rafmacalaba/datause-encoder-data huggingface dataset:
new locality identifier that equals the 1997 identifier in case of splits and equals the 2007 identifier in case of a merge (i.e. the most dis-aggregated identifier that can be tracked over time). This new identifier counts 533 unique “stable” localities that can be tracked over time. A number of localities are dropped from the analysis, including Jersualem localities that lie outside of the West Bank and localities with a 2007 population count below 1000. This reduces the number of stable localities that will be used in the regression analysis to 241. Annual population counts _Pjt_ are obtained by interpolating (and extrapolating) the log of the 1997 and 2007 counts for each locality separately. For the geographical coordinates of the localities we work with centroids, which were “snapped” to our road network.

# _Road network data_

The geo-referenced road network data were kindly provided to us by UNOCHA (who further developed the road network data produced by the European Commission’s Joint Research Center (JRC)). UNOCHA’s road network data counts approximately 26,000 geographic features for the West Bank (compared to approximately 20,000 geographic features for JRC’s data). Each road segment is classified into one of 6 road types with corresponding estimates of the average amount of time required for a typical civilian/commercial vehicle to traverse the segment: Regional (60kph), Primary (50kph), Secondary (40kph), Tertiary (30kph), Residential (20kph) and Track (10kph). The data also record different categories of restrictiveness for each road segment concerning use by Palestinians: No Restriction, Restricted Use, Partially Prohibited, and Totally Prohibited. We drop road segments classified as “Totally Prohibited”, and assume that the necessary permits have been acquired to permit use of all remaining roads. The analysis requires road segments to be perfectly connected (and origin and destination localities to be perfectly connected to the road network) for the the network analyst algorithm to traverse the road network. Road segments that did not exactly connect were modified to connect based on our best judgment.

# _Travel times_

The computation of market access as defined in eq. (1) requires data on travel times _Tijt_ . We used ArcGIS 10.3’s Network Analyst software package to solve for the optimal route that minimizes travel time between all origin and destination pairs of interest. Selected examples of these optimal routes (depicted in bold purple lines) are plotted in Figure 2.

10
units of analysis and all our models likewise cluster standard errors at the level of PRIOGRID cells. In Appendix C, we also cluster standard errors at the level of subnational administrative regions (admin1), showing that our results remain robust. While the resolution of the grid is a feature that comes with the PRIO-GRID data structure, there is no theoretical reason for choosing a particular degree of resolution, perhaps aside from picking inappropriately large cells that would introduce distances at which the effect of climate on population movements would be improbable. The 0.5°resolution represents a standard adopted by the literature relying on grid structures.

# **3.1 Measuring Drought**

To measure exposure to drought at the grid cell level, we use a metric of the proportion of months out of 12 months that are part of the longest streak of consecutive months ending in a given year with SPEI values of less than _−_ 1.5.<sup>2</sup> SPEI is the _Standardized Pre-_ . _cipitation Evapotranspiration Index_ SPEI is an extension of the Standardized Precipitation Index (SPI) but its advantage is that it takes both precipitation and evapotranspiration into account when determining the presence of drought. The measure we use captures drought in a given grid cell, thus conveying information about inclement weather that is most likely to affect agricultural yields in that cell. All SPEI values are taken from the SPEI Global Drought Monitor which we downloaded from the PRIO-GRID website.<sup>3</sup> As an illustration, the left part of Figure 2 displays the average value of our drought measure (i.e. the mean fraction of the year that a given grid cell experienced drought) for South America, averaging values for the entire period between 1985 and 2014.

To account for spatial correlation in our variables and, therefore, to discern the cumulative effect of drought in the cells that are adjacent to urban areas, we calculate spatial lags for both drought and the relevant control variables.<sup>4</sup> Here our approach is similar to other studies that have explored the impact of climatic shocks using disaggregated analyses that rely on grid cells.<sup>5</sup> The advantage of this approach is that it brings the tools

> 2 The SPEI’s range runs from _−_ 5 to 5.

> 3For details, see: https://grid.prio.org

> 4 We discuss our control variables immediately below in section 3.3.

> 5See, for instance, Harari & Ferrara (2018).

7
13 consumption taxes all have broadly similar effects on employment (cf. Nickell 2004; Nickell _et al._ 2005).

On the other hand, SHI can provide a disincentive for people to join and stay in formal employment, while the same is not true of general revenue-financing (cf. e.g. Belev 2003; Baeza and Packard 2006; Datta 2006; Levy 2007). The incentive is likely to be especially large in the case where among informal sector workers SHI enrollment is voluntary (as in Germany) or is only weakly enforced (more likely to be the case in countries like Mexico and Turkey). In such a setting, people can rely on private insurance (in some countries they are required by law to have it if they do not join the SHI scheme), or they may opt for informal employment arrangements and resort to using the health ministry’s public health care system, albeit facing higher out-ofpocket payments than they would have done under the SHI system and probably ending up with worse quality care. These incentives become even more of an issue if being in formal employment means being drawn into the tax system and having to contribute to a pension scheme that because of limited life expectancy may only pay out for a few years if any.

# **3. Methods**

The methods used are the same as those used by WMS (2008a; 2008b) to which the reader is referred for more detail. Let _yit_ be the outcome of interest in country _i_ at time _t_ , _SHIit_ be a dummy variable taking on a value of 1 if country _i_ has a SHI health financing system at time _t_ , and _Xit_ be a vector of covariates thought to potentially influence both outcomes and the SHI adoption decision. The basic model takes the form:
A number of studies examine privatization in developing nations. Some conduct cross-country analyses, using data from the transition nations of Eastern Europe (Bonin, Hasan, and Wachtel 2004, 2005) or the crisis nations of East Asia (Nguyen and Williams 2005). In many of the transition nations, control of many of the privatized banks was shifted from state ownership to foreign ownership. Others studies of individual nations – including Brazil (Beck, Crivelli, and Summerhill 2005, Nakane and Bauhmol-Weintraub 2005), Mexico (Haber 2005), Nigeria (Beck, Cull, and Jerome 2005), and Pakistan (Bonaccorsi di Patti and Hardy 2005) – generally find that at least one bank performance measure improved following privatization, although some measures show no change. Some of these studies also find signs of greater prudence in lending after privatization in the form of reduced nonperforming loans or lending (e.g. Beck, Cull, and Jerome 2005, Haber 2005). One study of Argentine privatization finds that cost efficiency improves following privatization (Delfino 2003).

# **3.  The Argentine banking system in the 1990s**

Ownership changes and other organizational restructuring significantly altered the Argentine banking system during the 1990s. The changes included domestic M&As, foreign acquisitions, privatization, and restructuring of state-owned banks without privatization. Most privatized banks were previously provincially-owned, although in some cases, they had been nationally- or municipally-owned. Between 1993 and 1999, assets held by provincially-owned banks fell from about 21.6% to 13.0% of total assets, while assets held by foreign-owned banks grew from 15.6% to 50.3% of total bank assets.

These governance changes were driven both by shifts in government policies and local and regional financial crises. In April 1991, in response to macroeconomic crisis, the Menem administration initiated a radical reform – known as the Convertibility Plan – that pegged the Argentine peso to the U.S. dollar, granted the central bank greater independence, privatized state-owned firms, and removed trade barriers. To address concerns about the country’s banking system, in September 1992 the Argentine Congress restructured the Banco Central de la República Argentina (BCRA), the central bank of Argentina, in a way that had a significant impact on prudential regulation. The new legislation constrained the BCRA by: i) preventing it from guaranteeing commercial bank deposits, ii) preventing it from taking on any new financial assets, and iii) restricting its extension of credit to commercial banks to liquidity rediscounts for a period of 15 days (Dillinger and Webb 1999). This legislation threatened the sustainability of the provincially-owned banks, many of which were in relatively weak financial health as a result of the pursuit of various political and social objectives. The legislative constraints on the BCRA 7
Table 3

# Poverty Mleasures based on Alternative Price Indices

||N|PO<br>G|M|N|PI<br>G|M|N|P2<br>G|M|
|---|---|---|---|---|---|---|---|---|---|
|Abidjan|.034|.034|.034|.009|.009|.009|.004|.004|.004|
|Other Cities|.236|.204|.119|.075|.064|.042|.037|.033|.022|
|East Forest|.479|.463|.285|_.155_|.144|.076|.069|.063|.032|
|West Forest|.178|.214|.062|.036|.043|.015|.013|.015|.006|
|Savannah|.502|.591|.322|.183|.221|.093|.088|.111|.037|
|Export Crop Farmers|.366|.366|.147|.094|.095|.042|.038|.039|.015|
|Food Crop Farmers|.434|.468|.263|.144|.156|.071|.065|.072|.028|
|Public Sector Employees|.049|.038|.003|.007|.004|.001|.001|.001|.000|
|Formal Private Sector Employees|.071|.070|.023|.014|.012|.005|.005|.004|.002|
|Informal Private Sector Employees|.262|.225|.225|.075|.069|.037|.028|.024|.008|
|Self-Employed|.262|.260|.185|.104|.103|.067|.058|.058|.038|
|Inactive|.183|.191|.152|.075|.072|.053|.043|.040|.030|
|Unemployed|.041|.041|.041|.005|.005|.005|.001|.001|.001|
|C6te d'Ivoire|.300|.314|.176|.098|.102|.050|.045|.048|.022|

# **_5._** Disafgregated Price **Indices**

For certain policy purposes it may be useful to have a price index available which only covers a sub-set of the consumption basket (for example, food or non-food only) or which covers the entire basket but pertains only to certain groups of households (for example, farmers or the poor). The detailed price information in the ICP data in 10
states both have large flows in and out of them. Additionally, unlike Egypt, the public sector sees the most transitions in and out of it. This might be due to its small size, employing only around 5% of the workforce in this sample; small movements in and out of the sector would therefore have a large effect on transitions. Figure 9 shows the simulation results among men aged 20-53 in 2005 Q1. The lower panel reports the comparable transition matrices for Egypt (focusing on men in the same age group observed empirically between 2012 and 2018). The lower panel was previously presented in Figure 3.

**Figure 9: Six-yearly transition matrices for males aged 20-53 in Mexico and Egypt**

||Private informal|37|**45**<br>7<br>3<br>7|30% (37%)|
|---|---|---|---|---|
||Private formal|37|**46**<br>7<br>3<br>7|36% (45%)|
|Mexico|Public formal|36|**45**<br>8<br>3<br>7|6% (7%)|
||Unemployed|37|**46**<br>7<br>3<br>7|3% (3%)|
||Out of labor force|37|**45**<br>7<br>3<br>**7**|24% (7%)|
||Private Info|rmal<br>Private Formal|Public<br>Unemployed<br>OL|F|
||Private informal|**76**|10<br>5<br>4<br>5|53% (55%)|
||Private formal|43|**39**<br>12<br>3<br>3|14% (14%)|
|Egypt|Public formal|8<br>8|**82**<br>2|21% (23%)|
||Unemployed|56|17<br>11<br>**8**<br>8|4% (3%)|
||Out of labor force|56|14<br>11<br>5<br>**14**|9% (5%)|
||Private informal|Private formal<br>Public|formal<br>Un-employed<br>Out of lab|or force|

_Notes_ . The upper panel is simulated by Monte Carlo Method described in Section 4 Methodology. 100,000 simulations of transitions after six years were performed for each initial state, where the transition probability matrix per period is taken from the empirical annual transition matrix in Figure 8. The annual transition matrix relies on panel data from the ENOE in 2005 Q1 and 2006 Q1. We restrict our analysis to men aged 20 to 53 who were interviewed in both rounds. We use transition matrices to examine individuals’ transition of their work status/sector between the two dates. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used. The lower panel relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to individuals who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices by sex to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

17

**Official Use**
Table 7: Placebo: Agricultural outcomes Pre-CADENA

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||∆|log ha sow|ed||log yield||
|Below trigger|0.000433|0.0104|0.0451|-0.0110|-0.0152|-0.0819<sup>_∗_</sup>|
||(0.0498)|(0.0304)|(0.0279)|(0.0596)|(0.0509)|(0.0425)|
|Bandwidth|50|70|all|50|70|all|
|Polynomial|linear|linear|quadratic|linear|linear|quadratic|
|N|3047|4425|13962|3047|4425|13962|

_∗ ∗∗_ Note. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _p <_ 0 _._ 10, _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Observations are at the municipality-crop-year-level and are restricted to pre-CADENA years for each municipality. Agricultural production is for the corresponding insurable crop (maize, beans, sorghum, and barley). All regressions include crop fixed effects. Columns 1 and 4 are estimated with a linear polynomial on a sample of observations within the optimal bandwidth of 50 mm. Columns 2 and 5 are estimated with a linear polynomial on a sample of observations within an alternative bandwidth of 70 mm. Columns 3 and 6 are estimated using a quadratic polynomial on the entire sample.

31
|**Table 5: Separate tests using**<br>Hypothesis<br>|**SP responses**<br>Estimation<br>method|**to the pandem**<br>Log GDP<br>Coefficient|**ic **<br>per capita<br> _Std. error_|Test on<br>f(lnGDPit)|Control<br>Coefficient|variable<br> _Std. error_|Number of<br>observations|
|---|---|---|---|---|---|---|---|
|Baseline|OLS|0.840<sup>***</sup>|_0.148_||||154|
|Relative prices 1||||||||
|_Health/Food price ratio_|OLS|0.820<sup>***</sup>|_0.182_||0.783|_0.794_|146|
||PLREG|||2.035<sup>††</sup>|0.030|_1.233_|146|
|Relative prices 2||||||||
|_Education/Food price ratio_|OLS|0.798<sup>***</sup>|_0.183_||0.584|_0.505_|146|
||PLREG|||1.863<sup>††</sup>|-0.032|_0.822_|146|
|Accountability 1||||||||
|<br>_Polyarchy index_|OLS|0.701<sup>***</sup>|_0.149_||0.061<sup>**</sup>|_0.029_|141|
||PLREG|||0.345|0.059|_0.042_|141|
|Accountability 2||||||||
|_Political competition index_|OLS|0.726<sup>***</sup>|_0.149_||0.099|_0.062_|141|
||PLREG|||0.447|0.075|_0.090_|141|
|Accountability 3||||||||
|_Voice and Accountability_|OLS|0.632<sup>***</sup>|_0.181_||0.449<sup>**</sup>|_0.227_|154|
|_Index_|PLREG|||0.987<sup>††</sup>|0.359|_0.294_|154|
|Governance: govt. effectiveness||||||||
|_Government Effectiveness_|OLS|-0.217|_0.278_||1.465<sup>***</sup>|_0.333_|154|
|_Index_|PLREG|||0.129|1.617<sup>***</sup>|_0.454_|154|
|Governance: domestic resources<br>_Tax revenue over GDP_|OLS|0.713<sup>***</sup>|_0.175_||0.038|_0.030_|125|
||PLREG|||0.267|0.026|_0.039_|125|
|Technology 1||||||||
|_Mobile phone suscriptions_|OLS|0.606<sup>***</sup>|_0.186_||0.013<sup>**</sup>|_0.006_|150|
|_per 100 inhabitants_|PLREG|||1.082<sup>†††</sup>|0.014<sup>**</sup>|_0.007_|150|
|Technology 2||||||||
|<br>_Percentage of population_|OLS|0.934<sup>***</sup>|_0.395_||-0.004|_0.017_|151|
|_that uses internet_|PLREG|||0.534|-0.001|_0.020_|151|
|Aging||||||||
|<br>_Share of the population_|OLS|0.492<sup>***</sup>|_0.213_||0.105<sup>**</sup>|_0.045_|147|
|<br>_age 65+_|PLREG|||0.594<sup>†</sup>|0.089|_0.060_|147|
|<br>Covid-19 hypothesis||||||||
|<br>_Total COVID19 death_|OLS|0.931|_0.166_||-0.245|_0.197_|153|
|_per 1,000,000_<br>Note:Standard errors are clustered a<br>test p-value <0.001, †† p-value <0.0|PLREG<br>t the country leve<br>1, and † p-value <|l.<sup>***</sup>indicates that<br>0.1.|the coefficient is s|2.643<sup>†††</sup><br>ignificant at 1% level,|-0.029<br><sup>**</sup>at 5% level,|_0.265_<br> <sup>*</sup>at 10% level.|153<br>††† indicates the|

<u><mark>Note: Standard errors are clustered at the country level.</mark></u><sup>***</sup> <mark>indicates that the coefficient is significant at 1% level,</mark><sup>**</sup> <mark>at 5% level,</mark><sup>*</sup> <mark>at 10% level. ††† indicates the test p-value <0.001, †† p-value <0.01, and † p-value < 0.1.</mark> 41
||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
||(0.510)|(0.191)|(0.258)|(0.258)|(0.152)|
|Blue collar craft|2.229*|1.000|0.960|0.955|1.184|
||(0.828)|(0.208)|(0.212)|(0.212)|(0.162)|
|Blue collar non-craft|1.029|1.317|1.667*|1.669*|1.377*|
||(0.397)|(0.331)|(0.382)|(0.382)|(0.206)|
|**Age group (<30 omit.)**||||||
|30-49|0.738|0.662*|0.614*|0.613*|0.657***|
||(0.246)|(0.116)|(0.119)|(0.118)|(0.078)|
|50+|1.226|0.959|0.893|0.896|0.964|
||(0.467)|(0.219)|(0.214)|(0.215)|(0.145)|
|**N**|806|1503|1832|1832|4141|
|**Pseudo R-squared**|0.120|0.070|0.085|0.085|0.075|

Source: Author’s calculations based on ELMPS 1998-2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.5.2. Entry of firms**

We turn now to entry of firms. We consider new firms to be those that started between waves. Figure 17 shows the percentage of firms that are new by subsequent wave formality. Informal firms are more likely to be new firms. In 2018, 73% of all informal firms were new compared to 55% for formal firms. Figure 18 shows the percentage of firms that are formal by whether they are new. The figure confirms that already established firms are more likely to be formal compared to new ones. Formality has fallen over time across both established and new firms. In 2018, 44% of established firms were formal compared to 26% of new firms. There is suggestive evidence that firms may start as informal (and later formalize). Taken with the higher rates of exit for informal firms, it may also indicate that this sector is more dynamic, has greater churn, or is a fallback form of employment (necessity entrepreneurship).

57
**Figure 7: Road Density in Terms of Population -** **<u>(road km/1000 people)</u>**

<!-- Start of picture text --> 20<br>17.3<br>15<br>10 9.2<br>7<br>6.7<br>6.1<br>5<br>3.3<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

**Figure 8: Paved Roads (% of total roads)**

<!-- Start of picture text --> 100<br>82<br>80<br>60 57<br>52<br>50<br>40<br>25<br>21<br>20<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

# **Large differences from the benchmark**

As a conclusion to the detailed benchmarking of South Africa’s infrastructure performance discussed above, it might be useful to highlight some of the _large_ differences—positive and negative—against the areas where performance differences are small between South Africa and comparator groups of countries, especially the benchmark upper-middle-income group. With 27
> capitalization of emerging markets.<sup>12</sup> **13** Moreover, some of the outstanding equity in

> emerging markets-as well as in many developed countries-is not publicly traded because it belongs to the families or corporations that control the companies. In sum, even though we cannot provide precise estimates, one can argue that international mutual funds hold a large and significant proportion of the publicly available equity.

Table 2 shows that the presence of mutual funds has grown substantially during the 1990s. Though net equity flows have declined from their **1993** peak-about **27** billion dollars to Latin America and 21 billion dollars to Asia-the relative importance of mutual funds has grown substantially. For example, dedicated emerging market equity funds held $21 billion in Latin-American stocks at the end of **_1995._ By** December **1997,** their holdings had increased to $40 billion. While mutual funds' growth in Asia has been less pronounced, the presence of mutual funds is still important in many countries. Overall, dedicated emerging market mutual funds held **$77** billion in Asia at the onset of the crisis (December **1996).** While the absolute amount of mutual fund investment in transition economies is not comparable to that in Asia and Latin America, funds' growth in these transition economies has been remarkable. In fact, in terms of market capitalization, mutual

> 12 The data come from Emerging Market Funds Research, which collects aggregate data of emerging market mutual funds to track the net cash flows of nearly 1,400 international emerging market equity funds, with an average position of about $120 billion in **1996.** The data set covers both **U.S.** registered and offshore funds as well as funds registered in Luxembourg, United Kingdom, Ireland, Cayman Islands, Canada, and Switzerland. It includes both open and closed-end funds. Our data start with the Mexican crisis and end in March **1999.** Thus, it includes observations on the major currency crises of the 1990s.

- **13** For example, the mutual fund industry estimates that international mutual funds hold between **60** and **70** percent of the market capitalization in Hungary, instead of the estimates obtained in Table 2, which all are below **30** percent. We thank Jonathan Garner, from **DLJ,** for raising this point.

**10**