rafmacalaba/datause-encoder-data huggingface dataset:
and private capital, and municipal-level electricity availability. In particular, taking logs, we derive from equation (7) the following regression: where _Yjt_ , _Kjt_ and _Lit_ represent the levels of production, private capital and labor in agriculture, industry or services in municipality _j_ at time _t_ ; and _ηj_ is a municipality fixed effect.

The proxy that we employ for _Gjt_ is a combination of the electricity access and generation capacity indices described previously. In particular, we consider the fraction of electrified grid points (i.e., electricity access) multiplied by generation capacity. We call _grid capacity_ to this compounded measure. The reason behind its choice is that electricity access has an upper bound equal to 1, which is somehow inconsistent with our definition of _Gt_ , whereas generation capacity is not bounded above. A combination of the two then seems desirable.

We estimate (42) for each sector separately using the panel of Brazilian municipalities. The panel is shorter in the time dimension than in the previous regressions, because values for the stock of capital at the sectoral and municipal levels can be obtained from IPEA only for the years 1970, 1975 and 1980 for the manufacturing and services sectors, and for 1970, 1975, 1980 and 1985 for the agricultural sector. In addition, for agriculture, we use investment information for the years 1985 and 1996 also from IPEA to impute the capital stock in 1996.<sup>19</sup> As described previously, employment numbers were collected by IBGE and published by IPEA for the years 1970, 1975, 1980, 1985 and 1995. For the purpose of matching up the employment data with the data on capital stock, the agricultural employment in 1995 was used as a proxy for the agricultural employment in 1996.<sup>20</sup> In the IV regression, there

> 19In particular, we created annual investment numbers between 1985 and 1996 by using linear interpolation, and then employed these interpolated investments, together with a depreciation rate of 7%, to bring the capital stock from 1985 forward to 1996.

> 20Although a central registry of formal employment contracts exists in Brazil, named Relação Anual de Informações Sociais (RAIS), we have chosen to use employment numbers from IBGE, which are based on a series of surveys conducted by IBGE. The reason is that the RAIS data may lack coverage of informal employment and the survey-based IBGE numbers are believed to reflect both formal and informal employment somewhat better.

35
17 Two features make the Danish experience fundamentally<sup>different from that of most Latin</sup> American expansionary ERBS. The first,<sup>is that real wages fell during the initial stage of the program.</sup> The second, is that although the current account<sup>worsened (reaching a deficit of 5.4 per cent of GDP in</sup> 1986) it was brought under control and<sup>the plan did not end in a balance of payments crisis.</sup> Table 2. Growth Rates of GDP and its<sup>Components in Denmark: 1978-90.</sup>

|Growth of<br>l_|GDP<br>____________|Government<br>Consumption|Private<br>Consumption|Private<br>Investment|
|---|---|---|---|---|
|1979-81|0.7|4.2|_-1.5_|-9.4|
|1982|3.0|3.1|1.4|19.9|
|1983|2.5|0.0|2.6|2.7|
|1984|4.4|0.0|3.4|12.1|
|1985|4.3|2.5|_5.0_|18.9|
|1986|3.6|_0.5_|5.7|18.8|
|1987|_-0.5_|2.5|_-1.5_|0.0|
|1988|0.9|0.2|0.0|0.0|
|1989|1.1|0.0|0.0|2.7|

_Source:_ OECD National Income Accounts.

# _3.3.2. Ireland._

The case of Ireland sharply contrasts with that of Denmark.<sup>While GDP did not grow on average</sup> during the period 1982-84 (after being negative in 1982<sup>and 1983), consumption fell by 7.1 per cent in</sup> 1982 and remained almost flat during the following two<sup>years. Business investment experienced negative</sup> growth rates from 1982 to 1987. Figure 6 shows that<sup>the behavior of manufacturing employment and</sup> inflation fit the classical recessionary scenario. If the<sup>recession was not deeper, it was because in the</sup> 1982-84 period Irish exports fared exceptionally well on international<sup>markets.</sup>
commercial microfinance bank thus can reduce unit costs to 11 percent -- versus 18 percent for the median NGO.

<!-- Start of picture text --> Percent<br>100<br>80<br>60<br>40<br>20<br>0<br>0 1 2 3 4 5<br>Avg Loan Balance per borrower / GNI per capita for the poorest 20%<br>NGO NBFI Bank<br><!-- End of picture text -->

**Figure 2: Operating expense per unit lent**

Source: Cull, et al. (2017). Notes: Observations: 446 NGOs, 380 non-bank financial institutions (NBFIs), and 82 microfinance banks. The figure is restricted to observations in which the ratio of the lender’s average loan size divided by the per capita gross national income at the country’s 20<sup>th</sup> percentile is under 5. Original, underlying data provided by Microfinance Information eXchange, Inc. (MIX).

Following “best practices” promoted by donors, institutions respond by raising interest rates. Figure 3 shows that, after adjusting for inflation, the median microfinance lender charged borrowers 21 percent per year, as measured by the average real portfolio yield. Strikingly, NGOs, the institutions that tend to serve the poorest customers, lent at an average of 28 percent 18
could not conduct their agricultural activities as usual in the reference agricultural season, and the reasons for disruption to agricultural activities.

Second, we collected detailed information on extensive (whether the household used any inorganic fertilizer) and intensive margin fertilizer use (how much inorganic fertilizer was used across all plots of the household; and how much was purchased). Depending on country and survey round, we also collected information on the different types of fertilizer applied. For intensive margin fertilizer use, respondents reported the total quantity of fertilizer used across all plots of the households in a unit of their choice. Wherever a quantity was reported in non-standard units, we use standardized conversion factors to express the quantity in kilograms. For those households that used no inorganic fertilizer, we collect information on their reasons. For those who used any fertilizer, we further ask whether households were able to acquire the full amount of fertilizer they desired. Those that responded that they could not acquire as much fertilizer as desired were asked how much more fertilizer (in kilograms or a unit of their choice) they would have liked to acquire. They were also asked about the reasons for the fertilizer shortfall and their coping strategies for having had less fertilizer than desired. We normalize all quantities reported (fertilizer quantity per hectare, purchased fertilizer quantity per hectare, fertilizer shortfall per hectare) by the total selfreported area under cultivation and winsorize at the 2.5 and 97.5 percentiles within each country.

Third, we collected information on fertilizer prices from two sources depending on the survey round. The first source of fertilizer price information comes from a dedicated prices module which, among others, collected information on the current price of inorganic fertilizer for all households reporting that fertilizer was available for sale in their community or a nearby location.<sup>4</sup> This question was not specific to any fertilizer type. The second source is the HFPS agriculture module which asked all respondents in crop-farming households to report the current price of inorganic fertilizer by fertilizer type.<sup>5</sup> Respondents reported on prices for either the types of fertilizer they used or, if they did not use any, for any type of fertilizer they could report on. Prices were reported in local currency units (LCU) for a certain amount of fertilizer and unit. We standardize all prices by expressing them in LCU per kilogram. If a household reported prices for

> 4 Respondents did have the option to answer that they did not know the price.

> 5 In Ethiopia (round 19) and Uganda (round 15), only those that used any inorganic fertilizer were asked to report on the price.

5
**Table 6a Trade gains of European partner countries (relative to their respective total trade volume) resulted from port efficiency improvement (half-way to EU15) of each new member and candidate member countries**

|Port Efficiency||EU15||N|ewmembe|rs|Candida|temember|countries|
|---|---|---|---|---|---|---|---|---|---|
||Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|
|Czech||||||||||
|Republic|6.75|21.30|13.77|24.16|82.94|56.04|3.60|18.78|11.83|
|Estonia|0.22|0.61|0.41|0.60|1.58|1.12|0.11|0.05|0.08|
|Hungary|7.68|27.25|17.13|17.12|47.33|33.41|10.14|39.64|26.13|
|Latvia|0.24|0.53|0.38|1.61|2.43|2.05|0.16|0.01|0.08|
|Lithuania|0.78|1.51|1.13|4.34|4.99|4.68|0.41|2.57|1.58|
|Poland|8.79|20.74|14.55|22.94|55.31|39.73|7.44|17.38|12.83|
|Slovakia|4.99|15.50|10.06|36.47|93.18|66.84|3.08|18.13|11.24|
|Slovenia|1.17|3.13|2.11|2.41|6.44|4.57|0.96|3.14|2.14|
|Bulgaria|1.61|4.90|3.20|3.13|3.92|3.55|7.25|19.86|14.00|
|Romania|1.22|3.60|2.37|3.10|3.30|3.21|3.76|8.70|6.41|
|Turkey|3.43|11.01|7.09|2.19|8.22|5.41|14.04|33.98|25.44|

Note: Figures are presented in 1/10000. For example, if the port efficiency development level of the Czech Republic reaches half-way of EU15 average, the export volume of EU15 will increase 0.000675.

**Table 6b Relative trade gains of European partner countries (relative to their respective total trade volume) resulted from customs regimes improvement (half-way to EU15) of each new member and candidate member countries**

|Customs<br>regimes||EU15||N|ewmembe|rs|Candida|temember|countries|
|---|---|---|---|---|---|---|---|---|---|
||Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|
|Czech||||||||||
|Republic|6.90|7.23|7.06|24.70|28.17|26.58|3.68|6.38|5.14|
|Estonia|0.16|0.15|0.15|0.44|0.38|0.41|0.08|0.01|0.04|
|Hungary|0.95|1.12|1.03|2.11|1.94|2.02|1.25|1.63|1.45|
|Latvia|0.86|0.63|0.75|5.81|2.91|4.27|0.59|0.02|0.28|
|Lithuania|0.59|0.38|0.49|3.32|1.27|2.23|0.31|0.65|0.50|
|Poland|12.29|9.64|11.01|32.10|25.71|28.78|10.41|8.08|9.15|
|Slovakia|1.24|1.28|1.26|9.09|7.72|8.35|0.77|1.50|1.16|
|Slovenia|0.85|0.76|0.81|1.76|1.56|1.66|0.70|0.76|0.73|
|Bulgaria|1.26|1.28|1.27|2.46|1.02|1.69|5.69|5.18|5.42|
|Romania|6.34|6.22|6.28|16.13|5.70|10.57|19.56|15.03|17.13|
|Turkey|8.20|8.73|8.46|5.23|6.52|5.92|33.52|26.95|29.76|

Note: Figures are presented in 1/10000. For example, if the customs regimes development level of the Czech Republic reaches halfway of EU15 average, the export volume of EU15 will increase 0.000690.

22
# **Introduction**

After a decade of financial repression and stagnant international financial flows, the 1990s saw a resurgence in financial globalization. Countries opened up their economies to capital flows and liberalized their financial sectors. As part of this process of financial integration, multinational banking gained momentum once again and international banks’ foreign claims—those extended on residents outside the country in which these banks are headquartered—took off. According to Bank for International Settlement statistics, which monitor foreign claims held by banks from OECD countries vis-à-vis the rest of the world, these claims rose from 1.3 trillion dollars in 1990 to close to 3 trillion dollars in 2002.<sup>1</sup> In real terms, foreign claims to all non-BIS reporting countries rose by 120 percent over this period and claims to developing countries increased by 104 percent. These claims consist of financial assets such as loans, debt securities and equities, including equity participations in subsidiaries.

International banks may grow their foreign claims portfolio in two ways. First, they may establish affiliates in different countries and extend claims locally through their branches and subsidiaries in these countries. Second, international banks may also extend cross-border claims by financing and booking the claims from outside the recipient or host countries (e.g., originating the claim in their home countries, where their headquarters are located). While the first type of international bank claims involve some form of foreign direct investment in the host country’s financial sector, cross-border claims do not. In practice, we observe significant disparities in how banks conduct their business across countries. In some instance, like in the case of Albania, Burundi, Bhutan, Cambodia, and Moldova, foreign banks extend only cross-border claims.

> 1 BIS-reporting countries over this period include: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and U.S.

1
projects in 2014. This was followed by financial services (18.1 percent) and consumer products and retail (14.1 percent). The natural resource sector (coal, oil, and natural gas) ranked 9<sup>th</sup> and accounted for only 3.5 percent of all Greenfield projects in Africa in 2014. As shown in Table 1 above, consumer products and retail (33.6 percent), and real estate, hospitality and construction (33.6 percent) sectors are the biggest creators of Greenfield FDI-related jobs; yet, these sectors are ranked 3<sup>rd</sup> and 4<sup>th</sup> on the list of attractive sectors for FDI in the region (based on fDi Market surveys). The two most attractive sectors to foreign investors are technology, media, and telecommunications, and financial services created only 6.4 and 2 percent respectively, of Africa’s Greenfield FDI-related jobs in 2014. Overall, US, UK, and France’s direct Greenfield investments tend to be concentrated in: technology, media and telecommunication, transport and logistics, consumer products and retail, business services, and financial services sectors. Chinese FDI is also predominantly in technology, media and telecommunications sector, where approximately 33 percent of its projects in Africa were concentrated in 2014 (EY 2015).

Many analysts have tended to assume that much of FDI to Africa is in the natural resource sector. However, the preceding and recent evidence on Greenfield investments suggests that there has been a change over time. In the past, foreign investors may have been attracted to Africa solely because of its abundance of natural resources, especially in oil and ores. However, recent evidence puts rising urbanization and an expanding consumer base as some of the major determining factors for FDI to Africa. As a result, the large economies of the continent: South Africa, Nigeria, Kenya, Ghana, Egypt, Morocco and recently, Ethiopia and Mozambique have attracted the majority of the FDI, with South Africa leading the group.

UNCTAD data (covering both Greenfield and non-Greenfield FDI) shows that a large proportion of FDI is from Western Europe (which makes up the bulk of G7 countries) and at a 14
# **1. Introduction**

The impacts of climate change are expected to become more severe, especially on the agriculture sector. Changes in the weather pattern are more and more associated with extreme events that have short- and medium-term impacts (Day et al., 2019; Stevanović et al., 2016; Key and Sneeringer, 2014). However, the distribution of these impacts varies widely by region. Southeast Asia and Sub-Saharan Africa are more prone to floods, while the Middle East and North Africa (MENA) regions are experiencing recurrent droughts. The MENA region is already one of the driest areas on the planet and drought shocks are expected to become more frequent, longer-lasting, and more intense in the future (Fischer et al., 2021; Chiang et al., 2021, Pörtner et al., 2022). Recently, Alfani et al. (2022) have documented that severe drought shocks significantly and persistently reduced the agricultural employment in Morocco during the period 2000-2009, with a limited reemployment of displaced workers and residual unemployment. However, while the impact of climate change on the agriculture sector in Africa is well documented (Abidoye and Odusola, 2015; Asafu-Adjaye, 2014), we know much less about the effects of climate extremes in the MENA region.

In this paper we investigate the effects of drought shocks on the labor market in Tunisia using rich laborforce survey data aligned with granular weather information at the governorate level. Building on Alfani et al. (2022), we test whether agricultural workers - who are the most exposed to climate variability - faced a job displacement as a consequence of severe drought events that occurred in Tunisia from 2000 to 2019. We also look at the employment rate of other economic sectors and at the unemployment rate to investigate whether a drop in agricultural employment is offset by job reallocation or translates into higher unemployment. We frame the analysis in a difference-in-difference setting by considering multiple and non-absorbing shocks at different points in time to account for differential treatment dynamics (Chaisemartin and D'Haultfœuille, 2020).

Tunisia is a representative case of other MENA countries. Despite a decline from 20% to 14% in the employment rate during the last decade, the agriculture sector still absorbs a relevant fraction of the labor force. Overall, the labor market is characterized by several frictions, mainly due to the gender gap in labor market participation, low education returns and informal employment. According with the recent Tunisia’s Jobs Landscape (World Bank, 2022), less than half of the working-age population is actively 2
The vast majority of firms report it is significantly more difficult to get credit since the crisis. For SMEs this response was universal (100% of firms), but even for large firms it was also pervasive (95.7 percent).<sup>24</sup> On access to foreign exchange, not surprisingly, every single firm that answered the question on ease of obtaining foreign currency reported that there were significant restrictions when compared to before the crisis.

There are striking changes in the source of credit since the crisis, with use of bank credit decreasing most. Figure 5 below compares pre‐crisis to 2017 sources of credit as reported in the 2017 survey. Most notably, the use of bank credit has decreased from 23.7 percent before the crisis to just 8.5 percent in 2017. The use of supplier credit has also decreased from 7.6 percent to 3.4 percent. Most of this lost credit from banks and suppliers does not appear to have been replaced, instead the share without credit rose from 56 percent to 75 percent.

**Figure 5: Sources of credit in 2010 and 2017**

<!-- Start of picture text --> 80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>No credit Bank credit Friends, family or Supplier credit Other sources<br>informal lenders<br>2010 2017<br><!-- End of picture text --> Firms largely use the credit they obtain for working capital. Fully 65 percent of firms report using it only for working capital, 31 percent use it for both working capital and investment, and just 3 percent using it for investments only. About 68 percent of firms have canceled planned investments in building, plant, or equipment due to this crisis. However, despite this, about 1 in 5 firms still report making some investments since the beginning of the crisis. There were no significant differences across region, sector, or size with regard to investments made or canceled.

The need for working capital is also heightened by a strong increase in delinquent or nonpaying customers. This is clearly a large problem as 67.5 percent of firms report that the share of delinquent customers has increased since the beginning of the crisis. This is a particularly big problem for medium sized firms where 80.6 percent of firms report an increase in delinquency and a smaller issue for large firms where 46.7 percent of firms

> 24 Statistically significant at the 5% level.

14
The overall targeting differentials across all 510 departments were $41, $110 and $76 per capita for TI, TII and Tll respectively; all three are significant at the 1% level. To help interpret these numbers, compare the poorest department, namely Figueroa (in Santiago Del Estero province) where the incidence of unmet basic needs is 75.5%, with the least poor department, namely Chacabuco (in Chaco province) where the poverty measure is 3.3%. The expected difference in spending was $30 under TI, $79 under TII, and $55 under TIII.

So the expansion to the programn between TI and TII was associated with a more propoor allocation of funds geographically, while the contraction between TII and TIII came with a less pro-poor allocation. Next we will see if this aggregate correlation is borne out when we compare provinces over times.

# **_5._ Program spending and poor-area targeting across provinces**

With the extra degrees of freedom made possible by exploiting the changes in the inter-provincial allocation of spending, it is possible to test for statistically significant effects of fiscal expansion and contraction on the program's targeting perfonnance. The better information system for TII and Tlll allows a breakdown of the aggregates into sub-periods by province. Intervals of five months were chosen. A data addendum is available from the author giving the detailed breakdown of the aggregate targeting differentials by these intervals, as well as program spending per capita for each five-month period.<sup>13</sup> To assess the effect of the cuts on targeting performance, one can regress the province and period-specific targeting differentials on program spending per capita across provinces, pooling all five-month periods and all provinces. The targeting differential will, however, vary across provinces according to other factors, such as the strength of provincial concern for the poor, how poor the province is as a whole, the history of the provincial efforts at

> 13 This is an extended version of the data used in Ravallion (1999). The latter paper only used data for TIH. Adding TII more than doubles the number of degrees of freedom in the data.

19