiopia

|**Country**|**Maize classification**<br>**model specifications**|**Seasons trained on**|**Seasons predicted on**|
|---|---|---|---|
|Malawi|Plot mean geolocation<br>method,<br>0.05 ha area threshold,<br>Optical features only|2017/18 rainy season,<br>2018/19 rainy season|2015/16 rainy season<br>2016/17 rainy season<br>2017/18 rainy season<br>2018/19 rainy season|
|Ethiopia|Corner point geolocation<br>method,<br>No area threshold,<br>Optical features only|2018 meher season|2016 meher season<br>2017 meher season<br>2018 meher season<br>2019 meher season|

# **5. Conclusions**

Satellite data sources have tremendous potential for amplifying the insights available from household and farm surveys. The research presented here advances our understanding of how to collect optimal plot-level survey data that can train and validate remote sensing models for highresolution crop type mapping. Specifically, we quantify the interactive effects of (i) plot size, (ii) approach to georeferencing plot locations, and (iii) size of the training data set on the performance of a machine learning-based maize classification model.

The results show that collecting a complete plot boundary is preferable to competing approaches to georeferencing plot locations in large-scale household surveys and that seemingly-small erosion in maize classification accuracy under less preferable approaches to georeferencing plot locations consistently results in total area under maize cultivation to be overestimated - in the range of 0.16 33
of lowering densities<sup>17</sup> . While it may not be possible or desirable to reconfigure all such plots so identified, the scenario of valuing the excess land in plots over 1 hectare in size with only 50 percent of plot coverage offers some indication of the potential additional land value that could be realized with selective relaxation of what can be excessive regulation.

# **2.8 Valuation of Potentially Marketable Public Land**

The basic source of data for land values are the Jantri rates. These are the values assessed and published by the Government authorities for the purposes of Stamp Duty levied at the time of transaction in land or property. Jantri includes assessed values of (i) vacant land; (ii) built property used for residential use and (iii) built property used for commercial purpose. Out of these, the values for vacant land have been used, as they appear to be the most consistent across different parcels and do not bring in the uncertainty introduced from assessing the value brought about by different types of use. This method leaves out variation that may come about due to differences that may be introduced due to the specific land use and is consistent with our approach of identifying and valuing only public land that is currently not in use or whose use would change.

However, the Jantri rates are known to be lower than the real market values. In order to get an estimate based on the real market values, valuation of three plots in each TPS was obtained from approved assessors. Based on their reports, the difference between the Jantri rate and real market values were calculated as a deviation factor. The estimated market rate is then the Jantri rate multiplied by the deviation factor applied to all plots in a TPS.

The Jantri rates, estimated market values and estimated deviation factors for a sample of five of the TPS covered in the study are presented in Table 5 below.

|**No.**|**TPS No. and Name**|**FP**<br>**No.**|**Jantri**<br>**Rate**|**Market**<br>**Rate**|**Deviation**<br>**Factor**|**Multiplier**|
|---|---|---|---|---|---|---|
|1|TPS No. 56 - Sola Gota Oganaj|229|10,000|15,000|1.50||
|2|TPS No. 56 - Sola Gota Oganaj|239|10,000|15,000|1.50|1.51|
|3|TPS No. 56 - Sola Gota Oganaj|281|16,500|25,000|1.52||
|4|TPS No. 43 - Sola|40|14,000|65,000|4.64||
|5|TPS No. 43 - Sola|221/1|9,500|10,000|1.05|2.84|
|6|TPS No. 43 - Sola|209|16,000|45,000|2.81||
|7|TPS No. 28 - Ghatlodia|81|12,500|20,000|1.60||
|8|TPS No. 28 - Ghatlodia|68|12,500|20,000|1.60|2.40|
|9|TPSNo. 28-Ghatlodia|106|12,500|50,000|4.00||
|10|TPS No. 29 - Chandlodia Sola|62|14,250|60,000|4.21||
|11|TPS No. 29 - Chandlodia Sola|82|12,750|20,000|1.57|2.45|
|12|TPS No. 29 - Chandlodia Sola|73|12,750|20,000|1.57||
|13|TPS No. 30 - Gota|119|15,000|20,000|1.33||
|14|TPS No. 30 - Gota|120|15,000|20,000|1.33|1.38|

> 17 Annez et alia 2012 op cit.

24
Virtual Windows Through Glass Walls? Digitalization for Low-Mobility Female Entrepreneurs Layane Alhorr<sup>∗</sup> Latest version here (updated frequently) _Keywords_ : Business Training and Outsourcing; Firm Growth; Female Entrepreneurship; Digital Marketing.

_JEL Codes:_ O12, O17, J16, L25, L26, M31.

> ∗Layane ~~A~~ lhorr@g.harvard.edu. I thank Lara Naber for excellent research assistance in the field. I am grateful to Emily Breza, Edward Glaeser, Rema Hanna, Asim Khwaja, and Tavneet Suri for their comments and mentorship throughout this project. I also greatly benefited from discussions with Gie Bai, Anke Becker, Shweta Bhogale, Claudia Goldin, Larry Katz, Rembrand Koning, Gabriel Krindler, Eliana La Ferrara, Louise Paul Delvaux, Gautam Rao, and Ambra Awa Seck. I thank conference participants at ASREC, PacDev, DevPec, CEPR, SSRC, CFXS, MNACE, AFE, and NEUDC, in addition to participants at the Harvard development economics, labor economics, political economy and culture, and economics and social policy workshops for feedback. This research was funded by the Weiss Fund, Sanad Fund, Harvard’s Middle East Initiative, and the World Bank MENA Chief Economist Office under the labor and gender research programs (TTLs: Nelly Elmallakh and Nazmul Chaudhury). The experiment in this paper was approved by the Harvard Institutional Review Board and is listed under AEA registry 9177.
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